Network Biology Lab - User contributions [en]

People

2025-03-05T05:33:49Z

Bsb0613: /* Scientists & Research Staff */

__NOTOC__
=='''Scientists & Research Staff'''==
{|border="0" width="1080"
|align="center"|[[File:prof_Insuk_fixed.png|100px|link=people:IS_Lee]]
|align="center"|[[File:SM_fixed.png|100px|link=people:SM_Yang]]
|align="center"|[[File:Junha_fixed.png|100px|link=People:JH_Cha]]
|align="center"|[[File:Nayeon_fixed.png|100px|link=People:NY_Kim]]
|align="center"|[[File:Seungbyn_fixed.png|100px|link=People:SB_Baek]]
|align="center"|[[File:Ilseok_fixed.png|100px|link=People:IS_Choi]]
|align="center"|[[File:JunHyeong_fixed.png|100px|link=People:Jun_Hyung_Cha]]
|align="center"|[[File:JunYeong_fixed.png|100px|link=People:Junyeong_Ma]]
|align="center"|[[File:Hanjune_fixed.png|100px|link=People:Han-June_KIM]]
|-

|width=160px; align="center"|[[people:IS_Lee|<big>'''Insuk Lee'''</big>]]
|width=160px; align="center"|[[people:SM_Yang|<big>'''Sunmo Yang'''</big>]]
|width=160px; align="center"|[[People:JH_Cha|<big>'''Junha Cha'''</big>]]
|width=160px; align="center"|[[People:NY_Kim|<big>'''Nayeon Kim'''</big>]]
|width=160px; align="center"|[[People:SB_Baek|<big>'''Seungbyn Baek'''</big>]]
|width=160px; align="center"|[[People:IS_Choi|<big>'''Ilseok Choi'''</big>]]
|width=160px; align="center"|[[People:Jun_Hyung_Cha|<big>'''Jun Hyung Cha'''</big>]]
|width=160px; align="center"|[[People:Junyeong_Ma|<big>'''Junyeong Ma'''</big>]]
|width=160px; align="center"|[[People:Hanjune_Kim|<big>'''Hanjune Kim'''</big>]]
|-

|align="center"|[[people:IS_Lee|Principal Investigator Professor]]
|align="center"|[[people:SM_Yang|Bioinformatics Programmer & Sys. Admin]]
|align="center"|[[People:JH_Cha|Ph.D candidate (Single-cell Biology) (2019.9 - )]]
|align="center"|[[People:NY_Kim|Ph.D candidate (Microbiome) (2019.9 - )]]
|align="center"|[[People:SB_Baek|Ph.D candidate (Single-cell Biology) (2019.9 - )]]
|align="center"|[[People:IS_Choi|Ph.D candidate (Single-cell Biology) (2020.9 - )]]
|align="center"|[[People:Jun_Hyung_Cha|Ph.D candidate (Microbiome) (2020.9 - )]]
|align="center"|[[People:Junyeong_Ma|Ph.D candidate (Microbiome) (2021.3 - )]]
|align="center"|[[People:Hanjune_Kim|Ph.D candidate (Microbiome) (2021.3 - )]]
|}
{|border="0" width="1080"

|align="center"|[[File:EuiJeong_fixed.png|100px|link=People:EuiJeong_Sung]]
|align="center"|[[File:Sehun_fixed.png|100px|link=People:Sehun_Ahn]]
|align="center"|[[File:Geon_fixed.png|100px|link=People:Geon_Koh]]
|align="center"|[[File:Wonjong_fixed.png|100px|link=People:WonJong_Kim]]
|align="center"|[[File:Jungyeon_fixed.png|100px|link=People:Jungyeon_Kim]]
|align="center"|[[File:Yerin_fixed.png|100px|link=People:Yerin_Kim]]
|align="center"|[[File:YurimJung_fixed.jpg|100px|link=People:Yurim_Jung]]
|align="center"|[[File:HaeBeen_fixed.jpg|100px|link=People:Hae_Been_lee]]
|align="center"|[[File:HaeBeen_fixed.jpg|100px|link=People:Hae_Been_lee]]
|-
|width=160px; align="center"|[[People:Euijeong_Sung|<big>'''Euijeong Sung'''</big>]]
|width=160px; align="center"|[[People:Sehun_Ahn|<big>'''Sehun Ahn'''</big>]]
|width=160px; align="center"|[[People:Geon Koh|<big>'''Geon Koh'''</big>]]
|width=160px; align="center"|[[People:WonJong_Kim|<big>'''Wonjong Kim'''</big>]]
|width=160px; align="center"|[[People:Jungyeon_Kim|<big>'''Jungyeon Kim'''</big>]]
|width=160px; align="center"|[[People:Yerin_Kim|<big>'''Yerin Kim'''</big>]]
|width=160px; align="center"|[[People:Yurim_Jung|<big>'''Yurim Jung'''</big>]]
|width=160px; align="center"|[[People:Hae_Been_lee|<big>'''Hae Been Lee'''</big>]]
|width=160px; align="center"|[[People:Hae_Been_lee|<big>'''Hae Been Lee'''</big>]]
|-
|align="center"|[[People:Euijeong_Sung|Ph.D candidate (Single-cell Biology) (2021.9 - )]]
|align="center"|[[People:Sehun_Ahn|Ph.D candidate (Microbiome) (2022.3 - )]]
|align="center"|[[People:Geon_Koh|Master course (Microbiome) (2022.9 - )]]
|align="center"|[[People:WonJong_Kim|Master course (Microbiome) (2023.3 - )]]
|align="center"|[[People:Jungyeon_Kim|Integrated Ph.D course (Microbiome) (2023.3 - )]]
|align="center"|[[People:Yerin_Kim|Integrated Ph.D course (Microbiome) (2024.3 - )]]
|align="center"|[[People:Yurim_Jung|Integrated Ph.D course (Single-cell Biology) (2024.9 - )]]
|align="center"|[[People:Hae_Been_lee|Integrated Ph.D course (Microbiome) (2024.9 - )]]
|align="center"|[[People:Hae_Been_lee|Integrated Ph.D course (Microbiome) (2024.9 - )]]
|}

=='''Administrative Assistant'''==
*Seomi Lee

=='''Undergraduate Students'''==
*Zunu An

=='''Alumni'''==
*'''Former Post-docs and Staff Scientists'''
**1. Taeyun Oh (2009.5-2011.12), currently Senior Researcher at Cowellbiogim, Korea
**2. Sohyun Hwang (2010.3-2015.8), currently Professor at [https://grad.cha.ac.kr/%ed%99%a9%ec%86%8c%ed%98%84/ Department of Biomedical Science, CHA University], Korea [https://scholar.google.com/citations?user=z5yC9cwAAAAJ&hl=en Google Scholar] (차의과대학 의과학과 교수)
**3. Samuel Beck (2011.2-2011.12), currently Associate Professor at Boston University, USA, [https://beck310.wixsite.com/becklab Lab Homepage] (미 보스턴 대학교 부교수)
**4. Jawon Song (2011.1-2012.5), currently Research Associate at [https://www.tacc.utexas.edu/about/directory/jawon-song Texas Advanced Computing Center], Austin, TX, USA [https://scholar.google.com/citations?user=wv61wGoAAAAJ&hl=en Google Scholar] (미국 텍사스슈퍼컴퓨팅센터 선임연구원)
**5. Yoonhee Ko (2012.8-2014.2), currently Associate Professor at [https://hufsbiomed.weebly.com/ Department of Biomedical Engineering, Hankuk University of Foreign Studies], Korea [https://scholar.google.com/citations?user=GBxCYX8AAAAJ&hl=en Google Scholar] (한국외국어대 바이오공학과 교수)
**6. Jonghoon Lee (2013.10-2014.2), currently Professor at [https://www.gachon.ac.kr/foodbiotech/6142/subview.do Department of Food Biotechnology, Gachon University], Korea (가천대학교 식품생명공학과 교수)
**7. Jung Eun Shim (2009.8-2017.11), currently Staff Scientist at Yonsei Genomics Center Bioinformatics Data Analysis Core (연세의료원 유전체센터 생명정보분석코어 책임연구원)

*'''Former Graduate Students (Ph.D)'''
**1. Junha Shin (2008.9-2014.2), PhD; currently Scientist I at [https://wid.wisc.edu/people/junha-shin/ Wisconsin Institute for Discovery], USA (미국 위스콘신 대학연구소 연구원)
**2. Hanhae Kim (2009.9-2015.2), PhD; currently Research Fellow at [http://www.kistep.re.kr Korea Institute of S&T Evaluation and Planning (KISTEP)] (한국과학기술기획평가원 연구위원)
**3. Ara Cho (2009.9-2015.2), PhD; currently Research Fellow at [http://www.kistep.re.kr Korea Institute of S&T Evaluation and Planning (KISTEP)] (한국과학기술기획평가원 연구위원)
**4. Eiru Kim (2010.9-2016.8), PhD; currently Senior Expert in Data Science, [https://www.gnf.org/ The Genomics Institute of the Novartis Research Foundation], San Diego, USA (노바티스 선임데이터분석연구원)
**5. Tak Lee (2011.3-2017.2), PhD; currently Senior Bioinformatician, [https://www.mpipz.mpg.de/en Max Planck Institute for Plant Breeding Research] (독일 막스플랭크 연구소 선임생명정보학자)
**6. Heonjong Han (2013.3~2019.8), PhD; currently Bioinformatics engineer at [https://3billion.io/ 3billion] (바이오기업 데이터사이언티스트)
**7. Kyungsoo Kim (2013.3~2020.2), PhD; currently Research Professor at Severance Hospital (강남세브란스의료원 유방암센터 연구교수)
**8. Jae-Won Cho (2016.9~2021.2), PhD; currently Assistant Professor at [https://ibb.hanyang.ac.kr/ Hanyang Institute of Bioscience and Biotechnology] (한양대학교 한양생명과학기술원 조교수)
**9. Chan Yeong Kim (2015.3~2021.2), PhD; currently Postdoc Fellow at European Molecular Biology Laboratory in Heidelberg [http://www.bork.embl.de/j/ Bork Group] (유럽연합분자생물학연구소 박사후 연구원)
**10. Sungho Lee (2017.3-2024.2), PhD; currently Data Scientist at [http://www.mogam.re.kr/kor/ MOGAM Institute for Biomedical Research] (목암생명과학연구소, (주)녹십자 출연 연구소, 선임연구원)
**11. Junha Cha (2019.9-2025.2), PhD;
**12. Nayeon Kim (2019.9-2025.2), PhD;
**13. Seungbyn Baek (2019.9-2025.2), PhD;

*'''Former Graduate Students (Master's degree)'''
**1. Sun-Gou Ji (2009.9-2011.8), M.Eng.; PhD from University of Cambridge (Sanger Genome center); currently Vice President, Computational Genetics at [https://bridgebio.com/ BridgeBio Pharma], Boston, USA (미 바이오기업 부대표)
**2. Hyojin Kim (2013.3-2015.2), M.Eng.; PhD from University of Aachen; currently Scientist at Bayer, Germany [https://www.linkedin.com/in/hyojin-kim-b3bb711ab Linkedin] (독일 바이엘 제약회사 선임 연구원)
**3. Hongseok Shim (2013.9-2015.8), M.Eng.; currently PhD student, Arizona State University, Biomedical Informatics program (애리조나주립대 생물정보학 박사과정)
**4. Sunmo Yang (2014.9-2017.2), M.Eng. currently Bioinformatics programmer, Yonsei University (연세대 네트워크생명공학 연구실 책임데이터연구원)
**5. Muyoung Lee (2016.3-2018.2), M.Eng. currently PhD student, University of Texas at Austin, Cellular and Molecular Biology Program (텍사스주립대 생물정보학 박사과정)
**6. Jiwon Yu (2021.9-2023.8), M.Eng. currently Data Scientist at [https://www.samsungbioepis.com/kr/index.do SAMSUNG BIOEPIS] (삼성바이오에피스 데이터사이언티스트)
**7. Geon_Koh (2022.9-2025.2), M.Eng.

*'''Former Visiting Scientists/Students'''
**1. Minkyung Shin (2012.3-2012.7), went to University of Southern California graduate school
**2. Paul Chung (2012 summer), currently E-Commerce Analyst/Coordinator at [http://www.riotgames.com/ Riot Games], USA
**3. Hyunjin Cho (2014 summer), currently Bioinformatics research assistant at [http://www.libd.org/ The Lieber Institute for Brain Development | LIBD], USA
**4. Michael Chung (2016.8-2016.12), currently work at [https://about.meta.com/ Meta], USA

*'''Former Undergraduate Students'''
**1. Ila Shin, went to medical school (Yonsei Univ.) Currently Professor, Kyunghee University School of Medicine (경희대학교 의과대학 교수)
**2. Yoonkyung Ko
**3. Sangyo Park, went to law school (Kyounghee Univ.)
**4. Jayoon Shin, went to medical school (Pusan National Univ.)
**5. Dongjoo Sun, went to dental school (Wongwang Univ.)
**6. Eulsoo Kim
**7. Myungwhan Lee, went to medical school (Yonsei Univ.)
**8. Moonhee Lee, went to graduate school (University of Amsterdam, Netherlands)
**9. Jinhyun Ju, went to graduate school (Cornell Medical School, Computational Systems Biology); Currently Principal Computational Biologist, Cancer Genomics at [https://bridgebio.com/ BridgeBio Pharma], Seattle, USA (미 바이오기업 선임연구원)
**10. Haeyoung Shin, went to graduate school (Brown University, Computational Neuroscience); Currently Professor, Seoul National University (서울대학교 생명과학부 교수)
**11. Joonghee Soh, went to graduate school (KAIST, Culture Technology)
**12. Hee Jung Cho, went to graduate school (UC Berkeley)
**13. TaeHwan Kim, went to medical school
**14. Joohyung Kim, went to medical school (Kyungpook National Univ.)
**15. Donghyun Shin, went to medical school (Yonsei Univ.)
**16. Byunghee Kang (2015 spring - 2016 summer), went to graduate school (PosTech)
**17. Sunphil Kim (2016 winter)
**18. Jaeho Shim (2016 winter)
**19. Seunghyun Shin (2016 spring)
**20. Aejoo Hong (2016 spring)
**21. Changbae Bang (2016 summer), went to medical school (Yonsei Univ.)
**22. Seoyoung Choi (2016 summer)
**23. Dabin Jung (2016 summer)
**24. Boreum Nam (2016 summer)
**25. Seungun Lee (2016 summer)
**26. Yeaji Kim (2016 summer and fall)
**27. Sangyoung Lee (2017 winter)
**28. Heejun Jang (2017 winter & spring), went to KIST as Research Assistant
**29. Jungha Lee (2017 summer), went to graduate school (Seoul National Univ.)
**30. Seunghyun Wang (2017 summer), went to graduate school (KAIST)
**31. Dong-Min Yang (2017 fall)
**32. Doo-Hee Lee (2017 fall)
**33. Suk-Jae Han (2018 winter)
**34. Ji-Eun Han (2018 winter)
**35. Yuri Ko (2018 spring)
**36. Sung-Woo Kim (2018 spring)
**37. Woosung Kwon (2018 summer)
**38. Soyun Kong (2018 summer)
**39. Hojeong Keum (2018 summer and fall)
**40. Sungjun Lim (2018 summer and fall)
**41. Jiyoon Lee (2018 summer and fall)
**42. Ilseok Choi(2019 winter, spring, summer and fall, 2020 winter, spring)
**43. Junik Park (2019 winter)
**44. Lee Yoo (2019 winter & spring)
**45. Yeonwha Kim (2019 winter & spring)
**46. Sohee Kim (2019 spring)
**47. Juseong Lee (2019 spring)
**48. Junyoung Yang (2019 spring & summer)
**49. Junyoung Ma (2019 summer, 2020 winter, summer, fall, 2021 winter)
**50. Dayun Jung (2019 summer)
**51. Hanjoon Kim (2019 summer, 2021 winter)
**52. Hyungjin Kim (2020 winter, spring)
**53. Sujin Hyun (2020 winter)
**54. Hyuki Lee (2020 winter)
**55. Donghwan Lee (2020 winter)
**56. Jun Hyung Cha (2020 spring, summer)
**57. Jiwon Yu (2020 summer, 2021 Spring, summer)
**58. Dong Seok Kim (2020 summer)
**59. Donggeun Lee (2020 summer)
**60. Sehun Ahn (2020 summer, 2021 winter, spring, summer, 2022 winter)
**61. Jiho Kim (2020 summer)
**62. Euijung Seong (2020 fall, 2021 spring, summer)
**63. Junhan Kim (2021 winter & spring)
**64. Suyong Choi (2021 winter)
**65. Sujin Pyeon (2021 Spring)
**66. Hyeon Hee Ji (2021 Spring)
**67. Semin Kim (2021 Summer)
**68. Su Hwan Kim (2021 Summer)
**69. Yongbin Kim (2021 Summer)
**70. Nan Park (2021 Summer)
**71. Hoyong Jung (2021 Summer)
**72. Ingyeong Koh (2021 Summer)
**73. Jongbin Jeong (2022 Winter)
**74. Sungchul Yang (2022 Winter, Spring, Summer)
**75. Hyojung Lee (2022 Winter, Spring)
**76. Jun Hyeong Kim (2022 Winter, Spring)
**77. Young Ji Roh (2022 Spring, Summer)
**78. Geon Koh (2022 Spring, Summer)
**79. Hayoung Kang (2022 Summer, 2023 Spring)
**80. Won Jong Kim (2022 Summer, Fall, 2023 Winter)
**81. Christian Lee (2022 Summer)
**82. Je Yun Kang (2022 Summer)
**83. Sumin Lee (2022 Fall)
**84. Jungyeon Kim (2022 Fall, 2023 Winter)
**85. Seol Song (2023 Winter)
**86. Soyeon Kim (2023 Summer)
**87. Sieun Park (2023 Summer)
**88. Gunhyeong Lee (2023 Summer)
**89. Yunseo Han (2023 Summer)
**90. Jae Hyun Kim (2023 Summer)
**91. Jeong-min Seo (2023 Summer)
**92. Yerin Kim (2023 Fall, 2024 Winter)
**93. Sang Min Park (2024 Winter)
**94. Minsun Seo (2024 Winter)
**95. Jaeryun Sim (2024 Winter)
**96. Sun Min Lim (2024 Winter)
**97. Yurim Jung (2024 Spring, Summer)
**98. Hae Been Lee (2024 Spring, Summer)
**99. Zunu An (2024 Summer, Fall, 2025 Winter)
**100. Se Bin Lim (2024 Summer, Fall, 2025 Winter)
**101. Sang Min Yoon (2024 Summer)
**102. Yena Kim (2024 Summer)
**103. Kang Eon Lee (2024 Fall)
**104. Soyoon Park (2025 Winter)
**105. Sojung Lee (2025 Winter)
**106. Leanne Ma (2025 Winter)

People:SB Baek

2025-02-10T05:12:58Z

Bsb0613: /* Publications */

===Seungbyn Baek===
{|
|[[File:SB pic.jpg|300px]]
|
*'''Ph.D program''' (2019~): Department of Biotechnology, Yonsei University

*'''B.E.''' (2014-2017): Bio-Convergence, Underwood International College, Yonsei University

<pre>*E-mail : bsb0613(at)yonsei.ac.kr</pre>

'''Research Interest'''
* Single-cell ATAC-seq
* Single-cell RNA-seq
* Cancer Genomics
* COVID-19 Immunology
|}

==Publications==
* '''Seungbyn Baek*''', Euijeong Sung, Gamin Kim, Min-Hee Hong, Chang Young Lee, Hyo Sup Shim, Seong Yong Park**, Hye Ryun Kim**, Insuk Lee**, Single-cell multi-omics reveals tumor microenvironment factors underlying poor immunotherapy responses in ALK-positive lung cancer '''''Cancer Communications''''' 2025 Jan 4. Online ahead of print [https://pubmed.ncbi.nlm.nih.gov/39754710/ pubmed] [https://www.biorxiv.org/content/10.1101/2024.09.24.614708v1 bioRxiv]
* '''Seungbyn Baek*''', Junha Cha, Min-Hee Hong, Gamin Kim, Yoon Woo Koh, Dahee Kim, Martin Hemberg, Seong Yong Park**, Hye Ryun Kim**, Insuk Lee**, Tumor microenvironment distinctions between esophageal cancer subtypes explain varied immunotherapy responses '''''Under Review''''' [https://www.biorxiv.org/content/10.1101/2024.09.24.614705v1 bioRxiv]
* Euijeong Sung, Junha Cha, '''Seungbyn Baek''', Insuk Lee**, Augmenting the human interactome for disease prediction through gene networks inferred from human cell atlas '''''Under Review''''' [https://www.biorxiv.org/content/10.1101/2024.12.12.628105v1 bioRxiv]
* Chan Yeong Kim*, Dong Jin Park*, Beung Chul Ahn*, '''Seungbyn Baek''', Min Hee Hong, Linh Thanh Nguyen, Sun Ha Hwang, Nayeon Kim, Daniel Podlesny, Askarbek Orakov, Christian Schudoma, Shahriyar Mahdi Robbani, Hyo Sup Shim, Hong In Yoon, Chang Young Lee, Seong Yong Park, Dongeun Yong, Mina Han, Peer Bork, Byoung Choul Kim**, Sang-Jun Ha**, Hye Ryun Kim**, Insuk Lee**, A conserved pilin from uncultured gut bacterial clade TANB77 enhances cancer immunotherapy, '''''Nature Communications''''' 2024 Dec 27;15:10726 [https://pubmed.ncbi.nlm.nih.gov/39730328/ PubMed]
* Randall T. Mertens*, Aditya Misra*, Peng Xiao*, '''Seungbyn Baek''', Joseph M. Rone, Davide Mangani, Kisha N. Sivanathan, Adedamola S. Arojojoye, Samuel G. Awuah, Insuk Lee, Guo-Ping Shi, Boryana Petrova, Jeannette R. Brook, Ana C. Anderson, Richard A. Flavell, Naama Kanarek, Martin Hemberg, Roni Nowarski**, A metabolic switch orchestrated by IL-18 and cGAMP programs intestinal tolerance, '''''Immunity''''' 2024 Jun 19:S1074-7613(24)00305-4 [https://pubmed.ncbi.nlm.nih.gov/38906145/ PubMed]
* Junha Cha*, Da Hee Kim*, Gamin Kim*, Jae-Won Cho, Euijeong Sung, '''Seungbyn Baek''', Min Hee Hong, Chang Gon Kim, Nam Suk Sim, Hyun Jun Hong, Jung Eun Lee, Martin Hemberg, Seyeon Park, Sun Ock Yoon, Sang-Jun Ha**, Yoon Woo Koh**, Hye Ryun Kim**, and Insuk Lee**, Single-cell analysis reveals cellular and molecular factors counteracting HPV-positive oropharyngeal cancer immunotherapy outcomes, '''''Journal for Immunotherapy of Cancer'''''. 2024 Jun 10;12(6):e008667 [https://pubmed.ncbi.nlm.nih.gov/38857913/ PubMed]
* Wei E Gordon*, '''Seungbyn Baek*''', Hai P Nguyen, Yien-Ming Kuo, Rachael Bradley, Sarah L. Fong, Nayeon Kim, Alex Galazyuk, Insuk Lee, Melissa Ingala, Nancy B Simmons, Tony Schountz, Lisa Cooper, Ilias Georgakopoulos-Soares, Martin Hemberg**, Nadav Ahituv**, Integrative single-cell characterization of a frugivorous and an insectivorous bat kidney and pancreas '''''Nature Communications''''' 2024 Jan 9;15(1):12 [https://pubmed.ncbi.nlm.nih.gov/38195585/ PubMed]
* '''Seungbyn Baek*''', Sunmo Yang, and Insuk Lee** COVID-GWAB: A Web-Based Prediction of COVID-19 Host Genes via Network Boosting of Genome-Wide Association Data '''''Biomolecules''''' 2022 Oct 12;12(10):1446 [https://pubmed.ncbi.nlm.nih.gov/36291657/ PubMed]
* Chan Yeong Kim*, '''Seungbyn Baek*''', Junha Cha, Sunmo Yang, Eiru Kim, Edward M. Marcotte, Traver Hart, and Insuk Lee** HumanNet v3: An improved database of human gene networks for disease research '''''Nucleic Acids Research''''' 2022 Jan 7;50(D1):D632-D639 [https://pubmed.ncbi.nlm.nih.gov/34747468/ PubMed]
* '''Seungbyn Baek*''', Insuk Lee** Single-cell ATAC sequencing analysis: from data preprocessing to hypothesis generation '''''Computational and Structural Biotechnology Journal''''' 2020 June 28;18:1429-1439 [https://pubmed.ncbi.nlm.nih.gov/32637041 Pubmed]

Journal Club

2025-01-10T02:54:36Z

Bsb0613:

{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2024-2 scOmics
|-
!scope="col" style="padding:.4em" | Date
!scope="col" style="padding:.4em" | Team
!scope="col" style="padding:.4em" | Paper index
!scope="col" style="padding:.4em" | Presenter
!scope="col" style="padding:.4em" | Paper title
|-
|style="padding:.4em;" rowspan=1|2025/3/5
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|25-8
|style="padding:.4em;"|YR Jung
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2024.11.18.624166 Learning multi-cellular representations of single-cell transcriptomics data enables characterization of patient-level disease states]
|-
|style="padding:.4em;" rowspan=1|2025/2/26
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|25-7
|style="padding:.4em;"|EJ Sung
|style="padding:.4em;text-align:left"|
[https://www.medrxiv.org/content/10.1101/2024.04.04.24305313v1 Single-cell RNA sequencing of human tissue supports successful drug targets]
|-
|style="padding:.4em;" rowspan=1|2025/2/19
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|25-6
|style="padding:.4em;"|IS Choi
|style="padding:.4em;text-align:left"|
[https://www.biorxiv.org/content/10.1101/2023.09.08.555192v7 Evaluating the Utilities of Foundation Models in Single-cell Data Analysis]
|-
|style="padding:.4em;" rowspan=1|2025/2/12
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|25-5
|style="padding:.4em;"|SB Baek
|style="padding:.4em;text-align:left"|
[https://www.nature.com/articles/s41586-024-08328-6 Accurate predictions on small data with a tabular foundation model]
|-
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[https://doi.org/10.1093/bioinformatics/btad663 TT3D: Leveraging precomputed protein 3D sequence models to predict protein–protein interactions]
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[https://doi.org/10.1093/bioinformatics/btac258 Topsy-Turvy: integrating a global view into sequence-based PPI prediction]
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[https://doi.org/10.1016/j.cels.2021.08.010 D-SCRIPT translates genome to phenome with sequence-based, structure-aware, genome-scale predictions of protein-protein interactions]
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[https://doi.org/10.1101/2024.08.16.608180 Quantized multi-task learning for context-specific representations of gene network dynamics]
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[https://doi.org/10.1101/2023.11.21.568145 ANDES: a novel best-match approach for enhancing gene set analysis in embedding spaces]
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[https://doi.org/10.1038/s41592-024-02303-9 CellRank 2: unified fate mapping in multiview single-cell data]
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[https://doi.org/10.1101/2024.07.29.605556 scPRINT: pre-training on 50 million cells allows robust gene network predictions]
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[https://doi.org/10.1038/s41467-024-46440-3 Bidirectional generation of structure and properties through a single molecular foundation model]
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[https://doi.org/10.1101/2025.01.07.631644 A Single-Graph Visualization to Reveal Hidden Explainability Patterns of SHAP Feature Interactions in Machine Learning for Biomedical Issues]
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[https://doi.org/10.1016/j.cell.2024.08.037 Emergence of community behaviors in the gut microbiota upon drug treatment]
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[https://doi.org/10.1038/s41564-024-01832-5 Prediction of strain level phage–host interactions across the Escherichia genus using only genomic information]
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[https://doi.org/10.1101/2025.01.07.631807 Metagenomic estimation of absolute bacterial biomass in the mammalian gut through host-derived read normalization]
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[https://doi.org/10.1101/2024.10.02.616292 De novo discovery of conserved gene clusters in microbial genomes with Spacedust]
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[https://doi.org/10.1101/2024.04.14.589414 Rapid and Sensitive Protein Complex Alignment with Foldseek-Multimer]
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[https://doi.org/10.1101/2024.12.30.630825 MGM as a large-scale pretrained foundation model for microbiome analyses in diverse contexts ]
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[https://doi.org/10.1126/science.ado9336 Sequence modeling and design from molecular to genome scale with Evo]
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[https://doi.org/10.1101/2024.12.18.629142 Human gut microbiome gene co-expression network reveals a loss in taxonomic and functional diversity in Parkinson’s disease]
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[https://doi.org/10.1101/2025.01.06.631550 Quantifying Metagenomic Strain Associations from Microbiomes with Anpan]
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[https://doi.org/10.1101/2024.12.13.628459 MaAsLin 3: Refining and extending generalized multivariable linear models for meta-omic association discovery]
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[https://doi.org/10.1101/2024.11.05.622169 PLM-interact: extending protein language models to predict protein-protein interactions]
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[https://doi.org/10.1101/2023.07.17.549267 Learning a deep language model for microbiomes: the power of large scale unlabeled microbiome data]
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[https://doi.org/10.1101/2024.12.30.630844 The genetic diversity and populational specificity of the human gut virome at single nucleotide resolution]
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[https://doi.org/10.1186/s13059-024-03425-1 pan-Draft: automated reconstruction of species-representative metabolic models from multiple genomes]
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[https://doi.org/10.1016/j.cell.2024.09.019 A core microbiome signature as an indicator of health]
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[https://doi.org/10.1016/j.cell.2024.09.027 Using artificial intelligence to document the hidden RNA virosphere]
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[https://doi.org/10.1101/2024.05.30.596740 ProTrek: Navigating the Protein Universe through Tri-Modal Contrastive Learning]
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[https://doi.org/10.1038/s41564-024-01739-1 Multikingdom and functional gut microbiota markers for autism spectrum disorder]
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[https://doi.org/10.1186/s13059-024-03320-9 VirRep: a hybrid language representation learning framework for identifying viruses from human gut metagenomes]
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[https://doi.org/10.1038/s41592-023-02117-1 SEVtras delineates small extracellular vesicles at droplet resolution from single-cell transcriptomes]
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[https://doi.org/10.1038/s41587-023-01728-5 A relay velocity model infers cell-dependent RNA velocity]
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[https://doi.org/10.1038/s41591-024-03067-7 Strain-specific gut microbial signatures in type 2 diabetes identified in a cross-cohort analysis of 8,117 metagenomes]
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[https://doi.org/10.1186/s40168-024-01832-x Gut virome-wide association analysis identifes cross-population viral signatures for infammatory bowel disease]
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[https://doi.org/10.48550/arXiv.1806.00064 Efficient Low-rank Multimodal Fusion with Modality-Specific Factors]
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[https://doi.org/10.48550/arXiv.1707.07250 Tensor Fusion Network for Multimodal Sentiment Analysis]
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[https://doi.org/10.1016/j.cell.2024.03.034 Gut symbionts alleviate MASH through a secondary bile acid biosynthetic pathway]
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[https://doi.org/10.1186/s13059-024-03325-4 Gut microbiota DPP4-like enzymes are increased in type-2 diabetes and contribute to incretin inactivation]
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[https://pubmed.ncbi.nlm.nih.gov/31510656 Deep learning with multimodal representation for pancancer prognosis prediction]
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[https://pubmed.ncbi.nlm.nih.gov/32881682 Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosis]
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[https://doi.org/10.1016/j.ccell.2022.07.004 Pan-cancer integrative histology-genomic analysis via multimodal deep learning]
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[https://doi.org/10.1016/j.chom.2024.03.005 A metagenomics pipeline reveals insertion sequence-driven evolution of the microbiota]
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[https://doi.org/10.48550/arXiv.2303.00915 BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs]
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[https://arxiv.org/abs/2103.00020 Learning Transferable Visual Models From Natural Language Supervision]
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[https://doi.org/10.1038/s41592-022-01616-x BIONIC: biological network integration using convolutions]
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[https://doi.org/10.1038/s41587-023-01917-2 Protein remote homology detection and structural alignment using deep learning]
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[https://doi.org/10.1038/s41586-024-07487-w Accurate structure prediction of biomolecular interactions with AlphaFold 3]
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[https://doi.org/10.1038/s41591-024-02963-2 Microbiome confounders and quantitative profiling challenge predicted microbial targets in colorectal cancer development]
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[https://doi.org/10.1016/j.cell.2024.05.029 Custom scoring based on ecological topology of gut microbiota associated with cancer immunotherapy outcome]
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[https://doi.org/10.1016/j.chom.2024.02.010 Stratification of Fusobacterium nucleatum by localhealth status in the oral cavity defines its subspeciesdisease association]
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[https://doi.org/10.1038/s41586-024-07182-w A distinct Fusobacterium nucleatum clade dominates the colorectal cancer niche]
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[https://doi.org/10.1016/j.cell.2024.01.039 A cryptic plasmid is among the most numerous genetic elements in the human gut]
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[https://doi.org/10.1016/j.cell.2024.03.014 Gut microbiome and metabolome profiling in Framingham heart study reveals cholesterol-metabolizing bacteria]
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[https://doi.org/10.1038/s41467-024-45793-z A metagenomic catalog of the early-life human gut virome]
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[https://doi.org/10.1038/s41467-024-44720-6 Defining the biogeographical map and potential bacterial translocation of microbiome in human ‘surface organs’]
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[https://doi.org/10.1038/s41467-023-42997-7 Gut microbial structural variation associates with immune checkpoint inhibitor response]
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[https://doi.org/10.1080/19490976.2024.2307586 Fungal signature differentiates alcohol-associated liver disease from nonalcoholic fatty liver disease]
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[https://doi.org/10.1038/s41467-023-40719-7 Microdiversity of the vaginal microbiome is associated with preterm birth]
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[https://doi.org/10.1038/s41564-023-01584-8 Large language models improve annotation of prokaryotic viral proteins]
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[https://academic.oup.com/nargab/article/2/2/lqaa023/5826153 Visualizing ’omic feature rankings and log-ratios using Qurro]
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[https://doi.org/10.1038/s41559-020-01353-4 Polarization of microbial communities between competitive and cooperative metabolism]
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[https://onlinelibrary.wiley.com/doi/full/10.1002/advs.202303925 Metagenomic Insight into The Global Dissemination of The Antibiotic Resistome]
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[https://doi.org/10.1073/pnas.2008731118 Conjugative plasmids interact with insertion sequences to shape the horizontal transfer of antimicrobial resistance genes]
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|style="padding:.4em;"|EB Hong
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[https://doi.org/10.1038/s41586-023-07011-6 Spatial transcriptomics reveal neuron–astrocyte synergy in long-term memory]
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|style="padding:.4em;"|JJ Heo
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[https://doi.org/10.1038/s41467-021-22197-x scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses]
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|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|24-30
|style="padding:.4em;"|SM Han
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[https://doi.org/10.1126/science.abi4882 Spatial transcriptomics of planktonic and sessile bacterial populations at single-cell resolution]
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|style="padding:.4em;"|HJ Choi
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[https://doi.org/10.1038/s41590-024-01792-2 Human lung cancer harbors spatially organized stem-immunity hubs associated with response to immunotherapy]
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[https://doi.org/10.1038/s41467-021-27464-5 Single-cell transcriptomics captures features of human midbrain development and dopamine neuron diversity in brain organoids]
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|style="padding:.4em;"|HJ Cha
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[https://doi.org/10.1016/j.chom.2023.08.019 Cell-type-specific responses to fungal infection in plants revealed by single-cell transcriptomics]
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|style="padding:.4em;"|YK Jung
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[https://www.sciencedirect.com/science/article/pii/S1534580722002519?via%3Dihub The single-cell stereo-seq reveals region-specific cell subtypes and transcriptome profiling in Arabidopsis leaves]
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|style="padding:.4em;"|HJ Lee
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[https://doi.org/10.1038/s41588-022-01100-4 Single-cell and bulk transcriptome sequencing identifies two epithelial tumor cell states and refines the consensus molecular classification of colorectal cancer]
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|style="padding:.4em;"|HK Lee
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[https://doi.org/10.1038/s42255-023-00876-x Delineating mouse β-cell identity during lifetime and in diabetes with a single cell atlas]
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|style="padding:.4em;"|JI Lee
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[https://doi.org/10.1038/s41587-023-01747-2 Multimodal spatiotemporal phenotyping of human retinal organoid development]
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|style="padding:.4em;"|JH Lee
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[https://doi.org/10.1038/s41586-024-07251-0 Immune microniches shape intestinal Treg function]
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|style="padding:.4em;"|JH Lee
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[https://doi.org/10.1016/j.devcel.2021.02.021 A single-cell analysis of the Arabidopsis vegetative shoot apex]
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[https://doi.org/10.1038/s41467-023-40137-9 Droplet-based high-throughput single microbe RNA sequencing by smRandom-seq]
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[https://doi.org/10.1038/s41564-023-01462-3 Single-cell massively-parallel multiplexed microbial sequencing (M3-seq) identifies rare bacterial populations and profiles phage infection]
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|style="padding:.4em;"|EB Yu
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[https://doi.org/10.1016/j.celrep.2022.111736 Spatial transcriptomics demonstrates the role of CD4 T cells in effector CD8 T cell differentiation during chronic viral infection]
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|style="padding:.4em;"|DY Won
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[https://doi.org/10.1038/s41587-023-01979-2 Spatial metatranscriptomics resolves host–bacteria–fungi interactomes]
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[https://doi.org/10.1038/s41467-023-36325-2 Dissecting the immune suppressive human prostate tumor microenvironment via integrated single-cell and spatial transcriptomic analyses]
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[https://doi.org/10.1038/s41593-023-01452-y Single-nucleus genomics in outbred rats with divergent cocaine addiction-like behaviors reveals changes in amygdala GABAergic inhibition]
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[https://doi.org/10.1038/s41593-023-01455-9 Spatial transcriptomics reveals the distinct organization of mouse prefrontal cortex and neuronal subtypes regulating chronic pain]
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[https://doi.org/10.1038/s41467-023-39933-0 Spatial cellular architecture predicts prognosis in glioblastoma]
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[https://doi.org/10.1016/j.celrep.2024.113784 Single-cell spatial transcriptomic and translatomic profiling of dopaminergic neurons in health, aging, and disease]
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[https://doi.org/10.1038/s41467-022-30511-4 Transcriptional adaptation of olfactory sensory neurons to GPCR identity and activity]
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[https://doi.org/10.1038/s41467-021-26271-2 Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions]
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[https://doi.org/10.1021/acscentsci.3c01169 Single-Cell Analysis Reveals Cxcl14+ Fibroblast Accumulation in Regenerating Diabetic Wounds Treated by Hydrogel-Delivering Carbon Monoxide]
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[https://doi.org/10.1038/s41477-022-01291-y Single-cell RNA sequencing provides a high-resolution roadmap for understanding the multicellular compartmentation of specialized metabolism]
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[https://doi.org/10.1038/s41556-023-01316-4 Single-cell spatial multi-omics and deep learning dissect enhancer-driven gene regulatory networks in liver zonation]
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[https://doi.org/10.1038/s41467-022-35319-w Spatial transcriptomics landscape of lesions from non-communicable inflammatory skin diseases]
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[https://doi.org/10.1016/j.cmet.2022.07.010 Neuregulin 4 suppresses NASH-HCC development by restraining tumor-prone liver microenvironment]
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[https://doi.org/10.1038/s41593-023-01334-3 Single-nucleus multiregion transcriptomic analysis of brain vasculature in Alzheimer’s disease]
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|style="padding:.4em;"|SH Ahn
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[https://doi.org/10.1136/gutjnl-2023-330243 Single-cell transcriptomic analysis deciphers heterogenous cancer stem-like cells in colorectal cancer and their organ-specific metastasis]
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[https://doi.org/10.1038/s41467-022-31519-6 Single cell sequencing identifies clonally expanded synovial CD4+ TPH cells expressing GPR56 in rheumatoid arthritis]
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[https://doi.org/10.1016/j.ccell.2023.09.011 Progenitor-like exhausted SPRY1+CD8+ T cells potentiate responsiveness to neoadjuvant PD-1 blockade in esophageal squamous cell carcinoma]
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[https://doi.org/10.1038/s41592-023-02035-2 Population-level integration of single-cell datasets enables multi-scale analysis across samples]
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|style="padding:.4em;"|SB Baek
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[https://doi.org/10.1038/s43587-023-00514-x scDiffCom: a tool for differential analysis of cell–cell interactions provides a mouse atlas of aging changes in intercellular communication]
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|style="padding:.4em;"|JH Cha
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[https://doi.org/10.1038/s41587-022-01467-z Modeling intercellular communication in tissues using spatial graphs of cells]
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[https://doi.org/10.1038/s41588-023-01523-7 Precise identification of cell states altered in disease using healthy single-cell references]
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[https://aacrjournals.org/clincancerres/article/29/19/3924/729105/Learning-Individual-Survival-Models-from-PanCancer Learning Individual Survival Models from PanCancer Whole Transcriptome Data]
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[https://doi.org/10.1038/s41592-023-01971-3 Dictys: dynamic gene regulatory network dissects developmental continuum with single-cell multiomics]
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|style="padding:.4em;"|JH Cha
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[https://www.science.org/doi/10.1126/sciimmunol.adf4968 Preexisting tumor-resident T cells with cytotoxic potential associate with response to neoadjuvant anti–PD-1 in head and neck cancer]
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[https://doi.org/10.1038/s41588-022-01273-y MHC II immunogenicity shapes the neoepitope landscape in human tumors]
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[https://doi.org/10.1038/s41586-023-06130-4 Hallmarks of transcriptional intratumour heterogeneity across a thousand tumours]
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[https://doi.org/10.1038/s41467-023-37353-8 Pan-cancer classification of single cells in the tumour microenvironment]
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[https://doi.org/10.1038/s41587-023-01686-y Single-cell mapping of combinatorial target antigens for CAR switches using logic gates]
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[https://doi.org/10.1038/s41587-023-01782-z Comparative analysis of cell–cell communication at single-cell resolution]
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|style="padding:.4em;" rowspan=1|Single-cell
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[https://doi.org/10.1038/s43018-023-00566-3 Phenotypic diversity of T cells in human primary and metastatic brain tumors revealed by multiomic interrogation]
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[https://doi.org/10.1038/s41591-023-02324-5 An integrated tumor, immune and microbiome atlas of colon cancer]
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[https://doi.org/10.1038/s41587-022-01476-y Multi-omic single-cell velocity models epigenome–transcriptome interactions and improves cell fate prediction]
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|style="padding:.4em;" rowspan=1|2023/09/05
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[https://doi.org/10.1016/j.immuni.2023.04.010 Recruitment of epitope-specific T cell clones with a low-avidity threshold supports efficacy against mutational escape upon re-infection]
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[https://doi.org/10.1186/s40168-023-01607-w Phages are unrecognized players in the ecology of the oral pathogen Porphyromonas gingivalis]
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[https://doi.org/10.1038/s41564-023-01439-2 A predicted CRISPR-mediated symbiosis between uncultivated archaea]
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[https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-023-01692-x Integrating compositional and functional content to describe vaginal microbiomes in health and disease]
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[https://doi.org/10.1038/s41587-023-01696-w Contamination source modeling with SCRuB improves cancer phenotype prediction from microbiome data]
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[https://doi.org/10.1038/s41586-023-06431-8 Mapping the T cell repertoire to a complex gut bacterial community]
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[https://doi.org/10.1101/2023.07.03.547607 Multi-view integration of microbiome data for identifying disease-associated modules]
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[https://doi.org/10.1101/2023.09.28.559994 Phage-bacteria dynamics during the first years of life revealed by trans-kingdom marker gene analysis]
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[https://doi.org/10.1038/s41593-023-01361-0 Multi-level analysis of the gut–brain axis shows autism spectrum disorder-associated molecular and microbial profiles]
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[https://doi.org/10.1101/2023.11.21.568153 Metagenomic Immunoglobulin Sequencing (MIG-Seq) Exposes Patterns of IgA Antibody Binding in the Healthy Human Gut Microbiome]
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[https://doi.org/10.1038/s41467-023-41042-x Impact of dietary interventions on pre-diabetic oral and gut microbiome, metabolites and cytokines]
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[https://doi.org/10.1038/s41592-023-02018-3 Fast and robust metagenomic sequence comparison through sparse chaining with skani]
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[https://doi.org/10.1038/s41591-023-02599-8 Bacterial SNPs in the human gut microbiome associate with host BMI]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|JH Cha
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[https://doi.org/10.1080/19490976.2023.2245562 Multimodal metagenomic analysis reveals microbial single nucleotide variants as superior biomarkers for early detection of colorectal cancer]
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|style="padding:.4em;"|23-53
|style="padding:.4em;"|NY Kim
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[https://doi.org/10.1016/j.chom.2023.10.005 Multi-kingdom gut microbiota analyses define bacterial-fungal interplay and microbial markers of pan-cancer immunotherapy across cohorts]
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|style="padding:.4em;"|YR Kim
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[https://doi.org/10.1016/j.xcrm.2023.101251 Prior antibiotic administration disrupts anti-PD-1 responses in advanced gastric cancer by altering the gut microbiome and systemic immune response]
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|style="padding:.4em;"|JY Kim
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[https://doi.org/10.1016/j.cell.2023.05.046 Ultra-deep sequencing of Hadza hunter-gatherers recovers vanishing gut microbes]
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[https://doi.org/10.1186/s40168-023-01472-7 Altered infective competence of the human gut microbiome in COVID-19]
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|style="padding:.4em;"|G Koh
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[https://onlinelibrary.wiley.com/doi/full/10.1002/aisy.202300342 Host-Variable-Embedding Augmented Microbiome-Based Simultaneous Detection of Multiple Diseases by Deep Learning]
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[https://doi.org/10.1038/s41467-023-39264-0 A data-driven approach for predicting the impact of drugs on the human microbiome]
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[https://doi.org/10.1101/2023.04.06.535777 Activation of programmed cell death and counter-defense functions of phage accessory genes]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|JH Cha
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[https://doi.org/10.1038/s41467-023-39459-5 Top-down identification of keystone taxa in the microbiome]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-45
|style="padding:.4em;"|JY Ma
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[https://doi.org/10.1016/j.cels.2022.12.007 Pitfalls of genotyping microbial communities with rapidly growing genome collections]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-44
|style="padding:.4em;"|NY Kim
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[https://doi.org/10.1186/s13059-023-03028-2 Reconstruction of the last bacterial common ancestor from 183 pangenomes reveals a versatile ancient core genome]
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|style="padding:.4em;"|23-43
|style="padding:.4em;"|SH Lee
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[https://doi.org/10.1038/s41587-023-01868-8 Generation of accurate, expandable phylogenomic trees with uDance]
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|style="padding:.4em;"|WJ Kim
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[https://doi.org/10.1016/j.immuni.2023.04.003 Phage display sequencing reveals that genetic, environmental, and intrinsic factors influence variation of human antibody epitope repertoire]
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|style="padding:.4em;"|23-41
|style="padding:.4em;"|JY Kim
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[https://doi.org/10.1016/j.immuni.2023.04.017 Phage-display immunoprecipitation sequencing of the antibody epitope repertoire in inflammatory bowel disease reveals distinct antibody signatures]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-40
|style="padding:.4em;"|G Koh
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[https://doi.org/10.15252/msb.202311525 Consistency across multi-omics layers in a drug-perturbed gut microbial community]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-39
|style="padding:.4em;"|HJ Kim
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[https://doi.org/10.1038/s41587-023-01953-y Identification of mobile genetic elements with geNomad]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-38
|style="padding:.4em;"|SH Ahn
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[https://doi.org/10.1038/s41591-023-02407-3 Microbiome-derived cobalamin and succinyl-CoA as biomarkers for improved screening of anal cancer]
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|style="padding:.4em;"|23-37
|style="padding:.4em;"|JH Cha
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[https://doi.org/10.1038/s41591-023-02424-2 The airway microbiome mediates the interaction between environmental exposure and respiratory health in humans]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-36
|style="padding:.4em;"|JY Ma
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[https://doi.org/10.1080/19490976.2023.2224474 Ordering taxa in image convolution networks improves microbiome-based machine learning accuracy]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-35
|style="padding:.4em;"|NY Kim
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[https://doi.org/10.1101/2023.08.12.553040 The defensome of complex bacterial communities]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-34
|style="padding:.4em;"|SH Lee
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[https://doi.org/10.1016/j.cell.2023.03.011 Dietary tryptophan metabolite released by intratumoral Lactobacillus reuteri facilitates immune checkpoint inhibitor treatment]
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|style="padding:.4em;"|WJ Kim
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[https://doi.org/10.1038/s43587-022-00306-9 Toward an improved definition of a healthy microbiome for healthy aging]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-32
|style="padding:.4em;"|JY Kim
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[https://doi.org/10.1038/s43587-022-00287-9 Associations of the skin, oral and gut microbiome with aging, frailty and infection risk reservoirs in older adults]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-31
|style="padding:.4em;"|G Koh
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[https://doi.org/10.1186/s40168-023-01614-x Statistical modeling of gut microbiota for personalized health status monitoring]
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|style="padding:.4em;"|SH Ahn
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[https://doi.org/10.7554/eLife.50240 Adjusting for age improves identification of gut microbiome alterations in multiple diseases]
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|style="padding:.4em;"|HJ Kim
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[https://doi.org/10.1038/s41564-023-01370-6 Centenarians have a diverse gut virome with the potential to modulate metabolism and promote healthy lifespan]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-28
|style="padding:.4em;"|JH Cha
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[https://doi.org/10.1016/j.chom.2023.01.003 Longitudinal comparison of the developing gut virome in infants and their mothers]
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{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2023-1 ADVANCED MICROBIOME DATA ANALYSIS
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[https://doi.org/10.1016/j.chom.2020.03.005 Structure of the Mucosal and Stool Microbiome in Lynch Syndrome]
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[https://doi.org/10.1038/s41586-019-1237-9 Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases]
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|style="padding:.4em;"|SH Ahn
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[https://doi.org/10.1016/j.jare.2022.03.007 Roles of oral microbiota and oral-gut microbial transmission in hypertension]
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[https://doi.org/10.1016/j.chom.2022.08.009 Human gut microbiota stimulate defined innate immune responses that vary from phylum to strain]
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[https://doi.org/10.1038/s41591-023-02217-7 Gut microbial metabolism of 5-ASA diminishes its clinical efficacy in inflammatory bowel disease]
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[https://doi.org/10.1016/j.chom.2023.01.004 Deficient butyrate-producing capacity in the gut microbiome is associated with bacterial network disturbances and fatigue symptoms in ME/CFS]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|EJ Sung
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[https://doi.org/10.1016/j.ccell.2022.11.013 Gut microbiota-mediated nucleotide synthesis attenuates the response to neoadjuvant chemoradiotherapy in rectal cancer]
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|style="padding:.4em;"|G Koh
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[https://doi.org/10.1016/j.chom.2021.06.019 Multi-omics reveal microbial determinants impacting responses to biologic therapies in inflammatory bowel disease]
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|style="padding:.4em;"|SH Lee
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[https://doi.org/10.1038/s41586-022-05181-3 Identification of trypsin-degrading commensals in the large intestine]
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|style="padding:.4em;"|JP Hong
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[https://doi.org/10.1038/s41586-022-05546-8 Questioning the fetal microbiome illustrates pitfalls of low-biomass microbial studies]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-14
|style="padding:.4em;"|MR Jang
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[https://doi.org/10.1016/j.chom.2023.01.013 Tissue-resident Lachnospiraceae family bacteria protect against colorectal carcinogenesis by promoting tumor immune surveillance]
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|style="padding:.4em;"|23-13
|style="padding:.4em;"|JW Yu
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[https://doi.org/10.1016/j.cell.2022.09.005 Pan-cancer analyses reveal cancer-type-specific fungal ecologies and bacteriome interactions]
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|style="padding:.4em;" rowspan=1|2023/05/09
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-12
|style="padding:.4em;"|NY Kim
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[https://doi.org/10.1016/j.cell.2022.09.015 A pan-cancer mycobiome analysis reveals fungal involvement in gastrointestinal and lung tumors]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-11
|style="padding:.4em;"|HR Shin
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[https://doi.org/10.1038/s41564-021-01030-7 Multi-kingdom microbiota analyses identify bacterial–fungal interactions and biomarkers of colorectal cancer across cohorts]
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|style="padding:.4em;" rowspan=1|Microbiome
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[https://doi.org/10.1038/s42255-022-00716-4 The antitumour effects of caloric restriction are mediated by the gut microbiome]
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|style="padding:.4em;" rowspan=1|Microbiome
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[https://doi.org/10.1038/s41591-022-01964-3 Variability of strain engraftment and predictability of microbiome composition after fecal microbiota transplantation across different diseases]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|SM Han
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[https://doi.org/10.1038/s41591-022-01913-0 Drivers and determinants of strain dynamics following fecal microbiota transplantation]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|YY Kim
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[https://doi.org/10.1016/j.cell.2022.11.023 Mobile genetic elements from the maternal microbiome shape infant gut microbial assembly and metabolism]
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|style="padding:.4em;" rowspan=1|2023/04/18
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-6
|style="padding:.4em;"|SH Heo
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[https://doi.org/10.1016/j.chom.2023.01.018 Mother-to-infant microbiota transmission and infant microbiota development across multiple body sites]
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|style="padding:.4em;" rowspan=1|2023/04/18
|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|SY Yang
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[https://doi.org/10.1038/s41467-023-36633-7 Population-level impacts of antibiotic usage on the human gut microbiome]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|YH Yoon
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[https://doi.org/10.1038/s41586-022-05438-x Enterococci enhance Clostridioides difficile pathogenesis]
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|style="padding:.4em;"|23-3
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[https://doi.org/10.1002/imt2.61 Targeting keystone species helps restore the dysbiosis of butyrate‐producing bacteria in nonalcoholic fatty liver disease]
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[https://doi.org/10.1002/advs.202203115 Differential Oral Microbial Input Determines Two Microbiota Pneumo-Types Associated with Health Status]
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[https://doi.org/10.1016/j.cell.2022.08.021 Gut microbiome of multiple sclerosis patients and paired household healthy controls reveal associations with disease risk and course]
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{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2023-1 scOmics
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|style="padding:.4em;"|JW Yu
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[https://doi.org/10.1101/2023.01.17.524482 Decoupling the correlation between cytotoxic and exhausted T lymphocyte transcriptomic signatures enhances melanoma immunotherapy response prediction from tumor expression]
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|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|IS Choi
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[https://doi.org/10.1101/2023.07.28.550993 Major data analysis errors invalidate cancer microbiome findings]
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|style="padding:.4em;"|EJ Sung
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[https://doi.org/10.1038/s43018-022-00475-x A single-cell atlas of glioblastoma evolution under therapy reveals cell-intrinsic and cell-extrinsic therapeutic targets]
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[https://doi.org/10.1038/s43018-022-00433-7 Single-cell meta-analyses reveal responses of tumor-reactive CXCL13+ T cells to immune-checkpoint blockade]
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[https://doi.org/10.1038/s41587-022-01342-x Estimation of tumor cell total mRNA expression in 15 cancer types predicts disease progression]
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|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|JH Cha
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[https://doi.org/10.1038/s41587-022-01288-0 DIALOGUE maps multicellular programs in tissue from single-cell or spatial transcriptomics data]
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|style="padding:.4em;"|SB Baek
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[https://doi.org/10.1038/s41591-023-02371-y Pan-cancer T cell atlas links a cellular stress response state to immunotherapy resistance]
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[https://doi.org/10.1016/j.patter.2022.100651 Self-supervised graph representation learning integrates multiple molecular networks and decodes gene-disease relationships]
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[https://doi.org/10.1016/j.ccell.2022.10.008 High-resolution single-cell atlas reveals diversity and plasticity of tissue-resident neutrophils in non-small cell lung cancer]
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{|class=wikitable style="text-align:center;"
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!scope="col" style="padding:.4em" | Date
!scope="col" style="padding:.4em" | Team
!scope="col" style="padding:.4em" | Paper index
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!scope="col" style="padding:.4em" | Paper title
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|style="padding:.4em;" rowspan=1|2023/08/25
|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|JY Ma
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[https://doi.org/10.1016/j.chom.2023.05.024 Enterosignatures define common bacterial guilds in the human gut microbiome]
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|style="padding:.4em;" rowspan=1|2023/08/25
|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|NY Kim
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|SH Lee
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[https://doi.org/10.1016/j.chom.2023.05.027 The TaxUMAP atlas: Efficient display of large clinical microbiome data reveals ecological competition in protection against bacteremia]
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|style="padding:.4em;" rowspan=1|2023/08/18
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-24
|style="padding:.4em;"|WJ Kim
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[http://dx.doi.org/10.1038/nbt.3704 Measurement of bacterial replication rates in microbial communities]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-23
|style="padding:.4em;"|JY Kim
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[https://doi.org/10.1186/s40168-023-01564-4 Skin microbiome diferentiates into distinct cutotypes with unique metabolic functions upon exposure to polycyclic aromatic hydrocarbons]
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|style="padding:.4em;" rowspan=1|2023/08/11
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-22
|style="padding:.4em;"|SH Ahn
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[https://doi.org/10.1016/j.xcrm.2023.100920 Enrichment of oral-derived bacteria in inflamed colorectal tumors and distinct associations of Fusobacterium in the mesenchymal subtype]
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|style="padding:.4em;" rowspan=1|2023/08/04
|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|HJ Kim
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[https://doi.org/10.1038/s41586-023-05989-7 Profiling the human intestinal environment under physiological conditions]
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|style="padding:.4em;" rowspan=1|Microbiome
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[https://doi.org/10.1186/s40168-023-01494-1 Genome-centric metagenomics reveals the host-driven dynamics and ecological role of CPR bacteria in an activated sludge system]
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|style="padding:.4em;" rowspan=1|2023/07/14
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[https://doi.org/10.1016/j.patter.2022.100658 Enhanced metagenomic deep learning for disease prediction and consistent signature recognition by restructured microbiome 2D representations]
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[https://doi.org/10.1186/s13059-022-02809-5 Gene fow and introgression are pervasive forces shaping the evolution of bacterial species]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|JH Cha
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[https://doi.org/10.1186/s40168-022-01435-4 Alterations of oral microbiota and impact on the gut microbiome in type 1 diabetes mellitus revealed by integrated multi‑omic analyses]
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[https://doi.org/10.1016/j.immuni.2022.08.016 The CD4+ T cell response to a commensal-derived epitope transitions from a tolerant to an inflammatory state in Crohn’s disease]
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[https://doi.org/10.1038/s41564-017-0084-4 Metatranscriptome of human faecal microbial communities in a cohort of adult men]
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|style="padding:.4em;"|SH Ahn
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[https://doi.org/10.1016/j.ccell.2022.09.009 Tumor microbiome links cellular programs and immunity in pancreatic cancer]
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[https://doi.org/10.1038/s41586-022-05620-1 The person-to-person transmission landscape of the gut and oral microbiomes]
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[https://doi.org/10.1101/2023.01.30.526328 BIRDMAn: A Bayesian differential abundance framework that enables robust inference of host-microbe associations]
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[https://www.nature.com/articles/s41564-022-01157-1 Phage–host coevolution in natural populations]
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[https://www.nature.com/articles/s41467-022-29968-0 A randomized controlled trial for response of microbiome network to exercise and diet intervention in patients with nonalcoholic fatty liver disease]
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[https://www.biorxiv.org/content/10.1101/2022.05.19.492684v1 Scalable power analysis and effect size exploration of microbiome community differences with Evident]
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[https://www.biorxiv.org/content/10.1101/2022.08.19.504505v1 SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks]
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|style="padding:.4em;"|JH Cha, SB Baek, IS Choi
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[https://www.pnas.org/doi/10.1073/pnas.2105859118 Representation learning of RNA velocity reveals robust cell transitions]
[https://www.nature.com/articles/s41467-022-34188-7 UniTVelo: temporally unified RNA velocity reinforces single-cell trajectory inference]
[https://www.sciencedirect.com/science/article/pii/S0092867421015774 Mapping transcriptomic vector fields of single cells]
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[https://www.nature.com/articles/s41467-022-31535-6 Network-based machine learning approach to predict immunotherapy response in cancer patients]
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|style="padding:.4em;" rowspan=1|Single-cell
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[https://www.nature.com/articles/s41586-022-04718-w Extricating human tumour immune alterations from tissue inflammation]
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[https://www.biorxiv.org/content/10.1101/2022.06.15.495325v1 T cell receptor convergence is an indicator of antigen-specific T cell response in cancer immunotherapies]
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[https://www.biorxiv.org/content/10.1101/2021.12.06.471401v1 MIRA: Joint regulatory modeling of multimodal expression and chromatin accessibility in single cells]
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|style="padding:.4em;" rowspan=1|Single-cell
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[https://www.nature.com/articles/s41564-022-01121-z Identification of shared and disease-specific host gene–microbiome associations across human diseases using multi-omic integration]
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[https://www.sciencedirect.com/science/article/pii/S193131282200049X Caudovirales bacteriophages are associated with improved executive function and memory in flies, mice, and humans]
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[https://www.nature.com/articles/s41587-022-01226-0 Identification of antimicrobial peptides from the human gut microbiome using deep learning]
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[https://www.nature.com/articles/s41586-022-04648-7 Discovery of bioactive microbial gene products in inflammatory bowel disease]
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[https://www.nature.com/articles/s41467-022-30512-3 Predicting cancer prognosis and drug response from the tumor microbiome]
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[https://www.biorxiv.org/content/10.1101/2022.02.21.480893v1 Integrating phylogenetic and functional data in microbiome studies]
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[https://pubmed.ncbi.nlm.nih.gov/34618582/ Commensal bacteria promote endocrine resistance in prostate cancer through androgen biosynthesis]
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[https://elifesciences.org/articles/42693 Extensive transmission of microbes along the gastrointestinal tract]
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|style="padding:.4em;" rowspan=1|Microbiome
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[https://www.cell.com/cell-host-microbe/fulltext/S1931-3128(19)30041-1?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS1931312819300411%3Fshowall%3Dtrue Distinct Genetic and Functional Traits of Human Intestinal Prevotella copri Strains Are Associated with Different Habitual Diets]
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[https://www.nature.com/articles/nmeth.3802 Strain-level microbial epidemiology and population genomics from shotgun metagenomics]
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[https://www.cell.com/cell/fulltext/S0092-8674(21)00942-9#secsectitle0025 Chromatin and gene-regulatory dynamics of the developing human cerebral cortex at single-cell resolution]
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[https://www.biorxiv.org/content/10.1101/2021.02.09.430114v2 Single-cell ATAC and RNA sequencing reveal pre-existing and persistent subpopulations of cells associated with relapse of prostate cancer]
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[https://www.biorxiv.org/content/10.1101/2021.03.16.435578v1 Integrated single-cell transcriptomics and epigenomics reveals strong germinal center-associated etiology of autoimmune risk loci]
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[https://www.biorxiv.org/content/10.1101/2021.07.28.453784v1 Functional Inference of Gene Regulation using Single-Cell Multi-Omics]
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[https://www.biorxiv.org/content/10.1101/2021.03.24.436532v1 Single-cell analyses reveal a continuum of cell state and composition changes in the malignant transformation of polyps to colorectal cancer]
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[https://www.cell.com/cancer-cell/fulltext/S1535-6108(21)00165-3 Single-cell sequencing links multiregional immune landscapes and tissue-resident T cells in ccRCC to tumor topology and therapy efficacy]
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[https://www.sciencedirect.com/science/article/pii/S1535610821001173 Tumor and immune reprogramming during immunotherapy in advanced renal cell carcinoma]
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[https://www.sciencedirect.com/science/article/pii/S1074761321001199 Single-cell chromatin accessibility landscape identifies tissue repair program in human regulatory T cells]
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[https://www.nature.com/articles/s41591-021-01232-w Identification of resistance pathways and therapeutic targets in relapsed multiple myeloma patients through single-cell sequencing]
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[https://www.nature.com/articles/s41591-021-01323-8 A single-cell map of intratumoral changes during anti-PD1 treatment of patients with breast cancer]
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[https://www.sciencedirect.com/science/article/abs/pii/S0092867420316135 Single-cell landscape of the ecosystem in early-relapse hepatocellular carcinoma]
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[https://www.nature.com/articles/s41586-021-03552-w Interpreting type 1 diabetes risk with genetics and single-cell epigenomics]
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[https://www.sciencedirect.com/science/article/pii/S0092867421000726 Massive expansion of human gut bacteriophage diversity]
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[https://www.sciencedirect.com/science/article/pii/S1931312821001451 The infant gut resistome associates with E. coli, environmental exposures, gut microbiome maturity, and asthma-associated bacterial composition]
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[https://www.sciencedirect.com/science/article/pii/S1931312820306703?dgcid=rss_sd_all Methotrexate impacts conserved pathways in diverse human gut bacteria leading to decreased host immune activation]
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[https://science.sciencemag.org/content/366/6471/eaax9176 A metagenomic strategy for harnessing the chemical repertoire of the human microbiome]
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[https://www.nature.com/articles/s41467-019-10927-1 Predictive metabolomic profiling of microbial communities using amplicon or metagenomic sequences]
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[https://www.nature.com/articles/s41564-018-0306-4 Gut microbiome structure and metabolic activity in inflammatory bowel disease]
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[https://www.nature.com/articles/s41591-020-01183-8 Microbiome connections with host metabolism and habitual diet from 1,098 deeply phenotyped individuals]
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[https://www.nature.com/articles/s41467-020-18476-8 A predictive index for health status using species-level gut microbiome profiling]
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[https://www.nature.com/articles/s41586-019-1237-9 Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases]
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[https://www.nature.com/articles/s41467-021-21475-y Gastrointestinal microbiota composition predicts peripheral inflammatory state during treatment of human tuberculosis]
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[https://www.sciencedirect.com/science/article/pii/S1931312820301694 Structure of the Mucosal and Stool Microbiome in Lynch Syndrome]
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[https://science.sciencemag.org/content/369/6506/936 Cross-reactivity between tumor MHC class 1-restricted antigens and an enterococcal bacteriophage]
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[https://www.nature.com/articles/s41564-020-00831-6 Bifidobacterium bifidum strains synergize with immune checkpoint inhibitors to reduce tumour burden in mice]
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[https://www.nature.com/articles/s41591-020-01223-3 The gut microbiome modulates the protective association between a Mediterranean diet and cardiometabolic disease risk]
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[https://www.nature.com/articles/s41467-019-14177-z Impact of commonly used drugs on the composition and metabolic function of the gut microbiota]
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[https://www.sciencedirect.com/science/article/pii/S0092867420305638 Personalized Mapping of Drug Metabolism by the Human Gut Microbiome]
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[https://www.cell.com/fulltext/S0092-8674(17)30107-1 Mining the Human Gut Microbiota for Immunomodulatory Organisms]
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[https://www.nature.com/articles/s41586-020-2095-1 Microbiome analyses of blood and tissues suggest cancer diagnostic approach]
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[https://science.sciencemag.org/content/368/6494/973 The human tumor microbiome is composed of tumor type-specific intracellular bacteria]
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[https://www.nature.com/articles/s41590-020-0784-4 Functional CRISPR dissection of gene networks controlling human regulatory T cell identity]
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[https://www.sciencedirect.com/science/article/pii/S0092867420306887 Molecular Pathways of Colon Inflammation Induced by Cancer Immunotherapy]
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[https://www.nature.com/articles/s41588-020-00721-x Single-cell epigenomic analyses implicate candidate causal variants at inherited risk loci for Alzheimer’s and Parkinson’s diseases]
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[https://www.nature.com/articles/s41467-020-14766-3 Trajectory-based differential expression analysis for single-cell sequencing data]
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[https://www.nature.com/articles/s41588-018-0156-2 Genetic determinants of co-accessible chromatin regions in activated T cells across humans]
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[https://www.sciencedirect.com/science/article/pii/S009286742030341X Single-Cell Analyses Inform Mechanisms of Myeloid-Targeted Therapies in Colon Cancer]
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[https://www.sciencedirect.com/science/article/pii/S0092867419308967?via%3Dihub Single-Cell Analysis of Crohn's Disease Lesions Identifies a Pathogenic Cellular Module Associated with Resistance to Anti-TNF Therapy]
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[https://www.nature.com/articles/s41467-020-15956-9 Multi-omic single-cell snapshots reveal multiple independent trajectories to drug tolerance in a melanoma cell line]
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[https://pubmed.ncbi.nlm.nih.gov/32393797/ Multi-omic single-cell snapshots reveal multiple independent trajectories to drug tolerance in a melanoma cell line]
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[https://pubmed.ncbi.nlm.nih.gov/30595452/ Dysfunctional CD8 T Cells Form a Proliferative, Dynamically Regulated Compartment Within Human Melanoma]
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[https://pubmed.ncbi.nlm.nih.gov/30388455/ A Cancer Cell Program Promotes T Cell Exclusion and Resistance to Checkpoint Blockade]
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[https://pubmed.ncbi.nlm.nih.gov/31974247/ Single-cell Transcriptional Diversity Is a Hallmark of Developmental Potential]
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[https://pubmed.ncbi.nlm.nih.gov/31675496/ Landscape and Dynamics of Single Immune Cells in Hepatocellular Carcinoma]
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[https://pubmed.ncbi.nlm.nih.gov/32103181/ Peripheral T Cell Expansion Predicts Tumour Infiltration and Clinical Response]
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[https://pubmed.ncbi.nlm.nih.gov/30388456/ Defining T Cell States Associated With Response to Checkpoint Immunotherapy in Melanoma]
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[https://pubmed.ncbi.nlm.nih.gov/31915379/ Rapid Non-Uniform Adaptation to Conformation-Specific KRAS(G12C) Inhibition]
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[https://pubmed.ncbi.nlm.nih.gov/31959990/ Targeted Therapy Guided by Single-Cell Transcriptomic Analysis in Drug-Induced Hypersensitivity Syndrome: A Case Report]
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[https://pubmed.ncbi.nlm.nih.gov/32066951/ Distinct Microbial and Immune Niches of the Human Colon]
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[https://pubmed.ncbi.nlm.nih.gov/31375813/ Massively Parallel Single-Cell Chromatin Landscapes of Human Immune Cell Development and Intratumoral T Cell Exhaustion]
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[https://pubmed.ncbi.nlm.nih.gov/29434354/ Single-cell Gene Expression Reveals a Landscape of Regulatory T Cell Phenotypes Shaped by the TCR]
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[https://pubmed.ncbi.nlm.nih.gov/30078704/ A Single-Cell Atlas of In Vivo Mammalian Chromatin Accessibility]
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[https://pubmed.ncbi.nlm.nih.gov/32014031/ scAI: An Unsupervised Approach for the Integrative Analysis of Parallel Single-Cell Transcriptomic and Epigenomic Profiles]
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[https://pubmed.ncbi.nlm.nih.gov/31792411/ Single-cell Multiomic Analysis Identifies Regulatory Programs in Mixed-Phenotype Acute Leukemia]
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[https://www.sciencedirect.com/science/article/pii/S1931312819303488 Obese Individuals with and without Type 2 Diabetes Show Different Gut Microbial Functional Capacity and Composition]
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[https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-019-0722-6 Clustering co-abundant genes identifies components of the gut microbiome that are reproducibly associated with colorectal cancer and inflammatory bowel disease]
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[https://www.nature.com/articles/s41587-019-0206-z Massively parallel single-cell chromatin landscapes of human immune cell development and intratumoral T cell exhaustion]
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[https://www.biorxiv.org/content/10.1101/739011v1 Assessment of computational methods for the analysis of single-cell ATAC-seq data]
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[https://www.sciencedirect.com/science/article/pii/S1931312819303026 Meta-Analysis Reveals Reproducible Gut Microbiome Alterations in Response to a High-Fat Diet]
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[https://www.sciencedirect.com/science/article/pii/S0092867419307731 Tumor Microbiome Diversity and Composition Influence Pancreatic Cancer Outcomes]
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[https://genome.cshlp.org/content/early/2019/04/01/gr.243725.118 The accessible chromatin landscape of the murine hippocampus at single-cell resolution]
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[https://www.sciencedirect.com/science/article/pii/S009286741830446X Integrated Single-Cell Analysis Maps the Continuous Regulatory Landscape of Human Hematopoietic Differentiation]
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|style="padding:.4em;"|SH Lee
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[https://science.sciencemag.org/content/365/6449/eaau4735 A sparse covarying unit that describes healthy and impaired human gut microbiota development]
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|style="padding:.4em;"|19-42
|style="padding:.4em;"|CY Kim
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[https://www.sciencedirect.com/science/article/pii/S0092867419307810 Large-Scale Analyses of Human Microbiomes Reveal Thousands of Small, Novel Genes]
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|style="padding:.4em;" rowspan=2|2019/09/10
|style="padding:.4em;" rowspan=2|Single-cell
|style="padding:.4em;"|19-41
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S0092867419307329?via%3Dihub Intra- and Inter-cellular Rewiring of the Human Colon during Ulcerative Colitis]
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|style="padding:.4em;"|19-40
|style="padding:.4em;"|IS Choi
|style="padding:.4em;text-align:left"|
[https://www.nature.com/articles/nbt.4042 Multiplexed droplet single-cell RNA-sequencing using natural genetic variation]
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|style="padding:.4em;" rowspan=2|2019/09/03
|style="padding:.4em;" rowspan=2|Microbiome
|style="padding:.4em;"|19-39
|style="padding:.4em;"|NY Kim
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S193131281930352X?via%3Dihub The Landscape of Genetic Content in the Gut and Oral Human Microbiome]
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|style="padding:.4em;"|19-38
|style="padding:.4em;"|CY Kim
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[https://www.sciencedirect.com/science/article/pii/S0092867419307755?via%3Dihub Benchmarking Metagenomics Tools for Taxonomic Classification]
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{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2019
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|style="padding:.4em;" rowspan=2|2019/08/20
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|style="padding:.4em;"|JH Cha
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[https://www.biorxiv.org/content/10.1101/719088v1 Coexpression uncovers a unified single-cell transcriptomic landscape]
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|style="padding:.4em;"|19-36
|style="padding:.4em;"|MY Lee
|style="padding:.4em;text-align:left"|
[https://www.biorxiv.org/content/10.1101/586859v1 Single-cell interactomes of the human brain reveal cell-type specific convergence of brain disorders]
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|style="padding:.4em;" rowspan=3|2019/08/14
|style="padding:.4em;"|19-35
|style="padding:.4em;"|SH Lee
|style="padding:.4em;text-align:left"|
[https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004075 Proportionality: a valid alternative to correlation for relative data]
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|style="padding:.4em;"|19-34
|style="padding:.4em;"|SH Lee
|style="padding:.4em;text-align:left"|
[https://www.nature.com/articles/s41598-017-16520-0 propr: An R-package for Identifying Proportionally Abundant Features Using Compositional Data Analysis]
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|style="padding:.4em;"|19-33
|style="padding:.4em;"|HJ Han
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[https://www.nature.com/articles/s41591-018-0157-9 Robust prediction of response to immune checkpoint blockade therapy in metastatic melanoma]
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|style="padding:.4em;" rowspan=2|2019/08/13
|style="padding:.4em;"|19-32
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[https://www.nature.com/articles/s41592-018-0254-1 A test metric for assessing single-cell RNA-seq batch correction]
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|style="padding:.4em;"|19-31
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S0092867419305598 Comprehensive Integration of Single-Cell Data]
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|style="padding:.4em;" rowspan=2|2019/08/08
|style="padding:.4em;"|19-30
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S0092867418313941 Defining T Cell States Associated with Response to Checkpoint Immunotherapy in Melanoma]
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|style="padding:.4em;"|19-29
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S009286741831242X High-Dimensional Analysis Delineates Myeloid and Lymphoid Compartment Remodeling during Successful Immune-Checkpoint Cancer Therapy]
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|style="padding:.4em;" rowspan=2|2019/08/07
|style="padding:.4em;"|19-28
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[https://www.biorxiv.org/content/10.1101/555557v1 A single-cell reference map for human blood and tissue T cell activation reveals functional states in health and disease]
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|style="padding:.4em;"|19-27
|style="padding:.4em;"|IS Choi
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S009286741831568X Dysfunctional CD8 T Cells Form a Proliferative, Dynamically Regulated Compartment within Human Melanoma]
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|style="padding:.4em;" rowspan=2|2019/07/30
|style="padding:.4em;"|19-26
|style="padding:.4em;"|IS Choi
|style="padding:.4em;text-align:left"|
[https://www.nature.com/articles/nm.4466 High-dimensional single-cell analysis predicts response to anti-PD-1 immunotherapy]
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|style="padding:.4em;"|19-25
|style="padding:.4em;"|JW Cho
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S0092867418311784 A Cancer Cell Program Promotes T Cell Exclusion and Resistance to Checkpoint Blockade]
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|style="padding:.4em;" rowspan=2|2019/07/23
|style="padding:.4em;"|19-24
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/26006008 COMPASS identifies T-cell subsets correlated with clinical outcomes.]
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|style="padding:.4em;"|19-23
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[https://www.nature.com/articles/ncomms14825 Sensitive detection of rare disease-associated cell subsets via representation learning]
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|style="padding:.4em;" rowspan=3|2019/05/30
|style="padding:.4em;"|19-22
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[https://www.ncbi.nlm.nih.gov/pubmed/30936547 Meta-analysis of fecal metagenomes reveals global microbial signatures that are specific for colorectal cancer]
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|style="padding:.4em;"|19-21
|style="padding:.4em;"|
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[https://www.ncbi.nlm.nih.gov/pubmed/30936548 Metagenomic analysis of colorectal cancer datasets identifies cross-cohort microbial diagnostic signatures and a link with choline degradation]
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|style="padding:.4em;"|19-20
|style="padding:.4em;"|
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[https://www.ncbi.nlm.nih.gov/pubmed/30664783 Microbial network disturbances in relapsing refractory Crohn's disease.]
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|style="padding:.4em;" rowspan=3|2019/05/23
|style="padding:.4em;"|19-19
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[https://www.ncbi.nlm.nih.gov/pubmed/30867587 New insights from uncultivated genomes of the global human gut microbiome]
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|style="padding:.4em;"|19-18
|style="padding:.4em;"|
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[https://www.ncbi.nlm.nih.gov/pubmed/30745586 A new genomic blueprint of the human gut microbiota]
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|style="padding:.4em;"|19-17
|style="padding:.4em;"|
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[https://www.ncbi.nlm.nih.gov/pubmed/30661755 Extensive Unexplored Human Microbiome Diversity Revealed by Over 150,000 Genomes from Metagenomes Spanning Age, Geography, and Lifestyle.]
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|style="padding:.4em;" rowspan=2|2019/05/16
|style="padding:.4em;"|19-16
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[https://www.ncbi.nlm.nih.gov/pubmed/29311644 Dynamics of metatranscription in the inflammatory bowel disease gut microbiome.]
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|style="padding:.4em;"|19-15
|style="padding:.4em;"|
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[https://www.ncbi.nlm.nih.gov/pubmed/29335555 Metatranscriptome of human faecal microbial communities in a cohort of adult men.]
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|style="padding:.4em;" rowspan=2|2019/05/09
|style="padding:.4em;"|19-14
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[https://www.ncbi.nlm.nih.gov/pubmed/30193113 Post-Antibiotic Gut Mucosal Microbiome Reconstitution Is Impaired by Probiotics and Improved by Autologous FMT.]
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|style="padding:.4em;"|19-13
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[https://www.ncbi.nlm.nih.gov/pubmed/30193112 Personalized Gut Mucosal Colonization Resistance to Empiric Probiotics Is Associated with Unique Host and Microbiome Features.]
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|style="padding:.4em;" rowspan=2|2019/05/02
|style="padding:.4em;"|19-12
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[https://www.ncbi.nlm.nih.gov/pubmed/30753825 Distinct Immune Cell Populations Define Response to Anti-PD-1 Monotherapy and Anti-PD-1/Anti-CTLA-4 Combined Therapy.]
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|style="padding:.4em;"|19-11
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[https://www.ncbi.nlm.nih.gov/pubmed/30778252 Subsets of exhausted CD8+ T cells differentially mediate tumor control and respond to checkpoint blockade.]
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|style="padding:.4em;" rowspan=2|2019/4/11
|style="padding:.4em;"|19-10
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[https://www.ncbi.nlm.nih.gov/pubmed/30479382 Lineage tracking reveals dynamic relationships of T cells in colorectal cancer.]
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|style="padding:.4em;"|19-9
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[https://www.ncbi.nlm.nih.gov/pubmed/30523328 Single cell dissection of plasma cell heterogeneity in symptomatic and asymptomatic myeloma.]
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|style="padding:.4em;" rowspan=2|2019/4/4
|style="padding:.4em;"|19-8
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[https://www.ncbi.nlm.nih.gov/pubmed/29942092 Single-cell profiling of breast cancer T cells reveals a tissue-resident memory subset associated with improved prognosis.]
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|style="padding:.4em;"|19-7
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[https://www.ncbi.nlm.nih.gov/pubmed/29942094 Global characterization of T cells in non-small-cell lung cancer by single-cell sequencing]
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|style="padding:.4em;" rowspan=2|2019/3/28
|style="padding:.4em;"|19-6
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[https://www.ncbi.nlm.nih.gov/pubmed/28319088 Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors.]
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|style="padding:.4em;"|19-5
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[https://www.ncbi.nlm.nih.gov/pubmed/28622514 Landscape of Infiltrating T Cells in Liver Cancer Revealed by Single-Cell Sequencing.]
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|style="padding:.4em;" rowspan=3|2019/3/21
|style="padding:.4em;"|19-4
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[https://www.ncbi.nlm.nih.gov/pubmed/29988129 Phenotype molding of stromal cells in the lung tumor microenvironment.]
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|style="padding:.4em;"|19-3-1
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[https://www.ncbi.nlm.nih.gov/pubmed/30787436 A single-cell molecular map of mouse gastrulation and early organogenesis]
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|style="padding:.4em;"|19-3
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[https://www.ncbi.nlm.nih.gov/pubmed/30787437 The single-cell transcriptional landscape of mammalian organogenesis]
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|style="padding:.4em;" rowspan=2|2019/3/14
|style="padding:.4em;"|19-2
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[https://www.ncbi.nlm.nih.gov/pubmed/29961579 Single-Cell Map of Diverse Immune Phenotypes in the Breast Tumor Microenvironment]
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|style="padding:.4em;"|19-1
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[https://www.ncbi.nlm.nih.gov/pubmed/29198524 Single-Cell Transcriptomic Analysis of Primary and Metastatic Tumor Ecosystems in Head and Neck Cancer]
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{|class=wikitable style="text-align:center;"
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|style="padding:.4em;" rowspan=2|2018/06/14
|style="padding:.4em;"|18-12
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=A+pan-cancer+analysis+of+enhancer+expression+in+nearly+9000+patient+samples A Pan-Cancer Analysis of Enhancer Expression in Nearly 9000 Patient Samples.]
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|style="padding:.4em;"|18-11
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=Machine+learning+identifies+stemness+features+associated+with+oncogenic+dedifferentiation Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation.]
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|style="padding:.4em;" rowspan=2|2018/06/07
|style="padding:.4em;"|18-10
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=Developmental+and+oncogenic+programs+in+H3K27M+gliomas+dissected+by+single-cell+RNA-seq Developmental and oncogenic programs in H3K27M gliomas dissected by single-cell RNA-seq.]
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|style="padding:.4em;"|18-9
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=Chemoresistance+evolution+in+triple-negative+breast+cancer+delineated+by+single-cell+sequencing Chemoresistance Evolution in Triple-Negative Breast Cancer Delineated by Single-Cell Sequencing.]
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|style="padding:.4em;" rowspan=2|2018/05/31
|style="padding:.4em;"|18-8
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=Mapping+human+pluripotent+stem+cell+differentiation+pathways+using+high+throughput+single-cell+RNA-sequencing Mapping human pluripotent stem cell differentiation pathways using high throughput single-cell RNA-sequencing.]
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|style="padding:.4em;"|18-7
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=A+single-cell+RNA-seq+survey+of+the+developmental+landscape+of+the+human+prefrontal+cortex A single-cell RNA-seq survey of the developmental landscape of the human prefrontal cortex.]
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|style="padding:.4em;" rowspan=2|2018/05/24
|style="padding:.4em;"|18-6
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=Single-cell+RNA+sequencing+identifies+celltype-specific+cis-eQTLs+and+co-expression+QTLs Single-cell RNA sequencing identifies celltype-specific cis-eQTLs and co-expression QTLs.]
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|style="padding:.4em;"|18-5
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=FOCS%3A+a+novel+method+for+analyzing+enhancer+and+gene+activity+patterns+infers+an+extensive+enhancer-promoter+map FOCS: a novel method for analyzing enhancer and gene activity patterns infers an extensive enhancer-promoter map.]
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|style="padding:.4em;" rowspan=2|2018/05/17
|style="padding:.4em;"|18-4
|style="padding:.4em;"|KS Kim
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[https://www.ncbi.nlm.nih.gov/pubmed/29149608 A Distinct Gene Module for Dysfunction Uncoupled from Activation in Tumor-Infiltrating T Cells.]
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|style="padding:.4em;"|18-3
|style="padding:.4em;"|SH Lee
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[https://www.ncbi.nlm.nih.gov/pubmed/29610481 A global transcriptional network connecting noncoding mutations to changes in tumor gene expression.]
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|style="padding:.4em;" rowspan=2|2018/05/10
|style="padding:.4em;"|18-2
|style="padding:.4em;"|JW Cho
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=Inferring+regulatory+element+landscapes+and+transcription+factor+networks+from+cancer+methylomes Inferring regulatory element landscapes and transcription factor networks from cancer methylomes.]
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|style="padding:.4em;"|18-1
|style="padding:.4em;"|DS Bae
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[https://www.ncbi.nlm.nih.gov/pubmed/28129544 Genomic and Transcriptomic Features of Response to Anti-PD-1 Therapy in Metastatic Melanoma.]
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{|class=wikitable style="text-align:center;"
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|style="padding:.4em;" rowspan=2|2017/06/28
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|style="padding:.4em;"|HJ Han
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[https://www.ncbi.nlm.nih.gov/pubmed/28104840 Tumor aneuploidy correlates with markers of immune evasion and with reduced response to immunotherapy.]
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|style="padding:.4em;"|17-35
|style="padding:.4em;"|JW Cho
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[https://www.ncbi.nlm.nih.gov/pubmed/27240091 Landscape of tumor-infiltrating T cell repertoire of human cancers.]
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|style="padding:.4em;" rowspan=2|2017/06/14
|style="padding:.4em;"|17-34
|style="padding:.4em;"|JW Cho
|style="padding:.4em;text-align:left"|
[http://biorxiv.org/content/early/2015/09/01/025908 The landscape of T cell infiltration in human cancer and its association with antigen presenting gene expression.]
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|style="padding:.4em;"|17-33
|style="padding:.4em;"|MY Lee
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.08.052 A Distinct Gene Module for Dysfunction Uncoupled from Activation in Tumor-Infiltrating T Cells.]
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|style="padding:.4em;" rowspan=2|2017/06/07
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[http://www.tandfonline.com/doi/full/10.1080/2162402X.2016.1253654 Identification of genetic determinants of breast cancer immune phenotypes by integrative genome-scale analysis.]
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|style="padding:.4em;"|17-31
|style="padding:.4em;"|MY Lee
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.celrep.2016.03.075 Genomic Correlates of Immune-Cell Infiltrates in Colorectal Carcinoma.]
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|style="padding:.4em;" rowspan=2|2017/05/31
|style="padding:.4em;"|17-30
|style="padding:.4em;"|DS Bae
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.02.065 Genomic and Transcriptomic Features of Response to Anti-PD-1 Therapy in Metastatic Melanoma.]
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|style="padding:.4em;"|17-29
|style="padding:.4em;"|JW Cho
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.celrep.2016.12.019 Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade.]
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|style="padding:.4em;" rowspan=2|2017/05/24
|style="padding:.4em;"|17-28
|style="padding:.4em;"|EB Kim
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.12.022 Systemic Immunity Is Required for Effective Cencer Immunotherapy.]
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|style="padding:.4em;"|17-27
|style="padding:.4em;"|CY Kim
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.10.020 Linking the Human Gut Microbiome to Inflammatory Cytokine Production Capacity.]
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|style="padding:.4em;" rowspan=2|2017/05/17
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.10.018 Host and Environmental Factors Influencing Individual Human Cytokine Responses.]
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|style="padding:.4em;"|17-25
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.10.017 A Functional Genomics Approach to Understand Variation in Cytokine Production in Humans.]
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|style="padding:.4em;" rowspan=2|2017/04/26
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1038%2FNMETH.4177 Pooled CRISPR screening with single-cell transcriptome readout.]
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|style="padding:.4em;"|17-23
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.11.039 Dissecting Immune Circuits by Linking CRISPR-Pooled Screens with Single-Cell RNA-Seq.]
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|style="padding:.4em;" rowspan=2|2017/04/12
|style="padding:.4em;"|17-22
|style="padding:.4em;"|SH Lee
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.11.038 Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens.]
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|style="padding:.4em;"|17-21
|style="padding:.4em;"|CY Kim
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1038%2Fnbt.3569 Wishbone identifies bifurcating developmental trajectories from single-cell data.]
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|style="padding:.4em;" rowspan=2|2017/04/05
|style="padding:.4em;"|17-20
|style="padding:.4em;"|KS Kim
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[http://biorxiv.org/content/early/2017/02/21/110668 Reversed graph embedding resolves complex single-cell developmental trajectories.]
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|style="padding:.4em;"|17-19
|style="padding:.4em;"|SH Lee
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1038%2Fnmeth.4150 Single-cell mRNA quantification and differential analysis with Census.]
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|style="padding:.4em;" rowspan=2|2017/03/29
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.celrep.2016.12.060 Single-Cell Transcriptomic Analysis Defines Heterogeneity and Transcriptional Dynamics in the Adult Neural Stem Cell Lineage.]
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|style="padding:.4em;"|17-17
|style="padding:.4em;"|DS Bae
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[https://www.ncbi.nlm.nih.gov/pubmed/26051941 Single-Cell Network Analysis Identifies DDIT3 as a Nodal Lineage Regulator in Hematopoiesis.]
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[https://www.ncbi.nlm.nih.gov/pubmed/27580035 Single-cell analysis of mixed-lineage states leading to a binary cell fate choice.]
|-
|style="padding:.4em;"|17-15
|style="padding:.4em;"|EB Kim
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/27281220 Dissecting direct reprogramming from fibroblast to neuron using single-cell RNA-seq.]
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|style="padding:.4em;" rowspan=2|2017/03/15
|style="padding:.4em;"|17-14
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1126%2Fscience.aad0501 Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq.]
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|style="padding:.4em;"|17-13
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/26084335 Single-cell mRNA sequencing identifies subclonal heterogeneity in anti-cancer drug responses of lung adenocarcinoma cells.]
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|style="padding:.4em;" rowspan=2|2017/02/28
|style="padding:.4em;"|17-12
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/27824113 A Comprehensive Characterization of the Function of LincRNAs in Transcriptional Regulation Through Long-Range Chromatin Interactions.]
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|style="padding:.4em;"|17-11
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[//pubmed.gov/27851969 Epigenomic Deconvolution of Breast Tumors Reveals Metabolic Coupling between Constituent Cell Types.]
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|style="padding:.4em;" rowspan=2|2017/02/21
|style="padding:.4em;"|17-10
|style="padding:.4em;"|EB Kim
|style="padding:.4em;text-align:left"|
[http://biorxiv.org/content/early/2016/12/30/097451 Convergence of dispersed regulatory mutations predicts driver genes in prostate cancer.]
|-
|style="padding:.4em;"|17-09
|style="padding:.4em;"|EB Kim
|style="padding:.4em;text-align:left"|
[//pubmed.gov/27723759 Chromatin structure-based prediction of recurrent noncoding mutations in cancer.]
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|style="padding:.4em;" rowspan=2|2017/02/07
|style="padding:.4em;"|17-08
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[http://biorxiv.org/content/early/2016/11/17/088286 Somatic Mutations and Neoepitope Homology in Melanomas Treated with CTLA-4 Blockade.]
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|style="padding:.4em;"|17-07
|style="padding:.4em;"|MY Lee
|style="padding:.4em;text-align:left"|
[http://biorxiv.org/content/early/2016/11/28/090134 Chemotherapy weakly contributes to predicted neoantigen expression in ovarian cancer.]
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|style="padding:.4em;" rowspan=1|2017/01/31
|style="padding:.4em;"|17-06
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[//www.ncbi.nlm.nih.gov/pubmed/27912059 Microbiota Diurnal Rhythmicity Programs Host Transcriptome Oscillations.]
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|style="padding:.4em;" rowspan=1|2017/01/24
|style="padding:.4em;"|17-05
|style="padding:.4em;"|JW Cho
|style="padding:.4em;text-align:left"|
[//www.ncbi.nlm.nih.gov/pubmed/27851914 Transcriptional Landscape of Human Tissue Lymphocytes Unveils Uniqueness of Tumor-Infiltrating T Regulatory Cells.]
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|style="padding:.4em;" rowspan=2|2017/01/17
|style="padding:.4em;"|17-04
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[//www.ncbi.nlm.nih.gov/pubmed/25938943 Mapping long-range promoter contacts in human cells with high-resolution capture Hi-C]
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|style="padding:.4em;"|17-03
|style="padding:.4em;"|EB Kim
|style="padding:.4em;text-align:left"|
[//www.ncbi.nlm.nih.gov/pubmed/27306882 CHiCAGO: robust detection of DNA looping interactions in Capture Hi-C data.]
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|style="padding:.4em;" rowspan=1|2017/01/10
|style="padding:.4em;"|17-02
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[//www.ncbi.nlm.nih.gov/pubmed/26527291 ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis]
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|style="padding:.4em;" rowspan=1|2017/01/03
|style="padding:.4em;"|17-01
|style="padding:.4em;"|SH Lee
|style="padding:.4em;text-align:left"|
[//www.ncbi.nlm.nih.gov/pubmed/26287467 Single-cell messenger RNA sequencing reveals rare intestinal cell types]
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{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2016
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!scope="col" style="padding:.4em" |Date
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!scope="col" style="padding:.4em" | Presenter
!scope="col" style="padding:.4em" | Paper title
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|style="padding:.4em;" rowspan=1|2016/12/27
|style="padding:.4em;"|2016-31
|style="padding:.4em;"|EB Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/25664528 Decoding the regulatory network of early blood development from single-cell gene expression measurements.]
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|style="padding:.4em;" rowspan=1|2016/12/6
|style="padding:.4em;"|2016-30
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26299571 Single-Cell RNA-Seq with Waterfall Reveals Molecular Cascades underlying Adult Neurogenesis.]
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|style="padding:.4em;" rowspan=1|2016/11/29
|style="padding:.4em;"|2016-29
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/24658644 The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells.]
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|style="padding:.4em;" rowspan=1|2016/11/22
|style="padding:.4em;"|2016-28
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26887813 Classification of low quality cells from single-cell RNA-seq data.]
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|style="padding:.4em;" rowspan=1|2016/11/15
|style="padding:.4em;"|2016-27
|style="padding:.4em;"|MY Lee
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26000487 Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells.]
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|style="padding:.4em;" rowspan=1|2016/11/8
|style="padding:.4em;"|2016-26
|style="padding:.4em;"|JW Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26000488 Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets.]
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|style="padding:.4em;" rowspan=1|2016/11/1
|style="padding:.4em;"|2016-25
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27426982 Causal Mechanistic Regulatory Network for Glioblastoma Deciphered Using Systems Genetics Network Analysis.]
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|style="padding:.4em;" rowspan=1|2016/10/25
|style="padding:.4em;"|2016-24
|style="padding:.4em;"|MY Lee
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27549193 Comprehensive analyses of tumor immunity: implications for cancer immunotherapy.]
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|style="padding:.4em;" rowspan=1|2016/10/11
|style="padding:.4em;"|2016-23
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=sidespread+parainflammation Widespread parainflammation in human cancer]
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|style="padding:.4em;" rowspan=1|2016/9/27
|style="padding:.4em;"|2016-22
|style="padding:.4em;"|ER Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27535533 Analysis of protein-coding genetic variation in 60,706 humans]
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|style="padding:.4em;" rowspan=1|2016/9/20
|style="padding:.4em;"|2016-21
|style="padding:.4em;"|SM Yang
|style="padding:.4em;text-align:left"|
Functional characterization of somatic mutations in cancer using network-based inference of protein activity
[http://www.ncbi.nlm.nih.gov/pubmed/27322546 pubmed]
[http://www.nature.com/ng/journal/v48/n8/full/ng.3593.html fulltext]
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|style="padding:.4em;" rowspan=1|2016/9/13
|style="padding:.4em;"|2016-20
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=exploiting+single-cell+expression+to+characterize Exploiting single-cell expression to characterize co-expression replicability.]
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|style="padding:.4em;" rowspan=1|2016/9/6
|style="padding:.4em;"|2016-19
|style="padding:.4em;"|T Lee
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26940869 Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade]
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|style="padding:.4em;" rowspan=1|2016/8/31
|style="padding:.4em;"|2016-18
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27264179 A Computational Drug Repositioning Approach for Targeting Oncogenic Transcription Factors]
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|style="padding:.4em;" rowspan=1|2016/8/16
|style="padding:.4em;"|2016-17
|style="padding:.4em;"|JW Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27309802 The landscape of accessible chromatin in mammalian preimplantation embryos]
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|style="padding:.4em;" rowspan=1|2016/8/8
|style="padding:.4em;"|2016-16
|style="padding:.4em;"|EB Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27064255 Enhancer-promoter interactions are encoded by complex genomic signatures on looping chromatin]
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|style="padding:.4em;" rowspan=1|2016/8/1
|style="padding:.4em;"|2016-15
|style="padding:.4em;"|MY Lee
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27040498 Personalized Immunomonitoring Uncovers Molecular Networks that Stratify Lupus Patients]
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|style="padding:.4em;" rowspan=1|2016/7/25
|style="padding:.4em;"|2016-14
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=integration+of+summary+data+from+gwas+and+eqtl+studies Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets]
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|style="padding:.4em;" rowspan=1|2016/7/18
|style="padding:.4em;"|2016-13
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/23624555 Identification of transcriptional regulators in the mouse immune system]
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|style="padding:.4em;" rowspan=2|2016/6/8
|style="padding:.4em;"|2016-12
|style="padding:.4em;"|DS Bae, CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26619012 Mapping the effects of drugs on the immune system]
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|style="padding:.4em;"| 2016-11
|style="padding:.4em;"|DS Bae, CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26186195 Elucidating compound mechanism of action by network perturbation analysis]
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|style="padding:.4em;" rowspan=2|2016/6/1
|style="padding:.4em;"|2016-10
|style="padding:.4em;"|MY Lee,SM Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26854917 Integrative approaches for large-scale transcriptome-wide association studies]
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|style="padding:.4em;"| 2016-9
|style="padding:.4em;"|MY Lee,SM Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26950747 Tissue-specific regulatory circuits reveal variable modular perturbations across complex diseases]
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|style="padding:.4em;" rowspan=2|2016/5/18
|style="padding:.4em;"|2016-8
|style="padding:.4em;"|CY Kim,SJ Kwon
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27013732 Survey of variation in human transcription factors reveals prevalent DNA binding changes]
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|style="padding:.4em;"| 2016-7
|style="padding:.4em;"| CY Kim,SJ Kwon
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26502339 Large-scale identification of sequence variants influencing human transcription factor occupancy in vivo]
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|style="padding:.4em;" rowspan=2|2016/5/11
|style="padding:.4em;"|2016-6
|style="padding:.4em;"|DS Bae, CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/25171417 Predicting Cancer-specific vulnerability via data-driven detection of synthetic lethality]
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|style="padding:.4em;"| 2016-5
|style="padding:.4em;"|DS Bae, CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/23467089 Dynamic regulatory network controlling Th17 cell differentiation]
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|style="padding:.4em;" rowspan=2|2016/5/4
|style="padding:.4em;"|2016-4
|style="padding:.4em;"|MY Lee,SM Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/25853550 Characterization of the immunophenotypes and antigenomes of colorectal cancers reveals distinct tumor escape mechanisms and novel targets for immunotherapy]
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|style="padding:.4em;"| 2016-3
|style="padding:.4em;"|MY Lee,SM Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26618344 Regulators of genetic risk of breast cancer identified by integrative network analysis]
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|style="padding:.4em;" rowspan=2|2016/4/27
|style="padding:.4em;"|2016-2
|style="padding:.4em;"|CY Kim,SJ Kwon
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26780608 A predictive computational framework for direct reprogramming between human cell types]
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|style="padding:.4em;"| 2016-1
|style="padding:.4em;"| CY Kim,SJ Kwon
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/25126793 CellNet: Network biology applied to stem cell engineering]
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{|class=wikitable style="text-align:center;"
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|style="padding:.4em;" rowspan=2|2015/06/11
|style="padding:.4em;"|2015-55
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[http://genome.cshlp.org/content/24/2/340.long Improved exome prioritization of disease genes through cross-species phenotype comparison.]
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|style="padding:.4em;"|2015-54
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|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0002929714001128 Phevor combines multiple biomedical ontologies for accurate identification of disease-causing alleles in single individuals and small nuclear families.]
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|style="padding:.4em;" rowspan=2|2015/06/04
|style="padding:.4em;"|2015-53
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[http://www.nature.com/nmeth/journal/v10/n11/full/nmeth.2656.html eXtasy: variant prioritization by genomic data fusion.]
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|style="padding:.4em;"|2015-52
|style="padding:.4em;"|
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[http://genome.cshlp.org/content/21/9/1529.long A probabilistic disease-gene finder for personal genomes.]
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|style="padding:.4em;" rowspan=2|2015/05/28
|style="padding:.4em;"|2015-51
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[http://www.nature.com/ng/journal/v46/n12/full/ng.3141.html Systematic analysis of noncoding somatic mutations and gene expression alterations across 14 tumor types.]
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|style="padding:.4em;"|2015-50
|style="padding:.4em;"|
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[http://www.nature.com/nbt/journal/v28/n5/full/nbt.1630.html GREAT improves functional interpretation of cis-regulatory regions.]
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|style="padding:.4em;" rowspan=2|2015/05/21
|style="padding:.4em;"|2015-49
|style="padding:.4em;"|
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[http://www.nature.com/nmeth/journal/v11/n3/full/nmeth.2832.html Functional annotation of noncoding sequence variants.]
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|style="padding:.4em;"|2015-48
|style="padding:.4em;"|
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[http://genomebiology.com/content/15/10/480 FunSeq2: A framework for prioritizing noncoding regulatory variants in cancer.]
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|style="padding:.4em;" rowspan=2|2015/05/14
|style="padding:.4em;"|2015-47
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nmeth/journal/v12/n2/full/nmeth.3215.html Selecting causal genes from genome-wide association studies via functionally coherent subnetworks.]
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|style="padding:.4em;"|2015-46
|style="padding:.4em;"|
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[http://www.nature.com/ncomms/2015/150119/ncomms6890/full/ncomms6890.html Biological interpretation of genome-wide association studies using predicted gene functions.]
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|style="padding:.4em;" rowspan=3|2015/05/07
|style="padding:.4em;"|2015-45
|style="padding:.4em;"|
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[http://www.nature.com/ncomms/2014/140626/ncomms5212/full/ncomms5212.html Human symptoms-disease network.]
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|style="padding:.4em;"|2015-44
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867413010246 A nondegenerate code of deleterious variants in Mendelian loci contributes to complex disease risk.]
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|style="padding:.4em;"|2015-43
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[http://www.sciencemag.org/content/347/6224/1257601.long Uncovering disease-disease relationships through the incomplete interactome.]

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|style="padding:.4em;" rowspan=2|2015/04/30
|style="padding:.4em;"|2015-42
|style="padding:.4em;"|
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[http://genome.cshlp.org/content/25/1/142.long The discovery of integrated gene networks for autism and related disorders.]
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|style="padding:.4em;"|2015-41
|style="padding:.4em;"|
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[http://msb.embopress.org/content/10/12/774.long Integrated systems analysis reveals a molecular network underlying autism spectrum disorders.]
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|style="padding:.4em;" rowspan=3|2015/04/23
|style="padding:.4em;"|2015-40
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v518/n7539/full/nature13990.html Dissecting neural differentiation regulatory networks through epigenetic footprinting.]
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|style="padding:.4em;"|2015-39
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[http://www.nature.com/nature/journal/v518/n7539/full/nature14221.html Cell-of-origin chromatin organization shapes the mutational landscape of cancer.]
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|style="padding:.4em;"|2015-38
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[http://www.nature.com/nature/journal/v518/n7539/full/nature14248.html Integrative analysis of 111 reference human epigenomes.]
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|style="padding:.4em;" rowspan=2|2015/04/09
|style="padding:.4em;"|2015-37
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[http://genome.cshlp.org/content/25/2/246.long Genome-wide analysis of local chromatin packing in Arabidopsis thaliana.]
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|style="padding:.4em;"|2015-36
|style="padding:.4em;"|
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[http://www.sciencedirect.com/science/article/pii/S0092867414014974 A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping.]
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|style="padding:.4em;" rowspan=2|2015/04/02
|style="padding:.4em;"|2015-35
|style="padding:.4em;"|
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[http://www.nature.com/nrg/journal/v14/n6/full/nrg3454.html Exploring the three-dimensional organization of genomes: interpreting chromatin interaction data.]
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|style="padding:.4em;"|2015-34
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[http://www.sciencemag.org/content/347/6225/1010.long Transcribed enhancers lead waves of coordinated transcription in transitioning mammalian cells.]
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|style="padding:.4em;" rowspan=2|2015/03/26
|style="padding:.4em;"|2015-33
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[http://genome.cshlp.org/content/25/2/257.long Cupid: simultaneous reconstruction of microRNA-target and ceRNA networks.]
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|style="padding:.4em;"|2015-32
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[http://genome.cshlp.org/content/25/1/41.long Characterization of the neural stem cell gene regulatory network identifies OLIG2 as a multifunctional regulator of self-renewal.]
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|style="padding:.4em;" rowspan=2|2015/03/19
|style="padding:.4em;"|2015-31
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nbt/journal/vaop/ncurrent/full/nbt.3154.html Decoding the regulatory network of early blood development from single-cell gene expression measurements.]
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|style="padding:.4em;"|2015-30
|style="padding:.4em;"|
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[http://www.nature.com/nrg/journal/v16/n3/full/nrg3833.html Computational and analytical challenges in single-cell transcriptomics.]
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|style="padding:.4em;" rowspan=3|2015/03/12
|style="padding:.4em;"|2015-29
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[http://www.sciencedirect.com/science/article/pii/S0092867415000136 Extensive Strain-Level Copy-Number Variation across Human Gut Microbiome Species.]
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|style="padding:.4em;"|2015-28
|style="padding:.4em;"|
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[http://www.nature.com/nature/journal/v500/n7464/full/nature12506.html Richness of human gut microbiome correlates with metabolic markers.]
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|style="padding:.4em;"|2015-27
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v490/n7418/full/nature11450.html A metagenome-wide association study of gut microbiota in type 2 diabetes.]
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|style="padding:.4em;" rowspan=3|2015/03/09
|style="padding:.4em;"|2015-26
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003326 Practical guidelines for the comprehensive analysis of ChIP-seq data.]
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|style="padding:.4em;"|2015-25
|style="padding:.4em;"|T Lee
|style="padding:.4em;text-align:left"|
[http://genome.cshlp.org/content/23/5/777.long Patterns of regulatory activity across diverse human cell types predict tissue identity, transcription factor binding, and long-range interactions.]
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|style="padding:.4em;"|2015-24
|style="padding:.4em;"|ER Kim
|style="padding:.4em;text-align:left"|
[http://msb.embopress.org/content/10/11/760.long Rapid neurogenesis through transcriptional activation in human stem cells.]
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|style="padding:.4em;" rowspan=3|2015/03/02
|style="padding:.4em;"|2015-23
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414011787 Genome-Scale CRISPR-Mediated Control of Gene Repression and Activation.]
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|style="padding:.4em;"|2015-22
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1004237 Integrating multiple genomic data to predict disease-causing nonsynonymous single nucleotide variants in exome sequencing studies.]
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|style="padding:.4em;"|2015-21
|style="padding:.4em;"|ER Kim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/ng/journal/v45/n6/full/ng.2653.html The Genotype-Tissue Expression (GTEx) project.]
|-
|style="padding:.4em;" rowspan=3|2015/02/24
|style="padding:.4em;"|2015-20
|style="padding:.4em;"|ER Kim
|style="padding:.4em;text-align:left"|
[http://www.sciencemag.org/content/346/6212/1007.long Mouse regulatory DNA landscapes reveal global principles of cis-regulatory evolution.]
|-
|style="padding:.4em;"|2015-19
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v515/n7527/full/nature13985.html Principles of regulatory information conservation between mouse and human.]
|-
|style="padding:.4em;"|2015-18
|style="padding:.4em;"|BH Kang
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v515/n7527/full/nature13972.html Conservation of trans-acting circuitry during mammalian regulatory evolution.]
|-
|style="padding:.4em;" rowspan=3|2015/02/16
|style="padding:.4em;"|2015-17
|style="padding:.4em;"|T Lee
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature13835.html Genetic and epigenetic fine mapping of causal autoimmune disease variants.]
|-
|style="padding:.4em;"|2015-16
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/ng/journal/v46/n3/full/ng.2892.html A general framework for estimating the relative pathogenicity of human genetic variants.]
|-
|style="padding:.4em;"|2015-15
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003825 A probabilistic model to predict clinical phenotypic traits from genome sequencing.]
|-
|style="padding:.4em;" rowspan=4|2015/02/02
|style="padding:.4em;"|2015-14
|style="padding:.4em;"|BH Kang
|style="padding:.4em;text-align:left"|
[http://www.pnas.org/content/111/22/E2329.long Relating the metatranscriptome and metagenome of the human gut.]
|-
|style="padding:.4em;"|2015-13
|style="padding:.4em;"|BH Kang
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v513/n7516/full/nature13568.html Alterations of the human gut microbiome in liver cirrhosis.]
|-
|style="padding:.4em;"|2015-12
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nbt/journal/v32/n8/full/nbt.2942.html An integrated catalog of reference genes in the human gut microbiome.]
|-
|style="padding:.4em;"|2015-11
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nbt/journal/v32/n8/full/nbt.2939.html Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes.]
|-
|style="padding:.4em;" rowspan=5|2015/01/26
|style="padding:.4em;"|2015-10
|style="padding:.4em;"|HH Kim
|style="padding:.4em;text-align:left"|
[http://msb.embopress.org/content/9/1/666.long Computational meta'omics for microbial community studies.]
|-
|style="padding:.4em;"|2015-09
|style="padding:.4em;"|HH Kim
|style="padding:.4em;text-align:left"|
[http://genomemedicine.com/content/5/7/65 Functional profiling of the gut microbiome in disease-associated inflammation.]
|-
|style="padding:.4em;"|2015-08
|style="padding:.4em;"|BH Kang
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S016895251200145X Biodiversity and functional genomics in the human microbiome.]
|-
|style="padding:.4em;"|2015-07
|style="padding:.4em;"|BH Kang
|style="padding:.4em;text-align:left"|
[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002808 Chapter 12: Human Microbiome Analysis.]
|-
|style="padding:.4em;"|2015-06
|style="padding:.4em;"|BH Kang
|style="padding:.4em;text-align:left"|
[http://www.cell.com/cell/abstract/S0092-8674%2814%2900864-2 Conducting a Microbiome Study.]
|-
|style="padding:.4em;" rowspan=3|2015/01/12
|style="padding:.4em;"|2015-05
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/ncomms/2014/141210/ncomms6522/full/ncomms6522.html Small RNA changes en route to distinct cellular states of induced pluripotency.]
|-
|style="padding:.4em;"|2015-04
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v516/n7530/full/nature14046.html Genome-wide characterization of the routes to pluripotency.]
|-
|style="padding:.4em;"|2015-03
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v516/n7530/full/nature14047.html Divergent reprogramming routes lead to alternative stem-cell states.]
|-
|style="padding:.4em;" rowspan=2|2015/01/05
|style="padding:.4em;"|2015-02
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.pnas.org/content/111/21/E2191.long Global view of enhancer-promoter interactome in human cells.]
|-
|style="padding:.4em;"|2015-01
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://nar.oxfordjournals.org/content/41/22/10391.long Combining Hi-C data with phylogenetic correlation to predict the target genes of distal regulatory elements in human genome.]
|}
{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2014
|-
!scope="col" style="padding:.4em" | Date
!scope="col" style="padding:.4em" | Paper index
!scope="col" style="padding:.4em" | Presenter
!scope="col" style="padding:.4em" | Paper title
|-
|style="padding:.4em;" rowspan=2|2014/12/23
|style="padding:.4em;"|2014-41
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867413012270 Super-enhancers in the control of cell identity and disease.]
|-
|style="padding:.4em;"|2014-40
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867413003929 Master transcription factors and mediator establish super-enhancers at key cell identity genes.]
|-
|style="padding:.4em;" rowspan=4|2014/12/09
|style="padding:.4em;"|2014-39
|style="padding:.4em;"|HH Kim
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414013713 Unraveling the biology of a fungal meningitis pathogen using chemical genetics.]
|-
|style="padding:.4em;"|2014-38
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414014226 A proteome-scale map of the human interactome network.]
|-
|style="padding:.4em;"|2014-37
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[http://msb.embopress.org/content/10/9/752.long The role of the interactome in the maintenance of deleterious variability in human populations.]
|-
|style="padding:.4em;"|2014-36
|style="padding:.4em;"|HS Shim
|style="padding:.4em;text-align:left"|
[http://www.sciencemag.org/content/342/6154/1235587.long Integrative annotation of variants from 1092 humans: application to cancer genomics.]
|-
|style="padding:.4em;" rowspan=2|2014/12/02
|style="padding:.4em;"|2014-35
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://genome.cshlp.org/content/23/8/1319.long Mapping functional transcription factor networks from gene expression data.]
|-
|style="padding:.4em;"|2014-34
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nrg/journal/v15/n7/full/nrg3684.html In pursuit of design principles of regulatory sequences.]
|-
|style="padding:.4em;" rowspan=2|2014/11/25
|style="padding:.4em;"|2014-33
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[http://genomemedicine.com/content/3/6/36 Epigenomics of human embryonic stem cells and induced pluripotent stem cells: insights into pluripotency and implications for disease.]
|-
|style="padding:.4em;"|2014-32
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S009286741300891X Developmental fate and cellular maturity encoded in human regulatory DNA landscapes.]
|-
|style="padding:.4em;" rowspan=2|2014/11/18
|style="padding:.4em;"|2014-31
|style="padding:.4em;"|SM Yang
|style="padding:.4em;text-align:left"|
[http://genomemedicine.com/content/6/8/64 The 'dnet' approach promotes emerging research on cancer patient survival.]
|-
|style="padding:.4em;"|2014-30
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414010368 Determination and inference of eukaryotic transcription factor sequence specificity.]
|-
|style="padding:.4em;" rowspan=3|2014/11/11
|style="padding:.4em;"|2014-29
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[http://www.pnas.org/content/110/16/6412.long Transcription factors interfering with dedifferentiation induce cell type-specific transcriptional profiles.]
|-
|style="padding:.4em;"|2014-28
|style="padding:.4em;"|HH Kim
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414009350 Dissecting engineered cell types and enhancing cell fate conversion via CellNet.]
|-
|style="padding:.4em;"|2014-27
|style="padding:.4em;"|HH Kim
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414009349 CellNet: network biology applied to stem cell engineering.]
|-
|style="padding:.4em;" rowspan=2|2014/11/04
|style="padding:.4em;"|2014-26
|style="padding:.4em;"|HS Shim
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414009775 Predicting Cancer-Specific Vulnerability via Data-Driven Detection of Synthetic Lethality.]
|-
|style="padding:.4em;"|2014-25
|style="padding:.4em;"|JH Shin
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nmeth/journal/v11/n9/full/nmeth.3046.html Phen-Gen: combining phenotype and genotype to analyze rare disorders.]
|-
|style="padding:.4em;" rowspan=2|2014/10/28
|style="padding:.4em;"|2014-24
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nbt/journal/vaop/ncurrent/full/nbt.2877.html A community effort to assess and improve drug sensitivity prediction algorithms.]
|-
|style="padding:.4em;"|2014-23
|style="padding:.4em;"|HS Shim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/ng/journal/v46/n9/full/ng.3051.html Multi-tiered genomic analysis of head and neck cancer ties TP53 mutation to 3p loss.]
|-
|style="padding:.4em;" rowspan=2|2014/09/30
|style="padding:.4em;"|2014-22
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nmeth/journal/v10/n11/full/nmeth.2651.html Network-based stratification of tumor mutations.]
|-
|style="padding:.4em;"|2014-21
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414001457 Synonymous mutations frequently act as driver mutations in human cancers.]
|-
|style="padding:.4em;" rowspan=2|2014/09/23
|style="padding:.4em;"|2014-20
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003460 VarWalker: Personalized Mutation Network Analysis of Putative Cancer Genes from Next-Generation Sequencing Data.]
|-
|style="padding:.4em;"|2014-19
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://genomebiology.com/content/13/12/R124 DriverNet: uncovering the impact of somatic driver mutations on transcriptional networks in cancer.]
|-
|style="padding:.4em;" rowspan=2|2014/09/16
|style="padding:.4em;"|2014-18
|style="padding:.4em;"|JH Shin
|style="padding:.4em;text-align:left"|
[http://genomebiology.com/content/14/5/R52 Integrated analysis of recurrent properties of cancer genes to identify novel drivers.]
|-
|style="padding:.4em;"|2014-17
|style="padding:.4em;"|AR Cho
|style="padding:.4em;text-align:left"|
[http://genomemedicine.com/content/4/11/89 Improving the prediction of the functional impact of cancer mutations by baseline tolerance transformation.]
|-
|style="padding:.4em;" rowspan=2|2014/09/02
|style="padding:.4em;"|2014-16
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=23228031 A network module-based method for identifying cancer prognostic signatures.]
|-
|style="padding:.4em;"|2014-15
|style="padding:.4em;"|AR Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=24429628 Realizing the promise of cancer predisposition genes.]
|-
|style="padding:.4em;" rowspan=2|2014/08/19
|style="padding:.4em;"|2014-14
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=24952901 Assessing the clinical utility of cancer genomic and proteomic data across tumor types.]
|-
|style="padding:.4em;"|2014-13
|style="padding:.4em;"|HS Shim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=24132290 Mutational landscape and significance across 12 major cancer types.]
|-
|style="padding:.4em;" rowspan=2|2014/08/12
|style="padding:.4em;"|2014-12
|style="padding:.4em;"|HS Shim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/23945592 Signatures of mutational processes in human cancer.]
|-
|style="padding:.4em;"|2014-11
|style="padding:.4em;"|HJ Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/24390350 Discovery and saturation analysis of cancer genes across 21 tumour types.]
|-
|style="padding:.4em;" rowspan=2|2014/08/05
|style="padding:.4em;"|2014-10
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=24084849 Comprehensive identification of mutational cancer driver genes across 12 tumor types.]
|-
|style="padding:.4em;"|2014-9
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=24037244 IntOGen-mutations identifies cancer drivers across tumor types.]
|-
|style="padding:.4em;" rowspan=3|2014/07/29
|style="padding:.4em;"|2014-8
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/23900255 Computational approaches to identify functional genetic variants in cancer genomes.]
|-
|style="padding:.4em;"|2014-7
|style="padding:.4em;"|AR Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=23770567 Mutational heterogeneity in cancer and the search for new cancer-associated genes.]
|-
|style="padding:.4em;"|2014-6
|style="padding:.4em;"|AR Cho
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nmeth/journal/v11/n4/abs/nmeth.2891.html Cancer genomes: discerning drivers from passengers]
|-
|style="padding:.4em;" rowspan=3|2014/07/22
|style="padding:.4em;"|2014-5
|style="padding:.4em;"|AR Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/24071849 The Cancer Genome Atlas Pan-Cancer analysis project.]
|-
|style="padding:.4em;"|2014-4
|style="padding:.4em;"|AR Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/23539594 Cancer genome landscapes.]
|-
|style="padding:.4em;"|2014-3
|style="padding:.4em;"|AR Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=21900272 Distinguishing driver and passenger mutations in an evolutionary history categorized by interference.]
|-
|style="padding:.4em;" rowspan=2|2014/07/15
|style="padding:.4em;"|2014-2
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=24670764 A promoter-level mammalian expression atlas.]
|-
|style="padding:.4em;"|2014-1
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=24670763 An atlas of active enhancers across human cell types and tissues.]
|}
{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2013
|-
!scope="col" style="padding:.4em" | Date
!scope="col" style="padding:.4em" | Paper index
!scope="col" style="padding:.4em" | Presenter
!scope="col" style="padding:.4em" | Paper title
|-
|style="padding:.4em;" rowspan=2|2013/06/26
|style="padding:.4em;"|2013-31
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|[http://nar.oxfordjournals.org/content/40/16/7690.long Integration of Hi-C and ChIP-seq data reveals distinct types of chromatin linkages]
|-
|style="padding:.4em;"|2013-30
|style="padding:.4em;"|YH Ko
|style="padding:.4em;text-align:left"|[http://www.nature.com/nbt/journal/v31/n4/full/nbt.2519.html Deciphering molecular circuits from genetic variation underlying transcriptional responsiveness to stimuli]
|-
|style="padding:.4em;" rowspan=2|2013/06/04
|style="padding:.4em;"|2013-29
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|[https://www.cell.com/abstract/S0092-8674(13)00439-X Mapping the Human miRNA Interactome by CLASH Reveals Frequent Noncanonical Binding]
|-
|style="padding:.4em;"|2013-28
|style="padding:.4em;"|ER Kim
|style="padding:.4em;text-align:left"|[http://www.nature.com/nbt/journal/v31/n3/full/nbt.2514.html Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples]
|-
|style="padding:.4em;" rowspan=2|2013/05/28
|style="padding:.4em;"|2013-27
|style="padding:.4em;"|YH Ko
|style="padding:.4em;text-align:left"|[http://www.pnas.org/content/102/38/13544.long Discovering statistically significant pathways in expression profiling studies]
|-
|style="padding:.4em;"|2013-26
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|[http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0058977 Prediction and Validation of Gene-Disease Associations Using Methods Inspired by Social Network Analyses]
|-
|style="padding:.4em;" rowspan=2|2013/05/21
|style="padding:.4em;"|2013-25
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|[http://www.nature.com/nature/journal/v496/n7446/full/nature11981.html Dynamic regulatory network controlling TH17 cell differentiation]
|-
|style="padding:.4em;"|2013-24
|style="padding:.4em;"|T Lee
|style="padding:.4em;text-align:left"|[http://www.sciencedirect.com/science/article/pii/S0092867412013529 Deciphering and Prediction of Transcriptome Dynamics under Fluctuating Field Conditions]

|-
|style="padding:.4em;" rowspan=2|2013/05/14
|style="padding:.4em;"|2013-23
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|[http://www.sciencedirect.com/science/article/pii/S0092867412015565 Integrative eQTL-Based Analyses Reveal the Biology of Breast Cancer Risk Loci]

|-
|style="padding:.4em;"|2013-22
|style="padding:.4em;"|HJ Kim
|style="padding:.4em;text-align:left"|[http://www.nature.com/ng/journal/v45/n2/full/ng.2504.html Chromatin marks identify critical cell types for fine mapping complex trait variants]
|-
|style="padding:.4em;" rowspan=2|'''2013/05/07'''
|style="padding:.4em;"|2013-21
|style="padding:.4em;"|ER Kim
|style="padding:.4em;text-align:left"|[http://www.cell.com/abstract/S0092-8674(12)01555-3 Genome-wide Chromatin State Transitions Associated with Developmental and Environmental Cues]
|-
|style="padding:.4em;"|2013-20
|style="padding:.4em;"|HS Shim
|style="padding:.4em;text-align:left"|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1003201 Human Disease-Associated Genetic Variation Impacts Large Intergenic Non-Coding RNA Expression]
|-
|style="padding:.4em;"|2013/04/09
|style="padding:.4em;"|2013-19
|style="padding:.4em;"|HJ Kim
|style="padding:.4em;text-align:left"|[http://genome.cshlp.org/content/22/9/1790.long Annotation of functional variation in personal genomes using RegulomeDB]
|-
|style="padding:.4em;"|2013/04/02
|style="padding:.4em;"|2013-18
|style="padding:.4em;"|HJ Kim
|style="padding:.4em;text-align:left"|[http://genome.cshlp.org/content/22/9/1775.long The GENCODE v7 catalog of human long noncoding RNAs: Analysis of their gene structure, evolution, and expression]
|-
|style="padding:.4em;" |2013/03/12
|style="padding:.4em;"|2013-17
|style="padding:.4em;"| ER Kim
|style="padding:.4em;text-align:left"|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002311 Global Mapping of Cell Type–Specific Open Chromatin by FAIRE-seq Reveals the Regulatory Role of the NFI Family in Adipocyte Differentiation]
|-
|style="padding:.4em;" rowspan=1|2013/03/05
|style="padding:.4em;"|2013-16
|style="padding:.4em;"| ER Kim
|style="padding:.4em;text-align:left"|[http://www.nature.com/nrg/journal/v10/n10/full/nrg2641.html ChIP–seq: advantages and challenges of a maturing technology]
|-
|style="padding:.4em;" rowspan=3|2013/02/21
|style="padding:.4em;"|2013-15
|style="padding:.4em;"| KS Kim
|style="padding:.4em;text-align:left"|[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002830 Novel Modeling of Combinatorial miRNA Targeting Identifies SNP with Potential Role in Bone Density]
|-
|style="padding:.4em;"|2013-14
|style="padding:.4em;"| KS Kim
|style="padding:.4em;text-align:left"|[http://www.cell.com/abstract/S0092-8674(12)01424-9 A Molecular Roadmap of Reprogramming Somatic Cells into iPS Cells]
|-
|style="padding:.4em;"|2013-13
|style="padding:.4em;"| HJ Han
|style="padding:.4em;text-align:left"|[http://genome.cshlp.org/content/22/6/1015.long Differential DNase I hypersensitivity reveals factor-dependent chromatin dynamics.]
|-
|style="padding:.4em;" rowspan=3|2013/02/15
|style="padding:.4em;"|2013-12
|style="padding:.4em;"| HJ Han
|style="padding:.4em;text-align:left"|[http://www.nature.com/ng/journal/v43/n3/full/ng.759.html Chromatin accessibility pre-determines glucocorticoid receptor binding patterns]
|-
|style="padding:.4em;"|2013-11
|style="padding:.4em;"| JE Shim, CY Kim
|style="padding:.4em;text-align:left"|[http://www.nature.com/nbt/journal/v30/n11/full/nbt.2422.html Interpreting noncoding genetic variation in complex traits and human disease]
|-
|style="padding:.4em;"|2013-10
|style="padding:.4em;"|HJ Han, JH Kim
|style="padding:.4em;text-align:left"|[http://genome.cshlp.org/content/21/3/447.full.pdf+html Accurate inference of transcription factor binding from DNA sequence and chromatin accessibility data]
|-
|style="padding:.4em;" rowspan=3|2013/02/08
|style="padding:.4em;"|2013-09
|style="padding:.4em;"|HJ Kim
|style="padding:.4em;text-align:left"|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002599 Widespread Site-Dependent Buffering of Human Regulatory Polymorphism]
|-
|style="padding:.4em;"|2013-08
|style="padding:.4em;"|JE Shim, CY Kim
|style="padding:.4em;text-align:left;"|[http://genome.cshlp.org/content/22/9/1748.long Linking disease associations with regulatory information in the human genome]
|-
|style="padding:.4em;"|2013-07
|style="padding:.4em;"|HJ Han, JH Kim
|style="padding:.4em;text-align:left"|[http://genome.cshlp.org/content/22/9/1658.long Understanding transcriptional regulation by integrative analysis of transcription factor binding data]
|-
|style="padding:.4em;" rowspan=3|2013/01/25
|style="padding:.4em;"|2013-06
|style="padding:.4em;"|HJ Han, '''JH Kim'''
|style="padding:.4em;text-align:left;"|[http://www.nature.com/nature/journal/v489/n7414/full/nature11279.html The long-range interaction landscape of gene promoters]
|-
|style="padding:.4em;"|2013-05
|style="padding:.4em;"|'''ER Kim''', HS Shim
|style="padding:.4em;text-align:left;"|[http://www.nature.com/nature/journal/v489/n7414/full/nature11233.html Landscape of transcription in human cells]
|-
|style="padding:.4em;"|2013-04
|style="padding:.4em;"|'''HJ Han''', JH Kim
|style="padding:.4em;text-align:left;"|[http://www.nature.com/nature/journal/v489/n7414/full/nature11245.html Architecture of the human regulatory network derived from ENCODE data]

|-
|style="padding:.4em;" rowspan=2|2013/01/18
|style="padding:.4em;"|2013-03
|style="padding:.4em;"|KS Kim, '''TH Kim'''
|style="padding:.4em;text-align:left;"|[http://www.nature.com/nature/journal/v489/n7414/full/nature11212.html An expansive human regulatory lexicon encoded in transcription factor footprints]
|-
|style="padding:.4em;"|2013-02
|style="padding:.4em;"|'''HJ Han''', JH Kim
|style="padding:.4em;text-align:left;"|[http://www.nature.com/nature/journal/v489/n7414/full/nature11232.html The accessible chromatin landscape of the human genome]
|-
|style="padding:.4em;" rowspan=1|2013/01/11
|style="padding:.4em;"|2013-01
|style="padding:.4em;"| '''JE Shim''', CY Kim
|style="padding:.4em;text-align:left"|[http://www.nature.com/nature/journal/v489/n7414/full/nature11247.html An integrated encyclopedia of DNA elements in the human genome]
|}

2012
{|border ="1"
|style="color:black; background-color:#dcdcdc; padding:5px;" align="center" |Date
|style="color:black; background-color:#dcdcdc;" align="center"|Paper index
|style="color:black; background-color:#dcdcdc;" align="center"|Paper title
|-
|rowspan = "1"|2013/01/11
|align ="center"|2012-81
|[http://genome.cshlp.org/content/22/8/1589.full (TH Kim) MuSiC: identifying mutational significance in cancer genomes.]
|-
|rowspan = "1"|2012/12/04
|align ="center"|2012-80
|[http://genome.cshlp.org/content/22/8/1383 (CY KIM) Human genomic disease variants: A neutral evolutionary explanation]
|-
|rowspan = "2"|2012/11/20
| align="center"|2012-79
|[http://www.cell.com/abstract/S0092-8674(12)00639-3 (HS Shim) Circuitry and Dynamics of Human Transcription Factor Regulatory Networks]
|-
| align="center"|2012-78
|[http://www.nature.com/nature/journal/v487/n7408/full/nature11288.html (HJ Kim) Interpreting cancer genomes using systematic host network perturbations by tumour virus proteins]
|-
|rowspan = "2"|2012/11/06
| align="center"|2012-77
|[http://www.sciencemag.org/content/337/6099/1190.short (HJ Han) Systematic Localization of Common Disease-Associated Variation in Regulatory DNA]
|-
| align="center"|2012-76
|[http://www.pnas.org/cgi/doi/10.1073/pnas.1201904109 (KS Kim) A public resource facilitating clinical use of genomes]
|-
|rowspan = "6" |2012/07/19
| align="center"|2012-75
|[http://genome.cshlp.org/content/22/7/1334 (HJ Han & YH Ko) Predictive regulatory models in Drosophila melanogaster by integrative inference of transcriptional networks]
|-
| align="center"|2012-74
|[http://www.sciencemag.org/content/337/6090/100.full (JE Shim) An Abundance of Rare Functional Variants in 202 Drug Target Genes Sequenced in 14,002 People]
|-
| align="center"|2012-73
|[http://www.sciencemag.org/content/337/6090/64.abstract (JE Shim) Evolution and Functional Impact of Rare Coding Variation from Deep Sequencing of Human Exomes]
|-
| align="center"|2012-72
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2063581/ (SH Hwang) Network-based classification of breast cancer metastasis]
|-
| align="center"|2012-71
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002707 (T Lee&CY Kim)Brain Expression Genome-Wide Association Study (eGWAS) Identifies Human Disease-Associated Variants]
|-
| align="center"|2012-70
|[http://genome.cshlp.org/content/20/9/1297 (ER Kim&TH Kim)The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data]
|-
|rowspan = "8" |2012/07/16
| align="center"|2012-69
|[http://www.nature.com/ng/journal/v43/n5/full/ng.806.html (ER Kim&TH Kim)A framework for variation discovery and genotyping using next-generation DNA sequencing data]
|-
| align="center"|2012-66
|[http://bioinformatics.oxfordjournals.org/content/25/16/2078.long (ER Kim&TH Kim)The Sequence Alignment/Map format and SAMtools]
|-
| align="center"|2012-65
|[http://bioinformatics.oxfordjournals.org/content/early/2011/06/07/bioinformatics.btr330 (ER Kim&TH Kim)The Variant Call Format and VCFtools]
|-
| align="center"|2012-64
|[http://www.cell.com/abstract/S0092-8674(12)00104-3 (YH Go&HJ Han)The Impact of the Gut Microbiota on Human Health: An Integrative View]
|-
|align="center"|2012-63
|[http://www.sciencemag.org/content/336/6086/1262.abstract (T Lee&CY Kim)Host-Gut Microbiota Metabolic Interactions]
|-
|align="center"|2012-62
|[http://www.sciencemag.org/content/336/6086/1268.full (AR Cho,JH Ju)Interactions Between the Microbiota and the Immune System]
|-
|align="center"|2012-61
|[http://www.sciencemag.org/content/336/6086/1255.abstract (SH Hwang&HJ Cho)The Application of Ecological Theory Toward an Understanding of the Human Microbiome]
|-
|align="center"|2012-60
|[http://stm.sciencemag.org/content/4/137/137rv5 (SH Hwang&HJ Cho)Microbiota-Targeted Therapies: An Ecological Perspective]
|-
|rowspan = "3" |2012/07/13
|align="center"|2012-59
|[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002358 (JH Shin&HJ Kim)Metabolic Reconstruction for Metagenomic Data and Its Application to the Human Microbiome]
|-
| align="center"|2012-58
|[http://www.nature.com/nature/journal/v486/n7402/full/nature11209.html (JH Shin&HJ Kim)A framework for human microbiome research]
|-
|align="center"|2012-57
|[http://www.nature.com/nature/journal/v486/n7402/full/nature11234.html (JH Shin&HJ Kim)Structure, function and diversity of the healthy human microbiome]
|-
|rowspan = "4" |2012/07/12
|align="center"|2012-56
|[http://nar.oxfordjournals.org/content/40/D1/D957.long (AR Cho&JH Ju)COLT-Cancer: functional genetic screening resource for essential genes in human cancer cell lines]
|-
|align="center"|2012-55
|[http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002511 (YH Go&HJ Han)Google Goes Cancer: Improving Outcome Prediction for Cancer Patients by Network-Based Ranking of Marker Genes]
|-
| align="center"|2012-54
|[http://www.nature.com/msb/journal/v7/n1/full/msb201147.html (YH Go&HJ Han)A pharmacogenomic method for individualized prediction of drug sensitivity]
|-
|align="center"|2012-53
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature10983.html (JH Soh)The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups]
|-
|rowspan = "6" |2012/07/09
|align="center"|2012-52
|[http://www.nature.com/nature/journal/v483/n7391/full/nature11003.html (ER Kim&TH Kim)The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity]
|-
|align="center"|2012-51
|[http://www.nature.com/nature/journal/v483/n7391/full/nature11005.html (ER Kim&TH Kim)Systematic identification of genomic markers of drug sensitivity in cancer cells]
|-
| align="center"|2012-50
|[http://www.pnas.org/content/early/2011/10/13/1018854108.abstract (ER Kim&TH Kim)Subtype and pathway specific responses to anticancer compounds in breast cancer]
|-
|align="center"|2012-49
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature10945.html (JE Shim&KS Kim)De novo mutations revealed by whole-exome sequencing are strongly associated with autism]
|-
|align="center"|2012-48
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature11011.html (JE Shim&KS Kim)Patterns and rates of exonic de novo mutations in autism spectrum disorders]
|-
|align="center"|2012-47
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature10989.html (JE Shim&KS Kim)Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations]
|-
|rowspan = "6" |2012/07/06
| align="center"|2012-46
|[http://www.g3journal.org/content/1/3/233.full (T Lee&CY Kim)Integrating Rare-Variant Testing, Function Prediction, and Gene Network in Composite Resequencing-Based Genome-Wide Association Studies (CR-GWAS)]
|-
|align="center"|2012-45
|[http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002621 (T Lee&CY Kim)Type 2 Diabetes Risk Alleles Demonstrate Extreme Directional Differentiation among Human Populations, Compared to Other Diseases]
|-
|align="center"|2012-44
|[http://www.nature.com/nature/journal/v480/n7376/full/nature10665.html (AR Cho&JH Ju)Predicting mutation outcome from early stochastic variation in genetic interaction partners]
|-
|align="center"|2012-43
|[http://www.sciencemag.org/content/335/6064/82 (AR Cho&JH Ju)Fitness Trade-Offs and Environmentally Induced Mutation Buffering in Isogenic C. elegans]
|-
| align="center"|2012-42
|[http://genome.cshlp.org/content/22/6/1163 (JE Shim&KS Kim)Identification of microRNA-regulated gene networks by expression analysis of target genes]
|-
|align="center"|2012-41
|[http://www.nature.com/ng/journal/v44/n6/full/ng.2303.html (JE Shim&KS Kim)Exome sequencing and the genetic basis of complex traits]
|-
|rowspan = "6" |2012/07/02
|align="center"|2012-40
|[http://www.cell.com/abstract/S0092-8674(12)00573-9 (JH Soh)Functional Repurposing Revealed by Comparing S. pombe and S. cerevisiae Genetic Interactions]
|-
|align="center"|2012-39
|[http://genome.cshlp.org/content/22/2/375.long (ER Kim&TH Kim)De novo discovery of mutated driver pathways in cancer]
|-
| align="center"|2012-38
|[http://www.nature.com/msb/journal/v4/n1/full/msb20082.html (YH Go&HJ Han)A systems biology approach to prediction of oncogenes and molecular perturbation targets in B-cell lymphomas]
|-
|align="center"|2012-37
|[http://www.nature.com/msb/journal/v7/n1/full/msb201126.html (SH Hwang&HJ Cho)PREDICT: a method for inferring novel drug indications with application to personalized medicine]
|-
|align="center"|2012-36
|[http://www.nature.com/nature/journal/v453/n7198/full/nature06973.html (SH Hwang&HJ Cho)Synergistic response to oncogenic mutations defines gene class critical to cancer phenotype]
|-
|align="center"|2012-35
|[http://www.sciencemag.org/content/334/6062/1518.full (JH Shin&HJ Kim)Detecting Novel Associations in Large Data Sets]
|-
|rowspan = "3" |2012/03/05
| align="center"|2012-34
|[http://www.ncbi.nlm.nih.gov/pubmed/18516045 (8,HH Kim)Mapping and quantifying mammalian transcriptomes by RNA-Seq.]
|-
|align="center"|2012-33
|[http://genomebiology.com/2010/11/10/R106 (11,Go&Ju)Differential expression analysis for sequence count data]
|-
|align="center"|2012-32
|[http://bioinformatics.oxfordjournals.org/content/26/1/139.short (12,Go&Ju)edgeR: a Bioconductor package for differential expression analysis of digital gene expression data ]
|-
|rowspan = "7" |2012/02/27 2012/02/28
| align="center"|2012-31
|[http://www.nature.com/nrg/journal/v10/n1/full/nrg2484.html (1,JW Song)RNA-Seq: a revolutionary tool for transcriptomics]
|-
|align="center"|2012-30
|[http://www.nature.com/nmeth/journal/v8/n6/full/nmeth.1613.html (2,JW Song)Computational methods for transcriptome annotation and quantification using RNA-seq]
|-
|align="center"|2012-29
|[http://genomebiology.com/2010/11/12/220 (3,HJ Han)From RNA-seq reads to differential expression results]
|-
|align="center"|2012-28
|[http://www.nature.com/nmeth/journal/v7/n9/full/nmeth.1491.html (4,AR Cho)Comprehensive comparative analysis of strand-specific RNA sequencing methods]
|-
|align="center"|2012-27
|[http://www.plosone.org/article/info:doi%2F10.1371%2Fjournal.pone.0026426 (5,T Lee)A Low-Cost Library Construction Protocol and Data Analysis Pipeline for Illumina-Based Strand-Specific Multiplex RNA-Seq]
|-
|align="center"|2012-26
|[http://www.nature.com/nbt/journal/v28/n5/abs/nbt.1621.html (6,So&Shin)Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation]
|-
|align="center"|2012-25
|[http://bioinformatics.oxfordjournals.org/content/25/9/1105.abstract (7,So&Shin)TopHat: discovering splice junctions with RNA-Seq]
|-
|rowspan = "5" |2012/02/06
| align="center"|2012-24
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3125733/ mirConnX: condition-specific mRNA-microRNA network integrator]
|-
|align="center"|2012-23
|[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002190 Construction and Analysis of an Integrated Regulatory Network Derived from High-Throughput Sequencing Data]
|-
|align="center"|2012-22
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002415 A Densely Interconnected Genome-Wide Network of MicroRNAs and Oncogenic Pathways Revealed Using Gene Expression Signatures]
|-
|align="center"|2012-21
|[http://genome.cshlp.org/content/20/5/589.short Reprogramming of miRNA networks in cancer and leukemia]
|-
|align="center"|2012-20
|[http://www.cell.com/abstract/S0092-8674(11)01152-4 An Extensive MicroRNA-Mediated Network of RNA-RNA Interactions Regulates Established Oncogenic Pathways in Glioblastoma]
|-
|rowspan = "5" |2012/01/30
|align="center"|2012-19
|[http://www.cell.com/abstract/S0092-8674(11)00237-6 Principles and Strategies for Developing Network Models in Cancer]
|-
|align="center"|2012-18
|[http://www.nature.com/ng/journal/v37/n4/full/ng1532.html Reverse engineering of regulatory networks in human B cells]
|-
|align="center"|2012-17
|[http://www.nature.com/nature/journal/v452/n7186/abs/nature06757.html Variations in DNA elucidate molecular networks that cause disease]
|-
|align="center"|2012-16
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2779083/ Harnessing gene expression to identify the genetic basis of drug resistance]
|-
|align="center"|2012-15
|[http://www.sciencedirect.com/science/article/pii/S0092867410012936 An Integrated Approach to Uncover Drivers of Cancer]
|-
|rowspan = "3" |2012/01/09
|align="center"|2012-14
|[http://www.ncbi.nlm.nih.gov/pubmed/18704161 Genetic variation in an individual human exome.]
|-
|align="center"|2012-13
|[http://www.ncbi.nlm.nih.gov/pubmed/22081227 Predicting phenotypic variation in yeast from individual genome sequences.]
|-
|align="center"|2012-12
|[http://www.ncbi.nlm.nih.gov/pubmed/20435227 Clinical assessment incorporating a personal genome.]
|-
|rowspan = "6" |2012/01/09 2012/01/16
| align="center"|2012-11
|[http://www.ncbi.nlm.nih.gov/pubmed/20399638 Human allelic variation: perspective from protein function, structure, and evolution.]
|-
|align="center"|2012-10
|[http://www.ncbi.nlm.nih.gov/pubmed/19561590 Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm.]
|-
|align="center"|2012-09
|[http://www.ncbi.nlm.nih.gov/pubmed/11230178 Prediction of deleterious human alleles.]
|-
|align="center"|2012-08
|[http://www.ncbi.nlm.nih.gov/pubmed/12202775 Human non-synonymous SNPs: server and survey.]
|-
|align="center"|2012-07
|[http://www.ncbi.nlm.nih.gov/pubmed/20354512 A method and server for predicting damaging missense mutations.]
|-
|align="center"|2012-06
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1920242/ SNAP: predict effect of non-synonymous polymorphisms on function]
|-
|rowspan = "3" |2012/01/09 2012/01/16
|align="center"|2012-05
|[http://www.ncbi.nlm.nih.gov/pubmed/21920052 Computational and statistical approaches to analyzing variants identified by exome sequencing.]
|-
|align="center"|2012-04
|[http://www.ncbi.nlm.nih.gov/pubmed/18179889 Shifting paradigm of association studies: value of rare single-nucleotide polymorphisms.]
|-
|align="center"|2012-03
|[http://www.ncbi.nlm.nih.gov/pubmed/19684571 Targeted capture and massively parallel sequencing of 12 human exomes.]
|-
|rowspan = "2" |2012/01/09 2012/01/16
|align="center"|2012-02
|[http://www.ncbi.nlm.nih.gov/pubmed/17637733 The distribution of fitness effects of new mutations.]
|-
|align="center"|2012-01
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1852724/ Most Rare Missense Alleles Are Deleterious in Humans: Implications for Complex Disease and Association Studies]
|}
2011
{|border ="1"
|style="color:black; background-color:#dcdcdc;" align="center" |Date
|style="color:black; background-color:#dcdcdc;" align="center"|Paper_index
|style="color:black; background-color:#dcdcdc;" align="center"|Paper_title
|-
|rowspan = "6" |2011/11/28
|align="center"|2011-49
|[http://bmir.stanford.edu/file_asset/index.php/1407/BMIR-2009-1355.pdf (Shin)Data-Driven Methods to Discover Molecular Determinants of Serious Adverse Drug Events]
|-
|align="center"|2011-48
|[http://www.nature.com/nchembio/journal/v4/n11/abs/nchembio.118.html (Shin)Network pharmacology: the next paradigm in drug discovery]
|-
|align="center"|2011-47
|[http://www.nature.com/msb/journal/v7/n1/full/msb201171.html (Oh)Systematic exploration of synergistic drug pairs]
|-
|align="center"|2011-46
|[http://www.nature.com/msb/journal/v5/n1/full/msb200995.html (Shim)Chemogenomic profiling predicts antifungal synergies]
|-
|align="center"|2011-45
|[http://www.pnas.org/content/104/32/13086 (Beck)A strategy for predicting the chemosensitivity of human cancers and its application to drug discovery]
|-
|align="center"|2011-44
|[http://www.nature.com/nchembio/journal/v1/n7/full/nchembio747.html (Hwang)Analysis of drug-induced effect patterns to link structure and side effects of medicines]
|-
|rowspan = "3" |2011/11/14
|align="center"|2011-43
|[http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000662 Network-Based Elucidation of Human Disease Similarities Reveals Common Functional Modules Enriched for Pluripotent Drug Targets]
|-
|align="center"|2011-42
|[http://www.nature.com/msb/journal/v7/n1/full/msb201131.html Cross-species discovery of syncretic drug combinations that potentiate the antifungal fluconazole]
|-
|align="center"|2011-41
|[http://www.nature.com/msb/journal/v7/n1/full/msb20115.html Analysis of multiple compound–protein interactions reveals novel bioactive molecules]
|-
|rowspan = "4" |2011/11/07
|align="center"|2011-40
|[http://www.nature.com/nbt/journal/v25/n10/abs/nbt1338.html Drug—target network]
|-
|align="center"|2011-39
|[http://www.nature.com/msb/journal/v6/n1/full/msb200998.html A side effect resource to capture phenotypic effects of drugs]
|-
|align="center"|2011-38
|[http://genome.cshlp.org/content/18/2/206.long Quantitative systems-level determinants of human genes targeted by successful drugs]
|-
|align="center"|2011-37
|[http://www.sciencemag.org/content/321/5886/263.short Drug Target Identification Using Side-Effect Similarity]
|-
|rowspan = "3" |2011/11/07
|align="center"|2011-36
|[http://stm.sciencemag.org/content/3/96/96ra77.short Discovery and Preclinical Validation of Drug Indications Using Compendia of Public Gene Expression Data]
|-
|align="center"|2011-35
|[http://bib.oxfordjournals.org/content/12/4/303.short Exploiting drug–disease relationships for computational drug repositioning]
|-
|align="center"|2011-34
|[http://www.springerlink.com/content/4489r051nu2t0ul1/ Drug Discovery in a Multidimensional World: Systems, Patterns, and Networks]
|-
|rowspan = "3" |2011/10/05
|align="center"|2011-33
|[http://genome.cshlp.org/content/20/7/960.full Analysis of membrane proteins in metagenomics: Networks of correlated environmental features and protein families]
|-
|align="center"|2011-32
|[http://www.pnas.org/content/106/5/1374.full Quantifying environmental adaptation of metabolic pathways in metagenomics]
|-
|align="center"|2011-31
|[http://www.pnas.org/content/104/35/13913.abstract Quantitative assessment of protein function prediction from metagenomics shotgun sequences]
|-
|rowspan = "4" |2011/10/04
|align="center"|2011-30
|[http://www.nature.com/nature/journal/v464/n7285/full/nature08821.html A human gut microbial gene catalogue established by metagenomic sequencing]
|-
|align="center"|2011-29
|[http://www.nature.com/nrmicro/journal/v6/n9/abs/nrmicro1935.html Molecular eco-systems biology: towards an understanding of community function]
|-
|align="center"|2011-28
|[http://genome.cshlp.org/content/early/2009/04/20/gr.085464.108 Microbial community profiling for human microbiome projects: Tools, techniques, and challenges]
|-
|align="center"|2011-27
|[http://www.nature.com/nrmicro/journal/v9/n4/full/nrmicro2540.html Unravelling the effects of the environment and host genotype on the gut microbiome]
|-
|rowspan = "2" |2011/09/19
|align="center"|2011-26
|[http://www.sciencemag.org/content/333/6042/596.full independently evolved virulence effectors converge onto hubs in a plant immune system Network]
|-
|align="center"|2011-25
|[http://www.sciencemag.org/content/333/6042/601.full Evidence for network evolution in an arabidopsis interactome Map]
|-
|rowspan = "2" |2011/09/05
|align="center"|2011-24
|[http://www.sciencedirect.com/science/article/pii/S0092867411005861 Exome Sequencing of Ion Channel Genes Reveals Complex Profiles Confounding Personal Risk Assessment in Epilepsy]
|-
|align="center"|2011-23
|[http://www.cell.com/abstract/S0092-8674(11)00543-5 Pluripotency factors in Embryonic stem cells Regulate Differentiation into Germ Layers]
|-
|rowspan = "2" |2011/09/22
|align="center"|2011-22
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002077 Integrated Genome-scale predition of Detrimental Mutations in Transcription Networks]
|-
|align="center"|2011-21
|[http://www.nature.com/nrg/journal/v12/n4/abs/nrg2969.html From expression QTLs to personalized transcriptomics]
|-
|2011/06/20
|align="center"|2011-20
|[http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001046 a user's guide to the encyclopedia of DNA elements(ENCODE)]
|-
|rowspan = "2" |2011/03/30
|align="center"|2011-19
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature09944.html enterotypes of the human gut microbiome]
|-
|align="center"|2011-18
|[http://www.nature.com/msb/journal/v7/n1/full/msb20116.html toward molecular trait-based ecology, through intergration of biogeochemical, geographical and metagenomic data]
|-
|rowspan = "2" |2011/03/16
|align="center"|2011-17
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002047 variable pathogenicity determines individual lifespan in ''caenorhabditis elegans'']
|-
|align="center"|2011-16
|[http://www.cell.com/abstract/S0092-8674(11)00371-0 a high-resolution ''c.elegans'' essential gene network based on phenotypic profiling of a complex tissue]
|-
|rowspan = "3" |2011/04/25
|align="center"|2011-15
|[http://www.ncbi.nlm.nih.gov/pubmed/21376230 Hallmarks of Cancer : The next generation]
|-
|align="center"|2011-14
|[http://www.cell.com/abstract/S0092-8674(11)00296-0 Mapping Cancer Origins]
|-
|align="center"|2011-13
|[http://www.cell.com/abstract/S0092-8674(11)00297-2 Genetic Interactions in Cancer Progression and Treatment]
|-
|rowspan = "2" |2011/04/11
|align="center"|2011-12
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1001273 Proteins encoded in genomic regions associated with immune-mediated disease physically interact and suggest underlying biology]
|-
|align="center"|2011-11
|***Changed!***
[http://www.ncbi.nlm.nih.gov/pubmed/19557189 Identifying relationships among genomic disease regions: predicting genes at pathogenic SNP associations and rare deletions]
|-
|rowspan = "2"|2011/03/28
|align="center"|2011-10
|[http://www.pnas.org/content/106/44/18843.long profiling translatomes of discrete cell populations resolves altered cellular priorities during hypoxia in Arabidopsis]
|-
|align="center"|2011-09
|[http://www.nature.com/msb/journal/v6/n1/full/msb201076.html cell-type specific analysis of translating RNAs in developing flowers reveals new levels of control]
|-
|rowspan = "2"|2011/03/14
|align="center"|2011-08
|[http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6WSN-51SFHJD-1&_user=44062&_coverDate=01%2F07%2F2011&_rdoc=1&_fmt=high&_orig=search&_origin=search&_sort=d&_docanchor=&view=c&_acct=C000004738&_version=1&_urlVersion=0&_userid=44062&md5=2f6017aab4c794ed7e78fbbd8447077a&searchtype=a phenotypic landscape of a bacterial cell]
|-
|align="center"|2011-07
|[http://www.nature.com/msb/journal/v6/n1/full/msb2010107.html cross-species chemogenomic profiling reveals evolutionarily conserved drug mode of action]
|-
|rowspan = "2"|2011/02/28
|align="center"|2011-06
|[http://www.pnas.org/content/early/2010/09/23/1004666107.abstract genomic patterns of pleiotropy and the evolution of complexity]
|-
|align="center"|2011-05
|[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1001009 simultaneous genome-wide inference of physical, genetic, regulatory, and functional pathway]
|-
|2011/02/21
|align="center"|2011-04
|[http://www.nature.com/msb/journal/v6/n1/full/msb201071.html dynamic interaction networks in a hierarchically organized tissue]
|-
|2011/02/14
|align="center"|2011-03
|[http://www.sciencemag.org/content/330/6009/1385.abstract rewiring of genetic networks in response to DNA damage]
|-
|rowspan = "2"|2011/01/31
|align="center"|2011-02
|[http://www.nature.com/nrg/journal/v10/n9/abs/nrg2633.html Applying mass spectrometry-based proteomics to genetics, genomics and network biology]
|-
|align="center"|2011-01
|[http://physiolgenomics.physiology.org/content/33/1/18.long Data analysis and bioinformatics tools for tandem mass spectrometry in proteomics]
|}
2010
{|border ="1"
|style="color:black; background-color:#dcdcdc;" align="center" |Date
|style="color:black; background-color:#dcdcdc;" align="center"|Paper_index
|style="color:black; background-color:#dcdcdc;" align="center"|Paper_title
|-
|rowspan = "2"|2010/12/27
|align="center"|2010-29
|[http://www.nature.com/nature/journal/v467/n7312/full/nature09326.html Functional_roles_fornoise_in_genetic_circuits]
|-
|align="center"|2010-28
|[http://www.nature.com/ng/journal/v42/n7/full/ng.610.html Estimation_of_effect_size_distribution_from_genome-wide_association_studies_and_implications_for_future_discoveries]
|-
|rowspan = "2"|2010/11/15
|align="center"|2010-27
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1000932 Liver_and_Adipose_Expression_associated_SNPs_are_enriched_for_association_to_type_2_diabetes]
|-
|align="center"|2010-26
|[http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B8JDC-50J4MRJ-2&_user=44062&_coverDate=10%2F10%2F2010&_rdoc=1&_fmt=high&_orig=search&_origin=search&_sort=d&_docanchor=&view=c&_acct=C000004738&_version=1&_urlVersion=0&_userid=44062&md5=7667a67cf8ba3f68117b8d0ff85a8887&searchtype=a It's_the_machine_that_matters:_predicting_gene_function_and_phenotype_from_protein_networks]
|-
|rowspan = "2"|2010/11/01
|align="center"|2010-25
|[http://genome.cshlp.org/content/early/2010/06/15/gr.104216.109 A_genome-wide_map_of_human_genetic_interactions_inferred_from_radiation_hybrid_genotypes]
|-
|align="center"|2010-24
|[http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0012139 A_Genome-Wide_Gene_Function_Prediction_Resource_for_Drosophila_melanogaster]
|-
|2010/10/11
|align="center"|2010-23
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2913399/?tool=pubmed Dissecting_spatio-temporal_protein_networks_driving_human_heart_development_and_related_disorders]
|-
|rowspan = "2"|2010/09/13
|align="center"|2010-22
|[http://www.nature.com/ng/journal/v42/n7/full/ng.600.html Transposable_elements_have_rewired_the_core_regulatory_network_of_human_embryonic_stem_cells]
|-
|align="center"|2010-21
|[http://www.nature.com/ng/journal/v42/n7/full/ng0710-557.html Limits_of_sequence_and_functional_conservation]
|-
|2010/05/26
|align="center"|2010-20
|[[media:100428_network_based_elucidation_of_human_disease_similarities_reveals_common_functional_modules_enriched_for_pluripotent_drug_targets.pdf|network_based_elucidation_of_human_disease_similarities_reveals_common_functional_modules_enriched_for_pluripotent_drug_targets.pdf]]
|-
|2010/05/19
|align="center"|2010-19
|[[media:100421_interpreting_metabolomic_profiles_using_unbiased_pathway_models.pdf|interpreting_metabolomic_profiles_using_unbiased_pathway_models.pdf]]
|-
|2010/04/21
|align="center"|2010-18
|[[media:100414_identification_of_networks_of_co_occurring_tumor_related_dna_copy_number_changes_using_a_genome_wide_scoringapproach.pdf|identification_of_networks_of_co_occurring_tumor_related_dna_copy_number_changes_using_a_genome_wide_scoringapproach.pdf]]
|-
|2010/04/14
|align="center"|2010-17
|[http://www.cell.com/retrieve/pii/S0092867410000796 an_atlas_of_combinatorial_transcriptional_regulation_in_mouse_and_man]
|-
|rowspan = "2" |2010/04/07
|align="center"|2010-16
|[http://www.pnas.org/content/early/2010/03/11/0910200107.long systematic_discovery_of_nonobvious_human_disease_models_through_orthologous_phenotypes]
|-
|align="center"|2010-15
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature08800.html genome_side_association_study_of_107_phenotypes_in_Arabidopsis_thaliana_inbred_lines]
|-
|rowspan="2"|2010/03/31
|align="center"|2010-14
|[[media:100331_toward_stem_cell_systems_biology_from_molecules_to_networks_and_landscapes.pdf|toward_stem_cell_systems_biology_from_molecules_to_networks_and_landscapes.pdf]]
|-
|align="center"|2010-13
|[http://www.nature.com/nrm/journal/v10/n10/abs/nrm2766.html systems_biology_of_stem_cell_fate_and_cellular_reprogramming]
|-
|2010/03/24
|align="center"|2010-12
|[http://www.nature.com/nbt/journal/v27/n2/abs/nbt.1522.html dynamic_modularity_in_protein_interaction_networks_predicts_breast_cancer_outcome]
|-
|2010/01/18
|align="center"|2010-11
|JournalClub_100118_a_tutorial_on_statistical_methods_for_population_association_studies
|-
|2010/01/16
|align="center"|2010-10
|systems-level_dinamic_analyses_of_fate_change_in_murine_embryonic_stem_cells
|-
|2010/01/15
|align="center"|2010-09
|distinguishing_direct_versus_indirect_transcription_factor-DNA-interactions
|-
|2010/01/14
|align="center"|2010-08
|chemogenomic_profiling_predicts_antifungal_synergies
|-
|2010/01/13
|align="center"|2010-07
|edgetic_perturbation_models_of_human_inherited_disorders
|-
|2010/01/12
|align="center"|2010-06
|analysis_of_cell_fate_from_single-cell_gene_expression_profiles_in_C.elegans
|-
|2010/01/11
|align="center"|2010-05
|predicting_new_molecular_targets_for_known_drugs.pdf
Reference:SEA(Similarity Ensemble Approach)
|-
|2010/01/09
|align="center"|2010-04
|harnessing_gene_expression_to_identify_the_genetic_basis_of_drug_resistance
|-
|2010/01/08
|align="center"|2010-03
|an_integrative_approach_to_reveal_driver_gene_fusions_from_paired_end_sequencing_data_in_cancer
|-
|2010/01/07
|align="center"|2010-02
|a_phenotypic_profile_of_the_candida_albicans_regulatory_network
|-
|2010/01/06
|align="center"|2010-01
|a_global_view_of_protein_expression_in_human_cells_tissues_and_organs
|}

People:SB Baek

2024-12-30T04:25:28Z

Bsb0613: /* Publications */

===Seungbyn Baek===
{|
|[[File:SB pic.jpg|300px]]
|
*'''Ph.D program''' (2019~): Department of Biotechnology, Yonsei University

*'''B.E.''' (2014-2017): Bio-Convergence, Underwood International College, Yonsei University

<pre>*E-mail : bsb0613(at)yonsei.ac.kr</pre>

'''Research Interest'''
* Single-cell ATAC-seq
* Single-cell RNA-seq
* Cancer Genomics
* COVID-19 Immunology
|}

==Publications==
* '''Seungbyn Baek*''', Euijeong Sung, Gamin Kim, Min-Hee Hong, Chang Young Lee, Hyo Sup Shim, Seong Yong Park**, Hye Ryun Kim**, Insuk Lee**, Single-cell multi-omics reveals tumor microenvironment factors underlying poor immunotherapy responses in ALK-positive lung cancer '''''Cancer Communications''''' In revision [https://www.biorxiv.org/content/10.1101/2024.09.24.614708v1 bioRxiv]
* '''Seungbyn Baek*''', Junha Cha, Min-Hee Hong, Gamin Kim, Yoon Woo Koh, Dahee Kim, Martin Hemberg, Seong Yong Park**, Hye Ryun Kim**, Insuk Lee**, Tumor microenvironment distinctions between esophageal cancer subtypes explain varied immunotherapy responses '''''Under Review''''' [https://www.biorxiv.org/content/10.1101/2024.09.24.614705v1 bioRxiv]
* Euijeong Sung, Junha Cha, '''Seungbyn Baek''', Insuk Lee**, Augmenting the human interactome for disease prediction through gene networks inferred from human cell atlas '''''Under Review''''' [https://www.biorxiv.org/content/10.1101/2024.12.12.628105v1 bioRxiv]
* Chan Yeong Kim*, Dong Jin Park*, Beung Chul Ahn*, '''Seungbyn Baek''', Min Hee Hong, Linh Thanh Nguyen, Sun Ha Hwang, Nayeon Kim, Daniel Podlesny, Askarbek Orakov, Christian Schudoma, Shahriyar Mahdi Robbani, Hyo Sup Shim, Hong In Yoon, Chang Young Lee, Seong Yong Park, Dongeun Yong, Mina Han, Peer Bork, Byoung Choul Kim**, Sang-Jun Ha**, Hye Ryun Kim**, Insuk Lee**, A conserved pilin from uncultured gut bacterial clade TANB77 enhances cancer immunotherapy, '''''Nature Communications''''' 2024 Dec 27;15:10726 [https://pubmed.ncbi.nlm.nih.gov/39730328/ PubMed]
* Randall T. Mertens*, Aditya Misra*, Peng Xiao*, '''Seungbyn Baek''', Joseph M. Rone, Davide Mangani, Kisha N. Sivanathan, Adedamola S. Arojojoye, Samuel G. Awuah, Insuk Lee, Guo-Ping Shi, Boryana Petrova, Jeannette R. Brook, Ana C. Anderson, Richard A. Flavell, Naama Kanarek, Martin Hemberg, Roni Nowarski**, A metabolic switch orchestrated by IL-18 and cGAMP programs intestinal tolerance, '''''Immunity''''' 2024 Jun 19:S1074-7613(24)00305-4 [https://pubmed.ncbi.nlm.nih.gov/38906145/ PubMed]
* Junha Cha*, Da Hee Kim*, Gamin Kim*, Jae-Won Cho, Euijeong Sung, '''Seungbyn Baek''', Min Hee Hong, Chang Gon Kim, Nam Suk Sim, Hyun Jun Hong, Jung Eun Lee, Martin Hemberg, Seyeon Park, Sun Ock Yoon, Sang-Jun Ha**, Yoon Woo Koh**, Hye Ryun Kim**, and Insuk Lee**, Single-cell analysis reveals cellular and molecular factors counteracting HPV-positive oropharyngeal cancer immunotherapy outcomes, '''''Journal for Immunotherapy of Cancer'''''. 2024 Jun 10;12(6):e008667 [https://pubmed.ncbi.nlm.nih.gov/38857913/ PubMed]
* Wei E Gordon*, '''Seungbyn Baek*''', Hai P Nguyen, Yien-Ming Kuo, Rachael Bradley, Sarah L. Fong, Nayeon Kim, Alex Galazyuk, Insuk Lee, Melissa Ingala, Nancy B Simmons, Tony Schountz, Lisa Cooper, Ilias Georgakopoulos-Soares, Martin Hemberg**, Nadav Ahituv**, Integrative single-cell characterization of a frugivorous and an insectivorous bat kidney and pancreas '''''Nature Communications''''' 2024 Jan 9;15(1):12 [https://pubmed.ncbi.nlm.nih.gov/38195585/ PubMed]
* '''Seungbyn Baek*''', Sunmo Yang, and Insuk Lee** COVID-GWAB: A Web-Based Prediction of COVID-19 Host Genes via Network Boosting of Genome-Wide Association Data '''''Biomolecules''''' 2022 Oct 12;12(10):1446 [https://pubmed.ncbi.nlm.nih.gov/36291657/ PubMed]
* Chan Yeong Kim*, '''Seungbyn Baek*''', Junha Cha, Sunmo Yang, Eiru Kim, Edward M. Marcotte, Traver Hart, and Insuk Lee** HumanNet v3: An improved database of human gene networks for disease research '''''Nucleic Acids Research''''' 2022 Jan 7;50(D1):D632-D639 [https://pubmed.ncbi.nlm.nih.gov/34747468/ PubMed]
* '''Seungbyn Baek*''', Insuk Lee** Single-cell ATAC sequencing analysis: from data preprocessing to hypothesis generation '''''Computational and Structural Biotechnology Journal''''' 2020 June 28;18:1429-1439 [https://pubmed.ncbi.nlm.nih.gov/32637041 Pubmed]

People:SB Baek

2024-12-19T06:26:20Z

Bsb0613: /* Publications */

People

2024-08-29T02:41:51Z

Bsb0613: /* Scientists & Research Staff */

__NOTOC__
=='''Scientists & Research Staff'''==
{|border="0" width="1080"
|align="center"|[[File:prof_Insuk_fixed.png|100px|link=people:IS_Lee]]
|align="center"|[[File:SM_fixed.png|100px|link=people:SM_Yang]]
|align="center"|[[File:Junha_fixed.png|100px|link=People:JH_Cha]]
|align="center"|[[File:Nayeon_fixed.png|100px|link=People:NY_Kim]]
|align="center"|[[File:Seungbyn_fixed.png|100px|link=People:SB_Baek]]
|align="center"|[[File:Ilseok_fixed.png|100px|link=People:IS_Choi]]
|align="center"|[[File:JunHyeong_fixed.png|100px|link=People:Jun_Hyung_Cha]]
|align="center"|[[File:JunYeong_fixed.png|100px|link=People:Junyeong_Ma]]
|align="center"|[[File:Hanjune_fixed.png|100px|link=People:Han-June_KIM]]
|-

|width=160px; align="center"|[[people:IS_Lee|<big>'''Insuk Lee'''</big>]]
|width=160px; align="center"|[[people:SM_Yang|<big>'''Sunmo Yang'''</big>]]
|width=160px; align="center"|[[People:JH_Cha|<big>'''Junha Cha'''</big>]]
|width=160px; align="center"|[[People:NY_Kim|<big>'''Nayeon Kim'''</big>]]
|width=160px; align="center"|[[People:SB_Baek|<big>'''Seungbyn Baek'''</big>]]
|width=160px; align="center"|[[People:IS_Choi|<big>'''Ilseok Choi'''</big>]]
|width=160px; align="center"|[[People:Jun_Hyung_Cha|<big>'''Jun Hyung Cha'''</big>]]
|width=160px; align="center"|[[People:Junyeong_Ma|<big>'''Junyeong Ma'''</big>]]
|width=160px; align="center"|[[People:Hanjune_Kim|<big>'''Hanjune Kim'''</big>]]
|-

|align="center"|[[people:IS_Lee|Principal Investigator Professor]]
|align="center"|[[people:SM_Yang|Bioinformatics Programmer & Sys. Admin]]
|align="center"|[[People:JH_Cha|Ph.D candidate (Single-cell Biology) (2019.9 - )]]
|align="center"|[[People:NY_Kim|Ph.D candidate (Microbiome) (2019.9 - )]]
|align="center"|[[People:SB_Baek|Ph.D candidate (Single-cell Biology) (2019.9 - )]]
|align="center"|[[People:IS_Choi|Ph.D candidate (Single-cell Biology) (2020.9 - )]]
|align="center"|[[People:Jun_Hyung_Cha|Ph.D candidate (Microbiome) (2020.9 - )]]
|align="center"|[[People:Junyeong_Ma|Ph.D candidate (Microbiome) (2021.3 - )]]
|align="center"|[[People:Hanjune_Kim|Integrated Ph.D course (Microbiome) (2021.3 - )]]
|}
{|border="0" width="960"

|align="center"|[[File:EuiJeong_fixed.png|100px|link=People:EuiJeong_Sung]]
|align="center"|[[File:Sehun_fixed.png|100px|link=People:Sehun_Ahn]]
|align="center"|[[File:Geon_fixed.png|100px|link=People:Geon_Koh]]
|align="center"|[[File:Wonjong_fixed.png|100px|link=People:WonJong_Kim]]
|align="center"|[[File:Jungyeon_fixed.png|100px|link=People:Jungyeon_Kim]]
|align="center"|[[File:Yerin_fixed.png|100px|link=People:Yerin_Kim]]
|align="center"|[[File:YurimJung_fixed.jpg|100px|link=People:Yurim_Jung]]
|align="center"|[[File:HaeBeen_fixed.jpg|100px|link=People:Hae_Been_lee]]
|-
|width=160px; align="center"|[[People:Euijeong_Sung|<big>'''Euijeong Sung'''</big>]]
|width=160px; align="center"|[[People:Sehun_Ahn|<big>'''Sehun Ahn'''</big>]]
|width=160px; align="center"|[[People:Geon Koh|<big>'''Geon Koh'''</big>]]
|width=160px; align="center"|[[People:WonJong_Kim|<big>'''Wonjong Kim'''</big>]]
|width=160px; align="center"|[[People:Jungyeon_Kim|<big>'''Jungyeon Kim'''</big>]]
|width=160px; align="center"|[[People:Yerin_Kim|<big>'''Yerin Kim'''</big>]]
|width=160px; align="center"|[[People:Yurim_Jung|<big>'''Yurim Jung'''</big>]]
|width=160px; align="center"|[[People:Hae_Been_lee|<big>'''Hae Been Lee'''</big>]]
|-
|align="center"|[[People:Euijeong_Sung|Integrated Ph.D course (Single-cell Biology) (2021.9 - )]]
|align="center"|[[People:Sehun_Ahn|Integrated Ph.D course (Microbiome) (2022.3 - )]]
|align="center"|[[People:Geon_Koh|Integrated Ph.D course (Microbiome) (2022.9 - )]]
|align="center"|[[People:WonJong_Kim|Integrated Ph.D course (Microbiome) (2023.3 - )]]
|align="center"|[[People:Jungyeon_Kim|Integrated Ph.D course (Microbiome) (2023.3 - )]]
|align="center"|[[People:Yerin_Kim|Integrated Ph.D course (Microbiome) (2024.3 - )]]
|align="center"|[[People:Yurim_Jung|Integrated Ph.D course (Single-cell Biology) (2024.9 - )]]
|align="center"|[[People:Hae_Been_lee|Integrated Ph.D course (Single-cell Biology) (2024.9 - )]]
|}

=='''Administrative Assistant'''==
*Seomi Lee

=='''Undergraduate Students'''==
*Zunu An
*Se Bin Lim
*Kang Eon Lee

=='''Alumni'''==
*'''Former Post-docs and Staff Scientists'''
**1. Taeyun Oh (2009.5-2011.12), currently Senior Researcher at Cowellbiogim, Korea
**2. Sohyun Hwang (2010.3-2015.8), currently Associate Professor (차의과대학 의과학과 교수), [https://grad.cha.ac.kr/%ed%99%a9%ec%86%8c%ed%98%84/ Department of Biomedical Science, CHA University], Korea [https://scholar.google.com/citations?user=z5yC9cwAAAAJ&hl=en Google Scholar]
**3. Samuel Beck (2011.2-2011.12), currently Associate Professor, Boston University, USA (미 보스턴 대학교 부교수), [https://beck310.wixsite.com/becklab Lab Homepage]
**4. Jawon Song (2011.1-2012.5), currently Research Associate (미국 텍사스슈퍼컴퓨팅센터 선임연구원), [https://www.tacc.utexas.edu/about/directory/jawon-song Texas Advanced Computing Center], Austin, TX, USA [https://scholar.google.com/citations?user=wv61wGoAAAAJ&hl=en Google Scholar]
**5. Yoonhee Ko (2012.8-2014.2), currently Associate Professor (한국외국어대 바이오공학과 교수), [https://hufsbiomed.weebly.com/ Department of Biomedical Engineering, Hankuk University of Foreign Studies], Korea [https://scholar.google.com/citations?user=GBxCYX8AAAAJ&hl=en Google Scholar]
**6. Jonghoon Lee (2013.10-2014.2), currently Associate Professor (가천대학교 식품생명공학과 교수), [https://www.gachon.ac.kr/foodbiotech/6142/subview.do Department of Food Biotechnology, Gachon University], Korea
**7. Jung Eun Shim (2009.8-2017.11), currently Staff Scientist at Yonsei Genomics Center Bioinformatics Data Analysis Core (연세의료원 유전체센터 생명정보분석코어 책임연구원)

*'''Former Graduate Students (Ph.D)'''
**1. Junha Shin (2008.9-2014.2), PhD; currently Scientist I at [https://wid.wisc.edu/people/junha-shin/ Wisconsin Institute for Discovery] (미국 위스콘신 대학연구소 연구원), USA
**2. Hanhae Kim (2009.9-2015.2), PhD; currently Research Fellow (한국과학기술기획평가원 연구위원), [http://www.kistep.re.kr Korea Institute of S&T Evaluation and Planning (KISTEP)]
**3. Ara Cho (2009.9-2015.2), PhD; currently Research Fellow (한국과학기술기획평가원 연구위원), [http://www.kistep.re.kr Korea Institute of S&T Evaluation and Planning (KISTEP)]
**4. Eiru Kim (2010.9-2016.8), PhD; currently Senior Expert in Data Science, [https://www.gnf.org/ The Genomics Institute of the Novartis Research Foundation], San Diego, USA (노바티스 선임데이터분석연구원)
**5. Tak Lee (2011.3-2017.2), PhD; currently Senior Bioinformatician, [https://www.mpipz.mpg.de/en Max Planck Institute for Plant Breeding Research] (독일 막스플랭크 연구소 선임생명정보학자)
**6. Heonjong Han (2013.3~2019.8), PhD; currently Bioinformatics engineer at [https://3billion.io/ 3billion] (바이오기업 데이터사이언티스트)
**7. Kyungsoo Kim (2013.3~2020.2), PhD; currently Research Professor at Severance Hospital (강남세브란스의료원 유방암센터 연구교수)
**8. Jae-Won Cho (2016.9~2021.2), PhD; currently Postdoc Fellow at Harvard Medical School (하버드의대 박사후 연구원) [https://scholar.google.com/citations?user=H4jO_DQAAAAJ&hl=en Hemberg Lab]
**9. Chan Yeong Kim (2015.3~2021.2), PhD; currently Postdoc Fellow at European Molecular Biology Laboratory in Heidelberg (유럽연합분자생물학연구소 박사후 연구원) [http://www.bork.embl.de/j/ Bork Group]
**10. Sungho Lee (2017.3-2024.2), PhD; currently Data Scientist at [http://www.mogam.re.kr/kor/ MOGAM Institute for Biomedical Research] (목암생명과학연구소, (주)녹십자 출연 연구소, 선임연구원)

*'''Former Graduate Students (Master's degree)'''
**1. Sun-Gou Ji (2009.9-2011.8), M.Eng.; PhD from University of Cambridge (Sanger Genome center); currently Vice President, Computational Genetics at [https://bridgebio.com/ BridgeBio Pharma], Boston, USA (미 바이오기업 부사장)
**2. Hyojin Kim (2013.3-2015.2), M.Eng.; PhD from University of Aachen; currently Computational Scientist, Uniklinikum Aachen (the university hospital of the city of Aachen), Germany (독일 아헨대학병원 데이터사이언티스트)
**3. Hongseok Shim (2013.9-2015.8), M.Eng.; currently PhD student, Arizona State University, Biomedical Informatics program (애리조나주립대 생물정보학 박사과정)
**4. Sunmo Yang (2014.9-2017.2), M.Eng. currently Bioinformatics programmer, Yonsei University (연세대 네트워크생명공학 연구실 책임데이터연구원)
**5. Muyoung Lee (2016.3-2018.2), M.Eng. currently PhD student, University of Texas at Austin, Cellular and Molecular Biology Program (텍사스주립대 생물정보학 박사과정)
**6. Jiwon Yu (2021.9-2023.8), M.Eng. currently Data Scientist at [https://www.samsungbioepis.com/kr/index.do SAMSUNG BIOEPIS] (삼성바이오에피스 데이터사이언티스트)

*'''Former Visiting Scientists/Students'''
**1. Minkyung Shin (2012.3-2012.7), went to University of Southern California graduate school
**2. Paul Chung (2012 summer), currently E-Commerce Analyst/Coordinator at [http://www.riotgames.com/ Riot Games], USA
**3. Hyunjin Cho (2014 summer), currently Bioinformatics research assistant at [http://www.libd.org/ The Lieber Institute for Brain Development | LIBD], USA
**4. Michael Chung (2016.8-2016.12), currently work at [https://about.meta.com/ Meta], USA

*'''Former Undergraduate Students'''
**1. Ila Shin, went to medical school (Yonsei Univ.) Currently Professor, Kyunghee University School of Medicine (경희대학교 의과대학 교수)
**2. Yoonkyung Ko
**3. Sangyo Park, went to law school (Kyounghee Univ.)
**4. Jayoon Shin, went to medical school (Pusan National Univ.)
**5. Dongjoo Sun, went to dental school (Wongwang Univ.)
**6. Eulsoo Kim
**7. Myungwhan Lee, went to medical school (Yonsei Univ.)
**8. Moonhee Lee, went to graduate school (University of Amsterdam, Netherlands)
**9. Jinhyun Ju, went to graduate school (Cornell Medical School, Computational Systems Biology); Currently Principal Computational Biologist, Cancer Genomics at [https://bridgebio.com/ BridgeBio Pharma], Seattle, USA (미 바이오기업 선임연구원)
**10. Haeyoung Shin, went to graduate school (Brown University, Computational Neuroscience); Currently Professor, Seoul National University (서울대학교 생명과학부 교수)
**11. Joonghee Soh, went to graduate school (KAIST, Culture Technology)
**12. Hee Jung Cho, went to graduate school (UC Berkeley)
**13. TaeHwan Kim, went to medical school
**14. Joohyung Kim, went to medical school (Kyungpook National Univ.)
**15. Donghyun Shin, went to medical school (Yonsei Univ.)
**16. Byunghee Kang (2015 spring - 2016 summer), went to graduate school (PosTech)
**17. Sunphil Kim (2016 winter)
**18. Jaeho Shim (2016 winter)
**19. Seunghyun Shin (2016 spring)
**20. Aejoo Hong (2016 spring)
**21. Changbae Bang (2016 summer), went to medical school (Yonsei Univ.)
**22. Seoyoung Choi (2016 summer)
**23. Dabin Jung (2016 summer)
**24. Boreum Nam (2016 summer)
**25. Seungun Lee (2016 summer)
**26. Yeaji Kim (2016 summer and fall)
**27. Sangyoung Lee (2017 winter)
**28. Heejun Jang (2017 winter & spring), went to KIST as Research Assistant
**29. Jungha Lee (2017 summer), went to graduate school (Seoul National Univ.)
**30. Seunghyun Wang (2017 summer), went to graduate school (KAIST)
**31. Dong-Min Yang (2017 fall)
**32. Doo-Hee Lee (2017 fall)
**33. Suk-Jae Han (2018 winter)
**34. Ji-Eun Han (2018 winter)
**35. Yuri Ko (2018 spring)
**36. Sung-Woo Kim (2018 spring)
**37. Woosung Kwon (2018 summer)
**38. Soyun Kong (2018 summer)
**39. Hojeong Keum (2018 summer and fall)
**40. Sungjun Lim (2018 summer and fall)
**41. Jiyoon Lee (2018 summer and fall)
**42. Ilseok Choi(2019 winter, spring, summer and fall, 2020 winter, spring)
**43. Junik Park (2019 winter)
**44. Lee Yoo (2019 winter & spring)
**45. Yeonwha Kim (2019 winter & spring)
**46. Sohee Kim (2019 spring)
**47. Juseong Lee (2019 spring)
**48. Junyoung Yang (2019 spring & summer)
**49. Junyoung Ma (2019 summer, 2020 winter, summer, fall, 2021 winter)
**50. Dayun Jung (2019 summer)
**51. Hanjoon Kim (2019 summer, 2021 winter)
**52. Hyungjin Kim (2020 winter, spring)
**53. Sujin Hyun (2020 winter)
**54. Hyuki Lee (2020 winter)
**55. Donghwan Lee (2020 winter)
**56. Jun Hyung Cha (2020 spring, summer)
**57. Jiwon Yu (2020 summer, 2021 Spring, summer)
**58. Dong Seok Kim (2020 summer)
**59. Donggeun Lee (2020 summer)
**60. Sehun Ahn (2020 summer, 2021 winter, spring, summer, 2022 winter)
**61. Jiho Kim (2020 summer)
**62. Euijung Seong (2020 fall, 2021 spring, summer)
**63. Junhan Kim (2021 winter & spring)
**64. Suyong Choi (2021 winter)
**65. Sujin Pyeon (2021 Spring)
**66. Hyeon Hee Ji (2021 Spring)
**67. Semin Kim (2021 Summer)
**68. Su Hwan Kim (2021 Summer)
**69. Yongbin Kim (2021 Summer)
**70. Nan Park (2021 Summer)
**71. Hoyong Jung (2021 Summer)
**72. Ingyeong Koh (2021 Summer)
**73. Jongbin Jeong (2022 Winter)
**74. Sungchul Yang (2022 Winter, Spring, Summer)
**75. Hyojung Lee (2022 Winter, Spring)
**76. Jun Hyeong Kim (2022 Winter, Spring)
**77. Young Ji Roh (2022 Spring, Summer)
**78. Geon Koh (2022 Spring, Summer)
**79. Hayoung Kang (2022 Summer, 2023 Spring)
**80. Won Jong Kim (2022 Summer, Fall, 2023 Winter)
**81. Christian Lee (2022 Summer)
**82. Je Yun Kang (2022 Summer)
**83. Sumin Lee (2022 Fall)
**84. Jungyeon Kim (2022 Fall, 2023 Winter)
**85. Seol Song (2023 Winter)
**86. Soyeon Kim (2023 Summer)
**87. Sieun Park (2023 Summer)
**88. Gunhyeong Lee (2023 Summer)
**89. Yunseo Han (2023 Summer)
**90. Jae Hyun Kim (2023 Summer)
**91. Jeong-min Seo (2023 Summer)
**92. Yerin Kim (2023 Fall, 2024 Winter)
**93. Sang Min Park (2024 Winter)
**94. Minsun Seo (2024 Winter)
**95. Jaeryun Sim (2024 Winter)
**96. Sun Min Lim (2024 Winter)
**97. Yurim Jung (2024 Spring, Summer)
**98. Hae Been Lee (2024 Spring, Summer)
**99. Zunu An (2024 Summer)
**100. Se Bin Lim (2024 Summer)
**101. Sang Min Yoon (2024 Summer)
**102. Yena Kim (2024 Summer)

People

2024-08-29T02:41:18Z

Bsb0613: /* Scientists & Research Staff */

__NOTOC__
=='''Scientists & Research Staff'''==
{|border="0" width="1080"
|align="center"|[[File:prof_Insuk_fixed.png|100px|link=people:IS_Lee]]
|align="center"|[[File:SM_fixed.png|100px|link=people:SM_Yang]]
|align="center"|[[File:Junha_fixed.png|100px|link=People:JH_Cha]]
|align="center"|[[File:Nayeon_fixed.png|100px|link=People:NY_Kim]]
|align="center"|[[File:Seungbyn_fixed.png|100px|link=People:SB_Baek]]
|align="center"|[[File:Ilseok_fixed.png|100px|link=People:IS_Choi]]
|align="center"|[[File:JunHyeong_fixed.png|100px|link=People:Jun_Hyung_Cha]]
|align="center"|[[File:JunYeong_fixed.png|100px|link=People:Junyeong_Ma]]
|align="center"|[[File:Hanjune_fixed.png|100px|link=People:Han-June_KIM]]
|-

|width=160px; align="center"|[[people:IS_Lee|<big>'''Insuk Lee'''</big>]]
|width=160px; align="center"|[[people:SM_Yang|<big>'''Sunmo Yang'''</big>]]
|width=160px; align="center"|[[People:JH_Cha|<big>'''Junha Cha'''</big>]]
|width=160px; align="center"|[[People:NY_Kim|<big>'''Nayeon Kim'''</big>]]
|width=160px; align="center"|[[People:SB_Baek|<big>'''Seungbyn Baek'''</big>]]
|width=160px; align="center"|[[People:IS_Choi|<big>'''Ilseok Choi'''</big>]]
|width=160px; align="center"|[[People:Jun_Hyung_Cha|<big>'''Jun Hyung Cha'''</big>]]
|width=160px; align="center"|[[People:Junyeong_Ma|<big>'''Junyeong Ma'''</big>]]
|width=160px; align="center"|[[People:Hanjune_Kim|<big>'''Hanjune Kim'''</big>]]
|-

|align="center"|[[people:IS_Lee|Principal Investigator Professor]]
|align="center"|[[people:SM_Yang|Bioinformatics Programmer & Sys. Admin]]
|align="center"|[[People:JH_Cha|Ph.D candidate (Single-cell Biology) (2019.9 - )]]
|align="center"|[[People:NY_Kim|Ph.D candidate (Microbiome) (2019.9 - )]]
|align="center"|[[People:SB_Baek|Ph.D candidate (Single-cell Biology) (2019.9 - )]]
|align="center"|[[People:IS_Choi|Ph.D candidate (Single-cell Biology) (2020.9 - )]]
|align="center"|[[People:Jun_Hyung_Cha|Ph.D candidate (Microbiome) (2020.9 - )]]
|align="center"|[[People:Junyeong_Ma|Ph.D candidate (Microbiome) (2021.3 - )]]
|align="center"|[[People:Hanjune_Kim|Integrated Ph.D course (Microbiome) (2021.3 - )]]
|}
{|border="0" width="960"

|align="center"|[[File:EuiJeong_fixed.png|100px|link=People:EuiJeong_Sung]]
|align="center"|[[File:Sehun_fixed.png|100px|link=People:Sehun_Ahn]]
|align="center"|[[File:Geon_fixed.png|100px|link=People:Geon_Koh]]
|align="center"|[[File:Wonjong_fixed.png|100px|link=People:WonJong_Kim]]
|align="center"|[[File:Jungyeon_fixed.png|100px|link=People:Jungyeon_Kim]]
|align="center"|[[File:Yerin_fixed.png|100px|link=People:Yerin_Kim]]
|align="center"|[[File:YurimJung_fixed.jpg|100px|link=People:Yurim_Jung]]
|align="center"|[[File:HaeBeen_fixed.jpg|100px|link=People:Hae_Been_Lee]]
|-
|width=160px; align="center"|[[People:Euijeong_Sung|<big>'''Euijeong Sung'''</big>]]
|width=160px; align="center"|[[People:Sehun_Ahn|<big>'''Sehun Ahn'''</big>]]
|width=160px; align="center"|[[People:Geon Koh|<big>'''Geon Koh'''</big>]]
|width=160px; align="center"|[[People:WonJong_Kim|<big>'''Wonjong Kim'''</big>]]
|width=160px; align="center"|[[People:Jungyeon_Kim|<big>'''Jungyeon Kim'''</big>]]
|width=160px; align="center"|[[People:Yerin_Kim|<big>'''Yerin Kim'''</big>]]
|width=160px; align="center"|[[People:Yurim_Jung|<big>'''Yurim Jung'''</big>]]
|width=160px; align="center"|[[People:Hae_Been_lee|<big>'''Hae Been Lee'''</big>]]
|-
|align="center"|[[People:Euijeong_Sung|Integrated Ph.D course (Single-cell Biology) (2021.9 - )]]
|align="center"|[[People:Sehun_Ahn|Integrated Ph.D course (Microbiome) (2022.3 - )]]
|align="center"|[[People:Geon_Koh|Integrated Ph.D course (Microbiome) (2022.9 - )]]
|align="center"|[[People:WonJong_Kim|Integrated Ph.D course (Microbiome) (2023.3 - )]]
|align="center"|[[People:Jungyeon_Kim|Integrated Ph.D course (Microbiome) (2023.3 - )]]
|align="center"|[[People:Yerin_Kim|Integrated Ph.D course (Microbiome) (2024.3 - )]]
|align="center"|[[People:Yurim_Jung|Integrated Ph.D course (Single-cell Biology) (2024.9 - )]]
|align="center"|[[People:Hae_Been_lee|Integrated Ph.D course (Single-cell Biology) (2024.9 - )]]
|}

=='''Administrative Assistant'''==
*Seomi Lee

=='''Undergraduate Students'''==
*Zunu An
*Se Bin Lim
*Kang Eon Lee

=='''Alumni'''==
*'''Former Post-docs and Staff Scientists'''
**1. Taeyun Oh (2009.5-2011.12), currently Senior Researcher at Cowellbiogim, Korea
**2. Sohyun Hwang (2010.3-2015.8), currently Associate Professor (차의과대학 의과학과 교수), [https://grad.cha.ac.kr/%ed%99%a9%ec%86%8c%ed%98%84/ Department of Biomedical Science, CHA University], Korea [https://scholar.google.com/citations?user=z5yC9cwAAAAJ&hl=en Google Scholar]
**3. Samuel Beck (2011.2-2011.12), currently Associate Professor, Boston University, USA (미 보스턴 대학교 부교수), [https://beck310.wixsite.com/becklab Lab Homepage]
**4. Jawon Song (2011.1-2012.5), currently Research Associate (미국 텍사스슈퍼컴퓨팅센터 선임연구원), [https://www.tacc.utexas.edu/about/directory/jawon-song Texas Advanced Computing Center], Austin, TX, USA [https://scholar.google.com/citations?user=wv61wGoAAAAJ&hl=en Google Scholar]
**5. Yoonhee Ko (2012.8-2014.2), currently Associate Professor (한국외국어대 바이오공학과 교수), [https://hufsbiomed.weebly.com/ Department of Biomedical Engineering, Hankuk University of Foreign Studies], Korea [https://scholar.google.com/citations?user=GBxCYX8AAAAJ&hl=en Google Scholar]
**6. Jonghoon Lee (2013.10-2014.2), currently Associate Professor (가천대학교 식품생명공학과 교수), [https://www.gachon.ac.kr/foodbiotech/6142/subview.do Department of Food Biotechnology, Gachon University], Korea
**7. Jung Eun Shim (2009.8-2017.11), currently Staff Scientist at Yonsei Genomics Center Bioinformatics Data Analysis Core (연세의료원 유전체센터 생명정보분석코어 책임연구원)

*'''Former Graduate Students (Ph.D)'''
**1. Junha Shin (2008.9-2014.2), PhD; currently Scientist I at [https://wid.wisc.edu/people/junha-shin/ Wisconsin Institute for Discovery] (미국 위스콘신 대학연구소 연구원), USA
**2. Hanhae Kim (2009.9-2015.2), PhD; currently Research Fellow (한국과학기술기획평가원 연구위원), [http://www.kistep.re.kr Korea Institute of S&T Evaluation and Planning (KISTEP)]
**3. Ara Cho (2009.9-2015.2), PhD; currently Research Fellow (한국과학기술기획평가원 연구위원), [http://www.kistep.re.kr Korea Institute of S&T Evaluation and Planning (KISTEP)]
**4. Eiru Kim (2010.9-2016.8), PhD; currently Senior Expert in Data Science, [https://www.gnf.org/ The Genomics Institute of the Novartis Research Foundation], San Diego, USA (노바티스 선임데이터분석연구원)
**5. Tak Lee (2011.3-2017.2), PhD; currently Senior Bioinformatician, [https://www.mpipz.mpg.de/en Max Planck Institute for Plant Breeding Research] (독일 막스플랭크 연구소 선임생명정보학자)
**6. Heonjong Han (2013.3~2019.8), PhD; currently Bioinformatics engineer at [https://3billion.io/ 3billion] (바이오기업 데이터사이언티스트)
**7. Kyungsoo Kim (2013.3~2020.2), PhD; currently Research Professor at Severance Hospital (강남세브란스의료원 유방암센터 연구교수)
**8. Jae-Won Cho (2016.9~2021.2), PhD; currently Postdoc Fellow at Harvard Medical School (하버드의대 박사후 연구원) [https://scholar.google.com/citations?user=H4jO_DQAAAAJ&hl=en Hemberg Lab]
**9. Chan Yeong Kim (2015.3~2021.2), PhD; currently Postdoc Fellow at European Molecular Biology Laboratory in Heidelberg (유럽연합분자생물학연구소 박사후 연구원) [http://www.bork.embl.de/j/ Bork Group]
**10. Sungho Lee (2017.3-2024.2), PhD; currently Data Scientist at [http://www.mogam.re.kr/kor/ MOGAM Institute for Biomedical Research] (목암생명과학연구소, (주)녹십자 출연 연구소, 선임연구원)

*'''Former Graduate Students (Master's degree)'''
**1. Sun-Gou Ji (2009.9-2011.8), M.Eng.; PhD from University of Cambridge (Sanger Genome center); currently Vice President, Computational Genetics at [https://bridgebio.com/ BridgeBio Pharma], Boston, USA (미 바이오기업 부사장)
**2. Hyojin Kim (2013.3-2015.2), M.Eng.; PhD from University of Aachen; currently Computational Scientist, Uniklinikum Aachen (the university hospital of the city of Aachen), Germany (독일 아헨대학병원 데이터사이언티스트)
**3. Hongseok Shim (2013.9-2015.8), M.Eng.; currently PhD student, Arizona State University, Biomedical Informatics program (애리조나주립대 생물정보학 박사과정)
**4. Sunmo Yang (2014.9-2017.2), M.Eng. currently Bioinformatics programmer, Yonsei University (연세대 네트워크생명공학 연구실 책임데이터연구원)
**5. Muyoung Lee (2016.3-2018.2), M.Eng. currently PhD student, University of Texas at Austin, Cellular and Molecular Biology Program (텍사스주립대 생물정보학 박사과정)
**6. Jiwon Yu (2021.9-2023.8), M.Eng. currently Data Scientist at [https://www.samsungbioepis.com/kr/index.do SAMSUNG BIOEPIS] (삼성바이오에피스 데이터사이언티스트)

*'''Former Visiting Scientists/Students'''
**1. Minkyung Shin (2012.3-2012.7), went to University of Southern California graduate school
**2. Paul Chung (2012 summer), currently E-Commerce Analyst/Coordinator at [http://www.riotgames.com/ Riot Games], USA
**3. Hyunjin Cho (2014 summer), currently Bioinformatics research assistant at [http://www.libd.org/ The Lieber Institute for Brain Development | LIBD], USA
**4. Michael Chung (2016.8-2016.12), currently work at [https://about.meta.com/ Meta], USA

*'''Former Undergraduate Students'''
**1. Ila Shin, went to medical school (Yonsei Univ.) Currently Professor, Kyunghee University School of Medicine (경희대학교 의과대학 교수)
**2. Yoonkyung Ko
**3. Sangyo Park, went to law school (Kyounghee Univ.)
**4. Jayoon Shin, went to medical school (Pusan National Univ.)
**5. Dongjoo Sun, went to dental school (Wongwang Univ.)
**6. Eulsoo Kim
**7. Myungwhan Lee, went to medical school (Yonsei Univ.)
**8. Moonhee Lee, went to graduate school (University of Amsterdam, Netherlands)
**9. Jinhyun Ju, went to graduate school (Cornell Medical School, Computational Systems Biology); Currently Principal Computational Biologist, Cancer Genomics at [https://bridgebio.com/ BridgeBio Pharma], Seattle, USA (미 바이오기업 선임연구원)
**10. Haeyoung Shin, went to graduate school (Brown University, Computational Neuroscience); Currently Professor, Seoul National University (서울대학교 생명과학부 교수)
**11. Joonghee Soh, went to graduate school (KAIST, Culture Technology)
**12. Hee Jung Cho, went to graduate school (UC Berkeley)
**13. TaeHwan Kim, went to medical school
**14. Joohyung Kim, went to medical school (Kyungpook National Univ.)
**15. Donghyun Shin, went to medical school (Yonsei Univ.)
**16. Byunghee Kang (2015 spring - 2016 summer), went to graduate school (PosTech)
**17. Sunphil Kim (2016 winter)
**18. Jaeho Shim (2016 winter)
**19. Seunghyun Shin (2016 spring)
**20. Aejoo Hong (2016 spring)
**21. Changbae Bang (2016 summer), went to medical school (Yonsei Univ.)
**22. Seoyoung Choi (2016 summer)
**23. Dabin Jung (2016 summer)
**24. Boreum Nam (2016 summer)
**25. Seungun Lee (2016 summer)
**26. Yeaji Kim (2016 summer and fall)
**27. Sangyoung Lee (2017 winter)
**28. Heejun Jang (2017 winter & spring), went to KIST as Research Assistant
**29. Jungha Lee (2017 summer), went to graduate school (Seoul National Univ.)
**30. Seunghyun Wang (2017 summer), went to graduate school (KAIST)
**31. Dong-Min Yang (2017 fall)
**32. Doo-Hee Lee (2017 fall)
**33. Suk-Jae Han (2018 winter)
**34. Ji-Eun Han (2018 winter)
**35. Yuri Ko (2018 spring)
**36. Sung-Woo Kim (2018 spring)
**37. Woosung Kwon (2018 summer)
**38. Soyun Kong (2018 summer)
**39. Hojeong Keum (2018 summer and fall)
**40. Sungjun Lim (2018 summer and fall)
**41. Jiyoon Lee (2018 summer and fall)
**42. Ilseok Choi(2019 winter, spring, summer and fall, 2020 winter, spring)
**43. Junik Park (2019 winter)
**44. Lee Yoo (2019 winter & spring)
**45. Yeonwha Kim (2019 winter & spring)
**46. Sohee Kim (2019 spring)
**47. Juseong Lee (2019 spring)
**48. Junyoung Yang (2019 spring & summer)
**49. Junyoung Ma (2019 summer, 2020 winter, summer, fall, 2021 winter)
**50. Dayun Jung (2019 summer)
**51. Hanjoon Kim (2019 summer, 2021 winter)
**52. Hyungjin Kim (2020 winter, spring)
**53. Sujin Hyun (2020 winter)
**54. Hyuki Lee (2020 winter)
**55. Donghwan Lee (2020 winter)
**56. Jun Hyung Cha (2020 spring, summer)
**57. Jiwon Yu (2020 summer, 2021 Spring, summer)
**58. Dong Seok Kim (2020 summer)
**59. Donggeun Lee (2020 summer)
**60. Sehun Ahn (2020 summer, 2021 winter, spring, summer, 2022 winter)
**61. Jiho Kim (2020 summer)
**62. Euijung Seong (2020 fall, 2021 spring, summer)
**63. Junhan Kim (2021 winter & spring)
**64. Suyong Choi (2021 winter)
**65. Sujin Pyeon (2021 Spring)
**66. Hyeon Hee Ji (2021 Spring)
**67. Semin Kim (2021 Summer)
**68. Su Hwan Kim (2021 Summer)
**69. Yongbin Kim (2021 Summer)
**70. Nan Park (2021 Summer)
**71. Hoyong Jung (2021 Summer)
**72. Ingyeong Koh (2021 Summer)
**73. Jongbin Jeong (2022 Winter)
**74. Sungchul Yang (2022 Winter, Spring, Summer)
**75. Hyojung Lee (2022 Winter, Spring)
**76. Jun Hyeong Kim (2022 Winter, Spring)
**77. Young Ji Roh (2022 Spring, Summer)
**78. Geon Koh (2022 Spring, Summer)
**79. Hayoung Kang (2022 Summer, 2023 Spring)
**80. Won Jong Kim (2022 Summer, Fall, 2023 Winter)
**81. Christian Lee (2022 Summer)
**82. Je Yun Kang (2022 Summer)
**83. Sumin Lee (2022 Fall)
**84. Jungyeon Kim (2022 Fall, 2023 Winter)
**85. Seol Song (2023 Winter)
**86. Soyeon Kim (2023 Summer)
**87. Sieun Park (2023 Summer)
**88. Gunhyeong Lee (2023 Summer)
**89. Yunseo Han (2023 Summer)
**90. Jae Hyun Kim (2023 Summer)
**91. Jeong-min Seo (2023 Summer)
**92. Yerin Kim (2023 Fall, 2024 Winter)
**93. Sang Min Park (2024 Winter)
**94. Minsun Seo (2024 Winter)
**95. Jaeryun Sim (2024 Winter)
**96. Sun Min Lim (2024 Winter)
**97. Yurim Jung (2024 Spring, Summer)
**98. Hae Been Lee (2024 Spring, Summer)
**99. Zunu An (2024 Summer)
**100. Se Bin Lim (2024 Summer)
**101. Sang Min Yoon (2024 Summer)
**102. Yena Kim (2024 Summer)

People

2024-08-23T08:18:23Z

Bsb0613: /* Scientists & Research Staff */

__NOTOC__
=='''Scientists & Research Staff'''==
{|border="0" width="1080"
|align="center"|[[File:prof_Insuk_fixed.png|100px|link=people:IS_Lee]]
|align="center"|[[File:SM_fixed.png|100px|link=people:SM_Yang]]
|align="center"|[[File:Junha_fixed.png|100px|link=People:JH_Cha]]
|align="center"|[[File:Nayeon_fixed.png|100px|link=People:NY_Kim]]
|align="center"|[[File:Seungbyn_fixed.png|100px|link=People:SB_Baek]]
|align="center"|[[File:Ilseok_fixed.png|100px|link=People:IS_Choi]]
|align="center"|[[File:JunHyeong_fixed.png|100px|link=People:Jun_Hyung_Cha]]
|align="center"|[[File:JunYeong_fixed.png|100px|link=People:Junyeong_Ma]]
|align="center"|[[File:Hanjune_fixed.png|100px|link=People:Han-June_KIM]]
|-

|width=160px; align="center"|[[people:IS_Lee|<big>'''Insuk Lee'''</big>]]
|width=160px; align="center"|[[people:SM_Yang|<big>'''Sunmo Yang'''</big>]]
|width=160px; align="center"|[[People:JH_Cha|<big>'''Junha Cha'''</big>]]
|width=160px; align="center"|[[People:NY_Kim|<big>'''Nayeon Kim'''</big>]]
|width=160px; align="center"|[[People:SB_Baek|<big>'''Seungbyn Baek'''</big>]]
|width=160px; align="center"|[[People:IS_Choi|<big>'''Ilseok Choi'''</big>]]
|width=160px; align="center"|[[People:Jun_Hyung_Cha|<big>'''Jun Hyung Cha'''</big>]]
|width=160px; align="center"|[[People:Junyeong_Ma|<big>'''Junyeong Ma'''</big>]]
|width=160px; align="center"|[[People:Hanjune_Kim|<big>'''Hanjune Kim'''</big>]]
|-

|align="center"|[[people:IS_Lee|Principal Investigator Professor]]
|align="center"|[[people:SM_Yang|Bioinformatics Programmer & Sys. Admin]]
|align="center"|[[People:JH_Cha|Ph.D candidate (Single-cell Biology) (2019.9 - )]]
|align="center"|[[People:NY_Kim|Ph.D candidate (Microbiome) (2019.9 - )]]
|align="center"|[[People:SB_Baek|Ph.D candidate (Single-cell Biology) (2019.9 - )]]
|align="center"|[[People:IS_Choi|Ph.D candidate (Single-cell Biology) (2020.9 - )]]
|align="center"|[[People:Jun_Hyung_Cha|Ph.D candidate (Microbiome) (2020.9 - )]]
|align="center"|[[People:Junyeong_Ma|Ph.D candidate (Microbiome) (2021.3 - )]]
|align="center"|[[People:Hanjune_Kim|Integrated Ph.D course (Microbiome) (2021.3 - )]]
|}
{|border="0" width="960"

|align="center"|[[File:EuiJeong_fixed.png|100px|link=People:EuiJeong_Sung]]
|align="center"|[[File:Sehun_fixed.png|100px|link=People:Sehun_Ahn]]
|align="center"|[[File:Geon_fixed.png|100px|link=People:Geon_Koh]]
|align="center"|[[File:Wonjong_fixed.png|100px|link=People:WonJong_Kim]]
|align="center"|[[File:Jungyeon_fixed.png|100px|link=People:Jungyeon_Kim]]
|align="center"|[[File:Yerin_fixed.png|100px|link=People:Yerin_Kim]]
|align="center"|[[File:EuiJeong_fixed.png|100px|link=People:Yurim_Jung]]
|align="center"|[[File:Jungyeon_fixed.png|100px|link=People:Hae_Been_lee]]
|-
|width=160px; align="center"|[[People:Euijeong_Sung|<big>'''Euijeong Sung'''</big>]]
|width=160px; align="center"|[[People:Sehun_Ahn|<big>'''Sehun Ahn'''</big>]]
|width=160px; align="center"|[[People:Geon Koh|<big>'''Geon Koh'''</big>]]
|width=160px; align="center"|[[People:WonJong_Kim|<big>'''Wonjong Kim'''</big>]]
|width=160px; align="center"|[[People:Jungyeon_Kim|<big>'''Jungyeon Kim'''</big>]]
|width=160px; align="center"|[[People:Yerin_Kim|<big>'''Yerin Kim'''</big>]]
|width=160px; align="center"|[[People:Yurim_Jung|<big>'''Yurim Jung'''</big>]]
|width=160px; align="center"|[[People:Hae_Been_lee|<big>'''Hae Been Lee'''</big>]]
|-
|align="center"|[[People:Euijeong_Sung|Integrated Ph.D course (Single-cell Biology) (2021.9 - )]]
|align="center"|[[People:Sehun_Ahn|Integrated Ph.D course (Microbiome) (2022.3 - )]]
|align="center"|[[People:Geon_Koh|Integrated Ph.D course (Microbiome) (2022.9 - )]]
|align="center"|[[People:WonJong_Kim|Integrated Ph.D course (Microbiome) (2023.3 - )]]
|align="center"|[[People:Jungyeon_Kim|Integrated Ph.D course (Microbiome) (2023.3 - )]]
|align="center"|[[People:Yerin_Kim|Integrated Ph.D course (Microbiome) (2024.3 - )]]
|align="center"|[[People:Yurim_Jung|Integrated Ph.D course (Single-cell Biology) (2024.9 - )]]
|align="center"|[[People:Hae_Been_lee|Integrated Ph.D course (Single-cell Biology) (2024.9 - )]]
|}

=='''Administrative Assistant'''==
*Seomi Lee

=='''Undergraduate Students'''==
*Yurim Jung
*Hae Been Lee
*Zunu An
*Se Bin Lim
*Sang Min Yoon
*Yena Kim

=='''Alumni'''==
*'''Former Post-docs and Staff Scientists'''
**1. Taeyun Oh (2009.5-2011.12), currently Senior Researcher at Cowellbiogim, Korea
**2. Sohyun Hwang (2010.3-2015.8), currently Associate Professor (차의과대학 의과학과 교수), [https://grad.cha.ac.kr/%ed%99%a9%ec%86%8c%ed%98%84/ Department of Biomedical Science, CHA University], Korea [https://scholar.google.com/citations?user=z5yC9cwAAAAJ&hl=en Google Scholar]
**3. Samuel Beck (2011.2-2011.12), currently Associate Professor, Boston University, USA (미 보스턴 대학교 부교수), [https://beck310.wixsite.com/becklab Lab Homepage]
**4. Jawon Song (2011.1-2012.5), currently Research Associate (미국 텍사스슈퍼컴퓨팅센터 선임연구원), [https://www.tacc.utexas.edu/about/directory/jawon-song Texas Advanced Computing Center], Austin, TX, USA [https://scholar.google.com/citations?user=wv61wGoAAAAJ&hl=en Google Scholar]
**5. Yoonhee Ko (2012.8-2014.2), currently Associate Professor (한국외국어대 바이오공학과 교수), [https://hufsbiomed.weebly.com/ Department of Biomedical Engineering, Hankuk University of Foreign Studies], Korea [https://scholar.google.com/citations?user=GBxCYX8AAAAJ&hl=en Google Scholar]
**6. Jonghoon Lee (2013.10-2014.2), currently Associate Professor (가천대학교 식품생명공학과 교수), [https://www.gachon.ac.kr/foodbiotech/6142/subview.do Department of Food Biotechnology, Gachon University], Korea
**7. Jung Eun Shim (2009.8-2017.11), currently Staff Scientist at Yonsei Genomics Center Bioinformatics Data Analysis Core (연세의료원 유전체센터 생명정보분석코어 책임연구원)

*'''Former Graduate Students (Ph.D)'''
**1. Junha Shin (2008.9-2014.2), PhD; currently Scientist I at [https://wid.wisc.edu/people/junha-shin/ Wisconsin Institute for Discovery] (미국 위스콘신 대학연구소 연구원), USA
**2. Hanhae Kim (2009.9-2015.2), PhD; currently Research Fellow (한국과학기술기획평가원 연구위원), [http://www.kistep.re.kr Korea Institute of S&T Evaluation and Planning (KISTEP)]
**3. Ara Cho (2009.9-2015.2), PhD; currently Research Fellow (한국과학기술기획평가원 연구위원), [http://www.kistep.re.kr Korea Institute of S&T Evaluation and Planning (KISTEP)]
**4. Eiru Kim (2010.9-2016.8), PhD; currently Senior Expert in Data Science, [https://www.gnf.org/ The Genomics Institute of the Novartis Research Foundation], San Diego, USA (노바티스 선임데이터분석연구원)
**5. Tak Lee (2011.3-2017.2), PhD; currently Senior Bioinformatician, [https://www.mpipz.mpg.de/en Max Planck Institute for Plant Breeding Research] (독일 막스플랭크 연구소 선임생명정보학자)
**6. Heonjong Han (2013.3~2019.8), PhD; currently Bioinformatics engineer at [https://3billion.io/ 3billion] (바이오기업 데이터사이언티스트)
**7. Kyungsoo Kim (2013.3~2020.2), PhD; currently Research Professor at Severance Hospital (강남세브란스의료원 유방암센터 연구교수)
**8. Jae-Won Cho (2016.9~2021.2), PhD; currently Postdoc Fellow at Harvard Medical School (하버드의대 박사후 연구원) [https://scholar.google.com/citations?user=H4jO_DQAAAAJ&hl=en Hemberg Lab]
**9. Chan Yeong Kim (2015.3~2021.2), PhD; currently Postdoc Fellow at European Molecular Biology Laboratory in Heidelberg (유럽연합분자생물학연구소 박사후 연구원) [http://www.bork.embl.de/j/ Bork Group]
**10. Sungho Lee (2017.3-2024.2), PhD; currently Data Scientist at [http://www.mogam.re.kr/kor/ MOGAM Institute for Biomedical Research] (목암생명과학연구소, (주)녹십자 출연 연구소, 선임연구원)

*'''Former Graduate Students (Master's degree)'''
**1. Sun-Gou Ji (2009.9-2011.8), M.Eng.; PhD from University of Cambridge (Sanger Genome center); currently Vice President, Computational Genetics at [https://bridgebio.com/ BridgeBio Pharma], Boston, USA (미 바이오기업 부사장)
**2. Hyojin Kim (2013.3-2015.2), M.Eng.; PhD from University of Aachen; currently Computational Scientist, Uniklinikum Aachen (the university hospital of the city of Aachen), Germany (독일 아헨대학병원 데이터사이언티스트)
**3. Hongseok Shim (2013.9-2015.8), M.Eng.; currently PhD student, Arizona State University, Biomedical Informatics program (애리조나주립대 생물정보학 박사과정)
**4. Sunmo Yang (2014.9-2017.2), M.Eng. currently Bioinformatics programmer, Yonsei University (연세대 네트워크생명공학 연구실 책임데이터연구원)
**5. Muyoung Lee (2016.3-2018.2), M.Eng. currently PhD student, University of Texas at Austin, Cellular and Molecular Biology Program (텍사스주립대 생물정보학 박사과정)
**6. Jiwon Yu (2021.9-2023.8), M.Eng. currently Data Scientist at [https://www.samsungbioepis.com/kr/index.do SAMSUNG BIOEPIS] (삼성바이오에피스 데이터사이언티스트)

*'''Former Visiting Scientists/Students'''
**1. Minkyung Shin (2012.3-2012.7), went to University of Southern California graduate school
**2. Paul Chung (2012 summer), currently E-Commerce Analyst/Coordinator at [http://www.riotgames.com/ Riot Games], USA
**3. Hyunjin Cho (2014 summer), currently Bioinformatics research assistant at [http://www.libd.org/ The Lieber Institute for Brain Development | LIBD], USA
**4. Michael Chung (2016.8-2016.12), currently work at [https://about.meta.com/ Meta], USA

*'''Former Undergraduate Students'''
**1. Ila Shin, went to medical school (Yonsei Univ.) Currently Professor, Kyunghee University School of Medicine (경희대학교 의과대학 교수)
**2. Yoonkyung Ko
**3. Sangyo Park, went to law school (Kyounghee Univ.)
**4. Jayoon Shin, went to medical school (Pusan National Univ.)
**5. Dongjoo Sun, went to dental school (Wongwang Univ.)
**6. Eulsoo Kim
**7. Myungwhan Lee, went to medical school (Yonsei Univ.)
**8. Moonhee Lee, went to graduate school (University of Amsterdam, Netherlands)
**9. Jinhyun Ju, went to graduate school (Cornell Medical School, Computational Systems Biology); Currently Principal Computational Biologist, Cancer Genomics at [https://bridgebio.com/ BridgeBio Pharma], Seattle, USA (미 바이오기업 선임연구원)
**10. Haeyoung Shin, went to graduate school (Brown University, Computational Neuroscience); Currently Professor, Seoul National University (서울대학교 생명과학부 교수)
**11. Joonghee Soh, went to graduate school (KAIST, Culture Technology)
**12. Hee Jung Cho, went to graduate school (UC Berkeley)
**13. TaeHwan Kim, went to medical school
**14. Joohyung Kim, went to medical school (Kyungpook National Univ.)
**15. Donghyun Shin, went to medical school (Yonsei Univ.)
**16. Byunghee Kang (2015 spring - 2016 summer), went to graduate school (PosTech)
**17. Sunphil Kim (2016 winter)
**18. Jaeho Shim (2016 winter)
**19. Seunghyun Shin (2016 spring)
**20. Aejoo Hong (2016 spring)
**21. Changbae Bang (2016 summer), went to medical school (Yonsei Univ.)
**22. Seoyoung Choi (2016 summer)
**23. Dabin Jung (2016 summer)
**24. Boreum Nam (2016 summer)
**25. Seungun Lee (2016 summer)
**26. Yeaji Kim (2016 summer and fall)
**27. Sangyoung Lee (2017 winter)
**28. Heejun Jang (2017 winter & spring), went to KIST as Research Assistant
**29. Jungha Lee (2017 summer), went to graduate school (Seoul National Univ.)
**30. Seunghyun Wang (2017 summer), went to graduate school (KAIST)
**31. Dong-Min Yang (2017 fall)
**32. Doo-Hee Lee (2017 fall)
**33. Suk-Jae Han (2018 winter)
**34. Ji-Eun Han (2018 winter)
**35. Yuri Ko (2018 spring)
**36. Sung-Woo Kim (2018 spring)
**37. Woosung Kwon (2018 summer)
**38. Soyun Kong (2018 summer)
**39. Hojeong Keum (2018 summer and fall)
**40. Sungjun Lim (2018 summer and fall)
**41. Jiyoon Lee (2018 summer and fall)
**42. Ilseok Choi(2019 winter, spring, summer and fall, 2020 winter, spring)
**43. Junik Park (2019 winter)
**44. Lee Yoo (2019 winter & spring)
**45. Yeonwha Kim (2019 winter & spring)
**46. Sohee Kim (2019 spring)
**47. Juseong Lee (2019 spring)
**48. Junyoung Yang (2019 spring & summer)
**49. Junyoung Ma (2019 summer, 2020 winter, summer, fall, 2021 winter)
**50. Dayun Jung (2019 summer)
**51. Hanjoon Kim (2019 summer, 2021 winter)
**52. Hyungjin Kim (2020 winter, spring)
**53. Sujin Hyun (2020 winter)
**54. Hyuki Lee (2020 winter)
**55. Donghwan Lee (2020 winter)
**56. Jun Hyung Cha (2020 spring, summer)
**57. Jiwon Yu (2020 summer, 2021 Spring, summer)
**58. Dong Seok Kim (2020 summer)
**59. Donggeun Lee (2020 summer)
**60. Sehun Ahn (2020 summer, 2021 winter, spring, summer, 2022 winter)
**61. Jiho Kim (2020 summer)
**62. Euijung Seong (2020 fall, 2021 spring, summer)
**63. Junhan Kim (2021 winter & spring)
**64. Suyong Choi (2021 winter)
**65. Sujin Pyeon (2021 Spring)
**66. Hyeon Hee Ji (2021 Spring)
**67. Semin Kim (2021 Summer)
**68. Su Hwan Kim (2021 Summer)
**69. Yongbin Kim (2021 Summer)
**70. Nan Park (2021 Summer)
**71. Hoyong Jung (2021 Summer)
**72. Ingyeong Koh (2021 Summer)
**73. Jongbin Jeong (2022 Winter)
**74. Sungchul Yang (2022 Winter, Spring, Summer)
**75. Hyojung Lee (2022 Winter, Spring)
**76. Jun Hyeong Kim (2022 Winter, Spring)
**77. Young Ji Roh (2022 Spring, Summer)
**78. Geon Koh (2022 Spring, Summer)
**79. Hayoung Kang (2022 Summer, 2023 Spring)
**80. Won Jong Kim (2022 Summer, Fall, 2023 Winter)
**81. Christian Lee (2022 Summer)
**82. Je Yun Kang (2022 Summer)
**83. Sumin Lee (2022 Fall)
**84. Jungyeon Kim (2022 Fall, 2023 Winter)
**85. Seol Song (2023 Winter)
**86. Soyeon Kim (2023 Summer)
**87. Sieun Park (2023 Summer)
**88. Gunhyeong Lee (2023 Summer)
**89. Yunseo Han (2023 Summer)
**90. Jae Hyun Kim (2023 Summer)
**91. Jeong-min Seo (2023 Summer)
**92. Yerin Kim (2023 Fall, 2024 Winter)
**93. Sang Min Park (2024 Winter)
**94. Minsun Seo (2024 Winter)
**95. Jaeryun Sim (2024 Winter)
**96. Sun Min Lim (2024 Winter)
**97. Yurim Jung (2024 Spring, Summer)
**98. Hae Been Lee (2024 Spring, Summer)
**99. Zunu An (2024 Summer)
**100. Se Bin Lim (2024 Summer)
**101. Sang Min Yoon (2024 Summer)
**102. Yena Kim (2024 Summer)

People

2024-08-23T08:15:46Z

Bsb0613: /* Scientists & Research Staff */

__NOTOC__
=='''Scientists & Research Staff'''==
{|border="0" width="1080"
|align="center"|[[File:prof_Insuk_fixed.png|100px|link=people:IS_Lee]]
|align="center"|[[File:SM_fixed.png|100px|link=people:SM_Yang]]
|align="center"|[[File:Junha_fixed.png|100px|link=People:JH_Cha]]
|align="center"|[[File:Nayeon_fixed.png|100px|link=People:NY_Kim]]
|align="center"|[[File:Seungbyn_fixed.png|100px|link=People:SB_Baek]]
|align="center"|[[File:Ilseok_fixed.png|100px|link=People:IS_Choi]]
|align="center"|[[File:JunHyeong_fixed.png|100px|link=People:Jun_Hyung_Cha]]
|align="center"|[[File:JunYeong_fixed.png|100px|link=People:Junyeong_Ma]]
|align="center"|[[File:Hanjune_fixed.png|100px|link=People:Han-June_KIM]]
|-

|width=160px; align="center"|[[people:IS_Lee|<big>'''Insuk Lee'''</big>]]
|width=160px; align="center"|[[people:SM_Yang|<big>'''Sunmo Yang'''</big>]]
|width=160px; align="center"|[[People:JH_Cha|<big>'''Junha Cha'''</big>]]
|width=160px; align="center"|[[People:NY_Kim|<big>'''Nayeon Kim'''</big>]]
|width=160px; align="center"|[[People:SB_Baek|<big>'''Seungbyn Baek'''</big>]]
|width=160px; align="center"|[[People:IS_Choi|<big>'''Ilseok Choi'''</big>]]
|width=160px; align="center"|[[People:Jun_Hyung_Cha|<big>'''Jun Hyung Cha'''</big>]]
|width=160px; align="center"|[[People:Junyeong_Ma|<big>'''Junyeong Ma'''</big>]]
|width=160px; align="center"|[[People:Hanjune_Kim|<big>'''Hanjune Kim'''</big>]]
|-

|align="center"|[[people:IS_Lee|Principal Investigator Professor]]
|align="center"|[[people:SM_Yang|Bioinformatics Programmer & Sys. Admin]]
|align="center"|[[People:JH_Cha|Ph.D candidate (Single-cell Biology) (2019.9 - )]]
|align="center"|[[People:NY_Kim|Ph.D candidate (Microbiome) (2019.9 - )]]
|align="center"|[[People:SB_Baek|Ph.D candidate (Single-cell Biology) (2019.9 - )]]
|align="center"|[[People:IS_Choi|Ph.D candidate (Single-cell Biology) (2020.9 - )]]
|align="center"|[[People:Jun_Hyung_Cha|Ph.D candidate (Microbiome) (2020.9 - )]]
|align="center"|[[People:Junyeong_Ma|Ph.D candidate (Microbiome) (2021.3 - )]]
|align="center"|[[People:Hanjune_Kim|Integrated Ph.D course (Microbiome) (2021.3 - )]]
|}
{|border="0" width="960"

|align="center"|[[File:EuiJeong_fixed.png|100px|link=People:EuiJeong_Sung]]
|align="center"|[[File:Sehun_fixed.png|100px|link=People:Sehun_Ahn]]
|align="center"|[[File:Geon_fixed.png|100px|link=People:Geon_Koh]]
|align="center"|[[File:Wonjong_fixed.png|100px|link=People:WonJong_Kim]]
|align="center"|[[File:Jungyeon_fixed.png|100px|link=People:Jungyeon_Kim]]
|align="center"|[[File:Yerin_fixed.png|100px|link=People:Yerin_Kim]]
|align="center"|[[File:Yerin_fixed.png|100px|link=People:Yurim_Jung]]
|align="center"|[[File:Yerin_fixed.png|100px|link=People:Hae_Been_lee]]
|-
|width=160px; align="center"|[[People:Euijeong_Sung|<big>'''Euijeong Sung'''</big>]]
|width=160px; align="center"|[[People:Sehun_Ahn|<big>'''Sehun Ahn'''</big>]]
|width=160px; align="center"|[[People:Geon Koh|<big>'''Geon Koh'''</big>]]
|width=160px; align="center"|[[People:WonJong_Kim|<big>'''Wonjong Kim'''</big>]]
|width=160px; align="center"|[[People:Jungyeon_Kim|<big>'''Jungyeon Kim'''</big>]]
|width=160px; align="center"|[[People:Yerin_Kim|<big>'''Yerin Kim'''</big>]]
|width=160px; align="center"|[[People:Yurim_Jung|<big>'''Yurim Jung'''</big>]]
|width=160px; align="center"|[[People:Hae_Been_lee|<big>'''Hae Been Lee'''</big>]]
|-
|align="center"|[[People:Euijeong_Sung|Integrated Ph.D course (Single-cell Biology) (2021.9 - )]]
|align="center"|[[People:Sehun_Ahn|Integrated Ph.D course (Microbiome) (2022.3 - )]]
|align="center"|[[People:Geon_Koh|Integrated Ph.D course (Microbiome) (2022.9 - )]]
|align="center"|[[People:WonJong_Kim|Integrated Ph.D course (Microbiome) (2023.3 - )]]
|align="center"|[[People:Jungyeon_Kim|Integrated Ph.D course (Microbiome) (2023.3 - )]]
|align="center"|[[People:Yerin_Kim|Integrated Ph.D course (Microbiome) (2024.3 - )]]
|align="center"|[[People:Yurim_Jung|Integrated Ph.D course (Single-cell Biology) (2024.9 - )]]
|align="center"|[[People:Hae_Been_lee|Integrated Ph.D course (Single-cell Biology) (2024.9 - )]]
|}

=='''Administrative Assistant'''==
*Seomi Lee

=='''Undergraduate Students'''==
*Yurim Jung
*Hae Been Lee
*Zunu An
*Se Bin Lim
*Sang Min Yoon
*Yena Kim

=='''Alumni'''==
*'''Former Post-docs and Staff Scientists'''
**1. Taeyun Oh (2009.5-2011.12), currently Senior Researcher at Cowellbiogim, Korea
**2. Sohyun Hwang (2010.3-2015.8), currently Associate Professor (차의과대학 의과학과 교수), [https://grad.cha.ac.kr/%ed%99%a9%ec%86%8c%ed%98%84/ Department of Biomedical Science, CHA University], Korea [https://scholar.google.com/citations?user=z5yC9cwAAAAJ&hl=en Google Scholar]
**3. Samuel Beck (2011.2-2011.12), currently Associate Professor, Boston University, USA (미 보스턴 대학교 부교수), [https://beck310.wixsite.com/becklab Lab Homepage]
**4. Jawon Song (2011.1-2012.5), currently Research Associate (미국 텍사스슈퍼컴퓨팅센터 선임연구원), [https://www.tacc.utexas.edu/about/directory/jawon-song Texas Advanced Computing Center], Austin, TX, USA [https://scholar.google.com/citations?user=wv61wGoAAAAJ&hl=en Google Scholar]
**5. Yoonhee Ko (2012.8-2014.2), currently Associate Professor (한국외국어대 바이오공학과 교수), [https://hufsbiomed.weebly.com/ Department of Biomedical Engineering, Hankuk University of Foreign Studies], Korea [https://scholar.google.com/citations?user=GBxCYX8AAAAJ&hl=en Google Scholar]
**6. Jonghoon Lee (2013.10-2014.2), currently Associate Professor (가천대학교 식품생명공학과 교수), [https://www.gachon.ac.kr/foodbiotech/6142/subview.do Department of Food Biotechnology, Gachon University], Korea
**7. Jung Eun Shim (2009.8-2017.11), currently Staff Scientist at Yonsei Genomics Center Bioinformatics Data Analysis Core (연세의료원 유전체센터 생명정보분석코어 책임연구원)

*'''Former Graduate Students (Ph.D)'''
**1. Junha Shin (2008.9-2014.2), PhD; currently Scientist I at [https://wid.wisc.edu/people/junha-shin/ Wisconsin Institute for Discovery] (미국 위스콘신 대학연구소 연구원), USA
**2. Hanhae Kim (2009.9-2015.2), PhD; currently Research Fellow (한국과학기술기획평가원 연구위원), [http://www.kistep.re.kr Korea Institute of S&T Evaluation and Planning (KISTEP)]
**3. Ara Cho (2009.9-2015.2), PhD; currently Research Fellow (한국과학기술기획평가원 연구위원), [http://www.kistep.re.kr Korea Institute of S&T Evaluation and Planning (KISTEP)]
**4. Eiru Kim (2010.9-2016.8), PhD; currently Senior Expert in Data Science, [https://www.gnf.org/ The Genomics Institute of the Novartis Research Foundation], San Diego, USA (노바티스 선임데이터분석연구원)
**5. Tak Lee (2011.3-2017.2), PhD; currently Senior Bioinformatician, [https://www.mpipz.mpg.de/en Max Planck Institute for Plant Breeding Research] (독일 막스플랭크 연구소 선임생명정보학자)
**6. Heonjong Han (2013.3~2019.8), PhD; currently Bioinformatics engineer at [https://3billion.io/ 3billion] (바이오기업 데이터사이언티스트)
**7. Kyungsoo Kim (2013.3~2020.2), PhD; currently Research Professor at Severance Hospital (강남세브란스의료원 유방암센터 연구교수)
**8. Jae-Won Cho (2016.9~2021.2), PhD; currently Postdoc Fellow at Harvard Medical School (하버드의대 박사후 연구원) [https://scholar.google.com/citations?user=H4jO_DQAAAAJ&hl=en Hemberg Lab]
**9. Chan Yeong Kim (2015.3~2021.2), PhD; currently Postdoc Fellow at European Molecular Biology Laboratory in Heidelberg (유럽연합분자생물학연구소 박사후 연구원) [http://www.bork.embl.de/j/ Bork Group]
**10. Sungho Lee (2017.3-2024.2), PhD; currently Data Scientist at [http://www.mogam.re.kr/kor/ MOGAM Institute for Biomedical Research] (목암생명과학연구소, (주)녹십자 출연 연구소, 선임연구원)

*'''Former Graduate Students (Master's degree)'''
**1. Sun-Gou Ji (2009.9-2011.8), M.Eng.; PhD from University of Cambridge (Sanger Genome center); currently Vice President, Computational Genetics at [https://bridgebio.com/ BridgeBio Pharma], Boston, USA (미 바이오기업 부사장)
**2. Hyojin Kim (2013.3-2015.2), M.Eng.; PhD from University of Aachen; currently Computational Scientist, Uniklinikum Aachen (the university hospital of the city of Aachen), Germany (독일 아헨대학병원 데이터사이언티스트)
**3. Hongseok Shim (2013.9-2015.8), M.Eng.; currently PhD student, Arizona State University, Biomedical Informatics program (애리조나주립대 생물정보학 박사과정)
**4. Sunmo Yang (2014.9-2017.2), M.Eng. currently Bioinformatics programmer, Yonsei University (연세대 네트워크생명공학 연구실 책임데이터연구원)
**5. Muyoung Lee (2016.3-2018.2), M.Eng. currently PhD student, University of Texas at Austin, Cellular and Molecular Biology Program (텍사스주립대 생물정보학 박사과정)
**6. Jiwon Yu (2021.9-2023.8), M.Eng. currently Data Scientist at [https://www.samsungbioepis.com/kr/index.do SAMSUNG BIOEPIS] (삼성바이오에피스 데이터사이언티스트)

*'''Former Visiting Scientists/Students'''
**1. Minkyung Shin (2012.3-2012.7), went to University of Southern California graduate school
**2. Paul Chung (2012 summer), currently E-Commerce Analyst/Coordinator at [http://www.riotgames.com/ Riot Games], USA
**3. Hyunjin Cho (2014 summer), currently Bioinformatics research assistant at [http://www.libd.org/ The Lieber Institute for Brain Development | LIBD], USA
**4. Michael Chung (2016.8-2016.12), currently work at [https://about.meta.com/ Meta], USA

*'''Former Undergraduate Students'''
**1. Ila Shin, went to medical school (Yonsei Univ.) Currently Professor, Kyunghee University School of Medicine (경희대학교 의과대학 교수)
**2. Yoonkyung Ko
**3. Sangyo Park, went to law school (Kyounghee Univ.)
**4. Jayoon Shin, went to medical school (Pusan National Univ.)
**5. Dongjoo Sun, went to dental school (Wongwang Univ.)
**6. Eulsoo Kim
**7. Myungwhan Lee, went to medical school (Yonsei Univ.)
**8. Moonhee Lee, went to graduate school (University of Amsterdam, Netherlands)
**9. Jinhyun Ju, went to graduate school (Cornell Medical School, Computational Systems Biology); Currently Principal Computational Biologist, Cancer Genomics at [https://bridgebio.com/ BridgeBio Pharma], Seattle, USA (미 바이오기업 선임연구원)
**10. Haeyoung Shin, went to graduate school (Brown University, Computational Neuroscience); Currently Professor, Seoul National University (서울대학교 생명과학부 교수)
**11. Joonghee Soh, went to graduate school (KAIST, Culture Technology)
**12. Hee Jung Cho, went to graduate school (UC Berkeley)
**13. TaeHwan Kim, went to medical school
**14. Joohyung Kim, went to medical school (Kyungpook National Univ.)
**15. Donghyun Shin, went to medical school (Yonsei Univ.)
**16. Byunghee Kang (2015 spring - 2016 summer), went to graduate school (PosTech)
**17. Sunphil Kim (2016 winter)
**18. Jaeho Shim (2016 winter)
**19. Seunghyun Shin (2016 spring)
**20. Aejoo Hong (2016 spring)
**21. Changbae Bang (2016 summer), went to medical school (Yonsei Univ.)
**22. Seoyoung Choi (2016 summer)
**23. Dabin Jung (2016 summer)
**24. Boreum Nam (2016 summer)
**25. Seungun Lee (2016 summer)
**26. Yeaji Kim (2016 summer and fall)
**27. Sangyoung Lee (2017 winter)
**28. Heejun Jang (2017 winter & spring), went to KIST as Research Assistant
**29. Jungha Lee (2017 summer), went to graduate school (Seoul National Univ.)
**30. Seunghyun Wang (2017 summer), went to graduate school (KAIST)
**31. Dong-Min Yang (2017 fall)
**32. Doo-Hee Lee (2017 fall)
**33. Suk-Jae Han (2018 winter)
**34. Ji-Eun Han (2018 winter)
**35. Yuri Ko (2018 spring)
**36. Sung-Woo Kim (2018 spring)
**37. Woosung Kwon (2018 summer)
**38. Soyun Kong (2018 summer)
**39. Hojeong Keum (2018 summer and fall)
**40. Sungjun Lim (2018 summer and fall)
**41. Jiyoon Lee (2018 summer and fall)
**42. Ilseok Choi(2019 winter, spring, summer and fall, 2020 winter, spring)
**43. Junik Park (2019 winter)
**44. Lee Yoo (2019 winter & spring)
**45. Yeonwha Kim (2019 winter & spring)
**46. Sohee Kim (2019 spring)
**47. Juseong Lee (2019 spring)
**48. Junyoung Yang (2019 spring & summer)
**49. Junyoung Ma (2019 summer, 2020 winter, summer, fall, 2021 winter)
**50. Dayun Jung (2019 summer)
**51. Hanjoon Kim (2019 summer, 2021 winter)
**52. Hyungjin Kim (2020 winter, spring)
**53. Sujin Hyun (2020 winter)
**54. Hyuki Lee (2020 winter)
**55. Donghwan Lee (2020 winter)
**56. Jun Hyung Cha (2020 spring, summer)
**57. Jiwon Yu (2020 summer, 2021 Spring, summer)
**58. Dong Seok Kim (2020 summer)
**59. Donggeun Lee (2020 summer)
**60. Sehun Ahn (2020 summer, 2021 winter, spring, summer, 2022 winter)
**61. Jiho Kim (2020 summer)
**62. Euijung Seong (2020 fall, 2021 spring, summer)
**63. Junhan Kim (2021 winter & spring)
**64. Suyong Choi (2021 winter)
**65. Sujin Pyeon (2021 Spring)
**66. Hyeon Hee Ji (2021 Spring)
**67. Semin Kim (2021 Summer)
**68. Su Hwan Kim (2021 Summer)
**69. Yongbin Kim (2021 Summer)
**70. Nan Park (2021 Summer)
**71. Hoyong Jung (2021 Summer)
**72. Ingyeong Koh (2021 Summer)
**73. Jongbin Jeong (2022 Winter)
**74. Sungchul Yang (2022 Winter, Spring, Summer)
**75. Hyojung Lee (2022 Winter, Spring)
**76. Jun Hyeong Kim (2022 Winter, Spring)
**77. Young Ji Roh (2022 Spring, Summer)
**78. Geon Koh (2022 Spring, Summer)
**79. Hayoung Kang (2022 Summer, 2023 Spring)
**80. Won Jong Kim (2022 Summer, Fall, 2023 Winter)
**81. Christian Lee (2022 Summer)
**82. Je Yun Kang (2022 Summer)
**83. Sumin Lee (2022 Fall)
**84. Jungyeon Kim (2022 Fall, 2023 Winter)
**85. Seol Song (2023 Winter)
**86. Soyeon Kim (2023 Summer)
**87. Sieun Park (2023 Summer)
**88. Gunhyeong Lee (2023 Summer)
**89. Yunseo Han (2023 Summer)
**90. Jae Hyun Kim (2023 Summer)
**91. Jeong-min Seo (2023 Summer)
**92. Yerin Kim (2023 Fall, 2024 Winter)
**93. Sang Min Park (2024 Winter)
**94. Minsun Seo (2024 Winter)
**95. Jaeryun Sim (2024 Winter)
**96. Sun Min Lim (2024 Winter)
**97. Yurim Jung (2024 Spring, Summer)
**98. Hae Been Lee (2024 Spring, Summer)
**99. Zunu An (2024 Summer)
**100. Se Bin Lim (2024 Summer)
**101. Sang Min Yoon (2024 Summer)
**102. Yena Kim (2024 Summer)

People

2024-08-23T08:14:56Z

Bsb0613: /* Scientists & Research Staff */

__NOTOC__
=='''Scientists & Research Staff'''==
{|border="0" width="200"
|align="center"|[[File:prof_Insuk_fixed.png|100px|link=people:IS_Lee]]
|align="center"|[[File:SM_fixed.png|100px|link=people:SM_Yang]]
|align="center"|[[File:Junha_fixed.png|100px|link=People:JH_Cha]]
|align="center"|[[File:Nayeon_fixed.png|100px|link=People:NY_Kim]]
|align="center"|[[File:Seungbyn_fixed.png|100px|link=People:SB_Baek]]
|align="center"|[[File:Ilseok_fixed.png|100px|link=People:IS_Choi]]
|align="center"|[[File:JunHyeong_fixed.png|100px|link=People:Jun_Hyung_Cha]]
|align="center"|[[File:JunYeong_fixed.png|100px|link=People:Junyeong_Ma]]
|align="center"|[[File:Hanjune_fixed.png|100px|link=People:Han-June_KIM]]
|-

|width=160px; align="center"|[[people:IS_Lee|<big>'''Insuk Lee'''</big>]]
|width=160px; align="center"|[[people:SM_Yang|<big>'''Sunmo Yang'''</big>]]
|width=160px; align="center"|[[People:JH_Cha|<big>'''Junha Cha'''</big>]]
|width=160px; align="center"|[[People:NY_Kim|<big>'''Nayeon Kim'''</big>]]
|width=160px; align="center"|[[People:SB_Baek|<big>'''Seungbyn Baek'''</big>]]
|width=160px; align="center"|[[People:IS_Choi|<big>'''Ilseok Choi'''</big>]]
|width=160px; align="center"|[[People:Jun_Hyung_Cha|<big>'''Jun Hyung Cha'''</big>]]
|width=160px; align="center"|[[People:Junyeong_Ma|<big>'''Junyeong Ma'''</big>]]
|width=160px; align="center"|[[People:Hanjune_Kim|<big>'''Hanjune Kim'''</big>]]
|-

|align="center"|[[people:IS_Lee|Principal Investigator Professor]]
|align="center"|[[people:SM_Yang|Bioinformatics Programmer & Sys. Admin]]
|align="center"|[[People:JH_Cha|Ph.D candidate (Single-cell Biology) (2019.9 - )]]
|align="center"|[[People:NY_Kim|Ph.D candidate (Microbiome) (2019.9 - )]]
|align="center"|[[People:SB_Baek|Ph.D candidate (Single-cell Biology) (2019.9 - )]]
|align="center"|[[People:IS_Choi|Ph.D candidate (Single-cell Biology) (2020.9 - )]]
|align="center"|[[People:Jun_Hyung_Cha|Ph.D candidate (Microbiome) (2020.9 - )]]
|align="center"|[[People:Junyeong_Ma|Ph.D candidate (Microbiome) (2021.3 - )]]
|align="center"|[[People:Hanjune_Kim|Integrated Ph.D course (Microbiome) (2021.3 - )]]
|}
{|border="0" width="180"

|align="center"|[[File:EuiJeong_fixed.png|100px|link=People:EuiJeong_Sung]]
|align="center"|[[File:Sehun_fixed.png|100px|link=People:Sehun_Ahn]]
|align="center"|[[File:Geon_fixed.png|100px|link=People:Geon_Koh]]
|align="center"|[[File:Wonjong_fixed.png|100px|link=People:WonJong_Kim]]
|align="center"|[[File:Jungyeon_fixed.png|100px|link=People:Jungyeon_Kim]]
|align="center"|[[File:Yerin_fixed.png|100px|link=People:Yerin_Kim]]
|align="center"|[[File:Yerin_fixed.png|100px|link=People:Yurim_Jung]]
|align="center"|[[File:Yerin_fixed.png|100px|link=People:Hae_Been_lee]]
|-
|width=160px; align="center"|[[People:Euijeong_Sung|<big>'''Euijeong Sung'''</big>]]
|width=160px; align="center"|[[People:Sehun_Ahn|<big>'''Sehun Ahn'''</big>]]
|width=160px; align="center"|[[People:Geon Koh|<big>'''Geon Koh'''</big>]]
|width=160px; align="center"|[[People:WonJong_Kim|<big>'''Wonjong Kim'''</big>]]
|width=160px; align="center"|[[People:Jungyeon_Kim|<big>'''Jungyeon Kim'''</big>]]
|width=160px; align="center"|[[People:Yerin_Kim|<big>'''Yerin Kim'''</big>]]
|width=160px; align="center"|[[People:Yurim_Jung|<big>'''Yurim Jung'''</big>]]
|width=160px; align="center"|[[People:Hae_Been_lee|<big>'''Hae Been Lee'''</big>]]
|-
|align="center"|[[People:Euijeong_Sung|Integrated Ph.D course (Single-cell Biology) (2021.9 - )]]
|align="center"|[[People:Sehun_Ahn|Integrated Ph.D course (Microbiome) (2022.3 - )]]
|align="center"|[[People:Geon_Koh|Integrated Ph.D course (Microbiome) (2022.9 - )]]
|align="center"|[[People:WonJong_Kim|Integrated Ph.D course (Microbiome) (2023.3 - )]]
|align="center"|[[People:Jungyeon_Kim|Integrated Ph.D course (Microbiome) (2023.3 - )]]
|align="center"|[[People:Yerin_Kim|Integrated Ph.D course (Microbiome) (2024.3 - )]]
|align="center"|[[People:Yurim_Jung|Integrated Ph.D course (Single-cell Biology) (2024.9 - )]]
|align="center"|[[People:Hae_Been_lee|Integrated Ph.D course (Single-cell Biology) (2024.9 - )]]
|}

=='''Administrative Assistant'''==
*Seomi Lee

=='''Undergraduate Students'''==
*Yurim Jung
*Hae Been Lee
*Zunu An
*Se Bin Lim
*Sang Min Yoon
*Yena Kim

=='''Alumni'''==
*'''Former Post-docs and Staff Scientists'''
**1. Taeyun Oh (2009.5-2011.12), currently Senior Researcher at Cowellbiogim, Korea
**2. Sohyun Hwang (2010.3-2015.8), currently Associate Professor (차의과대학 의과학과 교수), [https://grad.cha.ac.kr/%ed%99%a9%ec%86%8c%ed%98%84/ Department of Biomedical Science, CHA University], Korea [https://scholar.google.com/citations?user=z5yC9cwAAAAJ&hl=en Google Scholar]
**3. Samuel Beck (2011.2-2011.12), currently Associate Professor, Boston University, USA (미 보스턴 대학교 부교수), [https://beck310.wixsite.com/becklab Lab Homepage]
**4. Jawon Song (2011.1-2012.5), currently Research Associate (미국 텍사스슈퍼컴퓨팅센터 선임연구원), [https://www.tacc.utexas.edu/about/directory/jawon-song Texas Advanced Computing Center], Austin, TX, USA [https://scholar.google.com/citations?user=wv61wGoAAAAJ&hl=en Google Scholar]
**5. Yoonhee Ko (2012.8-2014.2), currently Associate Professor (한국외국어대 바이오공학과 교수), [https://hufsbiomed.weebly.com/ Department of Biomedical Engineering, Hankuk University of Foreign Studies], Korea [https://scholar.google.com/citations?user=GBxCYX8AAAAJ&hl=en Google Scholar]
**6. Jonghoon Lee (2013.10-2014.2), currently Associate Professor (가천대학교 식품생명공학과 교수), [https://www.gachon.ac.kr/foodbiotech/6142/subview.do Department of Food Biotechnology, Gachon University], Korea
**7. Jung Eun Shim (2009.8-2017.11), currently Staff Scientist at Yonsei Genomics Center Bioinformatics Data Analysis Core (연세의료원 유전체센터 생명정보분석코어 책임연구원)

*'''Former Graduate Students (Ph.D)'''
**1. Junha Shin (2008.9-2014.2), PhD; currently Scientist I at [https://wid.wisc.edu/people/junha-shin/ Wisconsin Institute for Discovery] (미국 위스콘신 대학연구소 연구원), USA
**2. Hanhae Kim (2009.9-2015.2), PhD; currently Research Fellow (한국과학기술기획평가원 연구위원), [http://www.kistep.re.kr Korea Institute of S&T Evaluation and Planning (KISTEP)]
**3. Ara Cho (2009.9-2015.2), PhD; currently Research Fellow (한국과학기술기획평가원 연구위원), [http://www.kistep.re.kr Korea Institute of S&T Evaluation and Planning (KISTEP)]
**4. Eiru Kim (2010.9-2016.8), PhD; currently Senior Expert in Data Science, [https://www.gnf.org/ The Genomics Institute of the Novartis Research Foundation], San Diego, USA (노바티스 선임데이터분석연구원)
**5. Tak Lee (2011.3-2017.2), PhD; currently Senior Bioinformatician, [https://www.mpipz.mpg.de/en Max Planck Institute for Plant Breeding Research] (독일 막스플랭크 연구소 선임생명정보학자)
**6. Heonjong Han (2013.3~2019.8), PhD; currently Bioinformatics engineer at [https://3billion.io/ 3billion] (바이오기업 데이터사이언티스트)
**7. Kyungsoo Kim (2013.3~2020.2), PhD; currently Research Professor at Severance Hospital (강남세브란스의료원 유방암센터 연구교수)
**8. Jae-Won Cho (2016.9~2021.2), PhD; currently Postdoc Fellow at Harvard Medical School (하버드의대 박사후 연구원) [https://scholar.google.com/citations?user=H4jO_DQAAAAJ&hl=en Hemberg Lab]
**9. Chan Yeong Kim (2015.3~2021.2), PhD; currently Postdoc Fellow at European Molecular Biology Laboratory in Heidelberg (유럽연합분자생물학연구소 박사후 연구원) [http://www.bork.embl.de/j/ Bork Group]
**10. Sungho Lee (2017.3-2024.2), PhD; currently Data Scientist at [http://www.mogam.re.kr/kor/ MOGAM Institute for Biomedical Research] (목암생명과학연구소, (주)녹십자 출연 연구소, 선임연구원)

*'''Former Graduate Students (Master's degree)'''
**1. Sun-Gou Ji (2009.9-2011.8), M.Eng.; PhD from University of Cambridge (Sanger Genome center); currently Vice President, Computational Genetics at [https://bridgebio.com/ BridgeBio Pharma], Boston, USA (미 바이오기업 부사장)
**2. Hyojin Kim (2013.3-2015.2), M.Eng.; PhD from University of Aachen; currently Computational Scientist, Uniklinikum Aachen (the university hospital of the city of Aachen), Germany (독일 아헨대학병원 데이터사이언티스트)
**3. Hongseok Shim (2013.9-2015.8), M.Eng.; currently PhD student, Arizona State University, Biomedical Informatics program (애리조나주립대 생물정보학 박사과정)
**4. Sunmo Yang (2014.9-2017.2), M.Eng. currently Bioinformatics programmer, Yonsei University (연세대 네트워크생명공학 연구실 책임데이터연구원)
**5. Muyoung Lee (2016.3-2018.2), M.Eng. currently PhD student, University of Texas at Austin, Cellular and Molecular Biology Program (텍사스주립대 생물정보학 박사과정)
**6. Jiwon Yu (2021.9-2023.8), M.Eng. currently Data Scientist at [https://www.samsungbioepis.com/kr/index.do SAMSUNG BIOEPIS] (삼성바이오에피스 데이터사이언티스트)

*'''Former Visiting Scientists/Students'''
**1. Minkyung Shin (2012.3-2012.7), went to University of Southern California graduate school
**2. Paul Chung (2012 summer), currently E-Commerce Analyst/Coordinator at [http://www.riotgames.com/ Riot Games], USA
**3. Hyunjin Cho (2014 summer), currently Bioinformatics research assistant at [http://www.libd.org/ The Lieber Institute for Brain Development | LIBD], USA
**4. Michael Chung (2016.8-2016.12), currently work at [https://about.meta.com/ Meta], USA

*'''Former Undergraduate Students'''
**1. Ila Shin, went to medical school (Yonsei Univ.) Currently Professor, Kyunghee University School of Medicine (경희대학교 의과대학 교수)
**2. Yoonkyung Ko
**3. Sangyo Park, went to law school (Kyounghee Univ.)
**4. Jayoon Shin, went to medical school (Pusan National Univ.)
**5. Dongjoo Sun, went to dental school (Wongwang Univ.)
**6. Eulsoo Kim
**7. Myungwhan Lee, went to medical school (Yonsei Univ.)
**8. Moonhee Lee, went to graduate school (University of Amsterdam, Netherlands)
**9. Jinhyun Ju, went to graduate school (Cornell Medical School, Computational Systems Biology); Currently Principal Computational Biologist, Cancer Genomics at [https://bridgebio.com/ BridgeBio Pharma], Seattle, USA (미 바이오기업 선임연구원)
**10. Haeyoung Shin, went to graduate school (Brown University, Computational Neuroscience); Currently Professor, Seoul National University (서울대학교 생명과학부 교수)
**11. Joonghee Soh, went to graduate school (KAIST, Culture Technology)
**12. Hee Jung Cho, went to graduate school (UC Berkeley)
**13. TaeHwan Kim, went to medical school
**14. Joohyung Kim, went to medical school (Kyungpook National Univ.)
**15. Donghyun Shin, went to medical school (Yonsei Univ.)
**16. Byunghee Kang (2015 spring - 2016 summer), went to graduate school (PosTech)
**17. Sunphil Kim (2016 winter)
**18. Jaeho Shim (2016 winter)
**19. Seunghyun Shin (2016 spring)
**20. Aejoo Hong (2016 spring)
**21. Changbae Bang (2016 summer), went to medical school (Yonsei Univ.)
**22. Seoyoung Choi (2016 summer)
**23. Dabin Jung (2016 summer)
**24. Boreum Nam (2016 summer)
**25. Seungun Lee (2016 summer)
**26. Yeaji Kim (2016 summer and fall)
**27. Sangyoung Lee (2017 winter)
**28. Heejun Jang (2017 winter & spring), went to KIST as Research Assistant
**29. Jungha Lee (2017 summer), went to graduate school (Seoul National Univ.)
**30. Seunghyun Wang (2017 summer), went to graduate school (KAIST)
**31. Dong-Min Yang (2017 fall)
**32. Doo-Hee Lee (2017 fall)
**33. Suk-Jae Han (2018 winter)
**34. Ji-Eun Han (2018 winter)
**35. Yuri Ko (2018 spring)
**36. Sung-Woo Kim (2018 spring)
**37. Woosung Kwon (2018 summer)
**38. Soyun Kong (2018 summer)
**39. Hojeong Keum (2018 summer and fall)
**40. Sungjun Lim (2018 summer and fall)
**41. Jiyoon Lee (2018 summer and fall)
**42. Ilseok Choi(2019 winter, spring, summer and fall, 2020 winter, spring)
**43. Junik Park (2019 winter)
**44. Lee Yoo (2019 winter & spring)
**45. Yeonwha Kim (2019 winter & spring)
**46. Sohee Kim (2019 spring)
**47. Juseong Lee (2019 spring)
**48. Junyoung Yang (2019 spring & summer)
**49. Junyoung Ma (2019 summer, 2020 winter, summer, fall, 2021 winter)
**50. Dayun Jung (2019 summer)
**51. Hanjoon Kim (2019 summer, 2021 winter)
**52. Hyungjin Kim (2020 winter, spring)
**53. Sujin Hyun (2020 winter)
**54. Hyuki Lee (2020 winter)
**55. Donghwan Lee (2020 winter)
**56. Jun Hyung Cha (2020 spring, summer)
**57. Jiwon Yu (2020 summer, 2021 Spring, summer)
**58. Dong Seok Kim (2020 summer)
**59. Donggeun Lee (2020 summer)
**60. Sehun Ahn (2020 summer, 2021 winter, spring, summer, 2022 winter)
**61. Jiho Kim (2020 summer)
**62. Euijung Seong (2020 fall, 2021 spring, summer)
**63. Junhan Kim (2021 winter & spring)
**64. Suyong Choi (2021 winter)
**65. Sujin Pyeon (2021 Spring)
**66. Hyeon Hee Ji (2021 Spring)
**67. Semin Kim (2021 Summer)
**68. Su Hwan Kim (2021 Summer)
**69. Yongbin Kim (2021 Summer)
**70. Nan Park (2021 Summer)
**71. Hoyong Jung (2021 Summer)
**72. Ingyeong Koh (2021 Summer)
**73. Jongbin Jeong (2022 Winter)
**74. Sungchul Yang (2022 Winter, Spring, Summer)
**75. Hyojung Lee (2022 Winter, Spring)
**76. Jun Hyeong Kim (2022 Winter, Spring)
**77. Young Ji Roh (2022 Spring, Summer)
**78. Geon Koh (2022 Spring, Summer)
**79. Hayoung Kang (2022 Summer, 2023 Spring)
**80. Won Jong Kim (2022 Summer, Fall, 2023 Winter)
**81. Christian Lee (2022 Summer)
**82. Je Yun Kang (2022 Summer)
**83. Sumin Lee (2022 Fall)
**84. Jungyeon Kim (2022 Fall, 2023 Winter)
**85. Seol Song (2023 Winter)
**86. Soyeon Kim (2023 Summer)
**87. Sieun Park (2023 Summer)
**88. Gunhyeong Lee (2023 Summer)
**89. Yunseo Han (2023 Summer)
**90. Jae Hyun Kim (2023 Summer)
**91. Jeong-min Seo (2023 Summer)
**92. Yerin Kim (2023 Fall, 2024 Winter)
**93. Sang Min Park (2024 Winter)
**94. Minsun Seo (2024 Winter)
**95. Jaeryun Sim (2024 Winter)
**96. Sun Min Lim (2024 Winter)
**97. Yurim Jung (2024 Spring, Summer)
**98. Hae Been Lee (2024 Spring, Summer)
**99. Zunu An (2024 Summer)
**100. Se Bin Lim (2024 Summer)
**101. Sang Min Yoon (2024 Summer)
**102. Yena Kim (2024 Summer)

People

2024-08-23T08:14:13Z

Bsb0613: /* Scientists & Research Staff */

__NOTOC__
=='''Scientists & Research Staff'''==
{|border="0" width="427.5"
|align="center"|[[File:prof_Insuk_fixed.png|100px|link=people:IS_Lee]]
|align="center"|[[File:SM_fixed.png|100px|link=people:SM_Yang]]
|align="center"|[[File:Junha_fixed.png|100px|link=People:JH_Cha]]
|align="center"|[[File:Nayeon_fixed.png|100px|link=People:NY_Kim]]
|align="center"|[[File:Seungbyn_fixed.png|100px|link=People:SB_Baek]]
|align="center"|[[File:Ilseok_fixed.png|100px|link=People:IS_Choi]]
|align="center"|[[File:JunHyeong_fixed.png|100px|link=People:Jun_Hyung_Cha]]
|align="center"|[[File:JunYeong_fixed.png|100px|link=People:Junyeong_Ma]]
|align="center"|[[File:Hanjune_fixed.png|100px|link=People:Han-June_KIM]]
|-

|width=160px; align="center"|[[people:IS_Lee|<big>'''Insuk Lee'''</big>]]
|width=160px; align="center"|[[people:SM_Yang|<big>'''Sunmo Yang'''</big>]]
|width=160px; align="center"|[[People:JH_Cha|<big>'''Junha Cha'''</big>]]
|width=160px; align="center"|[[People:NY_Kim|<big>'''Nayeon Kim'''</big>]]
|width=160px; align="center"|[[People:SB_Baek|<big>'''Seungbyn Baek'''</big>]]
|width=160px; align="center"|[[People:IS_Choi|<big>'''Ilseok Choi'''</big>]]
|width=160px; align="center"|[[People:Jun_Hyung_Cha|<big>'''Jun Hyung Cha'''</big>]]
|width=160px; align="center"|[[People:Junyeong_Ma|<big>'''Junyeong Ma'''</big>]]
|width=160px; align="center"|[[People:Hanjune_Kim|<big>'''Hanjune Kim'''</big>]]
|-

|align="center"|[[people:IS_Lee|Principal Investigator Professor]]
|align="center"|[[people:SM_Yang|Bioinformatics Programmer & Sys. Admin]]
|align="center"|[[People:JH_Cha|Ph.D candidate (Single-cell Biology) (2019.9 - )]]
|align="center"|[[People:NY_Kim|Ph.D candidate (Microbiome) (2019.9 - )]]
|align="center"|[[People:SB_Baek|Ph.D candidate (Single-cell Biology) (2019.9 - )]]
|align="center"|[[People:IS_Choi|Ph.D candidate (Single-cell Biology) (2020.9 - )]]
|align="center"|[[People:Jun_Hyung_Cha|Ph.D candidate (Microbiome) (2020.9 - )]]
|align="center"|[[People:Junyeong_Ma|Ph.D candidate (Microbiome) (2021.3 - )]]
|align="center"|[[People:Hanjune_Kim|Integrated Ph.D course (Microbiome) (2021.3 - )]]
|}
{|border="0" width="380"

|align="center"|[[File:EuiJeong_fixed.png|100px|link=People:EuiJeong_Sung]]
|align="center"|[[File:Sehun_fixed.png|100px|link=People:Sehun_Ahn]]
|align="center"|[[File:Geon_fixed.png|100px|link=People:Geon_Koh]]
|align="center"|[[File:Wonjong_fixed.png|100px|link=People:WonJong_Kim]]
|align="center"|[[File:Jungyeon_fixed.png|100px|link=People:Jungyeon_Kim]]
|align="center"|[[File:Yerin_fixed.png|100px|link=People:Yerin_Kim]]
|align="center"|[[File:Yerin_fixed.png|100px|link=People:Yurim_Jung]]
|align="center"|[[File:Yerin_fixed.png|100px|link=People:Hae_Been_lee]]
|-
|width=160px; align="center"|[[People:Euijeong_Sung|<big>'''Euijeong Sung'''</big>]]
|width=160px; align="center"|[[People:Sehun_Ahn|<big>'''Sehun Ahn'''</big>]]
|width=160px; align="center"|[[People:Geon Koh|<big>'''Geon Koh'''</big>]]
|width=160px; align="center"|[[People:WonJong_Kim|<big>'''Wonjong Kim'''</big>]]
|width=160px; align="center"|[[People:Jungyeon_Kim|<big>'''Jungyeon Kim'''</big>]]
|width=160px; align="center"|[[People:Yerin_Kim|<big>'''Yerin Kim'''</big>]]
|width=160px; align="center"|[[People:Yurim_Jung|<big>'''Yurim Jung'''</big>]]
|width=160px; align="center"|[[People:Hae_Been_lee|<big>'''Hae Been Lee'''</big>]]
|-
|align="center"|[[People:Euijeong_Sung|Integrated Ph.D course (Single-cell Biology) (2021.9 - )]]
|align="center"|[[People:Sehun_Ahn|Integrated Ph.D course (Microbiome) (2022.3 - )]]
|align="center"|[[People:Geon_Koh|Integrated Ph.D course (Microbiome) (2022.9 - )]]
|align="center"|[[People:WonJong_Kim|Integrated Ph.D course (Microbiome) (2023.3 - )]]
|align="center"|[[People:Jungyeon_Kim|Integrated Ph.D course (Microbiome) (2023.3 - )]]
|align="center"|[[People:Yerin_Kim|Integrated Ph.D course (Microbiome) (2024.3 - )]]
|align="center"|[[People:Yurim_Jung|Integrated Ph.D course (Single-cell Biology) (2024.9 - )]]
|align="center"|[[People:Hae_Been_lee|Integrated Ph.D course (Single-cell Biology) (2024.9 - )]]
|}

=='''Administrative Assistant'''==
*Seomi Lee

=='''Undergraduate Students'''==
*Yurim Jung
*Hae Been Lee
*Zunu An
*Se Bin Lim
*Sang Min Yoon
*Yena Kim

=='''Alumni'''==
*'''Former Post-docs and Staff Scientists'''
**1. Taeyun Oh (2009.5-2011.12), currently Senior Researcher at Cowellbiogim, Korea
**2. Sohyun Hwang (2010.3-2015.8), currently Associate Professor (차의과대학 의과학과 교수), [https://grad.cha.ac.kr/%ed%99%a9%ec%86%8c%ed%98%84/ Department of Biomedical Science, CHA University], Korea [https://scholar.google.com/citations?user=z5yC9cwAAAAJ&hl=en Google Scholar]
**3. Samuel Beck (2011.2-2011.12), currently Associate Professor, Boston University, USA (미 보스턴 대학교 부교수), [https://beck310.wixsite.com/becklab Lab Homepage]
**4. Jawon Song (2011.1-2012.5), currently Research Associate (미국 텍사스슈퍼컴퓨팅센터 선임연구원), [https://www.tacc.utexas.edu/about/directory/jawon-song Texas Advanced Computing Center], Austin, TX, USA [https://scholar.google.com/citations?user=wv61wGoAAAAJ&hl=en Google Scholar]
**5. Yoonhee Ko (2012.8-2014.2), currently Associate Professor (한국외국어대 바이오공학과 교수), [https://hufsbiomed.weebly.com/ Department of Biomedical Engineering, Hankuk University of Foreign Studies], Korea [https://scholar.google.com/citations?user=GBxCYX8AAAAJ&hl=en Google Scholar]
**6. Jonghoon Lee (2013.10-2014.2), currently Associate Professor (가천대학교 식품생명공학과 교수), [https://www.gachon.ac.kr/foodbiotech/6142/subview.do Department of Food Biotechnology, Gachon University], Korea
**7. Jung Eun Shim (2009.8-2017.11), currently Staff Scientist at Yonsei Genomics Center Bioinformatics Data Analysis Core (연세의료원 유전체센터 생명정보분석코어 책임연구원)

*'''Former Graduate Students (Ph.D)'''
**1. Junha Shin (2008.9-2014.2), PhD; currently Scientist I at [https://wid.wisc.edu/people/junha-shin/ Wisconsin Institute for Discovery] (미국 위스콘신 대학연구소 연구원), USA
**2. Hanhae Kim (2009.9-2015.2), PhD; currently Research Fellow (한국과학기술기획평가원 연구위원), [http://www.kistep.re.kr Korea Institute of S&T Evaluation and Planning (KISTEP)]
**3. Ara Cho (2009.9-2015.2), PhD; currently Research Fellow (한국과학기술기획평가원 연구위원), [http://www.kistep.re.kr Korea Institute of S&T Evaluation and Planning (KISTEP)]
**4. Eiru Kim (2010.9-2016.8), PhD; currently Senior Expert in Data Science, [https://www.gnf.org/ The Genomics Institute of the Novartis Research Foundation], San Diego, USA (노바티스 선임데이터분석연구원)
**5. Tak Lee (2011.3-2017.2), PhD; currently Senior Bioinformatician, [https://www.mpipz.mpg.de/en Max Planck Institute for Plant Breeding Research] (독일 막스플랭크 연구소 선임생명정보학자)
**6. Heonjong Han (2013.3~2019.8), PhD; currently Bioinformatics engineer at [https://3billion.io/ 3billion] (바이오기업 데이터사이언티스트)
**7. Kyungsoo Kim (2013.3~2020.2), PhD; currently Research Professor at Severance Hospital (강남세브란스의료원 유방암센터 연구교수)
**8. Jae-Won Cho (2016.9~2021.2), PhD; currently Postdoc Fellow at Harvard Medical School (하버드의대 박사후 연구원) [https://scholar.google.com/citations?user=H4jO_DQAAAAJ&hl=en Hemberg Lab]
**9. Chan Yeong Kim (2015.3~2021.2), PhD; currently Postdoc Fellow at European Molecular Biology Laboratory in Heidelberg (유럽연합분자생물학연구소 박사후 연구원) [http://www.bork.embl.de/j/ Bork Group]
**10. Sungho Lee (2017.3-2024.2), PhD; currently Data Scientist at [http://www.mogam.re.kr/kor/ MOGAM Institute for Biomedical Research] (목암생명과학연구소, (주)녹십자 출연 연구소, 선임연구원)

*'''Former Graduate Students (Master's degree)'''
**1. Sun-Gou Ji (2009.9-2011.8), M.Eng.; PhD from University of Cambridge (Sanger Genome center); currently Vice President, Computational Genetics at [https://bridgebio.com/ BridgeBio Pharma], Boston, USA (미 바이오기업 부사장)
**2. Hyojin Kim (2013.3-2015.2), M.Eng.; PhD from University of Aachen; currently Computational Scientist, Uniklinikum Aachen (the university hospital of the city of Aachen), Germany (독일 아헨대학병원 데이터사이언티스트)
**3. Hongseok Shim (2013.9-2015.8), M.Eng.; currently PhD student, Arizona State University, Biomedical Informatics program (애리조나주립대 생물정보학 박사과정)
**4. Sunmo Yang (2014.9-2017.2), M.Eng. currently Bioinformatics programmer, Yonsei University (연세대 네트워크생명공학 연구실 책임데이터연구원)
**5. Muyoung Lee (2016.3-2018.2), M.Eng. currently PhD student, University of Texas at Austin, Cellular and Molecular Biology Program (텍사스주립대 생물정보학 박사과정)
**6. Jiwon Yu (2021.9-2023.8), M.Eng. currently Data Scientist at [https://www.samsungbioepis.com/kr/index.do SAMSUNG BIOEPIS] (삼성바이오에피스 데이터사이언티스트)

*'''Former Visiting Scientists/Students'''
**1. Minkyung Shin (2012.3-2012.7), went to University of Southern California graduate school
**2. Paul Chung (2012 summer), currently E-Commerce Analyst/Coordinator at [http://www.riotgames.com/ Riot Games], USA
**3. Hyunjin Cho (2014 summer), currently Bioinformatics research assistant at [http://www.libd.org/ The Lieber Institute for Brain Development | LIBD], USA
**4. Michael Chung (2016.8-2016.12), currently work at [https://about.meta.com/ Meta], USA

*'''Former Undergraduate Students'''
**1. Ila Shin, went to medical school (Yonsei Univ.) Currently Professor, Kyunghee University School of Medicine (경희대학교 의과대학 교수)
**2. Yoonkyung Ko
**3. Sangyo Park, went to law school (Kyounghee Univ.)
**4. Jayoon Shin, went to medical school (Pusan National Univ.)
**5. Dongjoo Sun, went to dental school (Wongwang Univ.)
**6. Eulsoo Kim
**7. Myungwhan Lee, went to medical school (Yonsei Univ.)
**8. Moonhee Lee, went to graduate school (University of Amsterdam, Netherlands)
**9. Jinhyun Ju, went to graduate school (Cornell Medical School, Computational Systems Biology); Currently Principal Computational Biologist, Cancer Genomics at [https://bridgebio.com/ BridgeBio Pharma], Seattle, USA (미 바이오기업 선임연구원)
**10. Haeyoung Shin, went to graduate school (Brown University, Computational Neuroscience); Currently Professor, Seoul National University (서울대학교 생명과학부 교수)
**11. Joonghee Soh, went to graduate school (KAIST, Culture Technology)
**12. Hee Jung Cho, went to graduate school (UC Berkeley)
**13. TaeHwan Kim, went to medical school
**14. Joohyung Kim, went to medical school (Kyungpook National Univ.)
**15. Donghyun Shin, went to medical school (Yonsei Univ.)
**16. Byunghee Kang (2015 spring - 2016 summer), went to graduate school (PosTech)
**17. Sunphil Kim (2016 winter)
**18. Jaeho Shim (2016 winter)
**19. Seunghyun Shin (2016 spring)
**20. Aejoo Hong (2016 spring)
**21. Changbae Bang (2016 summer), went to medical school (Yonsei Univ.)
**22. Seoyoung Choi (2016 summer)
**23. Dabin Jung (2016 summer)
**24. Boreum Nam (2016 summer)
**25. Seungun Lee (2016 summer)
**26. Yeaji Kim (2016 summer and fall)
**27. Sangyoung Lee (2017 winter)
**28. Heejun Jang (2017 winter & spring), went to KIST as Research Assistant
**29. Jungha Lee (2017 summer), went to graduate school (Seoul National Univ.)
**30. Seunghyun Wang (2017 summer), went to graduate school (KAIST)
**31. Dong-Min Yang (2017 fall)
**32. Doo-Hee Lee (2017 fall)
**33. Suk-Jae Han (2018 winter)
**34. Ji-Eun Han (2018 winter)
**35. Yuri Ko (2018 spring)
**36. Sung-Woo Kim (2018 spring)
**37. Woosung Kwon (2018 summer)
**38. Soyun Kong (2018 summer)
**39. Hojeong Keum (2018 summer and fall)
**40. Sungjun Lim (2018 summer and fall)
**41. Jiyoon Lee (2018 summer and fall)
**42. Ilseok Choi(2019 winter, spring, summer and fall, 2020 winter, spring)
**43. Junik Park (2019 winter)
**44. Lee Yoo (2019 winter & spring)
**45. Yeonwha Kim (2019 winter & spring)
**46. Sohee Kim (2019 spring)
**47. Juseong Lee (2019 spring)
**48. Junyoung Yang (2019 spring & summer)
**49. Junyoung Ma (2019 summer, 2020 winter, summer, fall, 2021 winter)
**50. Dayun Jung (2019 summer)
**51. Hanjoon Kim (2019 summer, 2021 winter)
**52. Hyungjin Kim (2020 winter, spring)
**53. Sujin Hyun (2020 winter)
**54. Hyuki Lee (2020 winter)
**55. Donghwan Lee (2020 winter)
**56. Jun Hyung Cha (2020 spring, summer)
**57. Jiwon Yu (2020 summer, 2021 Spring, summer)
**58. Dong Seok Kim (2020 summer)
**59. Donggeun Lee (2020 summer)
**60. Sehun Ahn (2020 summer, 2021 winter, spring, summer, 2022 winter)
**61. Jiho Kim (2020 summer)
**62. Euijung Seong (2020 fall, 2021 spring, summer)
**63. Junhan Kim (2021 winter & spring)
**64. Suyong Choi (2021 winter)
**65. Sujin Pyeon (2021 Spring)
**66. Hyeon Hee Ji (2021 Spring)
**67. Semin Kim (2021 Summer)
**68. Su Hwan Kim (2021 Summer)
**69. Yongbin Kim (2021 Summer)
**70. Nan Park (2021 Summer)
**71. Hoyong Jung (2021 Summer)
**72. Ingyeong Koh (2021 Summer)
**73. Jongbin Jeong (2022 Winter)
**74. Sungchul Yang (2022 Winter, Spring, Summer)
**75. Hyojung Lee (2022 Winter, Spring)
**76. Jun Hyeong Kim (2022 Winter, Spring)
**77. Young Ji Roh (2022 Spring, Summer)
**78. Geon Koh (2022 Spring, Summer)
**79. Hayoung Kang (2022 Summer, 2023 Spring)
**80. Won Jong Kim (2022 Summer, Fall, 2023 Winter)
**81. Christian Lee (2022 Summer)
**82. Je Yun Kang (2022 Summer)
**83. Sumin Lee (2022 Fall)
**84. Jungyeon Kim (2022 Fall, 2023 Winter)
**85. Seol Song (2023 Winter)
**86. Soyeon Kim (2023 Summer)
**87. Sieun Park (2023 Summer)
**88. Gunhyeong Lee (2023 Summer)
**89. Yunseo Han (2023 Summer)
**90. Jae Hyun Kim (2023 Summer)
**91. Jeong-min Seo (2023 Summer)
**92. Yerin Kim (2023 Fall, 2024 Winter)
**93. Sang Min Park (2024 Winter)
**94. Minsun Seo (2024 Winter)
**95. Jaeryun Sim (2024 Winter)
**96. Sun Min Lim (2024 Winter)
**97. Yurim Jung (2024 Spring, Summer)
**98. Hae Been Lee (2024 Spring, Summer)
**99. Zunu An (2024 Summer)
**100. Se Bin Lim (2024 Summer)
**101. Sang Min Yoon (2024 Summer)
**102. Yena Kim (2024 Summer)

People

2024-08-23T08:12:23Z

Bsb0613: /* Scientists & Research Staff */

__NOTOC__
=='''Scientists & Research Staff'''==
{|border="0" width="1080"
|align="center"|[[File:prof_Insuk_fixed.png|100px|link=people:IS_Lee]]
|align="center"|[[File:SM_fixed.png|100px|link=people:SM_Yang]]
|align="center"|[[File:Junha_fixed.png|100px|link=People:JH_Cha]]
|align="center"|[[File:Nayeon_fixed.png|100px|link=People:NY_Kim]]
|align="center"|[[File:Seungbyn_fixed.png|100px|link=People:SB_Baek]]
|align="center"|[[File:Ilseok_fixed.png|100px|link=People:IS_Choi]]
|align="center"|[[File:JunHyeong_fixed.png|100px|link=People:Jun_Hyung_Cha]]
|align="center"|[[File:JunYeong_fixed.png|100px|link=People:Junyeong_Ma]]
|align="center"|[[File:Hanjune_fixed.png|100px|link=People:Han-June_KIM]]
|-

|width=160px; align="center"|[[people:IS_Lee|<big>'''Insuk Lee'''</big>]]
|width=160px; align="center"|[[people:SM_Yang|<big>'''Sunmo Yang'''</big>]]
|width=160px; align="center"|[[People:JH_Cha|<big>'''Junha Cha'''</big>]]
|width=160px; align="center"|[[People:NY_Kim|<big>'''Nayeon Kim'''</big>]]
|width=160px; align="center"|[[People:SB_Baek|<big>'''Seungbyn Baek'''</big>]]
|width=160px; align="center"|[[People:IS_Choi|<big>'''Ilseok Choi'''</big>]]
|width=160px; align="center"|[[People:Jun_Hyung_Cha|<big>'''Jun Hyung Cha'''</big>]]
|width=160px; align="center"|[[People:Junyeong_Ma|<big>'''Junyeong Ma'''</big>]]
|width=160px; align="center"|[[People:Hanjune_Kim|<big>'''Hanjune Kim'''</big>]]
|-

|align="center"|[[people:IS_Lee|Principal Investigator Professor]]
|align="center"|[[people:SM_Yang|Bioinformatics Programmer & Sys. Admin]]
|align="center"|[[People:JH_Cha|Ph.D candidate (Single-cell Biology) (2019.9 - )]]
|align="center"|[[People:NY_Kim|Ph.D candidate (Microbiome) (2019.9 - )]]
|align="center"|[[People:SB_Baek|Ph.D candidate (Single-cell Biology) (2019.9 - )]]
|align="center"|[[People:IS_Choi|Ph.D candidate (Single-cell Biology) (2020.9 - )]]
|align="center"|[[People:Jun_Hyung_Cha|Ph.D candidate (Microbiome) (2020.9 - )]]
|align="center"|[[People:Junyeong_Ma|Ph.D candidate (Microbiome) (2021.3 - )]]
|align="center"|[[People:Hanjune_Kim|Integrated Ph.D course (Microbiome) (2021.3 - )]]
|}
{|border="0" width="960"

|align="center"|[[File:EuiJeong_fixed.png|100px|link=People:EuiJeong_Sung]]
|align="center"|[[File:Sehun_fixed.png|100px|link=People:Sehun_Ahn]]
|align="center"|[[File:Geon_fixed.png|100px|link=People:Geon_Koh]]
|align="center"|[[File:Wonjong_fixed.png|100px|link=People:WonJong_Kim]]
|align="center"|[[File:Jungyeon_fixed.png|100px|link=People:Jungyeon_Kim]]
|align="center"|[[File:Yerin_fixed.png|100px|link=People:Yerin_Kim]]
|align="center"|[[File:Yerin_fixed.png|100px|link=People:Yurim_Jung]]
|align="center"|[[File:Yerin_fixed.png|100px|link=People:Hae_Been_lee]]
|-
|width=160px; align="center"|[[People:Euijeong_Sung|<big>'''Euijeong Sung'''</big>]]
|width=160px; align="center"|[[People:Sehun_Ahn|<big>'''Sehun Ahn'''</big>]]
|width=160px; align="center"|[[People:Geon Koh|<big>'''Geon Koh'''</big>]]
|width=160px; align="center"|[[People:WonJong_Kim|<big>'''Wonjong Kim'''</big>]]
|width=160px; align="center"|[[People:Jungyeon_Kim|<big>'''Jungyeon Kim'''</big>]]
|width=160px; align="center"|[[People:Yerin_Kim|<big>'''Yerin Kim'''</big>]]
|width=160px; align="center"|[[People:Yurim_Jung|<big>'''Yurim Jung'''</big>]]
|width=160px; align="center"|[[People:Hae_Been_lee|<big>'''Hae Been Lee'''</big>]]
|-
|align="center"|[[People:Euijeong_Sung|Integrated Ph.D course (Single-cell Biology) (2021.9 - )]]
|align="center"|[[People:Sehun_Ahn|Integrated Ph.D course (Microbiome) (2022.3 - )]]
|align="center"|[[People:Geon_Koh|Integrated Ph.D course (Microbiome) (2022.9 - )]]
|align="center"|[[People:WonJong_Kim|Integrated Ph.D course (Microbiome) (2023.3 - )]]
|align="center"|[[People:Jungyeon_Kim|Integrated Ph.D course (Microbiome) (2023.3 - )]]
|align="center"|[[People:Yerin_Kim|Integrated Ph.D course (Microbiome) (2024.3 - )]]
|align="center"|[[People:Yurim_Jung|Integrated Ph.D course (Single-cell Biology) (2024.9 - )]]
|align="center"|[[People:Hae_Been_lee|Integrated Ph.D course (Single-cell Biology) (2024.9 - )]]
|}

=='''Administrative Assistant'''==
*Seomi Lee

=='''Undergraduate Students'''==
*Yurim Jung
*Hae Been Lee
*Zunu An
*Se Bin Lim
*Sang Min Yoon
*Yena Kim

=='''Alumni'''==
*'''Former Post-docs and Staff Scientists'''
**1. Taeyun Oh (2009.5-2011.12), currently Senior Researcher at Cowellbiogim, Korea
**2. Sohyun Hwang (2010.3-2015.8), currently Associate Professor (차의과대학 의과학과 교수), [https://grad.cha.ac.kr/%ed%99%a9%ec%86%8c%ed%98%84/ Department of Biomedical Science, CHA University], Korea [https://scholar.google.com/citations?user=z5yC9cwAAAAJ&hl=en Google Scholar]
**3. Samuel Beck (2011.2-2011.12), currently Associate Professor, Boston University, USA (미 보스턴 대학교 부교수), [https://beck310.wixsite.com/becklab Lab Homepage]
**4. Jawon Song (2011.1-2012.5), currently Research Associate (미국 텍사스슈퍼컴퓨팅센터 선임연구원), [https://www.tacc.utexas.edu/about/directory/jawon-song Texas Advanced Computing Center], Austin, TX, USA [https://scholar.google.com/citations?user=wv61wGoAAAAJ&hl=en Google Scholar]
**5. Yoonhee Ko (2012.8-2014.2), currently Associate Professor (한국외국어대 바이오공학과 교수), [https://hufsbiomed.weebly.com/ Department of Biomedical Engineering, Hankuk University of Foreign Studies], Korea [https://scholar.google.com/citations?user=GBxCYX8AAAAJ&hl=en Google Scholar]
**6. Jonghoon Lee (2013.10-2014.2), currently Associate Professor (가천대학교 식품생명공학과 교수), [https://www.gachon.ac.kr/foodbiotech/6142/subview.do Department of Food Biotechnology, Gachon University], Korea
**7. Jung Eun Shim (2009.8-2017.11), currently Staff Scientist at Yonsei Genomics Center Bioinformatics Data Analysis Core (연세의료원 유전체센터 생명정보분석코어 책임연구원)

*'''Former Graduate Students (Ph.D)'''
**1. Junha Shin (2008.9-2014.2), PhD; currently Scientist I at [https://wid.wisc.edu/people/junha-shin/ Wisconsin Institute for Discovery] (미국 위스콘신 대학연구소 연구원), USA
**2. Hanhae Kim (2009.9-2015.2), PhD; currently Research Fellow (한국과학기술기획평가원 연구위원), [http://www.kistep.re.kr Korea Institute of S&T Evaluation and Planning (KISTEP)]
**3. Ara Cho (2009.9-2015.2), PhD; currently Research Fellow (한국과학기술기획평가원 연구위원), [http://www.kistep.re.kr Korea Institute of S&T Evaluation and Planning (KISTEP)]
**4. Eiru Kim (2010.9-2016.8), PhD; currently Senior Expert in Data Science, [https://www.gnf.org/ The Genomics Institute of the Novartis Research Foundation], San Diego, USA (노바티스 선임데이터분석연구원)
**5. Tak Lee (2011.3-2017.2), PhD; currently Senior Bioinformatician, [https://www.mpipz.mpg.de/en Max Planck Institute for Plant Breeding Research] (독일 막스플랭크 연구소 선임생명정보학자)
**6. Heonjong Han (2013.3~2019.8), PhD; currently Bioinformatics engineer at [https://3billion.io/ 3billion] (바이오기업 데이터사이언티스트)
**7. Kyungsoo Kim (2013.3~2020.2), PhD; currently Research Professor at Severance Hospital (강남세브란스의료원 유방암센터 연구교수)
**8. Jae-Won Cho (2016.9~2021.2), PhD; currently Postdoc Fellow at Harvard Medical School (하버드의대 박사후 연구원) [https://scholar.google.com/citations?user=H4jO_DQAAAAJ&hl=en Hemberg Lab]
**9. Chan Yeong Kim (2015.3~2021.2), PhD; currently Postdoc Fellow at European Molecular Biology Laboratory in Heidelberg (유럽연합분자생물학연구소 박사후 연구원) [http://www.bork.embl.de/j/ Bork Group]
**10. Sungho Lee (2017.3-2024.2), PhD; currently Data Scientist at [http://www.mogam.re.kr/kor/ MOGAM Institute for Biomedical Research] (목암생명과학연구소, (주)녹십자 출연 연구소, 선임연구원)

*'''Former Graduate Students (Master's degree)'''
**1. Sun-Gou Ji (2009.9-2011.8), M.Eng.; PhD from University of Cambridge (Sanger Genome center); currently Vice President, Computational Genetics at [https://bridgebio.com/ BridgeBio Pharma], Boston, USA (미 바이오기업 부사장)
**2. Hyojin Kim (2013.3-2015.2), M.Eng.; PhD from University of Aachen; currently Computational Scientist, Uniklinikum Aachen (the university hospital of the city of Aachen), Germany (독일 아헨대학병원 데이터사이언티스트)
**3. Hongseok Shim (2013.9-2015.8), M.Eng.; currently PhD student, Arizona State University, Biomedical Informatics program (애리조나주립대 생물정보학 박사과정)
**4. Sunmo Yang (2014.9-2017.2), M.Eng. currently Bioinformatics programmer, Yonsei University (연세대 네트워크생명공학 연구실 책임데이터연구원)
**5. Muyoung Lee (2016.3-2018.2), M.Eng. currently PhD student, University of Texas at Austin, Cellular and Molecular Biology Program (텍사스주립대 생물정보학 박사과정)
**6. Jiwon Yu (2021.9-2023.8), M.Eng. currently Data Scientist at [https://www.samsungbioepis.com/kr/index.do SAMSUNG BIOEPIS] (삼성바이오에피스 데이터사이언티스트)

*'''Former Visiting Scientists/Students'''
**1. Minkyung Shin (2012.3-2012.7), went to University of Southern California graduate school
**2. Paul Chung (2012 summer), currently E-Commerce Analyst/Coordinator at [http://www.riotgames.com/ Riot Games], USA
**3. Hyunjin Cho (2014 summer), currently Bioinformatics research assistant at [http://www.libd.org/ The Lieber Institute for Brain Development | LIBD], USA
**4. Michael Chung (2016.8-2016.12), currently work at [https://about.meta.com/ Meta], USA

*'''Former Undergraduate Students'''
**1. Ila Shin, went to medical school (Yonsei Univ.) Currently Professor, Kyunghee University School of Medicine (경희대학교 의과대학 교수)
**2. Yoonkyung Ko
**3. Sangyo Park, went to law school (Kyounghee Univ.)
**4. Jayoon Shin, went to medical school (Pusan National Univ.)
**5. Dongjoo Sun, went to dental school (Wongwang Univ.)
**6. Eulsoo Kim
**7. Myungwhan Lee, went to medical school (Yonsei Univ.)
**8. Moonhee Lee, went to graduate school (University of Amsterdam, Netherlands)
**9. Jinhyun Ju, went to graduate school (Cornell Medical School, Computational Systems Biology); Currently Principal Computational Biologist, Cancer Genomics at [https://bridgebio.com/ BridgeBio Pharma], Seattle, USA (미 바이오기업 선임연구원)
**10. Haeyoung Shin, went to graduate school (Brown University, Computational Neuroscience); Currently Professor, Seoul National University (서울대학교 생명과학부 교수)
**11. Joonghee Soh, went to graduate school (KAIST, Culture Technology)
**12. Hee Jung Cho, went to graduate school (UC Berkeley)
**13. TaeHwan Kim, went to medical school
**14. Joohyung Kim, went to medical school (Kyungpook National Univ.)
**15. Donghyun Shin, went to medical school (Yonsei Univ.)
**16. Byunghee Kang (2015 spring - 2016 summer), went to graduate school (PosTech)
**17. Sunphil Kim (2016 winter)
**18. Jaeho Shim (2016 winter)
**19. Seunghyun Shin (2016 spring)
**20. Aejoo Hong (2016 spring)
**21. Changbae Bang (2016 summer), went to medical school (Yonsei Univ.)
**22. Seoyoung Choi (2016 summer)
**23. Dabin Jung (2016 summer)
**24. Boreum Nam (2016 summer)
**25. Seungun Lee (2016 summer)
**26. Yeaji Kim (2016 summer and fall)
**27. Sangyoung Lee (2017 winter)
**28. Heejun Jang (2017 winter & spring), went to KIST as Research Assistant
**29. Jungha Lee (2017 summer), went to graduate school (Seoul National Univ.)
**30. Seunghyun Wang (2017 summer), went to graduate school (KAIST)
**31. Dong-Min Yang (2017 fall)
**32. Doo-Hee Lee (2017 fall)
**33. Suk-Jae Han (2018 winter)
**34. Ji-Eun Han (2018 winter)
**35. Yuri Ko (2018 spring)
**36. Sung-Woo Kim (2018 spring)
**37. Woosung Kwon (2018 summer)
**38. Soyun Kong (2018 summer)
**39. Hojeong Keum (2018 summer and fall)
**40. Sungjun Lim (2018 summer and fall)
**41. Jiyoon Lee (2018 summer and fall)
**42. Ilseok Choi(2019 winter, spring, summer and fall, 2020 winter, spring)
**43. Junik Park (2019 winter)
**44. Lee Yoo (2019 winter & spring)
**45. Yeonwha Kim (2019 winter & spring)
**46. Sohee Kim (2019 spring)
**47. Juseong Lee (2019 spring)
**48. Junyoung Yang (2019 spring & summer)
**49. Junyoung Ma (2019 summer, 2020 winter, summer, fall, 2021 winter)
**50. Dayun Jung (2019 summer)
**51. Hanjoon Kim (2019 summer, 2021 winter)
**52. Hyungjin Kim (2020 winter, spring)
**53. Sujin Hyun (2020 winter)
**54. Hyuki Lee (2020 winter)
**55. Donghwan Lee (2020 winter)
**56. Jun Hyung Cha (2020 spring, summer)
**57. Jiwon Yu (2020 summer, 2021 Spring, summer)
**58. Dong Seok Kim (2020 summer)
**59. Donggeun Lee (2020 summer)
**60. Sehun Ahn (2020 summer, 2021 winter, spring, summer, 2022 winter)
**61. Jiho Kim (2020 summer)
**62. Euijung Seong (2020 fall, 2021 spring, summer)
**63. Junhan Kim (2021 winter & spring)
**64. Suyong Choi (2021 winter)
**65. Sujin Pyeon (2021 Spring)
**66. Hyeon Hee Ji (2021 Spring)
**67. Semin Kim (2021 Summer)
**68. Su Hwan Kim (2021 Summer)
**69. Yongbin Kim (2021 Summer)
**70. Nan Park (2021 Summer)
**71. Hoyong Jung (2021 Summer)
**72. Ingyeong Koh (2021 Summer)
**73. Jongbin Jeong (2022 Winter)
**74. Sungchul Yang (2022 Winter, Spring, Summer)
**75. Hyojung Lee (2022 Winter, Spring)
**76. Jun Hyeong Kim (2022 Winter, Spring)
**77. Young Ji Roh (2022 Spring, Summer)
**78. Geon Koh (2022 Spring, Summer)
**79. Hayoung Kang (2022 Summer, 2023 Spring)
**80. Won Jong Kim (2022 Summer, Fall, 2023 Winter)
**81. Christian Lee (2022 Summer)
**82. Je Yun Kang (2022 Summer)
**83. Sumin Lee (2022 Fall)
**84. Jungyeon Kim (2022 Fall, 2023 Winter)
**85. Seol Song (2023 Winter)
**86. Soyeon Kim (2023 Summer)
**87. Sieun Park (2023 Summer)
**88. Gunhyeong Lee (2023 Summer)
**89. Yunseo Han (2023 Summer)
**90. Jae Hyun Kim (2023 Summer)
**91. Jeong-min Seo (2023 Summer)
**92. Yerin Kim (2023 Fall, 2024 Winter)
**93. Sang Min Park (2024 Winter)
**94. Minsun Seo (2024 Winter)
**95. Jaeryun Sim (2024 Winter)
**96. Sun Min Lim (2024 Winter)
**97. Yurim Jung (2024 Spring, Summer)
**98. Hae Been Lee (2024 Spring, Summer)
**99. Zunu An (2024 Summer)
**100. Se Bin Lim (2024 Summer)
**101. Sang Min Yoon (2024 Summer)
**102. Yena Kim (2024 Summer)

People

2024-08-23T08:11:38Z

Bsb0613: /* Scientists & Research Staff */

__NOTOC__
=='''Scientists & Research Staff'''==
{|border="0" width="540"
|align="center"|[[File:prof_Insuk_fixed.png|100px|link=people:IS_Lee]]
|align="center"|[[File:SM_fixed.png|100px|link=people:SM_Yang]]
|align="center"|[[File:Junha_fixed.png|100px|link=People:JH_Cha]]
|align="center"|[[File:Nayeon_fixed.png|100px|link=People:NY_Kim]]
|align="center"|[[File:Seungbyn_fixed.png|100px|link=People:SB_Baek]]
|align="center"|[[File:Ilseok_fixed.png|100px|link=People:IS_Choi]]
|align="center"|[[File:JunHyeong_fixed.png|100px|link=People:Jun_Hyung_Cha]]
|align="center"|[[File:JunYeong_fixed.png|100px|link=People:Junyeong_Ma]]
|align="center"|[[File:Hanjune_fixed.png|100px|link=People:Han-June_KIM]]
|-

|width=160px; align="center"|[[people:IS_Lee|<big>'''Insuk Lee'''</big>]]
|width=160px; align="center"|[[people:SM_Yang|<big>'''Sunmo Yang'''</big>]]
|width=160px; align="center"|[[People:JH_Cha|<big>'''Junha Cha'''</big>]]
|width=160px; align="center"|[[People:NY_Kim|<big>'''Nayeon Kim'''</big>]]
|width=160px; align="center"|[[People:SB_Baek|<big>'''Seungbyn Baek'''</big>]]
|width=160px; align="center"|[[People:IS_Choi|<big>'''Ilseok Choi'''</big>]]
|width=160px; align="center"|[[People:Jun_Hyung_Cha|<big>'''Jun Hyung Cha'''</big>]]
|width=160px; align="center"|[[People:Junyeong_Ma|<big>'''Junyeong Ma'''</big>]]
|width=160px; align="center"|[[People:Hanjune_Kim|<big>'''Hanjune Kim'''</big>]]
|-

|align="center"|[[people:IS_Lee|Principal Investigator Professor]]
|align="center"|[[people:SM_Yang|Bioinformatics Programmer & Sys. Admin]]
|align="center"|[[People:JH_Cha|Ph.D candidate (Single-cell Biology) (2019.9 - )]]
|align="center"|[[People:NY_Kim|Ph.D candidate (Microbiome) (2019.9 - )]]
|align="center"|[[People:SB_Baek|Ph.D candidate (Single-cell Biology) (2019.9 - )]]
|align="center"|[[People:IS_Choi|Ph.D candidate (Single-cell Biology) (2020.9 - )]]
|align="center"|[[People:Jun_Hyung_Cha|Ph.D candidate (Microbiome) (2020.9 - )]]
|align="center"|[[People:Junyeong_Ma|Ph.D candidate (Microbiome) (2021.3 - )]]
|align="center"|[[People:Hanjune_Kim|Integrated Ph.D course (Microbiome) (2021.3 - )]]
|}
{|border="0" width="480"

|align="center"|[[File:EuiJeong_fixed.png|100px|link=People:EuiJeong_Sung]]
|align="center"|[[File:Sehun_fixed.png|100px|link=People:Sehun_Ahn]]
|align="center"|[[File:Geon_fixed.png|100px|link=People:Geon_Koh]]
|align="center"|[[File:Wonjong_fixed.png|100px|link=People:WonJong_Kim]]
|align="center"|[[File:Jungyeon_fixed.png|100px|link=People:Jungyeon_Kim]]
|align="center"|[[File:Yerin_fixed.png|100px|link=People:Yerin_Kim]]
|align="center"|[[File:Yerin_fixed.png|100px|link=People:Yurim_Jung]]
|align="center"|[[File:Yerin_fixed.png|100px|link=People:Hae_Been_lee]]
|-
|width=160px; align="center"|[[People:Euijeong_Sung|<big>'''Euijeong Sung'''</big>]]
|width=160px; align="center"|[[People:Sehun_Ahn|<big>'''Sehun Ahn'''</big>]]
|width=160px; align="center"|[[People:Geon Koh|<big>'''Geon Koh'''</big>]]
|width=160px; align="center"|[[People:WonJong_Kim|<big>'''Wonjong Kim'''</big>]]
|width=160px; align="center"|[[People:Jungyeon_Kim|<big>'''Jungyeon Kim'''</big>]]
|width=160px; align="center"|[[People:Yerin_Kim|<big>'''Yerin Kim'''</big>]]
|width=160px; align="center"|[[People:Yurim_Jung|<big>'''Yurim Jung'''</big>]]
|width=160px; align="center"|[[People:Hae_Been_lee|<big>'''Hae Been Lee'''</big>]]
|-
|align="center"|[[People:Euijeong_Sung|Integrated Ph.D course (Single-cell Biology) (2021.9 - )]]
|align="center"|[[People:Sehun_Ahn|Integrated Ph.D course (Microbiome) (2022.3 - )]]
|align="center"|[[People:Geon_Koh|Integrated Ph.D course (Microbiome) (2022.9 - )]]
|align="center"|[[People:WonJong_Kim|Integrated Ph.D course (Microbiome) (2023.3 - )]]
|align="center"|[[People:Jungyeon_Kim|Integrated Ph.D course (Microbiome) (2023.3 - )]]
|align="center"|[[People:Yerin_Kim|Integrated Ph.D course (Microbiome) (2024.3 - )]]
|align="center"|[[People:Yurim_Jung|Integrated Ph.D course (Single-cell Biology) (2024.9 - )]]
|align="center"|[[People:Hae_Been_lee|Integrated Ph.D course (Single-cell Biology) (2024.9 - )]]
|}

=='''Administrative Assistant'''==
*Seomi Lee

=='''Undergraduate Students'''==
*Yurim Jung
*Hae Been Lee
*Zunu An
*Se Bin Lim
*Sang Min Yoon
*Yena Kim

=='''Alumni'''==
*'''Former Post-docs and Staff Scientists'''
**1. Taeyun Oh (2009.5-2011.12), currently Senior Researcher at Cowellbiogim, Korea
**2. Sohyun Hwang (2010.3-2015.8), currently Associate Professor (차의과대학 의과학과 교수), [https://grad.cha.ac.kr/%ed%99%a9%ec%86%8c%ed%98%84/ Department of Biomedical Science, CHA University], Korea [https://scholar.google.com/citations?user=z5yC9cwAAAAJ&hl=en Google Scholar]
**3. Samuel Beck (2011.2-2011.12), currently Associate Professor, Boston University, USA (미 보스턴 대학교 부교수), [https://beck310.wixsite.com/becklab Lab Homepage]
**4. Jawon Song (2011.1-2012.5), currently Research Associate (미국 텍사스슈퍼컴퓨팅센터 선임연구원), [https://www.tacc.utexas.edu/about/directory/jawon-song Texas Advanced Computing Center], Austin, TX, USA [https://scholar.google.com/citations?user=wv61wGoAAAAJ&hl=en Google Scholar]
**5. Yoonhee Ko (2012.8-2014.2), currently Associate Professor (한국외국어대 바이오공학과 교수), [https://hufsbiomed.weebly.com/ Department of Biomedical Engineering, Hankuk University of Foreign Studies], Korea [https://scholar.google.com/citations?user=GBxCYX8AAAAJ&hl=en Google Scholar]
**6. Jonghoon Lee (2013.10-2014.2), currently Associate Professor (가천대학교 식품생명공학과 교수), [https://www.gachon.ac.kr/foodbiotech/6142/subview.do Department of Food Biotechnology, Gachon University], Korea
**7. Jung Eun Shim (2009.8-2017.11), currently Staff Scientist at Yonsei Genomics Center Bioinformatics Data Analysis Core (연세의료원 유전체센터 생명정보분석코어 책임연구원)

*'''Former Graduate Students (Ph.D)'''
**1. Junha Shin (2008.9-2014.2), PhD; currently Scientist I at [https://wid.wisc.edu/people/junha-shin/ Wisconsin Institute for Discovery] (미국 위스콘신 대학연구소 연구원), USA
**2. Hanhae Kim (2009.9-2015.2), PhD; currently Research Fellow (한국과학기술기획평가원 연구위원), [http://www.kistep.re.kr Korea Institute of S&T Evaluation and Planning (KISTEP)]
**3. Ara Cho (2009.9-2015.2), PhD; currently Research Fellow (한국과학기술기획평가원 연구위원), [http://www.kistep.re.kr Korea Institute of S&T Evaluation and Planning (KISTEP)]
**4. Eiru Kim (2010.9-2016.8), PhD; currently Senior Expert in Data Science, [https://www.gnf.org/ The Genomics Institute of the Novartis Research Foundation], San Diego, USA (노바티스 선임데이터분석연구원)
**5. Tak Lee (2011.3-2017.2), PhD; currently Senior Bioinformatician, [https://www.mpipz.mpg.de/en Max Planck Institute for Plant Breeding Research] (독일 막스플랭크 연구소 선임생명정보학자)
**6. Heonjong Han (2013.3~2019.8), PhD; currently Bioinformatics engineer at [https://3billion.io/ 3billion] (바이오기업 데이터사이언티스트)
**7. Kyungsoo Kim (2013.3~2020.2), PhD; currently Research Professor at Severance Hospital (강남세브란스의료원 유방암센터 연구교수)
**8. Jae-Won Cho (2016.9~2021.2), PhD; currently Postdoc Fellow at Harvard Medical School (하버드의대 박사후 연구원) [https://scholar.google.com/citations?user=H4jO_DQAAAAJ&hl=en Hemberg Lab]
**9. Chan Yeong Kim (2015.3~2021.2), PhD; currently Postdoc Fellow at European Molecular Biology Laboratory in Heidelberg (유럽연합분자생물학연구소 박사후 연구원) [http://www.bork.embl.de/j/ Bork Group]
**10. Sungho Lee (2017.3-2024.2), PhD; currently Data Scientist at [http://www.mogam.re.kr/kor/ MOGAM Institute for Biomedical Research] (목암생명과학연구소, (주)녹십자 출연 연구소, 선임연구원)

*'''Former Graduate Students (Master's degree)'''
**1. Sun-Gou Ji (2009.9-2011.8), M.Eng.; PhD from University of Cambridge (Sanger Genome center); currently Vice President, Computational Genetics at [https://bridgebio.com/ BridgeBio Pharma], Boston, USA (미 바이오기업 부사장)
**2. Hyojin Kim (2013.3-2015.2), M.Eng.; PhD from University of Aachen; currently Computational Scientist, Uniklinikum Aachen (the university hospital of the city of Aachen), Germany (독일 아헨대학병원 데이터사이언티스트)
**3. Hongseok Shim (2013.9-2015.8), M.Eng.; currently PhD student, Arizona State University, Biomedical Informatics program (애리조나주립대 생물정보학 박사과정)
**4. Sunmo Yang (2014.9-2017.2), M.Eng. currently Bioinformatics programmer, Yonsei University (연세대 네트워크생명공학 연구실 책임데이터연구원)
**5. Muyoung Lee (2016.3-2018.2), M.Eng. currently PhD student, University of Texas at Austin, Cellular and Molecular Biology Program (텍사스주립대 생물정보학 박사과정)
**6. Jiwon Yu (2021.9-2023.8), M.Eng. currently Data Scientist at [https://www.samsungbioepis.com/kr/index.do SAMSUNG BIOEPIS] (삼성바이오에피스 데이터사이언티스트)

*'''Former Visiting Scientists/Students'''
**1. Minkyung Shin (2012.3-2012.7), went to University of Southern California graduate school
**2. Paul Chung (2012 summer), currently E-Commerce Analyst/Coordinator at [http://www.riotgames.com/ Riot Games], USA
**3. Hyunjin Cho (2014 summer), currently Bioinformatics research assistant at [http://www.libd.org/ The Lieber Institute for Brain Development | LIBD], USA
**4. Michael Chung (2016.8-2016.12), currently work at [https://about.meta.com/ Meta], USA

*'''Former Undergraduate Students'''
**1. Ila Shin, went to medical school (Yonsei Univ.) Currently Professor, Kyunghee University School of Medicine (경희대학교 의과대학 교수)
**2. Yoonkyung Ko
**3. Sangyo Park, went to law school (Kyounghee Univ.)
**4. Jayoon Shin, went to medical school (Pusan National Univ.)
**5. Dongjoo Sun, went to dental school (Wongwang Univ.)
**6. Eulsoo Kim
**7. Myungwhan Lee, went to medical school (Yonsei Univ.)
**8. Moonhee Lee, went to graduate school (University of Amsterdam, Netherlands)
**9. Jinhyun Ju, went to graduate school (Cornell Medical School, Computational Systems Biology); Currently Principal Computational Biologist, Cancer Genomics at [https://bridgebio.com/ BridgeBio Pharma], Seattle, USA (미 바이오기업 선임연구원)
**10. Haeyoung Shin, went to graduate school (Brown University, Computational Neuroscience); Currently Professor, Seoul National University (서울대학교 생명과학부 교수)
**11. Joonghee Soh, went to graduate school (KAIST, Culture Technology)
**12. Hee Jung Cho, went to graduate school (UC Berkeley)
**13. TaeHwan Kim, went to medical school
**14. Joohyung Kim, went to medical school (Kyungpook National Univ.)
**15. Donghyun Shin, went to medical school (Yonsei Univ.)
**16. Byunghee Kang (2015 spring - 2016 summer), went to graduate school (PosTech)
**17. Sunphil Kim (2016 winter)
**18. Jaeho Shim (2016 winter)
**19. Seunghyun Shin (2016 spring)
**20. Aejoo Hong (2016 spring)
**21. Changbae Bang (2016 summer), went to medical school (Yonsei Univ.)
**22. Seoyoung Choi (2016 summer)
**23. Dabin Jung (2016 summer)
**24. Boreum Nam (2016 summer)
**25. Seungun Lee (2016 summer)
**26. Yeaji Kim (2016 summer and fall)
**27. Sangyoung Lee (2017 winter)
**28. Heejun Jang (2017 winter & spring), went to KIST as Research Assistant
**29. Jungha Lee (2017 summer), went to graduate school (Seoul National Univ.)
**30. Seunghyun Wang (2017 summer), went to graduate school (KAIST)
**31. Dong-Min Yang (2017 fall)
**32. Doo-Hee Lee (2017 fall)
**33. Suk-Jae Han (2018 winter)
**34. Ji-Eun Han (2018 winter)
**35. Yuri Ko (2018 spring)
**36. Sung-Woo Kim (2018 spring)
**37. Woosung Kwon (2018 summer)
**38. Soyun Kong (2018 summer)
**39. Hojeong Keum (2018 summer and fall)
**40. Sungjun Lim (2018 summer and fall)
**41. Jiyoon Lee (2018 summer and fall)
**42. Ilseok Choi(2019 winter, spring, summer and fall, 2020 winter, spring)
**43. Junik Park (2019 winter)
**44. Lee Yoo (2019 winter & spring)
**45. Yeonwha Kim (2019 winter & spring)
**46. Sohee Kim (2019 spring)
**47. Juseong Lee (2019 spring)
**48. Junyoung Yang (2019 spring & summer)
**49. Junyoung Ma (2019 summer, 2020 winter, summer, fall, 2021 winter)
**50. Dayun Jung (2019 summer)
**51. Hanjoon Kim (2019 summer, 2021 winter)
**52. Hyungjin Kim (2020 winter, spring)
**53. Sujin Hyun (2020 winter)
**54. Hyuki Lee (2020 winter)
**55. Donghwan Lee (2020 winter)
**56. Jun Hyung Cha (2020 spring, summer)
**57. Jiwon Yu (2020 summer, 2021 Spring, summer)
**58. Dong Seok Kim (2020 summer)
**59. Donggeun Lee (2020 summer)
**60. Sehun Ahn (2020 summer, 2021 winter, spring, summer, 2022 winter)
**61. Jiho Kim (2020 summer)
**62. Euijung Seong (2020 fall, 2021 spring, summer)
**63. Junhan Kim (2021 winter & spring)
**64. Suyong Choi (2021 winter)
**65. Sujin Pyeon (2021 Spring)
**66. Hyeon Hee Ji (2021 Spring)
**67. Semin Kim (2021 Summer)
**68. Su Hwan Kim (2021 Summer)
**69. Yongbin Kim (2021 Summer)
**70. Nan Park (2021 Summer)
**71. Hoyong Jung (2021 Summer)
**72. Ingyeong Koh (2021 Summer)
**73. Jongbin Jeong (2022 Winter)
**74. Sungchul Yang (2022 Winter, Spring, Summer)
**75. Hyojung Lee (2022 Winter, Spring)
**76. Jun Hyeong Kim (2022 Winter, Spring)
**77. Young Ji Roh (2022 Spring, Summer)
**78. Geon Koh (2022 Spring, Summer)
**79. Hayoung Kang (2022 Summer, 2023 Spring)
**80. Won Jong Kim (2022 Summer, Fall, 2023 Winter)
**81. Christian Lee (2022 Summer)
**82. Je Yun Kang (2022 Summer)
**83. Sumin Lee (2022 Fall)
**84. Jungyeon Kim (2022 Fall, 2023 Winter)
**85. Seol Song (2023 Winter)
**86. Soyeon Kim (2023 Summer)
**87. Sieun Park (2023 Summer)
**88. Gunhyeong Lee (2023 Summer)
**89. Yunseo Han (2023 Summer)
**90. Jae Hyun Kim (2023 Summer)
**91. Jeong-min Seo (2023 Summer)
**92. Yerin Kim (2023 Fall, 2024 Winter)
**93. Sang Min Park (2024 Winter)
**94. Minsun Seo (2024 Winter)
**95. Jaeryun Sim (2024 Winter)
**96. Sun Min Lim (2024 Winter)
**97. Yurim Jung (2024 Spring, Summer)
**98. Hae Been Lee (2024 Spring, Summer)
**99. Zunu An (2024 Summer)
**100. Se Bin Lim (2024 Summer)
**101. Sang Min Yoon (2024 Summer)
**102. Yena Kim (2024 Summer)

People

2024-08-23T08:10:10Z

Bsb0613: /* Scientists & Research Staff */

__NOTOC__
=='''Scientists & Research Staff'''==
{|border="0" width="1080"
|align="center"|[[File:prof_Insuk_fixed.png|100px|link=people:IS_Lee]]
|align="center"|[[File:SM_fixed.png|100px|link=people:SM_Yang]]
|align="center"|[[File:Junha_fixed.png|100px|link=People:JH_Cha]]
|align="center"|[[File:Nayeon_fixed.png|100px|link=People:NY_Kim]]
|align="center"|[[File:Seungbyn_fixed.png|100px|link=People:SB_Baek]]
|align="center"|[[File:Ilseok_fixed.png|100px|link=People:IS_Choi]]
|align="center"|[[File:JunHyeong_fixed.png|100px|link=People:Jun_Hyung_Cha]]
|align="center"|[[File:JunYeong_fixed.png|100px|link=People:Junyeong_Ma]]
|align="center"|[[File:Hanjune_fixed.png|100px|link=People:Han-June_KIM]]
|-

|width=160px; align="center"|[[people:IS_Lee|<big>'''Insuk Lee'''</big>]]
|width=160px; align="center"|[[people:SM_Yang|<big>'''Sunmo Yang'''</big>]]
|width=160px; align="center"|[[People:JH_Cha|<big>'''Junha Cha'''</big>]]
|width=160px; align="center"|[[People:NY_Kim|<big>'''Nayeon Kim'''</big>]]
|width=160px; align="center"|[[People:SB_Baek|<big>'''Seungbyn Baek'''</big>]]
|width=160px; align="center"|[[People:IS_Choi|<big>'''Ilseok Choi'''</big>]]
|width=160px; align="center"|[[People:Jun_Hyung_Cha|<big>'''Jun Hyung Cha'''</big>]]
|width=160px; align="center"|[[People:Junyeong_Ma|<big>'''Junyeong Ma'''</big>]]
|width=160px; align="center"|[[People:Hanjune_Kim|<big>'''Hanjune Kim'''</big>]]
|-

|align="center"|[[people:IS_Lee|Principal Investigator Professor]]
|align="center"|[[people:SM_Yang|Bioinformatics Programmer & Sys. Admin]]
|align="center"|[[People:JH_Cha|Ph.D candidate (Single-cell Biology) (2019.9 - )]]
|align="center"|[[People:NY_Kim|Ph.D candidate (Microbiome) (2019.9 - )]]
|align="center"|[[People:SB_Baek|Ph.D candidate (Single-cell Biology) (2019.9 - )]]
|align="center"|[[People:IS_Choi|Ph.D candidate (Single-cell Biology) (2020.9 - )]]
|align="center"|[[People:Jun_Hyung_Cha|Ph.D candidate (Microbiome) (2020.9 - )]]
|align="center"|[[People:Junyeong_Ma|Ph.D candidate (Microbiome) (2021.3 - )]]
|align="center"|[[People:Hanjune_Kim|Integrated Ph.D course (Microbiome) (2021.3 - )]]
|}
{|border="0" width="960"

|align="center"|[[File:EuiJeong_fixed.png|100px|link=People:EuiJeong_Sung]]
|align="center"|[[File:Sehun_fixed.png|100px|link=People:Sehun_Ahn]]
|align="center"|[[File:Geon_fixed.png|100px|link=People:Geon_Koh]]
|align="center"|[[File:Wonjong_fixed.png|100px|link=People:WonJong_Kim]]
|align="center"|[[File:Jungyeon_fixed.png|100px|link=People:Jungyeon_Kim]]
|align="center"|[[File:Yerin_fixed.png|100px|link=People:Yerin_Kim]]
|align="center"|[[File:Yerin_fixed.png|100px|link=People:Yurim_Jung]]
|align="center"|[[File:Yerin_fixed.png|100px|link=People:Hae_Been_lee]]
|-
|width=160px; align="center"|[[People:Euijeong_Sung|<big>'''Euijeong Sung'''</big>]]
|width=160px; align="center"|[[People:Sehun_Ahn|<big>'''Sehun Ahn'''</big>]]
|width=160px; align="center"|[[People:Geon Koh|<big>'''Geon Koh'''</big>]]
|width=160px; align="center"|[[People:WonJong_Kim|<big>'''Wonjong Kim'''</big>]]
|width=160px; align="center"|[[People:Jungyeon_Kim|<big>'''Jungyeon Kim'''</big>]]
|width=160px; align="center"|[[People:Yerin_Kim|<big>'''Yerin Kim'''</big>]]
|width=160px; align="center"|[[People:Yurim_Jung|<big>'''Yurim Jung'''</big>]]
|width=160px; align="center"|[[People:Hae_Been_lee|<big>'''Hae Been Lee'''</big>]]
|-
|align="center"|[[People:Euijeong_Sung|Integrated Ph.D course (Single-cell Biology) (2021.9 - )]]
|align="center"|[[People:Sehun_Ahn|Integrated Ph.D course (Microbiome) (2022.3 - )]]
|align="center"|[[People:Geon_Koh|Integrated Ph.D course (Microbiome) (2022.9 - )]]
|align="center"|[[People:WonJong_Kim|Integrated Ph.D course (Microbiome) (2023.3 - )]]
|align="center"|[[People:Jungyeon_Kim|Integrated Ph.D course (Microbiome) (2023.3 - )]]
|align="center"|[[People:Yerin_Kim|Integrated Ph.D course (Microbiome) (2024.3 - )]]
|align="center"|[[People:Yurim_Jung|Integrated Ph.D course (Single-cell Biology) (2024.9 - )]]
|align="center"|[[People:Hae_Been_lee|Integrated Ph.D course (Single-cell Biology) (2024.9 - )]]
|}

=='''Administrative Assistant'''==
*Seomi Lee

=='''Undergraduate Students'''==
*Yurim Jung
*Hae Been Lee
*Zunu An
*Se Bin Lim
*Sang Min Yoon
*Yena Kim

=='''Alumni'''==
*'''Former Post-docs and Staff Scientists'''
**1. Taeyun Oh (2009.5-2011.12), currently Senior Researcher at Cowellbiogim, Korea
**2. Sohyun Hwang (2010.3-2015.8), currently Associate Professor (차의과대학 의과학과 교수), [https://grad.cha.ac.kr/%ed%99%a9%ec%86%8c%ed%98%84/ Department of Biomedical Science, CHA University], Korea [https://scholar.google.com/citations?user=z5yC9cwAAAAJ&hl=en Google Scholar]
**3. Samuel Beck (2011.2-2011.12), currently Associate Professor, Boston University, USA (미 보스턴 대학교 부교수), [https://beck310.wixsite.com/becklab Lab Homepage]
**4. Jawon Song (2011.1-2012.5), currently Research Associate (미국 텍사스슈퍼컴퓨팅센터 선임연구원), [https://www.tacc.utexas.edu/about/directory/jawon-song Texas Advanced Computing Center], Austin, TX, USA [https://scholar.google.com/citations?user=wv61wGoAAAAJ&hl=en Google Scholar]
**5. Yoonhee Ko (2012.8-2014.2), currently Associate Professor (한국외국어대 바이오공학과 교수), [https://hufsbiomed.weebly.com/ Department of Biomedical Engineering, Hankuk University of Foreign Studies], Korea [https://scholar.google.com/citations?user=GBxCYX8AAAAJ&hl=en Google Scholar]
**6. Jonghoon Lee (2013.10-2014.2), currently Associate Professor (가천대학교 식품생명공학과 교수), [https://www.gachon.ac.kr/foodbiotech/6142/subview.do Department of Food Biotechnology, Gachon University], Korea
**7. Jung Eun Shim (2009.8-2017.11), currently Staff Scientist at Yonsei Genomics Center Bioinformatics Data Analysis Core (연세의료원 유전체센터 생명정보분석코어 책임연구원)

*'''Former Graduate Students (Ph.D)'''
**1. Junha Shin (2008.9-2014.2), PhD; currently Scientist I at [https://wid.wisc.edu/people/junha-shin/ Wisconsin Institute for Discovery] (미국 위스콘신 대학연구소 연구원), USA
**2. Hanhae Kim (2009.9-2015.2), PhD; currently Research Fellow (한국과학기술기획평가원 연구위원), [http://www.kistep.re.kr Korea Institute of S&T Evaluation and Planning (KISTEP)]
**3. Ara Cho (2009.9-2015.2), PhD; currently Research Fellow (한국과학기술기획평가원 연구위원), [http://www.kistep.re.kr Korea Institute of S&T Evaluation and Planning (KISTEP)]
**4. Eiru Kim (2010.9-2016.8), PhD; currently Senior Expert in Data Science, [https://www.gnf.org/ The Genomics Institute of the Novartis Research Foundation], San Diego, USA (노바티스 선임데이터분석연구원)
**5. Tak Lee (2011.3-2017.2), PhD; currently Senior Bioinformatician, [https://www.mpipz.mpg.de/en Max Planck Institute for Plant Breeding Research] (독일 막스플랭크 연구소 선임생명정보학자)
**6. Heonjong Han (2013.3~2019.8), PhD; currently Bioinformatics engineer at [https://3billion.io/ 3billion] (바이오기업 데이터사이언티스트)
**7. Kyungsoo Kim (2013.3~2020.2), PhD; currently Research Professor at Severance Hospital (강남세브란스의료원 유방암센터 연구교수)
**8. Jae-Won Cho (2016.9~2021.2), PhD; currently Postdoc Fellow at Harvard Medical School (하버드의대 박사후 연구원) [https://scholar.google.com/citations?user=H4jO_DQAAAAJ&hl=en Hemberg Lab]
**9. Chan Yeong Kim (2015.3~2021.2), PhD; currently Postdoc Fellow at European Molecular Biology Laboratory in Heidelberg (유럽연합분자생물학연구소 박사후 연구원) [http://www.bork.embl.de/j/ Bork Group]
**10. Sungho Lee (2017.3-2024.2), PhD; currently Data Scientist at [http://www.mogam.re.kr/kor/ MOGAM Institute for Biomedical Research] (목암생명과학연구소, (주)녹십자 출연 연구소, 선임연구원)

*'''Former Graduate Students (Master's degree)'''
**1. Sun-Gou Ji (2009.9-2011.8), M.Eng.; PhD from University of Cambridge (Sanger Genome center); currently Vice President, Computational Genetics at [https://bridgebio.com/ BridgeBio Pharma], Boston, USA (미 바이오기업 부사장)
**2. Hyojin Kim (2013.3-2015.2), M.Eng.; PhD from University of Aachen; currently Computational Scientist, Uniklinikum Aachen (the university hospital of the city of Aachen), Germany (독일 아헨대학병원 데이터사이언티스트)
**3. Hongseok Shim (2013.9-2015.8), M.Eng.; currently PhD student, Arizona State University, Biomedical Informatics program (애리조나주립대 생물정보학 박사과정)
**4. Sunmo Yang (2014.9-2017.2), M.Eng. currently Bioinformatics programmer, Yonsei University (연세대 네트워크생명공학 연구실 책임데이터연구원)
**5. Muyoung Lee (2016.3-2018.2), M.Eng. currently PhD student, University of Texas at Austin, Cellular and Molecular Biology Program (텍사스주립대 생물정보학 박사과정)
**6. Jiwon Yu (2021.9-2023.8), M.Eng. currently Data Scientist at [https://www.samsungbioepis.com/kr/index.do SAMSUNG BIOEPIS] (삼성바이오에피스 데이터사이언티스트)

*'''Former Visiting Scientists/Students'''
**1. Minkyung Shin (2012.3-2012.7), went to University of Southern California graduate school
**2. Paul Chung (2012 summer), currently E-Commerce Analyst/Coordinator at [http://www.riotgames.com/ Riot Games], USA
**3. Hyunjin Cho (2014 summer), currently Bioinformatics research assistant at [http://www.libd.org/ The Lieber Institute for Brain Development | LIBD], USA
**4. Michael Chung (2016.8-2016.12), currently work at [https://about.meta.com/ Meta], USA

*'''Former Undergraduate Students'''
**1. Ila Shin, went to medical school (Yonsei Univ.) Currently Professor, Kyunghee University School of Medicine (경희대학교 의과대학 교수)
**2. Yoonkyung Ko
**3. Sangyo Park, went to law school (Kyounghee Univ.)
**4. Jayoon Shin, went to medical school (Pusan National Univ.)
**5. Dongjoo Sun, went to dental school (Wongwang Univ.)
**6. Eulsoo Kim
**7. Myungwhan Lee, went to medical school (Yonsei Univ.)
**8. Moonhee Lee, went to graduate school (University of Amsterdam, Netherlands)
**9. Jinhyun Ju, went to graduate school (Cornell Medical School, Computational Systems Biology); Currently Principal Computational Biologist, Cancer Genomics at [https://bridgebio.com/ BridgeBio Pharma], Seattle, USA (미 바이오기업 선임연구원)
**10. Haeyoung Shin, went to graduate school (Brown University, Computational Neuroscience); Currently Professor, Seoul National University (서울대학교 생명과학부 교수)
**11. Joonghee Soh, went to graduate school (KAIST, Culture Technology)
**12. Hee Jung Cho, went to graduate school (UC Berkeley)
**13. TaeHwan Kim, went to medical school
**14. Joohyung Kim, went to medical school (Kyungpook National Univ.)
**15. Donghyun Shin, went to medical school (Yonsei Univ.)
**16. Byunghee Kang (2015 spring - 2016 summer), went to graduate school (PosTech)
**17. Sunphil Kim (2016 winter)
**18. Jaeho Shim (2016 winter)
**19. Seunghyun Shin (2016 spring)
**20. Aejoo Hong (2016 spring)
**21. Changbae Bang (2016 summer), went to medical school (Yonsei Univ.)
**22. Seoyoung Choi (2016 summer)
**23. Dabin Jung (2016 summer)
**24. Boreum Nam (2016 summer)
**25. Seungun Lee (2016 summer)
**26. Yeaji Kim (2016 summer and fall)
**27. Sangyoung Lee (2017 winter)
**28. Heejun Jang (2017 winter & spring), went to KIST as Research Assistant
**29. Jungha Lee (2017 summer), went to graduate school (Seoul National Univ.)
**30. Seunghyun Wang (2017 summer), went to graduate school (KAIST)
**31. Dong-Min Yang (2017 fall)
**32. Doo-Hee Lee (2017 fall)
**33. Suk-Jae Han (2018 winter)
**34. Ji-Eun Han (2018 winter)
**35. Yuri Ko (2018 spring)
**36. Sung-Woo Kim (2018 spring)
**37. Woosung Kwon (2018 summer)
**38. Soyun Kong (2018 summer)
**39. Hojeong Keum (2018 summer and fall)
**40. Sungjun Lim (2018 summer and fall)
**41. Jiyoon Lee (2018 summer and fall)
**42. Ilseok Choi(2019 winter, spring, summer and fall, 2020 winter, spring)
**43. Junik Park (2019 winter)
**44. Lee Yoo (2019 winter & spring)
**45. Yeonwha Kim (2019 winter & spring)
**46. Sohee Kim (2019 spring)
**47. Juseong Lee (2019 spring)
**48. Junyoung Yang (2019 spring & summer)
**49. Junyoung Ma (2019 summer, 2020 winter, summer, fall, 2021 winter)
**50. Dayun Jung (2019 summer)
**51. Hanjoon Kim (2019 summer, 2021 winter)
**52. Hyungjin Kim (2020 winter, spring)
**53. Sujin Hyun (2020 winter)
**54. Hyuki Lee (2020 winter)
**55. Donghwan Lee (2020 winter)
**56. Jun Hyung Cha (2020 spring, summer)
**57. Jiwon Yu (2020 summer, 2021 Spring, summer)
**58. Dong Seok Kim (2020 summer)
**59. Donggeun Lee (2020 summer)
**60. Sehun Ahn (2020 summer, 2021 winter, spring, summer, 2022 winter)
**61. Jiho Kim (2020 summer)
**62. Euijung Seong (2020 fall, 2021 spring, summer)
**63. Junhan Kim (2021 winter & spring)
**64. Suyong Choi (2021 winter)
**65. Sujin Pyeon (2021 Spring)
**66. Hyeon Hee Ji (2021 Spring)
**67. Semin Kim (2021 Summer)
**68. Su Hwan Kim (2021 Summer)
**69. Yongbin Kim (2021 Summer)
**70. Nan Park (2021 Summer)
**71. Hoyong Jung (2021 Summer)
**72. Ingyeong Koh (2021 Summer)
**73. Jongbin Jeong (2022 Winter)
**74. Sungchul Yang (2022 Winter, Spring, Summer)
**75. Hyojung Lee (2022 Winter, Spring)
**76. Jun Hyeong Kim (2022 Winter, Spring)
**77. Young Ji Roh (2022 Spring, Summer)
**78. Geon Koh (2022 Spring, Summer)
**79. Hayoung Kang (2022 Summer, 2023 Spring)
**80. Won Jong Kim (2022 Summer, Fall, 2023 Winter)
**81. Christian Lee (2022 Summer)
**82. Je Yun Kang (2022 Summer)
**83. Sumin Lee (2022 Fall)
**84. Jungyeon Kim (2022 Fall, 2023 Winter)
**85. Seol Song (2023 Winter)
**86. Soyeon Kim (2023 Summer)
**87. Sieun Park (2023 Summer)
**88. Gunhyeong Lee (2023 Summer)
**89. Yunseo Han (2023 Summer)
**90. Jae Hyun Kim (2023 Summer)
**91. Jeong-min Seo (2023 Summer)
**92. Yerin Kim (2023 Fall, 2024 Winter)
**93. Sang Min Park (2024 Winter)
**94. Minsun Seo (2024 Winter)
**95. Jaeryun Sim (2024 Winter)
**96. Sun Min Lim (2024 Winter)
**97. Yurim Jung (2024 Spring, Summer)
**98. Hae Been Lee (2024 Spring, Summer)
**99. Zunu An (2024 Summer)
**100. Se Bin Lim (2024 Summer)
**101. Sang Min Yoon (2024 Summer)
**102. Yena Kim (2024 Summer)

People:SB Baek

2024-06-24T05:18:33Z

Bsb0613: /* Publications */

Publications

2024-06-24T05:16:20Z

Bsb0613: /* 2024 (9) */

*[http://scholar.google.com/citations?user=3uHQIOkAAAAJ Google Scholar citation report]
*[https://www.ibric.org/bric/hanbitsa/treatise.do?srOrder=&fixInOut=&fixCate=&srCate=&srStartYear=&srStartMonth=&srEndYear=&srEndMonth=&srSearchType=name&srSearchVal=%EC%9D%B4%EC%9D%B8%EC%84%9D BRIC 한빛사논문]
*[https://yonsei.pure.elsevier.com/en/persons/in-suk-lee Yonsei University Researcher Portal]
*[https://pubmed.ncbi.nlm.nih.gov/?term=Insuk+Lee%5BAuthor%5D&sort=date Pubmed article list]
* '*' First authors; '**' Corresponding authors;
==2024 (9)==
*114. '''Jiwon Yu*, Junha Cha*''', Geon Koh, '''Insuk Lee**''', HCNetlas: Human cell network atlas enabling cell type-resolved disease genetics '''''Under Review''''' [https://www.biorxiv.org/content/10.1101/2024.06.07.597878v1 bioRxiv]
 
*113. '''Nayeon Kim*''', Chan Yeong Kim, Summo Yang, Dongjin Park, Sang-Jun Ha, and '''Insuk Lee**''', MRGM: a mouse reference gut microbiome reveals a large functional discrepancy for gut bacteria of the same genus between mice and humans '''''Under Review''''' [https://www.biorxiv.org/content/10.1101/2021.10.24.465599v1 bioRxiv]
 
*112. '''Nayeon Kim*''', Junyeong Ma, Wonjong Kim, Jungyeon Kim, Peter Belenky** and '''Insuk Lee**''', Genome-Resolved Metagenomics: A Game Changer for Microbiome Medicine, '''''Experimental & Molecular Medicine''''' In press
 
*111. Randall T. Mertens*, Aditya Misra*, Peng Xiao*, '''Seungbyn Baek''', Joseph M. Rone, Davide Mangani, Kisha N. Sivanathan, Adedamola S. Arojojoye, Samuel G. Awuah, '''Insuk Lee''', Guo-Ping Shi, Boryana Petrova, Jeannette R. Brook, Ana C. Anderson, Richard A. Flavell, Naama Kanarek, Martin Hemberg, Roni Nowarski**, A metabolic switch orchestrated by IL-18 and cGAMP programs intestinal tolerance, '''''Immunity''''' 2024 Jun 19:S1074-7613(24)00305-4 [https://pubmed.ncbi.nlm.nih.gov/38906145/ PubMed]
 
*110. '''Junha Cha*''', Da Hee Kim*, Gamin Kim*, Jae-Won Cho, Euijeong Sung, Seungbyn Baek, Min Hee Hong, Chang Gon Kim, Nam Suk Sim, Hyun Jun Hong, Jung Eun Lee, Martin Hemberg, Seyeon Park, Sun Ock Yoon, Sang-Jun Ha**, Yoon Woo Koh**, Hye Ryun Kim**, and '''Insuk Lee**''', Single-cell analysis reveals cellular and molecular factors counteracting HPV-positive oropharyngeal cancer immunotherapy outcomes, '''''Journal for Immunotherapy of Cancer'''''. 2024 Jun 10;12(6):e008667 [https://pubmed.ncbi.nlm.nih.gov/38857913/ PubMed]
 
*109. Chang Gon Kim*, Min Hee Hong*, Dahee Kim*, Brian Hyohyoung Lee*, Hyunwook Kim*, Chan-Young Ock*, Geoffrey Kelly, Yoon Ji Bang, Gamin Kim, Jung Eun Lee, Chaeyeon Kim, Se-Heon Kim, Hyun Jun Hong, Young Min Park, Nam Suk Sim, Heejung Park, Jin Woo Park, Chang Geol Lee, Kyung Hwan Kim, Goeun Park, Inkyung Jung, Dawoon Han, Jong Hoon Kim, '''Junha Cha''', '''Insuk Lee''', Mingu Kang, Heon Song, Chiyoon Oum, Seulki Kim, Sukjun Kim, Yoojoo Lim, Seunghee Kim-Schulze, Miriam Merad, Sun Och Yoon**, Hyun Je Kim**, Yoon Woo Koh**, Hye Ryun Kim**, A phase II open-label randomized clinical trial of preoperative durvalumab or durvalumab plus tremelimumab in resectable head and neck squamous cell carcinoma '''''Clinical Cancer Research''''' 2024 May 15;30(10):2097-2110 [https://pubmed.ncbi.nlm.nih.gov/38457288/ PubMed]
 
*108. Da Hyun Kim*, '''Sungho Lee*''', Jisong Ahn, Jae Hwan Kim, Eunjung Lee**, '''Insuk Lee**''', and Sanguine Byun**, Transcriptomic and metabolomic analysis unveils nanoplastic-induced gut barrier dysfunction via STAT1/6 and ERK pathways '''''Environmental Research''''' 2024 May 15:249:118437 [https://pubmed.ncbi.nlm.nih.gov/38346486/ PubMed]
 
*107. Critical Assessment of Genome Interpretation Consortium, CAGI, the Critical Assessment of Genome Interpretation, establishes progress and prospects for computational genetic variant interpretation methods '''''Genome Biology''''' 2024 February 22;25(1):53 [https://pubmed.ncbi.nlm.nih.gov/38389099/ PubMed]
 
*106. Wei E Gordon*, '''Seungbyn Baek*''', Hai P Nguyen, Yien-Ming Kuo, Rachael Bradley, Sarah L. Fong, '''Nayeon Kim''', Alex Galazyuk, '''Insuk Lee''', Melissa Ingala, Nancy B Simmons, Tony Schountz, Lisa Cooper, Ilias Georgakopoulos-Soares, Martin Hemberg**, Nadav Ahituv**, Integrative single-cell characterization of a frugivorous and an insectivorous bat kidney and pancreas '''''Nature Communications''''' 2024 Jan 9;15(1):12 [https://pubmed.ncbi.nlm.nih.gov/38195585/ PubMed]
 

==2023 (3)==
*105. '''Junha Cha*''', Michael Lavi*, Junhan Kim, Noam Shomron**, '''Insuk Lee**''', Imputation of single-cell transcriptome data enables the reconstruction of networks predictive of breast cancer metastasis '''''Computational and Structural Biotechnology Journal''''' 2023 Mar 25;21:2296-2304 [https://pubmed.ncbi.nlm.nih.gov/37035549/ PubMed]
 
*104. '''Junha Cha*''', Jiwon Yu, Jae-Won Cho, Martin Hemberg**, '''Insuk Lee**''', scHumanNet: a single-cell network analysis platform for the study of cell-type specificity of disease genes '''''Nucleic Acids Research''''' 2023 Jan 25;51(2):e8 [https://pubmed.ncbi.nlm.nih.gov/36350625/ PubMed] [https://www.biorxiv.org/content/10.1101/2022.06.20.496836v1 bioRxiv]
 
*103. Myeong Joon Kim*, '''Kyungsoo Kim*''', Hyo Jin Park, Kyeong Hee Hong, Ji Hoon Oh, Jimin Son, '''Insuk Lee**''' and Sang-Jun Ha**, Deletion of PD-1 destabilizes the lineage identity and metabolic fitness of tumor-infiltrating regulatory T cells '''''Nature Immunology''''' 2023 Jan;24(1):148-161 [https://pubmed.ncbi.nlm.nih.gov/36577929/ PubMed]
 

==2022 (6)-Sabbatical leave==
*102. '''Chan Yeong Kim*, Junyeong Ma*''', and '''Insuk Lee**''' HiFi Metagenomic Sequencing Enables Assembly of Accurate and Complete Genomes from Human Gut Microbiota '''''Nature Communications''''' 2022 Oct 26;13(1):6367 [https://pubmed.ncbi.nlm.nih.gov/36289209/ PubMed] [https://www.biorxiv.org/content/10.1101/2022.02.09.479829v1 bioRxiv]
 
*101. '''Seungbyn Baek*''', Sunmo Yang, and '''Insuk Lee**''' COVID-GWAB: A Web-Based Prediction of COVID-19 Host Genes via Network Boosting of Genome-Wide Association Data '''''Biomolecules''''' 2022 Oct 12;12(10):1446 [https://pubmed.ncbi.nlm.nih.gov/36291657/ PubMed]
 
*100. Taeyun A Lee, '''Heonjong Han''', Ahsan Polash, Seok Keun Cho, Ji Won Lee, Eun A Ra, Eunhye Lee, Areum Park, Sujin Kang, Junhee L Choi, Ji Hyun Kim, Ji Eun Lee, Kyung-Won Min, Seong Wook Yang, Markus Hafner, '''Insuk Lee''', Je-Hyun Yoon, Sungwook Lee, Boyoun Park The nucleolus is the site for inflammatory RNA decay during infection '''''Nature Communications''''' 2022 Sep 3;13(1):5203 [https://pubmed.ncbi.nlm.nih.gov/36057640/ PubMed]
 
*99. Tae Gun Kang, Kee Woong Kwon, '''Kyungsoo Kim, Insuk Lee''', Myeong Joon Kim, Sang-Jun Ha, Sung Jae Shin Viral coinfection promotes tuberculosis immunopathogenesis by type I IFN signaling-dependent impediment of Th1 cell pulmonary influx, '''''Nature Communications''''' 2022 Jun 7;13(1):3155 [https://pubmed.ncbi.nlm.nih.gov/35672321/ PubMed]
 
*98. '''Jae-Won Cho*''', Hyo Sup Shim, Chang Young Lee, Seong Yong Park, Min Hee Hong, '''Insuk Lee**''', Hye Ryun Kim** The importance of enhancer methylation for epigenetic regulation of tumorigenesis in squamous lung cancer. '''''Experimental & Molecular Medicine''''' 2022 Jan 5;54(1):12-22 [https://pubmed.ncbi.nlm.nih.gov/34987166/ PubMed] [//netbiolab.org/wiki/Cho_etal_2021_Table_S1-10.zip Supplementary Table S1-10]
 
*97. '''Chan Yeong Kim*, Seungbyn Baek*''', Junha Cha, Sunmo Yang, Eiru Kim, Edward M. Marcotte, Traver Hart, and '''Insuk Lee**''' HumanNet v3: An improved database of human gene networks for disease research '''''Nucleic Acids Research''''' 2022 Jan 7;50(D1):D632-D639 [https://pubmed.ncbi.nlm.nih.gov/34747468/ PubMed]
 

==2021 (5)==
*96. '''Jae-Won Cho*''', Seyeon Park, Gamin Kim, Heonjong Han, Hyo Sup Shim, Sunhye Shin, Yong-Soo Bae, '''Seong Yong Park** Sang-Jun Ha**, Insuk Lee**, Hye Ryun Kim**''' Dysregulation of T FH-B-T RM lymphocyte cooperation is associated with unfavorable anti-PD-1 responses in EGFR-mutant lung cancer '''''Nature Communications''''' 2021 Oct 18;12(1):6068. [https://pubmed.ncbi.nlm.nih.gov/34663810/ PubMed]
 
*95. '''Chan Yeong Kim*, Muyoung Lee*''', Sunmo Yang, Kyungnam Kim, Dongeun Yong, Hye Ryun Kim, '''Insuk Lee**''' Human reference gut microbiome catalog including newly assembled genomes from under-represented Asian metagenomes '''''Genome Medicine''''' 2021 Aug 27;13(1):13 [https://pubmed.ncbi.nlm.nih.gov/34446072/ pubmed]
 
*94. Ho-Seok Lee, Ilyeong Choi, Young Jeon, Hee-Kyung Ahn, Huikyong Cho, JiWoo Kim, Jae-Hee Kim, Jung-Min Lee, SungHee Lee, Julian Bünting, Dong Hye Seo, '''Tak Lee''', Du-Hwa Lee, '''Insuk Lee''', Man-Ho Oh, Tae-Wuk Kim, Youssef Belkhadir, Hyun-Sook Pai Chaperone-like protein DAY plays critical roles in photomorphogenesis '''''Nature Communications''''' 2021 Jul 7;12(1):4194 [https://https://pubmed.ncbi.nlm.nih.gov/34234144/ pubmed]
 
*93. '''Jae-Won Cho*''', Jimin Son*, Sang-Jun Ha**, '''Insuk Lee**''', Systems biology analysis identifies TNFRSF9 as a functional marker of tumor-infiltrating regulatory T-cell enabling clinical outcome prediction in lung cancer '''''Computational and Structural Biotechnology Journal''''' 2021 Jan 21; 19:860-868 [https://pubmed.ncbi.nlm.nih.gov/33598101 pubmed]
 
*92. '''Tak Lee*''', '''Insuk Lee**''' Genome-wide Association Studies in Arabidopsis thaliana: Statistical Analysis and Network-Based Augmentation of Signals '''''Methods in Molecular Biology''''' 2020 Nov; 2200:187-210 [https://pubmed.ncbi.nlm.nih.gov/33175379 pubmed] [https://netbiolab.org/wiki/glucose_germrate.txt Supplementary File 1] [https://netbiolab.org/wiki/glucose_sig_accessions.txt Supplementary File 2]
 

==2020 (9)==
*91. '''Junha Cha*''', '''Insuk Lee**''' Single-cell Network Biology for Resolving Cellular Heterogeneity in Human Diseases '''''Experimental & Molecular Medicine''''' 2020 Nov;52(11):1798-1808 [https://pubmed.ncbi.nlm.nih.gov/33244151 pubmed]
 
*90. Jimin Son*, '''Jae-Won Cho*''', Hyo Jin Park, Jihyun Moon, Seyeon Park, Hoyoung Lee, Jeewon Lee, Ga min Kim, Su-Myeong Park, Sergio A. Lira, Andrew N. Mckenzie, Hye Young Kim, Cheol Yong Choi, Yong Taik Lim, Seong Yong Park, Hye Ryun Kim, Su-Hyung Park, Eui-Cheol Shin, '''Insuk Lee'''** & Sang-Jun Ha**, Tumor-Infiltrating Regulatory T Cell Accumulation in the Tumor Microenvironment is Mediated by IL33/ST2 Signaling '''''Cancer Immunology Research''''' 2020 Nov; 8(11):1393-1406 [https://pubmed.ncbi.nlm.nih.gov/32878747 pubmed] '''Spotlighted''' by [https://acir.org/journal-articles/cancer-immunobiology/immune-cell-biology?entryId=26357 Accelerating Cancer Immunotherapy Research]
 
*89. '''Jae-Won Cho*''', Min Hee Hong, Sang-Jun Ha, Young-Joon Kim, Byoung Chul Cho, '''Insuk Lee**''', and Hye Ryun Kim** Genome-wide identification of differentially methylated promoters and enhancers associated with response to anti-PD-1 therapy in non-small cell lung cancer [https://netbiolab.org/wiki/Supplementary_Table_S1-6.zip Supplementary File] '''''Experimental & Molecular Medicine''''' 2020 Sep 02;52(9):1550-1563 [https://pubmed.ncbi.nlm.nih.gov/32879421 pubmed]
 
*88. '''Seungbyn Baek*''', '''Insuk Lee**''' Single-cell ATAC sequencing analysis: from data preprocessing to hypothesis generation '''''Computational and Structural Biotechnology Journal''''' 2020 June 28;18:1429-1439 [https://pubmed.ncbi.nlm.nih.gov/32637041 pubmed]
 
*87. '''Eiru Kim*, Dasom Bae*, Sunmo Yang*''', Gunhwan Ko, Sungho Lee, Byungwook Lee**, '''Insuk Lee**'''. BiomeNet: A database for construction and analysis of functional interaction networks for any species with a sequenced genome '''''Bioinformatics''''' 2020 Mar 1:36(5):1584-1589 [https://www.ncbi.nlm.nih.gov/pubmed/31599923 pubmed]
 
*86. '''Kyungsoo Kim*''', Seyeon Park*, Seong Yong Park, Gamin Kim, Su Myeong Park, Jae-Won Cho, Da Hee Kim, Young Min Park, Yoon Woo Koh, Hye Ryun Kim, Sang-Jun Ha** and '''Insuk Lee**''', Single-cell transcriptome analysis reveals TOX as a promoting factor for T cell exhaustion and a predictor for anti-PD-1 responses in human cancer '''''Genome Medicine''''' 2020 Feb 28;12:22 [https://pubmed.ncbi.nlm.nih.gov/32111241 pubmed] '''Recommended''' by [https://facultyopinions.com/prime/737459051 F1000]
 
*85. '''Sungho Lee*, Tak Lee*''', Sunmo Yang and '''Insuk Lee**''', BarleyNet: a network-based functional omics analysis server for cultivated barley, Hordeum vulgare L. '''''Frontiers in Plant Science''''' 2020 Feb 18;11:98 [https://pubmed.ncbi.nlm.nih.gov/32133024/ pubmed]
 
*84. '''Kyungsoo Kim*''', Sunmo Yang, Sang-Jun Ha, '''Insuk Lee**''', VirtualCytometry: a webserver for evaluating immune cell differentiation using single-cell RNA sequencing data '''''Bioinformatics''''' 2020 Jan 15;36(2):546-551 [https://www.ncbi.nlm.nih.gov/pubmed/31373613 pubmed]
 
*83. '''Jung Eun Shim*, Insuk Lee**''', Construction of functional protein networks using domain profile associations, '''''Methods in Molecular Biology''''' 2020 Jan;2074:35-44 [https://www.ncbi.nlm.nih.gov/pubmed/31583628 pubmed]
 

==2019 (6)==
*82. '''Han H*''', Lee S, '''Lee I**''', NGSEA: network-based gene set enrichment analysis for interpreting gene expression phenotypes with functional gene sets '''''Mol Cells''''' 2019 Aug 31;42(8):579-588. [https://www.ncbi.nlm.nih.gov/pubmed/31307154 pubmed]
 
*81. '''Lee T*, Lee S*''', Yang S, '''Lee I**'''. MaizeNet: A co-functional network for network-assisted systems genetics in Zea mays '''''The Plant Journal''''' 2019 Aug 99(3):571-582 [https://www.ncbi.nlm.nih.gov/pubmed/31006149 pubmed]
 
*80. '''Lee M*''', Pinto NA*, Kim CY, Yang S, D'Souza R, Yong D**, Lee I**. Network integrative genomic and transcriptomic analysis of carbapenem-resistant Klebsiella pneumoniae strains identifies genes for antibiotic-resistance and virulence '''''mSystems''''' 2019 Aug;4(4):e00202-19. [https://www.ncbi.nlm.nih.gov/pubmed/31117026 pubmed]
 
*79. '''Shim JE*''', Kim JH, Shin J, Lee JE, '''Lee I**''' Pathway-specific protein domains are predictive for human diseases '''''PLOS Computational Biology''''' 2019 May 10;15(5):e1007052 [https://www.ncbi.nlm.nih.gov/pubmed/31075101 pubmed]
 
*78. '''Kim H*''', Joe A*, Lee M, Yang S, Ma X, Ronald PC**, '''Lee I**'''. A Genome-Scale Co-Functional Network of Xanthomonas Genes Can Accurately Reconstruct Regulatory Circuits Controlled by Two-Component Signaling Systems '''''Mol Cells''''' 2019 Feb 28;42(2):166-174 [https://www.ncbi.nlm.nih.gov/pubmed/30759970 pubmed]
 
*77. '''Hwang S*, Kim CY*''', Yang S, Kim E, Hart T, Marcotte EM, '''Lee I**'''. HumanNet v2: human gene networks for disease research '''''Nucleic Acids Research''''' 2019 Jan 8;47(D1):D573–D580.[https://www.ncbi.nlm.nih.gov/pubmed/30418591 pubmed]
 

==2018 (5)==
*76. '''Han H*''', Lehner B, '''Lee I**''', Cancer gene discovery by network analysis of somatic mutations using the MUFFINN server, '''''Methods in Molecular Biology''''' 2018 Dec 13; 1907:37-50.[https://www.ncbi.nlm.nih.gov/pubmed/30542989 pubmed]
 
*75. '''Kim CY*''', Lee M, Lee K, Yoon SS, '''Lee I**''', Network-based genetic investigation of virulence-associated phenotypes in methicillin-resistant Staphylococcus aureus, '''''Scientific Reports''''' 2018 July 17; 8:10796. [https://www.ncbi.nlm.nih.gov/pubmed/30018396 pubmed]
 
*74. Lim KT, Kim J, Hwang SI, Zhang L, Han H, Bae D, Kim KP, Hu YP, Schöler HR, '''Lee I''', Hui L, Han DW, Direct Conversion of Mouse Fibroblasts into Cholangiocyte Progenitor Cells. '''''Stem Cell Reports''''' 2018 Mar 27. 10:1-15.[https://www.ncbi.nlm.nih.gov/pubmed/29606616 pubmed]
 
*73. '''Lee T*, Lee I**''', araGWAB: A web application for network-based boosting of genome-wide association signals in Arabidopsis '''''Scientific Reports''''' 2018 Feb 13;8(1):2925.[https://www.ncbi.nlm.nih.gov/pubmed/29440686 pubmed]
 
*72. '''Han H*''', Cho JW, Lee S, Yun A, Kim H, Bae D, Yang S, Kim CY, Lee M, Kim E, Lee S, Kang B, Jeong D, Kim Y, Jeon HN, Jung H, Nam S, Chung M, Kim JH, '''Lee I**''', TRRUST v2: An expanded reference database of human and mouse transcriptional regulatory interactions '''''Nucleic Acids Research''''' 2018 Jan 4;46(D1):D380–D386.[https://www.ncbi.nlm.nih.gov/pubmed/29087512 pubmed]
 

==2017 (10)==
*71. '''Kim CY*, Lee I**''', Functional gene networks based on the gene neighborhood in metagenomes, '''''Animal Cells and Systems''''' 2017 Sept 29; 21:301-306;
 
*70. '''Lee T*, Hwang S*''', Kim CY, Shim H, Kim H, Ronald P**, Marcotte E**, '''Lee I**''', WheatNet: A genome-scale functional network for hexaploid bread wheat, Triticum aestivum, '''''Molecular Plant.''''' 2017 Aug 7;10(8):1133-1136; [https://www.ncbi.nlm.nih.gov/pubmed/28450181 pubmed]
 
*69. '''Lee T*, Lee I**''', AraNet: A Network Biology Server for Arabidopsis thaliana and Other Non-Model Plant Species, '''''Methods in Molecular Biology''''' 2017 June 17; 1629:225-238; [https://www.ncbi.nlm.nih.gov/pubmed/28623589 pubmed]
 
*68. '''Shim JE*''', Bang C, Yang S, Lee T, Hwang S, Kim CY, Singh-Blom M, Marcotte E, '''Lee I**''', GWAB: a web server for the network-based boosting of human genome-wide association data, '''''Nucleic Acids Research''''' 2017 July 3; 45 (W1):W154-W161; [https://www.ncbi.nlm.nih.gov/pubmed/28449091 pubmed]
 
*67. '''Kim E*, Lee I**''', Network-Based Gene Function Prediction in Mouse and Other Model Vertebrates Using MouseNet Server, '''''Methods in Molecular Biology''''' 2017 April 28; 1611:183-198; [https://www.ncbi.nlm.nih.gov/pubmed/28451980 pubmed] [http://f1000.com/prime/727562216?bd=1 {{#widget:AddHtml|content=<img src="http://cdn.f1000.com.s3.amazonaws.com/images/badges/badgef1000.gif" alt="Access the recommendation on F1000Prime" id="bg" width=70 />}}]
 
*66. '''Shim JE*, Lee T*, Lee I**''', From sequencing data to gene functions: co-functional network approaches, '''''Animal Cells and Systems''''' 2017 April 15; 20:77-83; [https://www.ncbi.nlm.nih.gov/pubmed/30460054 pubmed]
 
*65. '''Kim H*''', Kim BS*, Shim JE, Hwang S, Yang S, Kim E, Iyer-Pascuzzi AS**, '''Lee I**''', TomatoNet: A genome-wide co-functional network for unveiling complex traits of tomato, a model crop for fleshy fruits, '''''Molecular Plant.''''' 2017 April 3; 10:652–655; [https://www.ncbi.nlm.nih.gov/pubmed/27913317 pubmed]
 
*64. Jang K, Kim K, Cho A, '''Lee I''', Choi JK, Network perturbation by recurrent regulatory variants in cancer, '''''PLoS Compt. Biol.''''' 2017 Mar 23;13(3):e1005449; [https://www.ncbi.nlm.nih.gov/pubmed/28333928 pubmed]
 
*63. Yang S*, Kim CY, Hwang S, Kim E, Kim H, Shim H, '''Lee I**''', COEXPEDIA: exploring biomedical hypotheses via co-expressions associated with medical subject headings (MeSH), '''''Nucleic Acids Research''''' 2017 Jan 4; 45(D1):D389-D396 [https://www.ncbi.nlm.nih.gov/pubmed/27679477 pubmed]
 
*62. Kim E*, Hwang S, '''Lee I**''', SoyNet: a database of co-functional networks for soybean Glycine max, '''''Nucleic Acids Research''''' 2017 Jan 4; 45(D1):D1082-D1089. [http://www.ncbi.nlm.nih.gov/pubmed/27492285 pubmed]
 

==2016 (10)==
*61. '''Shin J**, Lee I**''', Construction of Functional Gene Networks Using Phylogenetic Profiles. '''''Methods in Molecular Biology''''' 2016 Nov 29; 1526:87-98 [http://www.ncbi.nlm.nih.gov/pubmed/27896737 pubmed]
 
*60. '''Shim H*''', Kim JH*, Kim CY*, Hwang S, Kim H, Yang S, Lee JE**, '''Lee I**''', Function-driven discovery of disease genes in zebraﬁsh using an integrated genomics big data resource,'''''Nucleic Acids Research''''' 2016 Nov 16;44(20):9611-9623 [http://www.ncbi.nlm.nih.gov/pubmed/27903883 pubmed]
 
*59. '''Shim JE*, Lee I**''' Weighted mutual information analysis substantially improves domain-based functional network models. '''''Bioinformatics''''' 2016 Sep 15;32(18):2824-30 [http://www.ncbi.nlm.nih.gov/pubmed/27207946 pubmed]
 
*58. Lee JY*, '''Kim E*''', Choi SM, Kim DW, Kim KP, '''Lee I**''', Kim HS** Microvesicles from brain-extract—treated mesenchymal stem cells improve neurological functions in a rat model of ischemic stroke. '''''Scientific Reports''''' 2016 Sep 9; 6:33038 [http://www.ncbi.nlm.nih.gov/pubmed/27609711 pubmed]
 
*57. Kim SM, Kim JW, Kwak TH, Park SW, Kim KP, Park H, Lim KT, Kang K, Kim J, Yang JH, Han H, '''Lee I''', Hyun JK, Bae YM, Schöler HR, Lee HT, Han DW Generation of Integration-free Induced Neural Stem Cells from Mouse Fibroblasts. '''''J Biol Chem'''''. 2016 Jul 1. 291(27):14199-212. [http://www.ncbi.nlm.nih.gov/pubmed/27189941 pubmed]
 
*56. '''Cho A*''', Shim JE, Kim E, Supek F, Lehner B**, '''Lee I**'''. MUFFINN: cancer gene discovery via network analysis of somatic mutation data. '''''Genome Biology''''' 2016 June 23;17(1):129 [http://www.ncbi.nlm.nih.gov/pubmed/27333808 pubmed]
 
*55. '''Hwang S*, Kim CY*''', Ji SG, Go J, Kim H, Yang S, Kim HJ, Cho A, Yoon SS**, '''Lee I**'''. Network-assisted investigation of virulence and antibiotic-resistance systems in Pseudomonas aeruginosa.'''''Scientific Reports''''' 2016 May 19; 6:26223. [http://www.ncbi.nlm.nih.gov/pubmed/27194047 pubmed]
 
*54. Yoon MY, Min KB, Lee KM, Yoon Y, Kim Y, Oh YT, Lee K, Chun J, Kim BY, Yoon SH, '''Lee I''', Kim CY, Yoon SS,. A single gene of a commensal microbe affects host susceptibility to enteric infection.'''''Nature Communications''''' 2016 May 13; 7:11606. [http://www.ncbi.nlm.nih.gov/pubmed/27173141 pubmed]
 
*53. Jo J*, Hwang S*, Kim HJ*, Hong S, Lee JE, Lee SG, Baek A, Han H, Lee JI, '''Lee I**''', Lee DR**. An integrated systems biology approach identifies positive cofactor 4 as a factor that increases reprogramming efficiency.'''''Nucleic Acids Research''''' 2016 Feb 18; 44(3):1203-15.[http://www.ncbi.nlm.nih.gov/pubmed/26740582 pubmed]
 
*52. Kim E*, Hwang S, Kim H, Shim H, Kang B, Yang S, Shim JH, Shin SY, Marcotte EM, '''Lee I**'''. MouseNet v2: A database of gene networks for studying the laboratory mouse and eight other model vertebrates,'''''Nucleic Acids Research''''' 2016 Jan 4;44(D1):D848-54.[http://www.ncbi.nlm.nih.gov/pubmed/26527726 pubmed]
 

==2015 (14)==
*51. '''Hwang S*, Kim E*, Lee I**''', Marcotte EM**, Systematic comparison of variant calling pipelines using gold standard personal exome variants, '''''Scientific Reports''''' 2015 Dec 5:17875 [http://www.ncbi.nlm.nih.gov/pubmed/?term=26639839 pubmed]
 
*50. '''Shin J*, Lee I**''', Co-Inheritance Analysis within the Domains of Life Substantially Improves Network Inference by Phylogenetic Profiling, '''''Plos One''''' 2015 Sep 22;10(9):e0139006 [http://www.ncbi.nlm.nih.gov/pubmed/26394049 pubmed]
 
*49. '''Shim JE*, Lee I**''', Network-assisted approaches for human disease research, '''''Animal Cells and Systems''''' 2015 Aug 19(4):231-235 [http://www.tandfonline.com/doi/abs/10.1080/19768354.2015.1074108 Link] [[media:19768354.2015.1074108.pdf|pdf]]
 
*48. '''Lee T*''', Oh T, Yang S, Shin J, Hwang S, Kim CY, Kim H, Shim H, Shim JE, Ronald PC**, '''Lee I**''', RiceNet v2: an improved network prioritization server for rice genes, '''''Nucleic Acids Research''''' 2015 Jul 1;43(W1):W122-7 [http://www.ncbi.nlm.nih.gov/pubmed/25813048 pubmed]
 
*47. '''Shin J*''', Yang S, Kim E, Kim CY, Shim H, Cho A, Kim H, Hwang S, Shim JE, '''Lee I**''', FlyNet: a versatile network prioritization server for the Drosophila community, '''''Nucleic Acids Research''''' 2015 Jul 1;43(W1):W91-7 [http://www.ncbi.nlm.nih.gov/pubmed/25943544 pubmed]
 
*46. '''Shim JE*''', Hwang S, '''Lee I**''', Pathway-Dependent Effectiveness of Network Algorithms for Gene Prioritization, '''''PLoS One''''' 2015 Jun 19;10(6):e0130589. [http://www.ncbi.nlm.nih.gov/pubmed/26091506 pubmed]
 
*45. '''Han H*''', Shim H, Shin D, Shim JE, Ko Y, Shin J, Kim H, Cho A, Kim E, Lee T, Kim H, Kim K, Yang S, Bae D, Yun A, Kim S, Kim CY, Cho HJ, Kang B, Shin S, '''Lee I**''', TRRUST: a reference database of human transcriptional regulatory interactions, '''''Scientific Reports''''' 2015 Jun 12;5:11432 [http://www.ncbi.nlm.nih.gov/pubmed/26066708 pubmed]
 
*44. '''Lee I**''', Mockler TC**, Editorial overview: Genome studies and molecular genetics: data-driven approaches to genotype-to-phenotype studies in crops, '''''Current Opinion in Plant Biology''''' 2015 Apr;24:iv-vi [http://www.ncbi.nlm.nih.gov/pubmed/25817324 pubmed]
 
*43. '''Lee I**''', Kim E, Marcotte EM**, Modes of Interaction between Individuals Dominate the Topologies of Real World Networks, '''''Plos One''''' 2015 Mar 20;10(3):e0121248 [http://www.ncbi.nlm.nih.gov/pubmed/25793969 Pubmed]
 
*42. '''Kim H*''', Jung KW*, Maeng S, Chen YL, Shin J, Shim JE, Hwang S, Janbon G, Kim T, Heitman J, Bahn YS**, '''Lee I**''', Network-assisted genetic dissection of pathogenicity and drug resistance in the opportunistic human pathogenic fungus Cryptococcus neoformans, '''''Scientific Reports''''' 2015 Mar 5;5:8767 [http://www.ncbi.nlm.nih.gov/pubmed/25739925 Pubmed] [http://f1000.com/prime/725377816?bd=1 {{#widget:AddHtml|content=<img src="http://cdn.f1000.com.s3.amazonaws.com/images/badges/badgef1000.gif" alt="Access the recommendation on F1000Prime" id="bg" width=70 />}}]
 
*41. '''Lee T*''', Kim H, '''Lee I**''', Network-assisted crop systems genetics: network inference and integrative analysis, '''''Current Opinion in Plant Biology''''' 2015 Mar 5;5:8767. [http://www.ncbi.nlm.nih.gov/pubmed/25698380 Pubmed]
 
*40. '''Kim T''', Dreher K, Nilo-Poyanco R, '''Lee I''', Fiehn O, Lange BM, Nikolau BJ, Sumner L, Welti R, Wurtele ES, Rhee SY, Patterns of metabolite changes identified from large-scale gene perturbations in Arabidopsis thaliana using a genome-scale metabolic network, '''''Plant Physiology''''' 2015 Apr;167(4):1685-98 [http://www.ncbi.nlm.nih.gov/pubmed/25670818 Pubmed]
 
*39. '''Kim H*''', Shim JE, Shin J, '''Lee I**''', EcoliNet: a database of cofunctional gene network for Escherichia coli, '''''Database''''' 2015 Feb 2;2015:bav001 [http://www.ncbi.nlm.nih.gov/pubmed/25650278 Pubmed]
 
*38. '''Lee T*''', Yang S, Kim E, Ko Y, Hwang S, Shin J, Shim JE, Shim H, Kim H, Kim C, '''Lee I**''', AraNet v2: an improved database of co-functional gene networks for the study of Arabidopsis thaliana and 27 other nonmodel plant species, '''''Nucleic Acids Research''''' 2015 Jan;43(Database issue):D996-1002 [http://www.ncbi.nlm.nih.gov/pubmed/25355510 Pubmed]
 

==2014 (5)-Sabbatical leave==
*37. '''Hwang S*''', Kim E, Yang S, Marcotte EM*, '''Lee I**''', MORPHIN: a web tool for human disease research by projecting model organism biology onto a human integrated gene network, '''''Nucleic Acids Res''''' 2014 Jul;42(Web server issue):W147-53. [http://www.ncbi.nlm.nih.gov/pubmed/24861622 Pubmed]
 
*36. '''Lee I**''', A showcase of future plant biology: moving towards next-generation plant genetics assisted by genome sequencing and systems biology, '''''Genome Biology''''', 2014 May 23;15(5):305. [http://www.ncbi.nlm.nih.gov/pubmed/25001400 Pubmed]
 
*35. '''Cho A*''', Shin J, Hwang S, Kim C, Shim H, Kim H, Kim H, '''Lee I**''' WormNet v3: a network-assisted hypothesis-generating server for Caenorhabditis elegans, '''''Nucleic Acids Res'''''. 2014 Jul;42(Web Server issue):W76-82 [http://www.ncbi.nlm.nih.gov/pubmed/24813450 Pubmed]
 
*34. '''Shin J*''', Lee T, Kim H, '''Lee I**''', Complementarity between distance- and probability-based methods of gene neighbourhood identification for pathway reconstruction. '''''Mol. BioSyst.''''', 2014 Jan;10(1):24-9 [http://www.ncbi.nlm.nih.gov/pubmed/24194096 Pubmed]
 
*33. '''Kim H*''', Shin J, Kim E, Kim H, Hwang S, Shim JE, '''Lee I**''', YeastNet v3: a public database of data-specific and integrated functional gene networks for Saccharomyces cerevisiae. '''''Nucleic Acids Research''''' 2014 Jan;42(Database issue):D731-6 [http://www.ncbi.nlm.nih.gov/pubmed/24165882 Pubmed]
 

==2013 (2)==
*32. '''Kim E*''', Kim H, '''Lee I**''', JiffyNet: a web-based instant protein network modeler for newly sequenced species. '''''Nucleic Acids Research''''' 2013 Jul;41(Web Server issue):W192-7 [http://www.ncbi.nlm.nih.gov/pubmed/23685435 Pubmed][http://nar.oxfordjournals.org/content/early/2013/05/17/nar.gkt419.full.pdf+html pdf]
 
*31. '''Lee I**''' , Network approaches to the genetic dissection of phenotypes in animals and humans. '''''Animal Cells and Systems''''' 2013 17(2)75-79 [http://www.tandfonline.com/doi/abs/10.1080/19768354.2013.789076 Link]
 

==2012 (3)==
*30. Wang PI*, '''Hwang S*''', Kincaid RP, Sullivan CS, '''Lee I**''', Marcotte EM**. RIDDLE: Reflective diffusion and local extension reveal functional associations for unannotated gene sets via proximity in a gene network. '''''Genome Biology''''' 2012 Dec 26;13(12):R125 [http://www.ncbi.nlm.nih.gov/pubmed/23268829 pubmed]
 
*29. Chae L**, '''Lee I**''', Shin J, Rhee SY**. Towards understanding how molecular networks evolve in plant. '''''Current Opinion in Plant Biology''''' 2012 Apr;15(2):177-84 [http://www.ncbi.nlm.nih.gov/pubmed/22280840 pubmed] [[media:CurrOpinPlantBiol 15-177.pdf|pdf]]
 
*28. Quanbeck SM1, Brachova L, Campbell AA, Guan X, Perera A, He K, Rhee SY, Bais P, Dickerson JA, Dixon P, Wohlgemuth G, Fiehn O, Barkan L, Lange I, Lange BM, '''Lee I''', Cortes D, Salazar C, Shuman J, Shulaev V, Huhman DV, Sumner LW, Roth MR, Welti R, Ilarslan H, Wurtele ES, Nikolau BJ. Metabolomics as a hypothesis-generating functional genomics tool for the annotation of Arabidopsis thaliana genes of “unknown function”. '''''Frontiers in Plant Science''''' 2012 Feb 10;3:15 [http://www.ncbi.nlm.nih.gov/pubmed/22645570/ pubmed][[media:Fpls-03-00015.pdf|pdf]]
 

==2011 (5)==
*27.'''Lee I**''', Seo YS, Coltrane D, Hwang S, Oh T, Marcotte EM**, Ronald PC**. Genetic dissection of the biotic stress response using a genome-scale gene network for rice. '''''PNAS''''' 2011 Nov 8;108(45):18548-53 [http://www.ncbi.nlm.nih.gov/pubmed/22042862 pubmed] [[media:PNAS-2011-Lee-18548-53.pdf|pdf]]
 
*26. '''Hwang S*''', Rhee SY**, Marcotte EM**, '''Lee I**'''. Systematic prediction of candidate gene function associated to phenotype traits using the probabilistic functional gene network of Arabidopsis thaliana. '''''Nature Protocols''''' 2011 Aug 25;6(9):1429-42 [http://www.ncbi.nlm.nih.gov/pubmed/21886106 pubmed][[media:2011.08.25.online.nprot.2011.372.pdf|pdf]]
 
*25. '''Lee I**''' , Probabilistic functional gene societies. '''''Progress in Biophysics and Molecular Biology''''' 2011 Aug;106(2):435-42 [http://www.ncbi.nlm.nih.gov/pubmed/21281658 pubmed] [[media:Publications_023_n.pdf|pdf]]
 
*24. '''Lee I**''', Blom UM*, Wang PI, Shim JE, Marcotte EM**. Prioritizing candidate disease genes by network-based boosting of genome-wide association data. '''''Genome Research''''' 2011 Jul;21(7):1109-21 [http://www.ncbi.nlm.nih.gov/pubmed/21536720 pubmed] [[media:Publications_025_n.pdf|pdf]]
 
*23. Seo YS*, Chern M, Bartley LE, Han M, Jung KH, '''Lee I''', Walia H, Richter T, Xu X, Cao P, Bai W, Ramanan R, Amonpant F, Arul L, Canlas PE, Ruan R, Park CJ, Chen X, Hwang S, Jeon JS, Ronald PC**. Towards establishment of a rice stress response interactome. '''''PLoS Genetics''''' 2011 Apr;7(4):e1002020 [http://www.ncbi.nlm.nih.gov/pubmed/21533176 pubmed] [[media:Publications_024.pdf|pdf]]
 

==2010 (4)==
*22. Huang N, '''Lee I''', Marcotte EM, Hurles ME. Characterising and predicting haploinsufficiency in the human genome, '''''PLoS Genet.''''' 2010 Oct 14;6(10):e1001154 [http://www.ncbi.nlm.nih.gov/pubmed/20976243 pubmed] [[media:Publications_022.pdf|pdf]]
 
*21. '''Kim E*''', Shin J, '''Lee I**'''. Assessment of effectiveness of the network-guided genetic screen, '''''Mol Biosyst.''''' 2010 Oct;6(10):1803-6 [http://www.ncbi.nlm.nih.gov/pubmed/20697632 pubmed] [[media:Publications_021.pdf|pdf]]
 
*20. '''Lee I**''', Lehner B**, Vavouri T, Shin J, Fraser AG**, Marcotte EM**. Andrew G. Fraser, and Edward M. Marcotte, Predicting genetic modifier loci using functional gene networks, '''''Genome Research''''' 2010 Aug;20(8):1143-53 ''co-corresponding author'' [http://www.ncbi.nlm.nih.gov/pubmed/20538624 pubmed] [[media:Publications_020.pdf|pdf]]
 
*19. '''Lee I**''', Ambaru B, Thakkar P, Marcotte EM**, Rhee SY**. Rational association of genes with traits using a genome-scale gene network for Arabidopsis thaliana '''''Nature Biotechnology''''', 2010 Feb;28(2):149-56 ''co-corresponding author'' [http://www.ncbi.nlm.nih.gov/pubmed/20118918 pubmed] [[media:Publications_019.pdf|pdf]]
 

==2009 (3)==
*18. '''Lee I*''', Marcotte EM. Effects of functional bias on supervised learning of a gene network model. '''In Methods in Molecular Biology''' Vol. 541, Computational Systems Biology (ed. R. Ireton, K. Montgomery, J. McDermott, R. Samudrala, R. Bumgarner). Totowa, THE HUMANA. 2009;541:463-75 [http://www.ncbi.nlm.nih.gov/pubmed/19381535 pubmed] [[media:Publications_018.pdf|pdf]]
 
*17. Li Z*, '''Lee I''', Moradi E, Hung NJ, Johnson AW, Marcotte EM. Rational Extension of the Ribosome Biogenesis Pathway Using Network-Guided Genetics, '''''PLoS Biology''''', 2009 Oct;7(10):e1000213 [http://www.ncbi.nlm.nih.gov/pubmed/19806183 pubmed] [[media:Publications_017.pdf|pdf]]
 
*16. Gray RS, Abitua PB, Wlodarczyk BJ, Szabo-Rogers HL, Blanchard O, '''Lee I''', Weiss GS, Liu KJ, Marcotte EM, Wallingford JB, Finnell RH. The planar cell polarity effector protein Fuz is essential for targeted membrane trafficking, ciliogenesis, and mouse embryonic development, '''''Nature Cell Biology''''' 2009 Oct;11(10):1225-32 '''''*Cover story''''' [http://www.ncbi.nlm.nih.gov/pubmed/19767740 pubmed] [[media:Publications_016.pdf|pdf]]
 

==2008 (3)==
*15. Lehner B**, '''Lee I**''' . Network-guided genetic screening: building, testing, and using gene networks to predict gene function. '''''Brief. Funct. Genomic'''''. 2008 May;7(3):217-27 '''''co-corresponding author''''' [http://www.ncbi.nlm.nih.gov/pubmed/18445637 pubmed] [[media:Publications_015.pdf|pdf]]
 
*14. '''Lee I*''', Marcotte EM. Integrating Functional Genomics Data. '''In Methods in Molecular Biology''' Vol. 453, Bioinformatics Vol. II (ed. Jonathan Keith). Totowa, THE HUMANA. 2008;453:267-78 [http://www.ncbi.nlm.nih.gov/pubmed/18712309 pubmed] [[media:Publications_014.pdf|pdf]]
 
*13. '''Lee I*''', Lehner B*, Crombie C, Wong W, Fraser AG, Marcotte EM. A single gene network accurately predicts phenotypic effects of gene perturbation in ''Caenorhabditis elegans''. '''''Nature Genetics''''' 2008 Feb;40(2):181-8 '''''*Cover story''''' [http://www.ncbi.nlm.nih.gov/pubmed/18223650 pubmed] [[media:Publications_013.pdf|pdf]] This paper was the subject of the following commentary (Natalie de Souza, Networking on organism. Nature Methods 5:217), and minireviews (Von Stetina S. E. and Mango S. E. Wormnet: a crystal ball for Caenorhabditis elegans. Genome Biology 9:226. Borgwardt K. Predicting phenotypic effects of gene perturbations in C. elegans using an integrated network model. BioEssays 30:707)
 

==Before 2007 (12)==
*12. '''Insuk Lee*''', Rammohan Narayanaswamy, Edward Marcotte. Bioinformatic prediction of yeast gene function. '''In METHOD IN MICROBIOLOGY''' Vol. 36, Yeast Gene Analysis (ed. Ian Stansfield and Mike Stark), Elsevier. p597-628, (2007) [[media:Publications_012.pdf|pdf]]
 
*11. Kris McGary, '''Insuk Lee''', Edward M. Marcotte. Broad network-based predictability of S. cerevisiae gene loss-of-function phenotypes. '''''Genome Biology''''' 8:R258 (2007) [http://www.ncbi.nlm.nih.gov/pubmed/18053250 pubmed] [[media:Publications_011.pdf|pdf]]
 
*10. '''Insuk Lee*''', Zhihua Li, and Edward M. Marcotte. An improved bias-reduced probabilistic functional gene network of baker’s yeast ''Saccharomyces cerevisiae''. '''''PLOS One''''' 2:e988 (2007) [http://www.ncbi.nlm.nih.gov/pubmed/17912365 pubmed] [[media:Publications_010.pdf|pdf]]
 
*9. Hart G. Traver, '''Insuk Lee''', Edward Marcotte. A high-accuracy consensus map of yeast protein complexes reveals modular nature of gene essentiality. '''''BMC Bioinformatic''''' 8:236 (2007) [http://www.ncbi.nlm.nih.gov/pubmed/17605818 pubmed] [[media:Publications_009.pdf|pdf]]
 
*8. '''Insuk Lee*''', Shailesh V. Date, Alex T. Adai, Edward Marcotte. A Probabilistic functional network of yeast genes. '''''Science''''' 306:1555-1558, (2004) [http://www.ncbi.nlm.nih.gov/pubmed/15567862 pubmed] [[media:Publications_008.pdf|pdf]]
 
*7. Bork, P., Jensen, L.J., Von Mering, C., Ramani, A.K., '''Lee I''', Marcotte, E.M. Protein interaction networks from yeast to human. '''''Curr. Opin. Struct. Biol.''''' 14:292-9, (2004) [http://www.ncbi.nlm.nih.gov/pubmed/15193308 pubmed] [[media:Publications_007.pdf|pdf]]
 
*6. '''Insuk Lee*''' and Rasika Harshey. Patterns of Sequence conservation at termini of LTR retrotransposons and DNA transposons in the human genome: Lessons from phage Mu. '''''Nucleic Acids Res.''''' 31:4531-4540, (2003)
 
*5. '''Insuk Lee*''' and Rasika Harshey. The conserved CA/TG motif at Mu termini: T specifies stable transpososome assembly. '''''J. Mol. Biol.''''' 330:261-275, (2003)
 
*4. '''Insuk Lee*''' and Rasika Harshey. Importance of the conserved CA dinucleotide at Mu termini. '''''J. Mol. Biol.''''' 314:433-444, (2001)
 
*3. Xue, Y., Bai, X., '''Lee, I.''', Kallstrom, G., Ho, J., Brown, J., Stevens, A., and Johnson, A. W. Saccharomyces cerevisiae RAI1 (YGL246c) is homologous to human DOM3Z and encodes a protein that binds the nuclear exoribonuclease Rat1p. '''''Mol. Cell. Biol.''''' 20:4006-4015, (2000)
 
*2. Liu, J., Gong, Y., Prakash, O., Wen, L., '''Lee, I'''., Huang J.,-K., and Krishnamoorthi, R. NMR studies of internal dynamics of serine proteinase protein inhibitors: Binding region mobilities of intact and reactive-site hydrolyzed CMTI-III of the squash family and comparison with those of counterparts of CMTI-V of the potato I family. '''''Protein Science''''' 7:132-141, (1998)
 
*1. Wen, L., '''Lee, I.''', Chen, G., Huang, J.,-K., Gong, Y., and Krishnamoorthi, R. Changing the inhibitory specificity and function of CMTI-V by site-directed mutagenesis. '''''Biochem. Biophys. Res. Commun.''''' 207:897-902, (1995)

Publications

2024-06-04T10:59:40Z

Bsb0613:

*[http://scholar.google.com/citations?user=3uHQIOkAAAAJ Google Scholar citation report]
*[https://www.ibric.org/bric/hanbitsa/treatise.do?srOrder=&fixInOut=&fixCate=&srCate=&srStartYear=&srStartMonth=&srEndYear=&srEndMonth=&srSearchType=name&srSearchVal=%EC%9D%B4%EC%9D%B8%EC%84%9D BRIC 한빛사논문]
*[https://yonsei.pure.elsevier.com/en/persons/in-suk-lee Yonsei University Researcher Portal]
*[https://pubmed.ncbi.nlm.nih.gov/?term=Insuk+Lee%5BAuthor%5D&sort=date Pubmed article list]
* '*' First authors; '**' Corresponding authors;
==2024==
*111. '''Nayeon Kim*''', Chan Yeong Kim, Summo Yang, Dongjin Park, Sang-Jun Ha, and '''Insuk Lee**''', MRGM: a mouse reference gut microbiome reveals a large functional discrepancy for gut bacteria of the same genus between mice and humans '''''Under Review''''' [https://www.biorxiv.org/content/10.1101/2021.10.24.465599v1 bioRxiv]
 
*110. Randall T. Mertens*, Peng Xiao, '''Seungbyn Baek''', Aditya Misra, Joseph M. Rone, Kisha N. Sivanathan, Davide Mangani, Adedamola S. Arojojoye, Samuel G. Awuah, '''Insuk Lee''', Guo-Ping Shi, Richard A. Flavell, Ana C. Anderson, Martin Hemberg, Roni Nowarski**, A metabolic switch orchestrated by IL-18 and cGAMP programs intestinal tolerance, '''''Immunity''''' In press
 
*109. '''Nayeon Kim*''', Junyeong Ma, Wonjong Kim, Jungyeon Kim, Peter Belenky** and '''Insuk Lee**''', Genome-Resolved Metagenomics: A Game Changer for Microbiome Medicine, '''''Experimental & Molecular Medicine''''' In press
 
*108. Chang Gon Kim*, Min Hee Hong*, Dahee Kim*, Brian Hyohyoung Lee*, Hyunwook Kim*, Chan-Young Ock*, Geoffrey Kelly, Yoon Ji Bang, Gamin Kim, Jung Eun Lee, Chaeyeon Kim, Se-Heon Kim, Hyun Jun Hong, Young Min Park, Nam Suk Sim, Heejung Park, Jin Woo Park, Chang Geol Lee, Kyung Hwan Kim, Goeun Park, Inkyung Jung, Dawoon Han, Jong Hoon Kim, '''Junha Cha''', '''Insuk Lee''', Mingu Kang, Heon Song, Chiyoon Oum, Seulki Kim, Sukjun Kim, Yoojoo Lim, Seunghee Kim-Schulze, Miriam Merad, Sun Och Yoon**, Hyun Je Kim**, Yoon Woo Koh**, Hye Ryun Kim**, A phase II open-label randomized clinical trial of preoperative durvalumab or durvalumab plus tremelimumab in resectable head and neck squamous cell carcinoma '''''Clinical Cancer Research''''' 2024 May 15;30(10):2097-2110[https://pubmed.ncbi.nlm.nih.gov/38457288/ PubMed]
 
*107. Da Hyun Kim*, '''Sungho Lee*''', Jisong Ahn, Jae Hwan Kim, Eunjung Lee**, '''Insuk Lee**''', and Sanguine Byun**, Transcriptomic and metabolomic analysis unveils nanoplastic-induced gut barrier dysfunction via STAT1/6 and ERK pathways '''''Environmental Research''''' 2024 May 15:249:118437 [https://pubmed.ncbi.nlm.nih.gov/38346486/ PubMed]
 
*106. Wei E Gordon*, '''Seungbyn Baek*''', Hai P Nguyen, Yien-Ming Kuo, Rachael Bradley, Sarah L. Fong, '''Nayeon Kim''', Alex Galazyuk, '''Insuk Lee''', Melissa Ingala, Nancy B Simmons, Tony Schountz, Lisa Cooper, Ilias Georgakopoulos-Soares, Martin Hemberg**, Nadav Ahituv**, Integrative single-cell characterization of a frugivorous and an insectivorous bat kidney and pancreas '''''Nature Communications''''' 2024 Jan 9;15(1):12 [https://pubmed.ncbi.nlm.nih.gov/38195585/ PubMed]
 

==2023 (3)==
*105. '''Junha Cha*''', Michael Lavi*, Junhan Kim, Noam Shomron**, '''Insuk Lee**''', Imputation of single-cell transcriptome data enables the reconstruction of networks predictive of breast cancer metastasis '''''Computational and Structural Biotechnology Journal''''' 2023 Mar 25;21:2296-2304 [https://pubmed.ncbi.nlm.nih.gov/37035549/ PubMed]
 
*104. '''Junha Cha*''', Jiwon Yu, Jae-Won Cho, Martin Hemberg**, '''Insuk Lee**''', scHumanNet: a single-cell network analysis platform for the study of cell-type specificity of disease genes '''''Nucleic Acids Research''''' 2023 Jan 25;51(2):e8 [https://pubmed.ncbi.nlm.nih.gov/36350625/ PubMed] [https://www.biorxiv.org/content/10.1101/2022.06.20.496836v1 bioRxiv]
 
*103. Myeong Joon Kim*, '''Kyungsoo Kim*''', Hyo Jin Park, Kyeong Hee Hong, Ji Hoon Oh, Jimin Son, '''Insuk Lee**''' and Sang-Jun Ha**, Deletion of PD-1 destabilizes the lineage identity and metabolic fitness of tumor-infiltrating regulatory T cells '''''Nature Immunology''''' 2023 Jan;24(1):148-161 [https://pubmed.ncbi.nlm.nih.gov/36577929/ PubMed]
 

==2022 (6)-Sabbatical leave==
*102. '''Chan Yeong Kim*, Junyeong Ma*''', and '''Insuk Lee**''' HiFi Metagenomic Sequencing Enables Assembly of Accurate and Complete Genomes from Human Gut Microbiota '''''Nature Communications''''' 2022 Oct 26;13(1):6367 [https://pubmed.ncbi.nlm.nih.gov/36289209/ PubMed] [https://www.biorxiv.org/content/10.1101/2022.02.09.479829v1 bioRxiv]
 
*101. '''Seungbyn Baek*''', Sunmo Yang, and '''Insuk Lee**''' COVID-GWAB: A Web-Based Prediction of COVID-19 Host Genes via Network Boosting of Genome-Wide Association Data '''''Biomolecules''''' 2022 Oct 12;12(10):1446 [https://pubmed.ncbi.nlm.nih.gov/36291657/ PubMed]
 
*100. Taeyun A Lee, '''Heonjong Han''', Ahsan Polash, Seok Keun Cho, Ji Won Lee, Eun A Ra, Eunhye Lee, Areum Park, Sujin Kang, Junhee L Choi, Ji Hyun Kim, Ji Eun Lee, Kyung-Won Min, Seong Wook Yang, Markus Hafner, '''Insuk Lee''', Je-Hyun Yoon, Sungwook Lee, Boyoun Park The nucleolus is the site for inflammatory RNA decay during infection '''''Nature Communications''''' 2022 Sep 3;13(1):5203 [https://pubmed.ncbi.nlm.nih.gov/36057640/ PubMed]
 
*99. Tae Gun Kang, Kee Woong Kwon, '''Kyungsoo Kim, Insuk Lee''', Myeong Joon Kim, Sang-Jun Ha, Sung Jae Shin Viral coinfection promotes tuberculosis immunopathogenesis by type I IFN signaling-dependent impediment of Th1 cell pulmonary influx, '''''Nature Communications''''' 2022 Jun 7;13(1):3155 [https://pubmed.ncbi.nlm.nih.gov/35672321/ PubMed]
 
*98. '''Jae-Won Cho*''', Hyo Sup Shim, Chang Young Lee, Seong Yong Park, Min Hee Hong, '''Insuk Lee**''', Hye Ryun Kim** The importance of enhancer methylation for epigenetic regulation of tumorigenesis in squamous lung cancer. '''''Experimental & Molecular Medicine''''' 2022 Jan 5;54(1):12-22 [https://pubmed.ncbi.nlm.nih.gov/34987166/ PubMed] [//netbiolab.org/wiki/Cho_etal_2021_Table_S1-10.zip Supplementary Table S1-10]
 
*97. '''Chan Yeong Kim*, Seungbyn Baek*''', Junha Cha, Sunmo Yang, Eiru Kim, Edward M. Marcotte, Traver Hart, and '''Insuk Lee**''' HumanNet v3: An improved database of human gene networks for disease research '''''Nucleic Acids Research''''' 2022 Jan 7;50(D1):D632-D639 [https://pubmed.ncbi.nlm.nih.gov/34747468/ PubMed]
 

==2021 (5)==
*96. '''Jae-Won Cho*''', Seyeon Park, Gamin Kim, Heonjong Han, Hyo Sup Shim, Sunhye Shin, Yong-Soo Bae, '''Seong Yong Park** Sang-Jun Ha**, Insuk Lee**, Hye Ryun Kim**''' Dysregulation of T FH-B-T RM lymphocyte cooperation is associated with unfavorable anti-PD-1 responses in EGFR-mutant lung cancer '''''Nature Communications''''' 2021 Oct 18;12(1):6068. [https://pubmed.ncbi.nlm.nih.gov/34663810/ PubMed]
 
*95. '''Chan Yeong Kim*, Muyoung Lee*''', Sunmo Yang, Kyungnam Kim, Dongeun Yong, Hye Ryun Kim, '''Insuk Lee**''' Human reference gut microbiome catalog including newly assembled genomes from under-represented Asian metagenomes '''''Genome Medicine''''' 2021 Aug 27;13(1):13 [https://pubmed.ncbi.nlm.nih.gov/34446072/ pubmed]
 
*94. Ho-Seok Lee, Ilyeong Choi, Young Jeon, Hee-Kyung Ahn, Huikyong Cho, JiWoo Kim, Jae-Hee Kim, Jung-Min Lee, SungHee Lee, Julian Bünting, Dong Hye Seo, '''Tak Lee''', Du-Hwa Lee, '''Insuk Lee''', Man-Ho Oh, Tae-Wuk Kim, Youssef Belkhadir, Hyun-Sook Pai Chaperone-like protein DAY plays critical roles in photomorphogenesis '''''Nature Communications''''' 2021 Jul 7;12(1):4194 [https://https://pubmed.ncbi.nlm.nih.gov/34234144/ pubmed]
 
*93. '''Jae-Won Cho*''', Jimin Son*, Sang-Jun Ha**, '''Insuk Lee**''', Systems biology analysis identifies TNFRSF9 as a functional marker of tumor-infiltrating regulatory T-cell enabling clinical outcome prediction in lung cancer '''''Computational and Structural Biotechnology Journal''''' 2021 Jan 21; 19:860-868 [https://pubmed.ncbi.nlm.nih.gov/33598101 pubmed]
 
*92. '''Tak Lee*''', '''Insuk Lee**''' Genome-wide Association Studies in Arabidopsis thaliana: Statistical Analysis and Network-Based Augmentation of Signals '''''Methods in Molecular Biology''''' 2020 Nov; 2200:187-210 [https://pubmed.ncbi.nlm.nih.gov/33175379 pubmed] [https://netbiolab.org/wiki/glucose_germrate.txt Supplementary File 1] [https://netbiolab.org/wiki/glucose_sig_accessions.txt Supplementary File 2]
 

==2020 (9)==
*91. '''Junha Cha*''', '''Insuk Lee**''' Single-cell Network Biology for Resolving Cellular Heterogeneity in Human Diseases '''''Experimental & Molecular Medicine''''' 2020 Nov;52(11):1798-1808 [https://pubmed.ncbi.nlm.nih.gov/33244151 pubmed]
 
*90. Jimin Son*, '''Jae-Won Cho*''', Hyo Jin Park, Jihyun Moon, Seyeon Park, Hoyoung Lee, Jeewon Lee, Ga min Kim, Su-Myeong Park, Sergio A. Lira, Andrew N. Mckenzie, Hye Young Kim, Cheol Yong Choi, Yong Taik Lim, Seong Yong Park, Hye Ryun Kim, Su-Hyung Park, Eui-Cheol Shin, '''Insuk Lee'''** & Sang-Jun Ha**, Tumor-Infiltrating Regulatory T Cell Accumulation in the Tumor Microenvironment is Mediated by IL33/ST2 Signaling '''''Cancer Immunology Research''''' 2020 Nov; 8(11):1393-1406 [https://pubmed.ncbi.nlm.nih.gov/32878747 pubmed] '''Spotlighted''' by [https://acir.org/journal-articles/cancer-immunobiology/immune-cell-biology?entryId=26357 Accelerating Cancer Immunotherapy Research]
 
*89. '''Jae-Won Cho*''', Min Hee Hong, Sang-Jun Ha, Young-Joon Kim, Byoung Chul Cho, '''Insuk Lee**''', and Hye Ryun Kim** Genome-wide identification of differentially methylated promoters and enhancers associated with response to anti-PD-1 therapy in non-small cell lung cancer [https://netbiolab.org/wiki/Supplementary_Table_S1-6.zip Supplementary File] '''''Experimental & Molecular Medicine''''' 2020 Sep 02;52(9):1550-1563 [https://pubmed.ncbi.nlm.nih.gov/32879421 pubmed]
 
*88. '''Seungbyn Baek*''', '''Insuk Lee**''' Single-cell ATAC sequencing analysis: from data preprocessing to hypothesis generation '''''Computational and Structural Biotechnology Journal''''' 2020 June 28;18:1429-1439 [https://pubmed.ncbi.nlm.nih.gov/32637041 pubmed]
 
*87. '''Eiru Kim*, Dasom Bae*, Sunmo Yang*''', Gunhwan Ko, Sungho Lee, Byungwook Lee**, '''Insuk Lee**'''. BiomeNet: A database for construction and analysis of functional interaction networks for any species with a sequenced genome '''''Bioinformatics''''' 2020 Mar 1:36(5):1584-1589 [https://www.ncbi.nlm.nih.gov/pubmed/31599923 pubmed]
 
*86. '''Kyungsoo Kim*''', Seyeon Park*, Seong Yong Park, Gamin Kim, Su Myeong Park, Jae-Won Cho, Da Hee Kim, Young Min Park, Yoon Woo Koh, Hye Ryun Kim, Sang-Jun Ha** and '''Insuk Lee**''', Single-cell transcriptome analysis reveals TOX as a promoting factor for T cell exhaustion and a predictor for anti-PD-1 responses in human cancer '''''Genome Medicine''''' 2020 Feb 28;12:22 [https://pubmed.ncbi.nlm.nih.gov/32111241 pubmed] '''Recommended''' by [https://facultyopinions.com/prime/737459051 F1000]
 
*85. '''Sungho Lee*, Tak Lee*''', Sunmo Yang and '''Insuk Lee**''', BarleyNet: a network-based functional omics analysis server for cultivated barley, Hordeum vulgare L. '''''Frontiers in Plant Science''''' 2020 Feb 18;11:98 [https://pubmed.ncbi.nlm.nih.gov/32133024/ pubmed]
 
*84. '''Kyungsoo Kim*''', Sunmo Yang, Sang-Jun Ha, '''Insuk Lee**''', VirtualCytometry: a webserver for evaluating immune cell differentiation using single-cell RNA sequencing data '''''Bioinformatics''''' 2020 Jan 15;36(2):546-551 [https://www.ncbi.nlm.nih.gov/pubmed/31373613 pubmed]
 
*83. '''Jung Eun Shim*, Insuk Lee**''', Construction of functional protein networks using domain profile associations, '''''Methods in Molecular Biology''''' 2020 Jan;2074:35-44 [https://www.ncbi.nlm.nih.gov/pubmed/31583628 pubmed]
 

==2019 (6)==
*82. '''Han H*''', Lee S, '''Lee I**''', NGSEA: network-based gene set enrichment analysis for interpreting gene expression phenotypes with functional gene sets '''''Mol Cells''''' 2019 Aug 31;42(8):579-588. [https://www.ncbi.nlm.nih.gov/pubmed/31307154 pubmed]
 
*81. '''Lee T*, Lee S*''', Yang S, '''Lee I**'''. MaizeNet: A co-functional network for network-assisted systems genetics in Zea mays '''''The Plant Journal''''' 2019 Aug 99(3):571-582 [https://www.ncbi.nlm.nih.gov/pubmed/31006149 pubmed]
 
*80. '''Lee M*''', Pinto NA*, Kim CY, Yang S, D'Souza R, Yong D**, Lee I**. Network integrative genomic and transcriptomic analysis of carbapenem-resistant Klebsiella pneumoniae strains identifies genes for antibiotic-resistance and virulence '''''mSystems''''' 2019 Aug;4(4):e00202-19. [https://www.ncbi.nlm.nih.gov/pubmed/31117026 pubmed]
 
*79. '''Shim JE*''', Kim JH, Shin J, Lee JE, '''Lee I**''' Pathway-specific protein domains are predictive for human diseases '''''PLOS Computational Biology''''' 2019 May 10;15(5):e1007052 [https://www.ncbi.nlm.nih.gov/pubmed/31075101 pubmed]
 
*78. '''Kim H*''', Joe A*, Lee M, Yang S, Ma X, Ronald PC**, '''Lee I**'''. A Genome-Scale Co-Functional Network of Xanthomonas Genes Can Accurately Reconstruct Regulatory Circuits Controlled by Two-Component Signaling Systems '''''Mol Cells''''' 2019 Feb 28;42(2):166-174 [https://www.ncbi.nlm.nih.gov/pubmed/30759970 pubmed]
 
*77. '''Hwang S*, Kim CY*''', Yang S, Kim E, Hart T, Marcotte EM, '''Lee I**'''. HumanNet v2: human gene networks for disease research '''''Nucleic Acids Research''''' 2019 Jan 8;47(D1):D573–D580.[https://www.ncbi.nlm.nih.gov/pubmed/30418591 pubmed]
 

==2018 (5)==
*76. '''Han H*''', Lehner B, '''Lee I**''', Cancer gene discovery by network analysis of somatic mutations using the MUFFINN server, '''''Methods in Molecular Biology''''' 2018 Dec 13; 1907:37-50.[https://www.ncbi.nlm.nih.gov/pubmed/30542989 pubmed]
 
*75. '''Kim CY*''', Lee M, Lee K, Yoon SS, '''Lee I**''', Network-based genetic investigation of virulence-associated phenotypes in methicillin-resistant Staphylococcus aureus, '''''Scientific Reports''''' 2018 July 17; 8:10796. [https://www.ncbi.nlm.nih.gov/pubmed/30018396 pubmed]
 
*74. Lim KT, Kim J, Hwang SI, Zhang L, Han H, Bae D, Kim KP, Hu YP, Schöler HR, '''Lee I''', Hui L, Han DW, Direct Conversion of Mouse Fibroblasts into Cholangiocyte Progenitor Cells. '''''Stem Cell Reports''''' 2018 Mar 27. 10:1-15.[https://www.ncbi.nlm.nih.gov/pubmed/29606616 pubmed]
 
*73. '''Lee T*, Lee I**''', araGWAB: A web application for network-based boosting of genome-wide association signals in Arabidopsis '''''Scientific Reports''''' 2018 Feb 13;8(1):2925.[https://www.ncbi.nlm.nih.gov/pubmed/29440686 pubmed]
 
*72. '''Han H*''', Cho JW, Lee S, Yun A, Kim H, Bae D, Yang S, Kim CY, Lee M, Kim E, Lee S, Kang B, Jeong D, Kim Y, Jeon HN, Jung H, Nam S, Chung M, Kim JH, '''Lee I**''', TRRUST v2: An expanded reference database of human and mouse transcriptional regulatory interactions '''''Nucleic Acids Research''''' 2018 Jan 4;46(D1):D380–D386.[https://www.ncbi.nlm.nih.gov/pubmed/29087512 pubmed]
 

==2017 (10)==
*71. '''Kim CY*, Lee I**''', Functional gene networks based on the gene neighborhood in metagenomes, '''''Animal Cells and Systems''''' 2017 Sept 29; 21:301-306;
 
*70. '''Lee T*, Hwang S*''', Kim CY, Shim H, Kim H, Ronald P**, Marcotte E**, '''Lee I**''', WheatNet: A genome-scale functional network for hexaploid bread wheat, Triticum aestivum, '''''Molecular Plant.''''' 2017 Aug 7;10(8):1133-1136; [https://www.ncbi.nlm.nih.gov/pubmed/28450181 pubmed]
 
*69. '''Lee T*, Lee I**''', AraNet: A Network Biology Server for Arabidopsis thaliana and Other Non-Model Plant Species, '''''Methods in Molecular Biology''''' 2017 June 17; 1629:225-238; [https://www.ncbi.nlm.nih.gov/pubmed/28623589 pubmed]
 
*68. '''Shim JE*''', Bang C, Yang S, Lee T, Hwang S, Kim CY, Singh-Blom M, Marcotte E, '''Lee I**''', GWAB: a web server for the network-based boosting of human genome-wide association data, '''''Nucleic Acids Research''''' 2017 July 3; 45 (W1):W154-W161; [https://www.ncbi.nlm.nih.gov/pubmed/28449091 pubmed]
 
*67. '''Kim E*, Lee I**''', Network-Based Gene Function Prediction in Mouse and Other Model Vertebrates Using MouseNet Server, '''''Methods in Molecular Biology''''' 2017 April 28; 1611:183-198; [https://www.ncbi.nlm.nih.gov/pubmed/28451980 pubmed] [http://f1000.com/prime/727562216?bd=1 {{#widget:AddHtml|content=<img src="http://cdn.f1000.com.s3.amazonaws.com/images/badges/badgef1000.gif" alt="Access the recommendation on F1000Prime" id="bg" width=70 />}}]
 
*66. '''Shim JE*, Lee T*, Lee I**''', From sequencing data to gene functions: co-functional network approaches, '''''Animal Cells and Systems''''' 2017 April 15; 20:77-83; [https://www.ncbi.nlm.nih.gov/pubmed/30460054 pubmed]
 
*65. '''Kim H*''', Kim BS*, Shim JE, Hwang S, Yang S, Kim E, Iyer-Pascuzzi AS**, '''Lee I**''', TomatoNet: A genome-wide co-functional network for unveiling complex traits of tomato, a model crop for fleshy fruits, '''''Molecular Plant.''''' 2017 April 3; 10:652–655; [https://www.ncbi.nlm.nih.gov/pubmed/27913317 pubmed]
 
*64. Jang K, Kim K, Cho A, '''Lee I''', Choi JK, Network perturbation by recurrent regulatory variants in cancer, '''''PLoS Compt. Biol.''''' 2017 Mar 23;13(3):e1005449; [https://www.ncbi.nlm.nih.gov/pubmed/28333928 pubmed]
 
*63. Yang S*, Kim CY, Hwang S, Kim E, Kim H, Shim H, '''Lee I**''', COEXPEDIA: exploring biomedical hypotheses via co-expressions associated with medical subject headings (MeSH), '''''Nucleic Acids Research''''' 2017 Jan 4; 45(D1):D389-D396 [https://www.ncbi.nlm.nih.gov/pubmed/27679477 pubmed]
 
*62. Kim E*, Hwang S, '''Lee I**''', SoyNet: a database of co-functional networks for soybean Glycine max, '''''Nucleic Acids Research''''' 2017 Jan 4; 45(D1):D1082-D1089. [http://www.ncbi.nlm.nih.gov/pubmed/27492285 pubmed]
 

==2016 (10)==
*61. '''Shin J**, Lee I**''', Construction of Functional Gene Networks Using Phylogenetic Profiles. '''''Methods in Molecular Biology''''' 2016 Nov 29; 1526:87-98 [http://www.ncbi.nlm.nih.gov/pubmed/27896737 pubmed]
 
*60. '''Shim H*''', Kim JH*, Kim CY*, Hwang S, Kim H, Yang S, Lee JE**, '''Lee I**''', Function-driven discovery of disease genes in zebraﬁsh using an integrated genomics big data resource,'''''Nucleic Acids Research''''' 2016 Nov 16;44(20):9611-9623 [http://www.ncbi.nlm.nih.gov/pubmed/27903883 pubmed]
 
*59. '''Shim JE*, Lee I**''' Weighted mutual information analysis substantially improves domain-based functional network models. '''''Bioinformatics''''' 2016 Sep 15;32(18):2824-30 [http://www.ncbi.nlm.nih.gov/pubmed/27207946 pubmed]
 
*58. Lee JY*, '''Kim E*''', Choi SM, Kim DW, Kim KP, '''Lee I**''', Kim HS** Microvesicles from brain-extract—treated mesenchymal stem cells improve neurological functions in a rat model of ischemic stroke. '''''Scientific Reports''''' 2016 Sep 9; 6:33038 [http://www.ncbi.nlm.nih.gov/pubmed/27609711 pubmed]
 
*57. Kim SM, Kim JW, Kwak TH, Park SW, Kim KP, Park H, Lim KT, Kang K, Kim J, Yang JH, Han H, '''Lee I''', Hyun JK, Bae YM, Schöler HR, Lee HT, Han DW Generation of Integration-free Induced Neural Stem Cells from Mouse Fibroblasts. '''''J Biol Chem'''''. 2016 Jul 1. 291(27):14199-212. [http://www.ncbi.nlm.nih.gov/pubmed/27189941 pubmed]
 
*56. '''Cho A*''', Shim JE, Kim E, Supek F, Lehner B**, '''Lee I**'''. MUFFINN: cancer gene discovery via network analysis of somatic mutation data. '''''Genome Biology''''' 2016 June 23;17(1):129 [http://www.ncbi.nlm.nih.gov/pubmed/27333808 pubmed]
 
*55. '''Hwang S*, Kim CY*''', Ji SG, Go J, Kim H, Yang S, Kim HJ, Cho A, Yoon SS**, '''Lee I**'''. Network-assisted investigation of virulence and antibiotic-resistance systems in Pseudomonas aeruginosa.'''''Scientific Reports''''' 2016 May 19; 6:26223. [http://www.ncbi.nlm.nih.gov/pubmed/27194047 pubmed]
 
*54. Yoon MY, Min KB, Lee KM, Yoon Y, Kim Y, Oh YT, Lee K, Chun J, Kim BY, Yoon SH, '''Lee I''', Kim CY, Yoon SS,. A single gene of a commensal microbe affects host susceptibility to enteric infection.'''''Nature Communications''''' 2016 May 13; 7:11606. [http://www.ncbi.nlm.nih.gov/pubmed/27173141 pubmed]
 
*53. Jo J*, Hwang S*, Kim HJ*, Hong S, Lee JE, Lee SG, Baek A, Han H, Lee JI, '''Lee I**''', Lee DR**. An integrated systems biology approach identifies positive cofactor 4 as a factor that increases reprogramming efficiency.'''''Nucleic Acids Research''''' 2016 Feb 18; 44(3):1203-15.[http://www.ncbi.nlm.nih.gov/pubmed/26740582 pubmed]
 
*52. Kim E*, Hwang S, Kim H, Shim H, Kang B, Yang S, Shim JH, Shin SY, Marcotte EM, '''Lee I**'''. MouseNet v2: A database of gene networks for studying the laboratory mouse and eight other model vertebrates,'''''Nucleic Acids Research''''' 2016 Jan 4;44(D1):D848-54.[http://www.ncbi.nlm.nih.gov/pubmed/26527726 pubmed]
 

==2015 (14)==
*51. '''Hwang S*, Kim E*, Lee I**''', Marcotte EM**, Systematic comparison of variant calling pipelines using gold standard personal exome variants, '''''Scientific Reports''''' 2015 Dec 5:17875 [http://www.ncbi.nlm.nih.gov/pubmed/?term=26639839 pubmed]
 
*50. '''Shin J*, Lee I**''', Co-Inheritance Analysis within the Domains of Life Substantially Improves Network Inference by Phylogenetic Profiling, '''''Plos One''''' 2015 Sep 22;10(9):e0139006 [http://www.ncbi.nlm.nih.gov/pubmed/26394049 pubmed]
 
*49. '''Shim JE*, Lee I**''', Network-assisted approaches for human disease research, '''''Animal Cells and Systems''''' 2015 Aug 19(4):231-235 [http://www.tandfonline.com/doi/abs/10.1080/19768354.2015.1074108 Link] [[media:19768354.2015.1074108.pdf|pdf]]
 
*48. '''Lee T*''', Oh T, Yang S, Shin J, Hwang S, Kim CY, Kim H, Shim H, Shim JE, Ronald PC**, '''Lee I**''', RiceNet v2: an improved network prioritization server for rice genes, '''''Nucleic Acids Research''''' 2015 Jul 1;43(W1):W122-7 [http://www.ncbi.nlm.nih.gov/pubmed/25813048 pubmed]
 
*47. '''Shin J*''', Yang S, Kim E, Kim CY, Shim H, Cho A, Kim H, Hwang S, Shim JE, '''Lee I**''', FlyNet: a versatile network prioritization server for the Drosophila community, '''''Nucleic Acids Research''''' 2015 Jul 1;43(W1):W91-7 [http://www.ncbi.nlm.nih.gov/pubmed/25943544 pubmed]
 
*46. '''Shim JE*''', Hwang S, '''Lee I**''', Pathway-Dependent Effectiveness of Network Algorithms for Gene Prioritization, '''''PLoS One''''' 2015 Jun 19;10(6):e0130589. [http://www.ncbi.nlm.nih.gov/pubmed/26091506 pubmed]
 
*45. '''Han H*''', Shim H, Shin D, Shim JE, Ko Y, Shin J, Kim H, Cho A, Kim E, Lee T, Kim H, Kim K, Yang S, Bae D, Yun A, Kim S, Kim CY, Cho HJ, Kang B, Shin S, '''Lee I**''', TRRUST: a reference database of human transcriptional regulatory interactions, '''''Scientific Reports''''' 2015 Jun 12;5:11432 [http://www.ncbi.nlm.nih.gov/pubmed/26066708 pubmed]
 
*44. '''Lee I**''', Mockler TC**, Editorial overview: Genome studies and molecular genetics: data-driven approaches to genotype-to-phenotype studies in crops, '''''Current Opinion in Plant Biology''''' 2015 Apr;24:iv-vi [http://www.ncbi.nlm.nih.gov/pubmed/25817324 pubmed]
 
*43. '''Lee I**''', Kim E, Marcotte EM**, Modes of Interaction between Individuals Dominate the Topologies of Real World Networks, '''''Plos One''''' 2015 Mar 20;10(3):e0121248 [http://www.ncbi.nlm.nih.gov/pubmed/25793969 Pubmed]
 
*42. '''Kim H*''', Jung KW*, Maeng S, Chen YL, Shin J, Shim JE, Hwang S, Janbon G, Kim T, Heitman J, Bahn YS**, '''Lee I**''', Network-assisted genetic dissection of pathogenicity and drug resistance in the opportunistic human pathogenic fungus Cryptococcus neoformans, '''''Scientific Reports''''' 2015 Mar 5;5:8767 [http://www.ncbi.nlm.nih.gov/pubmed/25739925 Pubmed] [http://f1000.com/prime/725377816?bd=1 {{#widget:AddHtml|content=<img src="http://cdn.f1000.com.s3.amazonaws.com/images/badges/badgef1000.gif" alt="Access the recommendation on F1000Prime" id="bg" width=70 />}}]
 
*41. '''Lee T*''', Kim H, '''Lee I**''', Network-assisted crop systems genetics: network inference and integrative analysis, '''''Current Opinion in Plant Biology''''' 2015 Mar 5;5:8767. [http://www.ncbi.nlm.nih.gov/pubmed/25698380 Pubmed]
 
*40. '''Kim T''', Dreher K, Nilo-Poyanco R, '''Lee I''', Fiehn O, Lange BM, Nikolau BJ, Sumner L, Welti R, Wurtele ES, Rhee SY, Patterns of metabolite changes identified from large-scale gene perturbations in Arabidopsis thaliana using a genome-scale metabolic network, '''''Plant Physiology''''' 2015 Apr;167(4):1685-98 [http://www.ncbi.nlm.nih.gov/pubmed/25670818 Pubmed]
 
*39. '''Kim H*''', Shim JE, Shin J, '''Lee I**''', EcoliNet: a database of cofunctional gene network for Escherichia coli, '''''Database''''' 2015 Feb 2;2015:bav001 [http://www.ncbi.nlm.nih.gov/pubmed/25650278 Pubmed]
 
*38. '''Lee T*''', Yang S, Kim E, Ko Y, Hwang S, Shin J, Shim JE, Shim H, Kim H, Kim C, '''Lee I**''', AraNet v2: an improved database of co-functional gene networks for the study of Arabidopsis thaliana and 27 other nonmodel plant species, '''''Nucleic Acids Research''''' 2015 Jan;43(Database issue):D996-1002 [http://www.ncbi.nlm.nih.gov/pubmed/25355510 Pubmed]
 

==2014 (5)-Sabbatical leave==
*37. '''Hwang S*''', Kim E, Yang S, Marcotte EM*, '''Lee I**''', MORPHIN: a web tool for human disease research by projecting model organism biology onto a human integrated gene network, '''''Nucleic Acids Res''''' 2014 Jul;42(Web server issue):W147-53. [http://www.ncbi.nlm.nih.gov/pubmed/24861622 Pubmed]
 
*36. '''Lee I**''', A showcase of future plant biology: moving towards next-generation plant genetics assisted by genome sequencing and systems biology, '''''Genome Biology''''', 2014 May 23;15(5):305. [http://www.ncbi.nlm.nih.gov/pubmed/25001400 Pubmed]
 
*35. '''Cho A*''', Shin J, Hwang S, Kim C, Shim H, Kim H, Kim H, '''Lee I**''' WormNet v3: a network-assisted hypothesis-generating server for Caenorhabditis elegans, '''''Nucleic Acids Res'''''. 2014 Jul;42(Web Server issue):W76-82 [http://www.ncbi.nlm.nih.gov/pubmed/24813450 Pubmed]
 
*34. '''Shin J*''', Lee T, Kim H, '''Lee I**''', Complementarity between distance- and probability-based methods of gene neighbourhood identification for pathway reconstruction. '''''Mol. BioSyst.''''', 2014 Jan;10(1):24-9 [http://www.ncbi.nlm.nih.gov/pubmed/24194096 Pubmed]
 
*33. '''Kim H*''', Shin J, Kim E, Kim H, Hwang S, Shim JE, '''Lee I**''', YeastNet v3: a public database of data-specific and integrated functional gene networks for Saccharomyces cerevisiae. '''''Nucleic Acids Research''''' 2014 Jan;42(Database issue):D731-6 [http://www.ncbi.nlm.nih.gov/pubmed/24165882 Pubmed]
 

==2013 (2)==
*32. '''Kim E*''', Kim H, '''Lee I**''', JiffyNet: a web-based instant protein network modeler for newly sequenced species. '''''Nucleic Acids Research''''' 2013 Jul;41(Web Server issue):W192-7 [http://www.ncbi.nlm.nih.gov/pubmed/23685435 Pubmed][http://nar.oxfordjournals.org/content/early/2013/05/17/nar.gkt419.full.pdf+html pdf]
 
*31. '''Lee I**''' , Network approaches to the genetic dissection of phenotypes in animals and humans. '''''Animal Cells and Systems''''' 2013 17(2)75-79 [http://www.tandfonline.com/doi/abs/10.1080/19768354.2013.789076 Link]
 

==2012 (3)==
*30. Wang PI*, '''Hwang S*''', Kincaid RP, Sullivan CS, '''Lee I**''', Marcotte EM**. RIDDLE: Reflective diffusion and local extension reveal functional associations for unannotated gene sets via proximity in a gene network. '''''Genome Biology''''' 2012 Dec 26;13(12):R125 [http://www.ncbi.nlm.nih.gov/pubmed/23268829 pubmed]
 
*29. Chae L**, '''Lee I**''', Shin J, Rhee SY**. Towards understanding how molecular networks evolve in plant. '''''Current Opinion in Plant Biology''''' 2012 Apr;15(2):177-84 [http://www.ncbi.nlm.nih.gov/pubmed/22280840 pubmed] [[media:CurrOpinPlantBiol 15-177.pdf|pdf]]
 
*28. Quanbeck SM1, Brachova L, Campbell AA, Guan X, Perera A, He K, Rhee SY, Bais P, Dickerson JA, Dixon P, Wohlgemuth G, Fiehn O, Barkan L, Lange I, Lange BM, '''Lee I''', Cortes D, Salazar C, Shuman J, Shulaev V, Huhman DV, Sumner LW, Roth MR, Welti R, Ilarslan H, Wurtele ES, Nikolau BJ. Metabolomics as a hypothesis-generating functional genomics tool for the annotation of Arabidopsis thaliana genes of “unknown function”. '''''Frontiers in Plant Science''''' 2012 Feb 10;3:15 [http://www.ncbi.nlm.nih.gov/pubmed/22645570/ pubmed][[media:Fpls-03-00015.pdf|pdf]]
 

==2011 (5)==
*27.'''Lee I**''', Seo YS, Coltrane D, Hwang S, Oh T, Marcotte EM**, Ronald PC**. Genetic dissection of the biotic stress response using a genome-scale gene network for rice. '''''PNAS''''' 2011 Nov 8;108(45):18548-53 [http://www.ncbi.nlm.nih.gov/pubmed/22042862 pubmed] [[media:PNAS-2011-Lee-18548-53.pdf|pdf]]
 
*26. '''Hwang S*''', Rhee SY**, Marcotte EM**, '''Lee I**'''. Systematic prediction of candidate gene function associated to phenotype traits using the probabilistic functional gene network of Arabidopsis thaliana. '''''Nature Protocols''''' 2011 Aug 25;6(9):1429-42 [http://www.ncbi.nlm.nih.gov/pubmed/21886106 pubmed][[media:2011.08.25.online.nprot.2011.372.pdf|pdf]]
 
*25. '''Lee I**''' , Probabilistic functional gene societies. '''''Progress in Biophysics and Molecular Biology''''' 2011 Aug;106(2):435-42 [http://www.ncbi.nlm.nih.gov/pubmed/21281658 pubmed] [[media:Publications_023_n.pdf|pdf]]
 
*24. '''Lee I**''', Blom UM*, Wang PI, Shim JE, Marcotte EM**. Prioritizing candidate disease genes by network-based boosting of genome-wide association data. '''''Genome Research''''' 2011 Jul;21(7):1109-21 [http://www.ncbi.nlm.nih.gov/pubmed/21536720 pubmed] [[media:Publications_025_n.pdf|pdf]]
 
*23. Seo YS*, Chern M, Bartley LE, Han M, Jung KH, '''Lee I''', Walia H, Richter T, Xu X, Cao P, Bai W, Ramanan R, Amonpant F, Arul L, Canlas PE, Ruan R, Park CJ, Chen X, Hwang S, Jeon JS, Ronald PC**. Towards establishment of a rice stress response interactome. '''''PLoS Genetics''''' 2011 Apr;7(4):e1002020 [http://www.ncbi.nlm.nih.gov/pubmed/21533176 pubmed] [[media:Publications_024.pdf|pdf]]
 

==2010 (4)==
*22. Huang N, '''Lee I''', Marcotte EM, Hurles ME. Characterising and predicting haploinsufficiency in the human genome, '''''PLoS Genet.''''' 2010 Oct 14;6(10):e1001154 [http://www.ncbi.nlm.nih.gov/pubmed/20976243 pubmed] [[media:Publications_022.pdf|pdf]]
 
*21. '''Kim E*''', Shin J, '''Lee I**'''. Assessment of effectiveness of the network-guided genetic screen, '''''Mol Biosyst.''''' 2010 Oct;6(10):1803-6 [http://www.ncbi.nlm.nih.gov/pubmed/20697632 pubmed] [[media:Publications_021.pdf|pdf]]
 
*20. '''Lee I**''', Lehner B**, Vavouri T, Shin J, Fraser AG**, Marcotte EM**. Andrew G. Fraser, and Edward M. Marcotte, Predicting genetic modifier loci using functional gene networks, '''''Genome Research''''' 2010 Aug;20(8):1143-53 ''co-corresponding author'' [http://www.ncbi.nlm.nih.gov/pubmed/20538624 pubmed] [[media:Publications_020.pdf|pdf]]
 
*19. '''Lee I**''', Ambaru B, Thakkar P, Marcotte EM**, Rhee SY**. Rational association of genes with traits using a genome-scale gene network for Arabidopsis thaliana '''''Nature Biotechnology''''', 2010 Feb;28(2):149-56 ''co-corresponding author'' [http://www.ncbi.nlm.nih.gov/pubmed/20118918 pubmed] [[media:Publications_019.pdf|pdf]]
 

==2009 (3)==
*18. '''Lee I*''', Marcotte EM. Effects of functional bias on supervised learning of a gene network model. '''In Methods in Molecular Biology''' Vol. 541, Computational Systems Biology (ed. R. Ireton, K. Montgomery, J. McDermott, R. Samudrala, R. Bumgarner). Totowa, THE HUMANA. 2009;541:463-75 [http://www.ncbi.nlm.nih.gov/pubmed/19381535 pubmed] [[media:Publications_018.pdf|pdf]]
 
*17. Li Z*, '''Lee I''', Moradi E, Hung NJ, Johnson AW, Marcotte EM. Rational Extension of the Ribosome Biogenesis Pathway Using Network-Guided Genetics, '''''PLoS Biology''''', 2009 Oct;7(10):e1000213 [http://www.ncbi.nlm.nih.gov/pubmed/19806183 pubmed] [[media:Publications_017.pdf|pdf]]
 
*16. Gray RS, Abitua PB, Wlodarczyk BJ, Szabo-Rogers HL, Blanchard O, '''Lee I''', Weiss GS, Liu KJ, Marcotte EM, Wallingford JB, Finnell RH. The planar cell polarity effector protein Fuz is essential for targeted membrane trafficking, ciliogenesis, and mouse embryonic development, '''''Nature Cell Biology''''' 2009 Oct;11(10):1225-32 '''''*Cover story''''' [http://www.ncbi.nlm.nih.gov/pubmed/19767740 pubmed] [[media:Publications_016.pdf|pdf]]
 

==2008 (3)==
*15. Lehner B**, '''Lee I**''' . Network-guided genetic screening: building, testing, and using gene networks to predict gene function. '''''Brief. Funct. Genomic'''''. 2008 May;7(3):217-27 '''''co-corresponding author''''' [http://www.ncbi.nlm.nih.gov/pubmed/18445637 pubmed] [[media:Publications_015.pdf|pdf]]
 
*14. '''Lee I*''', Marcotte EM. Integrating Functional Genomics Data. '''In Methods in Molecular Biology''' Vol. 453, Bioinformatics Vol. II (ed. Jonathan Keith). Totowa, THE HUMANA. 2008;453:267-78 [http://www.ncbi.nlm.nih.gov/pubmed/18712309 pubmed] [[media:Publications_014.pdf|pdf]]
 
*13. '''Lee I*''', Lehner B*, Crombie C, Wong W, Fraser AG, Marcotte EM. A single gene network accurately predicts phenotypic effects of gene perturbation in ''Caenorhabditis elegans''. '''''Nature Genetics''''' 2008 Feb;40(2):181-8 '''''*Cover story''''' [http://www.ncbi.nlm.nih.gov/pubmed/18223650 pubmed] [[media:Publications_013.pdf|pdf]] This paper was the subject of the following commentary (Natalie de Souza, Networking on organism. Nature Methods 5:217), and minireviews (Von Stetina S. E. and Mango S. E. Wormnet: a crystal ball for Caenorhabditis elegans. Genome Biology 9:226. Borgwardt K. Predicting phenotypic effects of gene perturbations in C. elegans using an integrated network model. BioEssays 30:707)
 

==Before 2007 (12)==
*12. '''Insuk Lee*''', Rammohan Narayanaswamy, Edward Marcotte. Bioinformatic prediction of yeast gene function. '''In METHOD IN MICROBIOLOGY''' Vol. 36, Yeast Gene Analysis (ed. Ian Stansfield and Mike Stark), Elsevier. p597-628, (2007) [[media:Publications_012.pdf|pdf]]
 
*11. Kris McGary, '''Insuk Lee''', Edward M. Marcotte. Broad network-based predictability of S. cerevisiae gene loss-of-function phenotypes. '''''Genome Biology''''' 8:R258 (2007) [http://www.ncbi.nlm.nih.gov/pubmed/18053250 pubmed] [[media:Publications_011.pdf|pdf]]
 
*10. '''Insuk Lee*''', Zhihua Li, and Edward M. Marcotte. An improved bias-reduced probabilistic functional gene network of baker’s yeast ''Saccharomyces cerevisiae''. '''''PLOS One''''' 2:e988 (2007) [http://www.ncbi.nlm.nih.gov/pubmed/17912365 pubmed] [[media:Publications_010.pdf|pdf]]
 
*9. Hart G. Traver, '''Insuk Lee''', Edward Marcotte. A high-accuracy consensus map of yeast protein complexes reveals modular nature of gene essentiality. '''''BMC Bioinformatic''''' 8:236 (2007) [http://www.ncbi.nlm.nih.gov/pubmed/17605818 pubmed] [[media:Publications_009.pdf|pdf]]
 
*8. '''Insuk Lee*''', Shailesh V. Date, Alex T. Adai, Edward Marcotte. A Probabilistic functional network of yeast genes. '''''Science''''' 306:1555-1558, (2004) [http://www.ncbi.nlm.nih.gov/pubmed/15567862 pubmed] [[media:Publications_008.pdf|pdf]]
 
*7. Bork, P., Jensen, L.J., Von Mering, C., Ramani, A.K., '''Lee I''', Marcotte, E.M. Protein interaction networks from yeast to human. '''''Curr. Opin. Struct. Biol.''''' 14:292-9, (2004) [http://www.ncbi.nlm.nih.gov/pubmed/15193308 pubmed] [[media:Publications_007.pdf|pdf]]
 
*6. '''Insuk Lee*''' and Rasika Harshey. Patterns of Sequence conservation at termini of LTR retrotransposons and DNA transposons in the human genome: Lessons from phage Mu. '''''Nucleic Acids Res.''''' 31:4531-4540, (2003)
 
*5. '''Insuk Lee*''' and Rasika Harshey. The conserved CA/TG motif at Mu termini: T specifies stable transpososome assembly. '''''J. Mol. Biol.''''' 330:261-275, (2003)
 
*4. '''Insuk Lee*''' and Rasika Harshey. Importance of the conserved CA dinucleotide at Mu termini. '''''J. Mol. Biol.''''' 314:433-444, (2001)
 
*3. Xue, Y., Bai, X., '''Lee, I.''', Kallstrom, G., Ho, J., Brown, J., Stevens, A., and Johnson, A. W. Saccharomyces cerevisiae RAI1 (YGL246c) is homologous to human DOM3Z and encodes a protein that binds the nuclear exoribonuclease Rat1p. '''''Mol. Cell. Biol.''''' 20:4006-4015, (2000)
 
*2. Liu, J., Gong, Y., Prakash, O., Wen, L., '''Lee, I'''., Huang J.,-K., and Krishnamoorthi, R. NMR studies of internal dynamics of serine proteinase protein inhibitors: Binding region mobilities of intact and reactive-site hydrolyzed CMTI-III of the squash family and comparison with those of counterparts of CMTI-V of the potato I family. '''''Protein Science''''' 7:132-141, (1998)
 
*1. Wen, L., '''Lee, I.''', Chen, G., Huang, J.,-K., Gong, Y., and Krishnamoorthi, R. Changing the inhibitory specificity and function of CMTI-V by site-directed mutagenesis. '''''Biochem. Biophys. Res. Commun.''''' 207:897-902, (1995)

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__NOTOC__
=='''Scientists & Research Staff'''==
{|border="0" width="1100"
|align="center"|[[File:prof_Insuk_fixed.png|100px|link=people:IS_Lee]]
|align="center"|[[File:SM_fixed.png|100px|link=people:SM_Yang]]
|align="center"|[[File:Junha_fixed.png|100px|link=People:JH_Cha]]
|align="center"|[[File:Nayeon_fixed.png|100px|link=People:NY_Kim]]
|align="center"|[[File:Seungbyn_fixed.png|100px|link=People:SB_Baek]]
|align="center"|[[File:Ilseok_fixed.png|100px|link=People:IS_Choi]]
|align="center"|[[File:JunHyeong_fixed.png|100px|link=People:Junhyeong_Cha]]
|align="center"|[[File:JunYeong_fixed.png|100px|link=People:Junyeong_Ma]]
|-

|width=160px; align="center"|[[people:IS_Lee|<big>'''Insuk Lee'''</big>]]
|width=160px; align="center"|[[people:SM_Yang|<big>'''Sunmo Yang'''</big>]]
|width=160px; align="center"|[[People:JH_Cha|<big>'''Junha Cha'''</big>]]
|width=160px; align="center"|[[People:NY_Kim|<big>'''Nayeon Kim'''</big>]]
|width=160px; align="center"|[[People:SB_Baek|<big>'''Seungbyn Baek'''</big>]]
|width=160px; align="center"|[[People:IS_Choi|<big>'''Ilseok Choi'''</big>]]
|width=160px; align="center"|[[People:Junhyeong_Cha|<big>'''Junhyeong Cha'''</big>]]
|width=160px; align="center"|[[People:Junyeong_Ma|<big>'''Junyeong Ma'''</big>]]
|-

|align="center"|[[people:IS_Lee|Principal Investigator Professor]]
|align="center"|[[people:SM_Yang|Bioinformatics Programmer & Sys. Admin]]
|align="center"|[[People:JH_Cha|Ph.D candidate (Single-cell Biology) (2019.9 - )]]
|align="center"|[[People:NY_Kim|Ph.D candidate (Microbiome) (2019.9 - )]]
|align="center"|[[People:SB_Baek|Ph.D candidate (Single-cell Biology) (2019.9 - )]]
|align="center"|[[People:IS_Choi|Ph.D candidate (Single-cell Biology) (2020.9 - )]]
|align="center"|[[People:Junhyeong_Cha|Ph.D candidate (Microbiome) (2020.9 - )]]
|align="center"|[[People:Junyeong_Ma|Ph.D candidate (Microbiome) (2021.3 - )]]
|}
{|border="0" width="962.5"

|align="center"|[[File:Hanjune_fixed.png|100px|link=People:Han-June_KIM]]
|align="center"|[[File:EuiJeong_fixed.png|100px|link=People:EuiJeong_Sung]]
|align="center"|[[File:Sehun_fixed.png|100px|link=People:Sehun_Ahn]]
|align="center"|[[File:Geon_fixed.png|100px|link=People:Geon_Koh]]
|align="center"|[[File:Wonjong_fixed.png|100px|link=People:WonJong_Kim]]
|align="center"|[[File:Jungyeon_fixed.png|100px|link=People:Jungyeon_Kim]]
|align="center"|[[File:Yerin_fixed.png|100px|link=People:Yerin_Kim]]
|-
|width=160px; align="center"|[[People:Hanjune_Kim|<big>'''Hanjune Kim'''</big>]]
|width=160px; align="center"|[[People:Euijeong_Sung|<big>'''Euijeong Sung'''</big>]]
|width=160px; align="center"|[[People:Sehun_Ahn|<big>'''Sehun Ahn'''</big>]]
|width=160px; align="center"|[[People:Geon Koh|<big>'''Geon Koh'''</big>]]
|width=160px; align="center"|[[People:WonJong_Kim|<big>'''Wonjong Kim'''</big>]]
|width=160px; align="center"|[[People:Jungyeon_Kim|<big>'''Jungyeon Kim'''</big>]]
|width=160px; align="center"|[[People:Yerin_Kim|<big>'''Yerin Kim'''</big>]]
|-
|align="center"|[[People:Hanjune_Kim|Integrated Ph.D course (Microbiome) (2021.3 - )]]
|align="center"|[[People:Euijeong_Sung|Integrated Ph.D course (Single-cell Biology) (2021.9 - )]]
|align="center"|[[People:Sehun_Ahn|Integrated Ph.D course (Microbiome) (2022.3 - )]]
|align="center"|[[People:Geon_Koh|Integrated Ph.D course (Microbiome) (2022.9 - )]]
|align="center"|[[People:WonJong_Kim|Integrated Ph.D course (Microbiome) (2023.3 - )]]
|align="center"|[[People:Jungyeon_Kim|Integrated Ph.D course (Microbiome) (2023.3 - )]]
|align="center"|[[People:Yerin_Kim|Integrated Ph.D course (Microbiome) (2024.3 - )]]
|}

=='''Administrative Assistant'''==
*Seomi Lee

=='''Undergraduate Students'''==
*Yerin Kim
*Sang Min Park
*Minsun Seo
*Jaeryun Sim
*Sun Min Lim
*Yurim Jung

=='''Alumni'''==
*'''Former Post-docs and Staff Scientists'''
**1. Taeyun Oh (2009.5-2011.12), currently Senior Researcher at Cowellbiogim, Korea
**2. Sohyun Hwang (2010.3-2015.8), currently Associate Professor (차의과대학 의과학과 교수), [https://grad.cha.ac.kr/%ed%99%a9%ec%86%8c%ed%98%84/ Department of Biomedical Science, CHA University], Korea [https://scholar.google.com/citations?user=z5yC9cwAAAAJ&hl=en Google Scholar]
**3. Samuel Beck (2011.2-2011.12), currently Associate Professor, Boston University, USA (미 보스턴 대학교 부교수), [https://beck310.wixsite.com/becklab Lab Homepage]
**4. Jawon Song (2011.1-2012.5), currently Research Associate (미국 텍사스슈퍼컴퓨팅센터 선임연구원), [https://www.tacc.utexas.edu/about/directory/jawon-song Texas Advanced Computing Center], Austin, TX, USA [https://scholar.google.com/citations?user=wv61wGoAAAAJ&hl=en Google Scholar]
**5. Yoonhee Ko (2012.8-2014.2), currently Associate Professor (한국외국어대 바이오공학과 교수), [https://hufsbiomed.weebly.com/ Department of Biomedical Engineering, Hankuk University of Foreign Studies], Korea [https://scholar.google.com/citations?user=GBxCYX8AAAAJ&hl=en Google Scholar]
**6. Jonghoon Lee (2013.10-2014.2), currently Associate Professor (가천대학교 식품생명공학과 교수), [https://www.gachon.ac.kr/foodbiotech/6142/subview.do Department of Food Biotechnology, Gachon University], Korea
**7. Jung Eun Shim (2009.8-2017.11), currently Staff Scientist at Yonsei Genomics Center Bioinformatics Data Analysis Core (연세의료원 유전체센터 생명정보분석코어 책임연구원)

*'''Former Graduate Students (Ph.D)'''
**1. Junha Shin (2008.9-2014.2), PhD; currently Postdoc Fellow, University of Wisconsin-Madison (미국 위스콘신주립대 박사후연구원), USA [https://roylab.discovery.wisc.edu/ Roy Lab]
**2. Hanhae Kim (2009.9-2015.2), PhD; currently Research Fellow (한국과학기술기획평가원 연구위원), [http://www.kistep.re.kr Korea Institute of S&T Evaluation and Planning (KISTEP)]
**3. Ara Cho (2009.9-2015.2), PhD; currently Research Fellow (한국과학기술기획평가원 연구위원), [http://www.kistep.re.kr Korea Institute of S&T Evaluation and Planning (KISTEP)]
**4. Eiru Kim (2010.9-2016.8), PhD; currently Senior Expert in Data Science, [https://www.gnf.org/ The Genomics Institute of the Novartis Research Foundation], San Diego, USA (노바티스 선임데이터분석연구원)
**5. Tak Lee (2011.3-2017.2), PhD; currently Senior Bioinformatician, [https://www.mpipz.mpg.de/en Max Planck Institute for Plant Breeding Research] (독일 막스플랭크 연구소 선임연구원)
**6. Heonjong Han (2013.3~2019.8), PhD; currently Bioinformatics engineer at [https://3billion.io/ 3billion] (바이오기업 데이터사이언티스트)
**7. Kyungsoo Kim (2013.3~2020.2), PhD; currently Research Professor at Severance Hospital (강남세브란스의료원 유방암센터 연구교수)
**8. Jae-Won Cho (2016.9~2021.2), PhD; currently Postdoc Fellow at Harvard Medical School (하버드의대 박사후 연구원) [https://scholar.google.com/citations?user=H4jO_DQAAAAJ&hl=en Hemberg Lab]
**9. Chan Yeong Kim (2015.3~2021.2), PhD; currently Postdoc Fellow at European Molecular Biology Laboratory in Heidelberg (유럽연합분자생물학연구소 박사후 연구원) [http://www.bork.embl.de/j/ Bork Group]
**10. Sungho Lee (2017.3-2024.2), PhD; currently Data Scientist at [http://www.mogam.re.kr/kor/ MOGAM Institute for Biomedical Research] (목암생명과학연구소, (주)녹십자 출연 연구소, 선임연구원)

*'''Former Graduate Students (Master's degree)'''
**1. Sun-Gou Ji (2009.9-2011.8), M.Eng.; PhD from University of Cambridge (Sanger Genome center); currently Vice President, Computational Genetics at [https://bridgebio.com/ BridgeBio Pharma], Boston, USA (미 바이오기업 임원)
**2. Hyojin Kim (2013.3-2015.2), M.Eng.; PhD from University of Aachen; currently Computational Scientist, Uniklinikum Aachen (the university hospital of the city of Aachen), Germany (독일 아헨대학병원 데이터사이언티스트)
**3. Hongseok Shim (2013.9-2015.8), M.Eng.; currently PhD student, Arizona State University, Biomedical Informatics program (애리조나주립대 생물정보학 박사과정)
**4. Sunmo Yang (2014.9-2017.2), M.Eng. currently Bioinformatics programmer, Yonsei University (연세대 네트워크생명공학 연구실 책임데이터연구원)
**5. Muyoung Lee (2016.3-2018.2), M.Eng. currently PhD student, University of Texas at Austin, Cellular and Molecular Biology Program (텍사스주립대 생물정보학 박사과정)
**6. Jiwon Yu (2021.9-2023.8), M.Eng. currently Data Scientist at [https://www.samsungbioepis.com/kr/index.do SAMSUNG BIOEPIS] (삼성바이오에피스 데이터사이언티스트)

*'''Former Visiting Scientists/Students'''
**1. Minkyung Shin (2012.3-2012.7), went to University of Southern California graduate school
**2. Paul Chung (2012 summer), currently E-Commerce Analyst/Coordinator at [http://www.riotgames.com/ Riot Games], USA
**3. Hyunjin Cho (2014 summer), currently Bioinformatics research assistant at [http://www.libd.org/ The Lieber Institute for Brain Development | LIBD], USA
**4. Michael Chung (2016.8-2016.12), currently work at [https://about.meta.com/ Meta], USA

*'''Former Undergraduate Students'''
**1. Ila Shin, went to medical school (Yonsei Univ.) Currently Professor, Kyunghee University School of Medicine (경희대학교 의과대학 교수)
**2. Yoonkyung Ko
**3. Sangyo Park, went to law school (Kyounghee Univ.)
**4. Jayoon Shin, went to medical school (Pusan National Univ.)
**5. Dongjoo Sun, went to dental school (Wongwang Univ.)
**6. Eulsoo Kim
**7. Myungwhan Lee, went to medical school (Yonsei Univ.)
**8. Moonhee Lee, went to graduate school (University of Amsterdam, Netherlands)
**9. Jinhyun Ju, went to graduate school (Cornell Medical School, Computational Systems Biology); Currently Principal Computational Biologist, Cancer Genomics at [https://bridgebio.com/ BridgeBio Pharma], Seattle, USA (미 바이오기업 선임연구원)
**10. Haeyoung Shin, went to graduate school (Brown University, Computational Neuroscience); Currently Professor, Seoul National University (서울대학교 생명과학부 교수)
**11. Joonghee Soh, went to graduate school (KAIST, Culture Technology)
**12. Hee Jung Cho, went to graduate school (UC Berkeley)
**13. TaeHwan Kim, went to medical school
**14. Joohyung Kim, went to medical school (Kyungpook National Univ.)
**15. Donghyun Shin, went to medical school (Yonsei Univ.)
**16. Byunghee Kang (2015 spring - 2016 summer), went to graduate school (PosTech)
**17. Sunphil Kim (2016 winter)
**18. Jaeho Shim (2016 winter)
**19. Seunghyun Shin (2016 spring)
**20. Aejoo Hong (2016 spring)
**21. Changbae Bang (2016 summer), went to medical school (Yonsei Univ.)
**22. Seoyoung Choi (2016 summer)
**23. Dabin Jung (2016 summer)
**24. Boreum Nam (2016 summer)
**25. Seungun Lee (2016 summer)
**26. Yeaji Kim (2016 summer and fall)
**27. Sangyoung Lee (2017 winter)
**28. Heejun Jang (2017 winter & spring), went to KIST as Research Assistant
**29. Jungha Lee (2017 summer), went to graduate school (Seoul National Univ.)
**30. Seunghyun Wang (2017 summer), went to graduate school (KAIST)
**31. Dong-Min Yang (2017 fall)
**32. Doo-Hee Lee (2017 fall)
**33. Suk-Jae Han (2018 winter)
**34. Ji-Eun Han (2018 winter)
**35. Yuri Ko (2018 spring)
**36. Sung-Woo Kim (2018 spring)
**37. Woosung Kwon (2018 summer)
**38. Soyun Kong (2018 summer)
**39. Hojeong Keum (2018 summer and fall)
**40. Sungjun Lim (2018 summer and fall)
**41. Jiyoon Lee (2018 summer and fall)
**42. Ilseok Choi(2019 winter, spring, summer and fall, 2020 winter, spring)
**43. Junik Park (2019 winter)
**44. Lee Yoo (2019 winter & spring)
**45. Yeonwha Kim (2019 winter & spring)
**46. Sohee Kim (2019 spring)
**47. Juseong Lee (2019 spring)
**48. Junyoung Yang (2019 spring & summer)
**49. Junyoung Ma (2019 summer, 2020 winter, summer, fall, 2021 winter)
**50. Dayun Jung (2019 summer)
**51. Hanjoon Kim (2019 summer, 2021 winter)
**52. Hyungjin Kim (2020 winter, spring)
**53. Sujin Hyun (2020 winter)
**54. Hyuki Lee (2020 winter)
**55. Donghwan Lee (2020 winter)
**56. Junhyung Cha (2020 spring, summer)
**57. Jiwon Yu (2020 summer, 2021 Spring, summer)
**58. Dong Seok Kim (2020 summer)
**59. Donggeun Lee (2020 summer)
**60. Sehun Ahn (2020 summer, 2021 winter, spring, summer, 2022 winter)
**61. Jiho Kim (2020 summer)
**62. Euijung Seong (2020 fall, 2021 spring, summer)
**63. Junhan Kim (2021 winter & spring)
**64. Suyong Choi (2021 winter)
**65. Sujin Pyeon (2021 Spring)
**66. Hyeon Hee Ji (2021 Spring)
**67. Semin Kim (2021 Summer)
**68. Su Hwan Kim (2021 Summer)
**69. Yongbin Kim (2021 Summer)
**70. Nan Park (2021 Summer)
**71. Hoyong Jung (2021 Summer)
**72. Ingyeong Koh (2021 Summer)
**73. Jongbin Jeong (2022 Winter)
**74. Sungchul Yang (2022 Winter, Spring, Summer)
**75. Hyojung Lee (2022 Winter, Spring)
**76. Jun Hyeong Kim (2022 Winter, Spring)
**77. Young Ji Roh (2022 Spring, Summer)
**78. Geon Koh (2022 Spring, Summer)
**79. Hayoung Kang (2022 Summer, 2023 Spring)
**80. Won Jong Kim (2022 Summer, Fall, 2023 Winter)
**81. Christian Lee (2022 Summer)
**82. Je Yun Kang (2022 Summer)
**83. Sumin Lee (2022 Fall)
**84. Jungyeon Kim (2022 Fall, 2023 Winter)
**85. Seol Song (2023 Winter)
**86. Soyeon Kim (2023 Summer)
**87. Sieun Park (2023 Summer)
**88. Gunhyeong Lee (2023 Summer)
**89. Yunseo Han (2023 Summer)
**90. Jae Hyun Kim (2023 Summer)
**91. Jeong-min Seo (2023 Summer)
**92. Yerin Kim (2023 Fall)

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__NOTOC__
=='''Scientists & Research Staff'''==
{|border="0" width="1100"
|align="center"|[[File:이인석교수님-상체.jpg|100px|link=people:IS_Lee]]
|align="center"|[[File:Smyang.jpg|70px|link=people:SM_Yang]]
|align="center"|[[File:Junha_Cha_profile2.png|70px|link=People:JH_Cha]]
|align="center"|[[File:나연.png|70px|link=People:NY_Kim]]
|align="center"|[[File:SeungbynBaek AACR.jpg|80px|link=People:SB_Baek]]
|align="center"|[[File:KakaoTalk 20200914 132021053.jpg|80px|link=People:IS_Choi]]
|align="center"|[[File:chajh profile.jpg|70px|link=People:Junhyeong_Cha]]
|align="center"|[[File:KakaoTalk 20210225 184422243.jpg|70px|link=People:Junyeong_Ma]]
|-

|width=160px; align="center"|[[people:IS_Lee|<big>'''Insuk Lee'''</big>]]
|width=160px; align="center"|[[people:SM_Yang|<big>'''Sunmo Yang'''</big>]]
|width=160px; align="center"|[[People:JH_Cha|<big>'''Junha Cha'''</big>]]
|width=160px; align="center"|[[People:NY_Kim|<big>'''Nayeon Kim'''</big>]]
|width=160px; align="center"|[[People:SB_Baek|<big>'''Seungbyn Baek'''</big>]]
|width=160px; align="center"|[[People:IS_Choi|<big>'''Ilseok Choi'''</big>]]
|width=160px; align="center"|[[People:Junhyeong_Cha|<big>'''Junhyeong Cha'''</big>]]
|width=160px; align="center"|[[People:Junyeong_Ma|<big>'''Junyeong Ma'''</big>]]
|-

|align="center"|[[people:IS_Lee|Principal Investigator Professor]]
|align="center"|[[people:SM_Yang|Bioinformatics Programmer & Sys. Admin]]
|align="center"|[[People:JH_Cha|Ph.D candidate (Single-cell Biology) (2019.9 - )]]
|align="center"|[[People:NY_Kim|Ph.D candidate (Microbiome) (2019.9 - )]]
|align="center"|[[People:SB_Baek|Ph.D candidate (Single-cell Biology) (2019.9 - )]]
|align="center"|[[People:IS_Choi|Ph.D candidate (Single-cell Biology) (2020.9 - )]]
|align="center"|[[People:Junhyeong_Cha|Ph.D candidate (Microbiome) (2020.9 - )]]
|align="center"|[[People:Junyeong_Ma|Ph.D candidate (Microbiome) (2021.3 - )]]
|}
{|border="0" width="962.5"

|align="center"|[[File:profile_front_resized.jpg|70px|link=People:Han-June_KIM]]
|align="center"|[[File:KakaoTalk_20210825_174338962.jpg|70px|link=People:EuiJeong_Sung]]
|align="center"|[[File:Hunsei.PNG|70px|link=People:Sehun_Ahn]]
|align="center"|[[File:고건.jpg|70px|link=People:Geon_Koh]]
|align="center"|[[File:원종.jpg|70px|link=People:WonJong_Kim]]
|align="center"|[[File:정연.jpg|70px|link=People:Jungyeon_Kim]]
|align="center"|[["NO IMAGE"]]
|-
|width=160px; align="center"|[[People:Hanjune_Kim|<big>'''Hanjune Kim'''</big>]]
|width=160px; align="center"|[[People:Euijeong_Sung|<big>'''Euijeong Sung'''</big>]]
|width=160px; align="center"|[[People:Sehun_Ahn|<big>'''Sehun Ahn'''</big>]]
|width=160px; align="center"|[[People:Geon Koh|<big>'''Geon Koh'''</big>]]
|width=160px; align="center"|[[People:WonJong_Kim|<big>'''Wonjong Kim'''</big>]]
|width=160px; align="center"|[[People:Jungyeon_Kim|<big>'''Jungyeon Kim'''</big>]]
|width=160px; align="center"|[[People:Yerin_Kim|<big>'''Yerin Kim'''</big>]]
|-
|align="center"|[[People:Hanjune_Kim|Integrated Ph.D course (Microbiome) (2021.3 - )]]
|align="center"|[[People:Euijeong_Sung|Integrated Ph.D course (Single-cell Biology) (2021.9 - )]]
|align="center"|[[People:Sehun_Ahn|Integrated Ph.D course (Microbiome) (2022.3 - )]]
|align="center"|[[People:Geon_Koh|Integrated Ph.D course (Microbiome) (2022.9 - )]]
|align="center"|[[People:WonJong_Kim|Integrated Ph.D course (Microbiome) (2023.3 - )]]
|align="center"|[[People:Jungyeon_Kim|Integrated Ph.D course (Microbiome) (2023.3 - )]]
|align="center"|[[People:Yerin_Kim|Integrated Ph.D course (Microbiome) (2024.3 - )]]
|}

=='''Administrative Assistant'''==
*Seomi Lee

=='''Undergraduate Students'''==
*Yerin Kim
*Sang Min Park
*Minsun Seo
*Jaeryun Sim
*Sun Min Lim
*Yurim Jung

=='''Alumni'''==
*'''Former Post-docs and Staff Scientists'''
**1. Taeyun Oh (2009.5-2011.12), currently Senior Researcher at Cowellbiogim, Korea
**2. Sohyun Hwang (2010.3-2015.8), currently Associate Professor (차의과대학 의과학과 교수), [https://grad.cha.ac.kr/%ed%99%a9%ec%86%8c%ed%98%84/ Department of Biomedical Science, CHA University], Korea [https://scholar.google.com/citations?user=z5yC9cwAAAAJ&hl=en Google Scholar]
**3. Samuel Beck (2011.2-2011.12), currently Associate Professor, Boston University, USA (미 보스턴 대학교 부교수), [https://beck310.wixsite.com/becklab Lab Homepage]
**4. Jawon Song (2011.1-2012.5), currently Research Associate (미국 텍사스슈퍼컴퓨팅센터 선임연구원), [https://www.tacc.utexas.edu/about/directory/jawon-song Texas Advanced Computing Center], Austin, TX, USA [https://scholar.google.com/citations?user=wv61wGoAAAAJ&hl=en Google Scholar]
**5. Yoonhee Ko (2012.8-2014.2), currently Associate Professor (한국외국어대 바이오공학과 교수), [https://hufsbiomed.weebly.com/ Department of Biomedical Engineering, Hankuk University of Foreign Studies], Korea [https://scholar.google.com/citations?user=GBxCYX8AAAAJ&hl=en Google Scholar]
**6. Jonghoon Lee (2013.10-2014.2), currently Associate Professor (가천대학교 식품생명공학과 교수), [https://www.gachon.ac.kr/foodbiotech/6142/subview.do Department of Food Biotechnology, Gachon University], Korea
**7. Jung Eun Shim (2009.8-2017.11), currently Staff Scientist at Yonsei Genomics Center Bioinformatics Data Analysis Core (연세의료원 유전체센터 생명정보분석코어 책임연구원)

*'''Former Graduate Students (Ph.D)'''
**1. Junha Shin (2008.9-2014.2), PhD; currently Postdoc Fellow, University of Wisconsin-Madison (미국 위스콘신주립대 박사후연구원), USA [https://roylab.discovery.wisc.edu/ Roy Lab]
**2. Hanhae Kim (2009.9-2015.2), PhD; currently Research Fellow (한국과학기술기획평가원 연구위원), [http://www.kistep.re.kr Korea Institute of S&T Evaluation and Planning (KISTEP)]
**3. Ara Cho (2009.9-2015.2), PhD; currently Research Fellow (한국과학기술기획평가원 연구위원), [http://www.kistep.re.kr Korea Institute of S&T Evaluation and Planning (KISTEP)]
**4. Eiru Kim (2010.9-2016.8), PhD; currently Senior Expert in Data Science, [https://www.gnf.org/ The Genomics Institute of the Novartis Research Foundation], San Diego, USA (노바티스 선임데이터분석연구원)
**5. Tak Lee (2011.3-2017.2), PhD; currently Senior Bioinformatician, [https://www.mpipz.mpg.de/en Max Planck Institute for Plant Breeding Research] (독일 막스플랭크 연구소 선임연구원)
**6. Heonjong Han (2013.3~2019.8), PhD; currently Bioinformatics engineer at [https://3billion.io/ 3billion] (바이오기업 데이터사이언티스트)
**7. Kyungsoo Kim (2013.3~2020.2), PhD; currently Research Professor at Severance Hospital (강남세브란스의료원 유방암센터 연구교수)
**8. Jae-Won Cho (2016.9~2021.2), PhD; currently Postdoc Fellow at Harvard Medical School (하버드의대 박사후 연구원) [https://scholar.google.com/citations?user=H4jO_DQAAAAJ&hl=en Hemberg Lab]
**9. Chan Yeong Kim (2015.3~2021.2), PhD; currently Postdoc Fellow at European Molecular Biology Laboratory in Heidelberg (유럽연합분자생물학연구소 박사후 연구원) [http://www.bork.embl.de/j/ Bork Group]
**10. Sungho Lee (2017.3-2024.2), PhD; currently Data Scientist at [http://www.mogam.re.kr/kor/ MOGAM Institute for Biomedical Research] (목암생명과학연구소, (주)녹십자 출연 연구소, 선임연구원)

*'''Former Graduate Students (Master's degree)'''
**1. Sun-Gou Ji (2009.9-2011.8), M.Eng.; PhD from University of Cambridge (Sanger Genome center); currently Vice President, Computational Genetics at [https://bridgebio.com/ BridgeBio Pharma], Boston, USA (미 바이오기업 임원)
**2. Hyojin Kim (2013.3-2015.2), M.Eng.; PhD from University of Aachen; currently Computational Scientist, Uniklinikum Aachen (the university hospital of the city of Aachen), Germany (독일 아헨대학병원 데이터사이언티스트)
**3. Hongseok Shim (2013.9-2015.8), M.Eng.; currently PhD student, Arizona State University, Biomedical Informatics program (애리조나주립대 생물정보학 박사과정)
**4. Sunmo Yang (2014.9-2017.2), M.Eng. currently Bioinformatics programmer, Yonsei University (연세대 네트워크생명공학 연구실 책임데이터연구원)
**5. Muyoung Lee (2016.3-2018.2), M.Eng. currently PhD student, University of Texas at Austin, Cellular and Molecular Biology Program (텍사스주립대 생물정보학 박사과정)
**6. Jiwon Yu (2021.9-2023.8), M.Eng. currently Data Scientist at [https://www.samsungbioepis.com/kr/index.do SAMSUNG BIOEPIS] (삼성바이오에피스 데이터사이언티스트)

*'''Former Visiting Scientists/Students'''
**1. Minkyung Shin (2012.3-2012.7), went to University of Southern California graduate school
**2. Paul Chung (2012 summer), currently E-Commerce Analyst/Coordinator at [http://www.riotgames.com/ Riot Games], USA
**3. Hyunjin Cho (2014 summer), currently Bioinformatics research assistant at [http://www.libd.org/ The Lieber Institute for Brain Development | LIBD], USA
**4. Michael Chung (2016.8-2016.12), currently work at [https://about.meta.com/ Meta], USA

*'''Former Undergraduate Students'''
**1. Ila Shin, went to medical school (Yonsei Univ.) Currently Professor, Kyunghee University School of Medicine (경희대학교 의과대학 교수)
**2. Yoonkyung Ko
**3. Sangyo Park, went to law school (Kyounghee Univ.)
**4. Jayoon Shin, went to medical school (Pusan National Univ.)
**5. Dongjoo Sun, went to dental school (Wongwang Univ.)
**6. Eulsoo Kim
**7. Myungwhan Lee, went to medical school (Yonsei Univ.)
**8. Moonhee Lee, went to graduate school (University of Amsterdam, Netherlands)
**9. Jinhyun Ju, went to graduate school (Cornell Medical School, Computational Systems Biology); Currently Principal Computational Biologist, Cancer Genomics at [https://bridgebio.com/ BridgeBio Pharma], Seattle, USA (미 바이오기업 선임연구원)
**10. Haeyoung Shin, went to graduate school (Brown University, Computational Neuroscience); Currently Professor, Seoul National University (서울대학교 생명과학부 교수)
**11. Joonghee Soh, went to graduate school (KAIST, Culture Technology)
**12. Hee Jung Cho, went to graduate school (UC Berkeley)
**13. TaeHwan Kim, went to medical school
**14. Joohyung Kim, went to medical school (Kyungpook National Univ.)
**15. Donghyun Shin, went to medical school (Yonsei Univ.)
**16. Byunghee Kang (2015 spring - 2016 summer), went to graduate school (PosTech)
**17. Sunphil Kim (2016 winter)
**18. Jaeho Shim (2016 winter)
**19. Seunghyun Shin (2016 spring)
**20. Aejoo Hong (2016 spring)
**21. Changbae Bang (2016 summer), went to medical school (Yonsei Univ.)
**22. Seoyoung Choi (2016 summer)
**23. Dabin Jung (2016 summer)
**24. Boreum Nam (2016 summer)
**25. Seungun Lee (2016 summer)
**26. Yeaji Kim (2016 summer and fall)
**27. Sangyoung Lee (2017 winter)
**28. Heejun Jang (2017 winter & spring), went to KIST as Research Assistant
**29. Jungha Lee (2017 summer), went to graduate school (Seoul National Univ.)
**30. Seunghyun Wang (2017 summer), went to graduate school (KAIST)
**31. Dong-Min Yang (2017 fall)
**32. Doo-Hee Lee (2017 fall)
**33. Suk-Jae Han (2018 winter)
**34. Ji-Eun Han (2018 winter)
**35. Yuri Ko (2018 spring)
**36. Sung-Woo Kim (2018 spring)
**37. Woosung Kwon (2018 summer)
**38. Soyun Kong (2018 summer)
**39. Hojeong Keum (2018 summer and fall)
**40. Sungjun Lim (2018 summer and fall)
**41. Jiyoon Lee (2018 summer and fall)
**42. Ilseok Choi(2019 winter, spring, summer and fall, 2020 winter, spring)
**43. Junik Park (2019 winter)
**44. Lee Yoo (2019 winter & spring)
**45. Yeonwha Kim (2019 winter & spring)
**46. Sohee Kim (2019 spring)
**47. Juseong Lee (2019 spring)
**48. Junyoung Yang (2019 spring & summer)
**49. Junyoung Ma (2019 summer, 2020 winter, summer, fall, 2021 winter)
**50. Dayun Jung (2019 summer)
**51. Hanjoon Kim (2019 summer, 2021 winter)
**52. Hyungjin Kim (2020 winter, spring)
**53. Sujin Hyun (2020 winter)
**54. Hyuki Lee (2020 winter)
**55. Donghwan Lee (2020 winter)
**56. Junhyung Cha (2020 spring, summer)
**57. Jiwon Yu (2020 summer, 2021 Spring, summer)
**58. Dong Seok Kim (2020 summer)
**59. Donggeun Lee (2020 summer)
**60. Sehun Ahn (2020 summer, 2021 winter, spring, summer, 2022 winter)
**61. Jiho Kim (2020 summer)
**62. Euijung Seong (2020 fall, 2021 spring, summer)
**63. Junhan Kim (2021 winter & spring)
**64. Suyong Choi (2021 winter)
**65. Sujin Pyeon (2021 Spring)
**66. Hyeon Hee Ji (2021 Spring)
**67. Semin Kim (2021 Summer)
**68. Su Hwan Kim (2021 Summer)
**69. Yongbin Kim (2021 Summer)
**70. Nan Park (2021 Summer)
**71. Hoyong Jung (2021 Summer)
**72. Ingyeong Koh (2021 Summer)
**73. Jongbin Jeong (2022 Winter)
**74. Sungchul Yang (2022 Winter, Spring, Summer)
**75. Hyojung Lee (2022 Winter, Spring)
**76. Jun Hyeong Kim (2022 Winter, Spring)
**77. Young Ji Roh (2022 Spring, Summer)
**78. Geon Koh (2022 Spring, Summer)
**79. Hayoung Kang (2022 Summer, 2023 Spring)
**80. Won Jong Kim (2022 Summer, Fall, 2023 Winter)
**81. Christian Lee (2022 Summer)
**82. Je Yun Kang (2022 Summer)
**83. Sumin Lee (2022 Fall)
**84. Jungyeon Kim (2022 Fall, 2023 Winter)
**85. Seol Song (2023 Winter)
**86. Soyeon Kim (2023 Summer)
**87. Sieun Park (2023 Summer)
**88. Gunhyeong Lee (2023 Summer)
**89. Yunseo Han (2023 Summer)
**90. Jae Hyun Kim (2023 Summer)
**91. Jeong-min Seo (2023 Summer)
**92. Yerin Kim (2023 Fall)

Journal Club

2024-02-09T05:16:09Z

Bsb0613:

{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2023-2 scOmics
|-
!scope="col" style="padding:.4em" | Date
!scope="col" style="padding:.4em" | Team
!scope="col" style="padding:.4em" | Paper index
!scope="col" style="padding:.4em" | Presenter
!scope="col" style="padding:.4em" | Paper title
|-
|style="padding:.4em;" rowspan=1|2024/03/12
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-43
|style="padding:.4em;"|SB Baek
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41592-023-01994-w Deep generative modeling of transcriptional dynamics for RNA velocity analysis in single cells]
|-
|style="padding:.4em;" rowspan=1|2024/03/05
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-42
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41587-021-00896-6 Gene signature extraction and cell identity recognition at the single-cell level with Cell-ID]
|-
|style="padding:.4em;" rowspan=1|2024/02/27
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-41
|style="padding:.4em;"|EJ Sung
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.xgen.2023.100383 Polygenic regression uncovers trait-relevant cellular contexts through pathway activation transformation of single-cell RNA sequencing data]
|-
|style="padding:.4em;" rowspan=1|2024/02/20
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-40
|style="padding:.4em;"|IS Choi
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41592-023-02035-2 Population-level integration of single-cell datasets enables multi-scale analysis across samples]
|-
|style="padding:.4em;" rowspan=1|2024/02/06
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-39
|style="padding:.4em;"|SB Baek
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s43587-023-00514-x scDiffCom: a tool for differential analysis of cell–cell interactions provides a mouse atlas of aging changes in intercellular communication]
|-
|style="padding:.4em;" rowspan=1|2024/01/30
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-38
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41587-022-01467-z Modeling intercellular communication in tissues using spatial graphs of cells]
|-
|style="padding:.4em;" rowspan=1|2024/01/16
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-37
|style="padding:.4em;"|EJ Sung
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41588-023-01523-7 Precise identification of cell states altered in disease using healthy single-cell references]
|-
|style="padding:.4em;" rowspan=1|2024/01/09
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-36
|style="padding:.4em;"|IS Choi
|style="padding:.4em;text-align:left"|
[https://aacrjournals.org/clincancerres/article/29/19/3924/729105/Learning-Individual-Survival-Models-from-PanCancer Learning Individual Survival Models from PanCancer Whole Transcriptome Data]
|-
|style="padding:.4em;" rowspan=1|2024/01/02
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-35
|style="padding:.4em;"|SB Baek
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41592-023-01971-3 Dictys: dynamic gene regulatory network dissects developmental continuum with single-cell multiomics]
|-
|style="padding:.4em;" rowspan=1|2023/12/12
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-34
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://www.science.org/doi/10.1126/sciimmunol.adf4968 Preexisting tumor-resident T cells with cytotoxic potential associate with response to neoadjuvant anti–PD-1 in head and neck cancer]
|-
|style="padding:.4em;" rowspan=1|2023/12/05
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-33
|style="padding:.4em;"|EJ Sung
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41588-022-01273-y MHC II immunogenicity shapes the neoepitope landscape in human tumors]
|-
|style="padding:.4em;" rowspan=1|2023/11/28
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-32
|style="padding:.4em;"|IS Choi
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41586-023-06130-4 Hallmarks of transcriptional intratumour heterogeneity across a thousand tumours]
|-
|style="padding:.4em;" rowspan=1|2023/11/21
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-31
|style="padding:.4em;"|JW Yu
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41467-023-37353-8 Pan-cancer classification of single cells in the tumour microenvironment]
|-
|style="padding:.4em;" rowspan=1|2023/10/31
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-30
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41587-023-01686-y Single-cell mapping of combinatorial target antigens for CAR switches using logic gates]
|-
|style="padding:.4em;" rowspan=1|2023/10/24
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-29
|style="padding:.4em;"|SB Baek
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41587-023-01782-z Comparative analysis of cell–cell communication at single-cell resolution]
|-
|style="padding:.4em;" rowspan=1|2023/09/26
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-28
|style="padding:.4em;"|EJ Sung
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s43018-023-00566-3 Phenotypic diversity of T cells in human primary and metastatic brain tumors revealed by multiomic interrogation]
|-
|style="padding:.4em;" rowspan=1|2023/09/19
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-27
|style="padding:.4em;"|IS Choi
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41591-023-02324-5 An integrated tumor, immune and microbiome atlas of colon cancer]
|-
|style="padding:.4em;" rowspan=1|2023/09/12
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-26
|style="padding:.4em;"|SB Baek
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41587-022-01476-y Multi-omic single-cell velocity models epigenome–transcriptome interactions and improves cell fate prediction]
|-
|style="padding:.4em;" rowspan=1|2023/09/05
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-25
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.immuni.2023.04.010 Recruitment of epitope-specific T cell clones with a low-avidity threshold supports efficacy against mutational escape upon re-infection]
|}

{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2023-2 Microbiome
|-
!scope="col" style="padding:.4em" | Date
!scope="col" style="padding:.4em" | Team
!scope="col" style="padding:.4em" | Paper index
!scope="col" style="padding:.4em" | Presenter
!scope="col" style="padding:.4em" | Paper title

|-
|style="padding:.4em;" rowspan=1|2024/02/21
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-66
|style="padding:.4em;"|HJ Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1186/s40168-023-01607-w Phages are unrecognized players in the ecology of the oral pathogen Porphyromonas gingivalis]
|-
|style="padding:.4em;" rowspan=1|2024/02/21
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-65
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41564-023-01439-2 A predicted CRISPR-mediated symbiosis between uncultivated archaea]
|-
|style="padding:.4em;" rowspan=1|2024/02/14
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-64
|style="padding:.4em;"|SH Ahn
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2023.09.18.558355 Structural and Functional Disparities within the Human Gut Virome in terms of Genome Topology and Representative Genome Selection]
|-
|style="padding:.4em;" rowspan=1|2024/02/14
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-63
|style="padding:.4em;"|JY Ma
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41587-023-01696-w Contamination source modeling with SCRuB improves cancer phenotype prediction from microbiome data]
|-
|style="padding:.4em;" rowspan=1|2024/02/07
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-62
|style="padding:.4em;"|NY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41586-023-06431-8 Mapping the T cell repertoire to a complex gut bacterial community]
|-
|style="padding:.4em;" rowspan=1|2024/02/07
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-61
|style="padding:.4em;"|YR Kim
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[https://doi.org/10.1101/2023.07.03.547607 Multi-view integration of microbiome data for identifying disease-associated modules]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-60
|style="padding:.4em;"|JY Kim
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[https://doi.org/10.1101/2023.09.28.559994 Phage-bacteria dynamics during the first years of life revealed by trans-kingdom marker gene analysis]
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[https://doi.org/10.1038/s41593-023-01361-0 Multi-level analysis of the gut–brain axis shows autism spectrum disorder-associated molecular and microbial profiles]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-58
|style="padding:.4em;"|G Koh
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[https://doi.org/10.1101/2023.11.21.568153 Metagenomic Immunoglobulin Sequencing (MIG-Seq) Exposes Patterns of IgA Antibody Binding in the Healthy Human Gut Microbiome]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-57
|style="padding:.4em;"|SH Ahn
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[https://doi.org/10.1038/s41467-023-41042-x Impact of dietary interventions on pre-diabetic oral and gut microbiome, metabolites and cytokines]
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|style="padding:.4em;" rowspan=1|2024/01/10
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-56
|style="padding:.4em;"|HJ Kim
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[https://doi.org/10.1038/s41592-023-02018-3 Fast and robust metagenomic sequence comparison through sparse chaining with skani]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-55
|style="padding:.4em;"|JY Ma
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41591-023-02599-8 Bacterial SNPs in the human gut microbiome associate with host BMI]
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|style="padding:.4em;" rowspan=1|2023/01/03
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-54
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1080/19490976.2023.2245562 Multimodal metagenomic analysis reveals microbial single nucleotide variants as superior biomarkers for early detection of colorectal cancer]
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|style="padding:.4em;" rowspan=1|2023/01/03
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-53
|style="padding:.4em;"|NY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.chom.2023.10.005 Multi-kingdom gut microbiota analyses define bacterial-fungal interplay and microbial markers of pan-cancer immunotherapy across cohorts]
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|style="padding:.4em;" rowspan=1|2023/12/27
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-52
|style="padding:.4em;"|YR Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.xcrm.2023.101251 Prior antibiotic administration disrupts anti-PD-1 responses in advanced gastric cancer by altering the gut microbiome and systemic immune response]
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|style="padding:.4em;" rowspan=1|2023/12/27
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-51
|style="padding:.4em;"|JY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.cell.2023.05.046 Ultra-deep sequencing of Hadza hunter-gatherers recovers vanishing gut microbes]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-50
|style="padding:.4em;"|WJ Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1186/s40168-023-01472-7 Altered infective competence of the human gut microbiome in COVID-19]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-49
|style="padding:.4em;"|G Koh
|style="padding:.4em;text-align:left"|
[https://onlinelibrary.wiley.com/doi/full/10.1002/aisy.202300342 Host-Variable-Embedding Augmented Microbiome-Based Simultaneous Detection of Multiple Diseases by Deep Learning]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-48
|style="padding:.4em;"|SH Ahn
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41467-023-39264-0 A data-driven approach for predicting the impact of drugs on the human microbiome]
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|style="padding:.4em;" rowspan=1|2023/12/06
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-47
|style="padding:.4em;"|HJ Kim
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[https://doi.org/10.1101/2023.04.06.535777 Activation of programmed cell death and counter-defense functions of phage accessory genes]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-46
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41467-023-39459-5 Top-down identification of keystone taxa in the microbiome]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-45
|style="padding:.4em;"|JY Ma
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.cels.2022.12.007 Pitfalls of genotyping microbial communities with rapidly growing genome collections]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-44
|style="padding:.4em;"|NY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1186/s13059-023-03028-2 Reconstruction of the last bacterial common ancestor from 183 pangenomes reveals a versatile ancient core genome]
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|style="padding:.4em;" rowspan=1|2023/11/22
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-43
|style="padding:.4em;"|SH Lee
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[https://doi.org/10.1038/s41587-023-01868-8 Generation of accurate, expandable phylogenomic trees with uDance]
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|style="padding:.4em;" rowspan=1|2023/11/08
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-42
|style="padding:.4em;"|WJ Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.immuni.2023.04.003 Phage display sequencing reveals that genetic, environmental, and intrinsic factors influence variation of human antibody epitope repertoire]
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|style="padding:.4em;" rowspan=1|2023/11/08
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-41
|style="padding:.4em;"|JY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.immuni.2023.04.017 Phage-display immunoprecipitation sequencing of the antibody epitope repertoire in inflammatory bowel disease reveals distinct antibody signatures]
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|style="padding:.4em;" rowspan=1|2023/11/01
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-40
|style="padding:.4em;"|G Koh
|style="padding:.4em;text-align:left"|
[https://doi.org/10.15252/msb.202311525 Consistency across multi-omics layers in a drug-perturbed gut microbial community]
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|style="padding:.4em;" rowspan=1|2023/11/01
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-39
|style="padding:.4em;"|HJ Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41587-023-01953-y Identification of mobile genetic elements with geNomad]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-38
|style="padding:.4em;"|SH Ahn
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41591-023-02407-3 Microbiome-derived cobalamin and succinyl-CoA as biomarkers for improved screening of anal cancer]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-37
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41591-023-02424-2 The airway microbiome mediates the interaction between environmental exposure and respiratory health in humans]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-36
|style="padding:.4em;"|JY Ma
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1080/19490976.2023.2224474 Ordering taxa in image convolution networks improves microbiome-based machine learning accuracy]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-35
|style="padding:.4em;"|NY Kim
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[https://doi.org/10.1101/2023.08.12.553040 The defensome of complex bacterial communities]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-34
|style="padding:.4em;"|SH Lee
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.cell.2023.03.011 Dietary tryptophan metabolite released by intratumoral Lactobacillus reuteri facilitates immune checkpoint inhibitor treatment]
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|style="padding:.4em;" rowspan=1|2023/09/20
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-33
|style="padding:.4em;"|WJ Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s43587-022-00306-9 Toward an improved definition of a healthy microbiome for healthy aging]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-32
|style="padding:.4em;"|JY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s43587-022-00287-9 Associations of the skin, oral and gut microbiome with aging, frailty and infection risk reservoirs in older adults]
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|style="padding:.4em;" rowspan=1|2023/09/13
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-31
|style="padding:.4em;"|G Koh
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[https://doi.org/10.1186/s40168-023-01614-x Statistical modeling of gut microbiota for personalized health status monitoring]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-30
|style="padding:.4em;"|SH Ahn
|style="padding:.4em;text-align:left"|
[https://doi.org/10.7554/eLife.50240 Adjusting for age improves identification of gut microbiome alterations in multiple diseases]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|HJ Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41564-023-01370-6 Centenarians have a diverse gut virome with the potential to modulate metabolism and promote healthy lifespan]
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|style="padding:.4em;" rowspan=1|2023/09/06
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-28
|style="padding:.4em;"|JH Cha
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[https://doi.org/10.1016/j.chom.2023.01.003 Longitudinal comparison of the developing gut virome in infants and their mothers]
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{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2023-1 ADVANCED MICROBIOME DATA ANALYSIS
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|style="padding:.4em;"|JY Kim
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[https://doi.org/10.1016/j.chom.2020.03.005 Structure of the Mucosal and Stool Microbiome in Lynch Syndrome]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|WJ Kim
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[https://doi.org/10.1038/s41586-019-1237-9 Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-22
|style="padding:.4em;"|SH Ahn
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[https://doi.org/10.1016/j.jare.2022.03.007 Roles of oral microbiota and oral-gut microbial transmission in hypertension]
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|style="padding:.4em;"|23-21
|style="padding:.4em;"|SY Lim
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[https://doi.org/10.1016/j.chom.2022.08.009 Human gut microbiota stimulate defined innate immune responses that vary from phylum to strain]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-20
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[https://doi.org/10.1038/s41591-023-02217-7 Gut microbial metabolism of 5-ASA diminishes its clinical efficacy in inflammatory bowel disease]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-19
|style="padding:.4em;"|JY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.chom.2023.01.004 Deficient butyrate-producing capacity in the gut microbiome is associated with bacterial network disturbances and fatigue symptoms in ME/CFS]
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|style="padding:.4em;" rowspan=1|2023/05/23
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-18
|style="padding:.4em;"|EJ Sung
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.ccell.2022.11.013 Gut microbiota-mediated nucleotide synthesis attenuates the response to neoadjuvant chemoradiotherapy in rectal cancer]
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|style="padding:.4em;" rowspan=1|2023/05/23
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-17
|style="padding:.4em;"|G Koh
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[https://doi.org/10.1016/j.chom.2021.06.019 Multi-omics reveal microbial determinants impacting responses to biologic therapies in inflammatory bowel disease]
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|style="padding:.4em;" rowspan=1|2023/05/23
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-16
|style="padding:.4em;"|SH Lee
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[https://doi.org/10.1038/s41586-022-05181-3 Identification of trypsin-degrading commensals in the large intestine]
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|style="padding:.4em;" rowspan=1|2023/05/16
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-15
|style="padding:.4em;"|JP Hong
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[https://doi.org/10.1038/s41586-022-05546-8 Questioning the fetal microbiome illustrates pitfalls of low-biomass microbial studies]
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|style="padding:.4em;" rowspan=1|2023/05/16
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-14
|style="padding:.4em;"|MR Jang
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[https://doi.org/10.1016/j.chom.2023.01.013 Tissue-resident Lachnospiraceae family bacteria protect against colorectal carcinogenesis by promoting tumor immune surveillance]
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|style="padding:.4em;" rowspan=1|2023/05/16
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-13
|style="padding:.4em;"|JW Yu
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.cell.2022.09.005 Pan-cancer analyses reveal cancer-type-specific fungal ecologies and bacteriome interactions]
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|style="padding:.4em;" rowspan=1|2023/05/09
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-12
|style="padding:.4em;"|NY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.cell.2022.09.015 A pan-cancer mycobiome analysis reveals fungal involvement in gastrointestinal and lung tumors]
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|style="padding:.4em;" rowspan=1|2023/05/09
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-11
|style="padding:.4em;"|HR Shin
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41564-021-01030-7 Multi-kingdom microbiota analyses identify bacterial–fungal interactions and biomarkers of colorectal cancer across cohorts]
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|style="padding:.4em;" rowspan=1|2023/05/09
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-10
|style="padding:.4em;"|SG Oh
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[https://doi.org/10.1038/s42255-022-00716-4 The antitumour effects of caloric restriction are mediated by the gut microbiome]
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|style="padding:.4em;" rowspan=1|2023/05/02
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-9
|style="padding:.4em;"|WJ Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41591-022-01964-3 Variability of strain engraftment and predictability of microbiome composition after fecal microbiota transplantation across different diseases]
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|style="padding:.4em;" rowspan=1|2023/05/02
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-8
|style="padding:.4em;"|SM Han
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[https://doi.org/10.1038/s41591-022-01913-0 Drivers and determinants of strain dynamics following fecal microbiota transplantation]
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|style="padding:.4em;" rowspan=1|2023/05/02
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-7
|style="padding:.4em;"|YY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.cell.2022.11.023 Mobile genetic elements from the maternal microbiome shape infant gut microbial assembly and metabolism]
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|style="padding:.4em;" rowspan=1|2023/04/18
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-6
|style="padding:.4em;"|SH Heo
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.chom.2023.01.018 Mother-to-infant microbiota transmission and infant microbiota development across multiple body sites]
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|style="padding:.4em;" rowspan=1|2023/04/18
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-5
|style="padding:.4em;"|SY Yang
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[https://doi.org/10.1038/s41467-023-36633-7 Population-level impacts of antibiotic usage on the human gut microbiome]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-4
|style="padding:.4em;"|YH Yoon
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[https://doi.org/10.1038/s41586-022-05438-x Enterococci enhance Clostridioides difficile pathogenesis]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-3
|style="padding:.4em;"|DH Lee
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[https://doi.org/10.1002/imt2.61 Targeting keystone species helps restore the dysbiosis of butyrate‐producing bacteria in nonalcoholic fatty liver disease]
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|style="padding:.4em;"|YJ Roh
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[https://doi.org/10.1002/advs.202203115 Differential Oral Microbial Input Determines Two Microbiota Pneumo-Types Associated with Health Status]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-1
|style="padding:.4em;"|SH Ahn
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[https://doi.org/10.1016/j.cell.2022.08.021 Gut microbiome of multiple sclerosis patients and paired household healthy controls reveal associations with disease risk and course]
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|+style="text-align:left;font-size:12pt" | 2023-1 scOmics
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|style="padding:.4em;"|JW Yu
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[https://doi.org/10.1101/2023.01.17.524482 Decoupling the correlation between cytotoxic and exhausted T lymphocyte transcriptomic signatures enhances melanoma immunotherapy response prediction from tumor expression]
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|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-23
|style="padding:.4em;"|IS Choi
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[https://doi.org/10.1101/2023.07.28.550993 Major data analysis errors invalidate cancer microbiome findings]
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|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|EJ Sung
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[https://doi.org/10.1038/s43018-022-00475-x A single-cell atlas of glioblastoma evolution under therapy reveals cell-intrinsic and cell-extrinsic therapeutic targets]
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|style="padding:.4em;" rowspan=1|Single-cell
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[https://doi.org/10.1038/s43018-022-00433-7 Single-cell meta-analyses reveal responses of tumor-reactive CXCL13+ T cells to immune-checkpoint blockade]
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|style="padding:.4em;"|JW Yu
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!scope="col" style="padding:.4em" | Paper title
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|style="padding:.4em;" rowspan=1|Microbiome
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[https://doi.org/10.1186/s40168-022-01435-4 Alterations of oral microbiota and impact on the gut microbiome in type 1 diabetes mellitus revealed by integrated multi‑omic analyses]
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[https://www.nature.com/articles/s41564-022-01157-1 Phage–host coevolution in natural populations]
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[https://www.nature.com/articles/s41467-022-34188-7 UniTVelo: temporally unified RNA velocity reinforces single-cell trajectory inference]
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[https://www.biorxiv.org/content/10.1101/2022.08.02.502504v1 A novel in silico method employs chemical and protein similarity algorithms to accurately identify chemical transformations in the human gut microbiome]
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[https://www.biorxiv.org/content/10.1101/2021.09.13.460160v3 Inference of disease-associated microbial biomarkers based on metagenomic and metatranscriptomic data]
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[https://www.sciencedirect.com/science/article/pii/S0092867422009199?via%3Dihub Personalized microbiome-driven effects of non-nutritive sweeteners on human glucose tolerance]
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[https://www.nature.com/articles/s41564-022-01121-z Identification of shared and disease-specific host gene–microbiome associations across human diseases using multi-omic integration]
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[https://www.biorxiv.org/content/10.1101/2021.10.06.463341v2.full SynTracker: a synteny based tool for tracking microbial strains]
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[https://www.nature.com/articles/s41587-022-01226-0 Identification of antimicrobial peptides from the human gut microbiome using deep learning]
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[https://www.nature.com/articles/s41586-022-04648-7 Discovery of bioactive microbial gene products in inflammatory bowel disease]
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[https://www.nature.com/articles/s43588-022-00247-8 Large-scale microbiome data integration enables robust biomarker identification]
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[https://www.nature.com/articles/s41467-022-30512-3 Predicting cancer prognosis and drug response from the tumor microbiome]
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[https://www.nature.com/articles/s41586-022-04862-3 Biosynthetic potential of the global ocean microbiome]
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[https://www.nature.com/articles/s41596-020-00480-3 Tutorial: assessing metagenomics software with the CAMI benchmarking toolkit disease]
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[https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02195-w Functional and genetic markers of niche partitioning among enigmatic members of the human oral microbiome]
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[https://www.biorxiv.org/content/10.1101/2022.02.21.480893v1 Integrating phylogenetic and functional data in microbiome studies]
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[https://genomebiology.biomedcentral.com/articles/10.1186/s13059-021-02473-1 Pandora: nucleotide-resolution bacterial pan-genomics with reference graphs]
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[https://journals.asm.org/doi/10.1128/mSystems.00252-19 Comprehensive Analysis Reveals the Evolution and Pathogenicity of Aeromonas, Viewed from Both Single Isolated Species and Microbial Communities]
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[https://genomebiology.biomedcentral.com/articles/10.1186/s13059-021-02576-9 Species-resolved sequencing of low-biomass or degraded microbiomes using 2bRAD-M]
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[https://pubmed.ncbi.nlm.nih.gov/34517888/ Gut microbial determinants of clinically important improvement in patients with rheumatoid arthritis]
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[https://pubmed.ncbi.nlm.nih.gov/34880502/ Gut microbiota modulates weight gain in mice after discontinued smoke exposure]
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[https://pubmed.ncbi.nlm.nih.gov/34819672/ The human microbiome encodes resistance to the antidiabetic drug acarbose]
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[https://pubmed.ncbi.nlm.nih.gov/34618582/ Commensal bacteria promote endocrine resistance in prostate cancer through androgen biosynthesis]
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[https://pubmed.ncbi.nlm.nih.gov/34912116/ Towards the biogeography of prokaryotic genes]
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[https://www.biorxiv.org/content/10.1101/2022.02.01.478746v2 Scalable microbial strain inference in metagenomic data using StrainFacts]
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[https://www.biorxiv.org/content/10.1101/2022.02.15.480535v1 StrainPanDA: linked reconstruction of strain composition and gene content profiles via pangenome-based decomposition of metagenomic data]
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[https://www.sciencedirect.com/science/article/pii/S1931312821002365 Dispersal strategies shape persistence and evolution of human gut bacteria]
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[https://www.sciencedirect.com/science/article/pii/S0092867421003524 The long-term genetic stability and individual specificity of the human gut microbiome]
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[https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02042-y Analysis of 1321 Eubacterium rectale genomes from metagenomes uncovers complex phylogeographic population structure and subspecies functional adaptations]
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[https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3000102 Evolutionary dynamics of bacteria in the gut microbiome within and across hosts]
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[https://elifesciences.org/articles/42693 Extensive transmission of microbes along the gastrointestinal tract]
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[https://www.cell.com/cell-host-microbe/fulltext/S1931-3128(19)30041-1?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS1931312819300411%3Fshowall%3Dtrue Distinct Genetic and Functional Traits of Human Intestinal Prevotella copri Strains Are Associated with Different Habitual Diets]
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[https://www.nature.com/articles/nmeth.3802 Strain-level microbial epidemiology and population genomics from shotgun metagenomics]
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[https://www.cell.com/cell/fulltext/S0092-8674(21)00942-9#secsectitle0025 Chromatin and gene-regulatory dynamics of the developing human cerebral cortex at single-cell resolution]
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[https://www.biorxiv.org/content/10.1101/2021.02.09.430114v2 Single-cell ATAC and RNA sequencing reveal pre-existing and persistent subpopulations of cells associated with relapse of prostate cancer]
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[https://www.biorxiv.org/content/10.1101/2021.03.16.435578v1 Integrated single-cell transcriptomics and epigenomics reveals strong germinal center-associated etiology of autoimmune risk loci]
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[https://www.biorxiv.org/content/10.1101/2021.07.28.453784v1 Functional Inference of Gene Regulation using Single-Cell Multi-Omics]
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[https://www.biorxiv.org/content/10.1101/2021.03.24.436532v1 Single-cell analyses reveal a continuum of cell state and composition changes in the malignant transformation of polyps to colorectal cancer]
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[https://www.cell.com/cancer-cell/fulltext/S1535-6108(21)00165-3 Single-cell sequencing links multiregional immune landscapes and tissue-resident T cells in ccRCC to tumor topology and therapy efficacy]
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[https://www.sciencedirect.com/science/article/pii/S1535610821001173 Tumor and immune reprogramming during immunotherapy in advanced renal cell carcinoma]
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[https://www.sciencedirect.com/science/article/pii/S1074761321001199 Single-cell chromatin accessibility landscape identifies tissue repair program in human regulatory T cells]
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[https://www.nature.com/articles/s41591-021-01232-w Identification of resistance pathways and therapeutic targets in relapsed multiple myeloma patients through single-cell sequencing]
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[https://www.nature.com/articles/s41591-021-01323-8 A single-cell map of intratumoral changes during anti-PD1 treatment of patients with breast cancer]
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[https://www.sciencedirect.com/science/article/abs/pii/S0092867420316135 Single-cell landscape of the ecosystem in early-relapse hepatocellular carcinoma]
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[https://www.nature.com/articles/s41586-021-03552-w Interpreting type 1 diabetes risk with genetics and single-cell epigenomics]
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[https://www.sciencedirect.com/science/article/pii/S0092867421000726 Massive expansion of human gut bacteriophage diversity]
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[https://www.sciencedirect.com/science/article/pii/S1931312821001451 The infant gut resistome associates with E. coli, environmental exposures, gut microbiome maturity, and asthma-associated bacterial composition]
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[https://www.sciencedirect.com/science/article/pii/S1931312820306703?dgcid=rss_sd_all Methotrexate impacts conserved pathways in diverse human gut bacteria leading to decreased host immune activation]
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[https://science.sciencemag.org/content/366/6471/eaax9176 A metagenomic strategy for harnessing the chemical repertoire of the human microbiome]
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[https://www.nature.com/articles/s41467-019-10927-1 Predictive metabolomic profiling of microbial communities using amplicon or metagenomic sequences]
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[https://www.nature.com/articles/s41564-018-0306-4 Gut microbiome structure and metabolic activity in inflammatory bowel disease]
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[https://www.nature.com/articles/s41591-020-01183-8 Microbiome connections with host metabolism and habitual diet from 1,098 deeply phenotyped individuals]
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[https://www.nature.com/articles/s41467-020-18476-8 A predictive index for health status using species-level gut microbiome profiling]
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[https://www.nature.com/articles/s41586-019-1237-9 Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases]
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[https://www.nature.com/articles/s41467-021-21475-y Gastrointestinal microbiota composition predicts peripheral inflammatory state during treatment of human tuberculosis]
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[https://www.sciencedirect.com/science/article/pii/S1931312820301694 Structure of the Mucosal and Stool Microbiome in Lynch Syndrome]
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[https://science.sciencemag.org/content/369/6506/936 Cross-reactivity between tumor MHC class 1-restricted antigens and an enterococcal bacteriophage]
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[https://www.nature.com/articles/s41564-020-00831-6 Bifidobacterium bifidum strains synergize with immune checkpoint inhibitors to reduce tumour burden in mice]
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[https://www.nature.com/articles/s41591-020-01223-3 The gut microbiome modulates the protective association between a Mediterranean diet and cardiometabolic disease risk]
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[https://www.nature.com/articles/s41467-019-14177-z Impact of commonly used drugs on the composition and metabolic function of the gut microbiota]
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[https://www.sciencedirect.com/science/article/pii/S0092867420305638 Personalized Mapping of Drug Metabolism by the Human Gut Microbiome]
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[https://www.cell.com/fulltext/S0092-8674(17)30107-1 Mining the Human Gut Microbiota for Immunomodulatory Organisms]
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[https://www.nature.com/articles/s41586-020-2095-1 Microbiome analyses of blood and tissues suggest cancer diagnostic approach]
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[https://science.sciencemag.org/content/368/6494/973 The human tumor microbiome is composed of tumor type-specific intracellular bacteria]
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[https://www.nature.com/articles/s41590-020-0784-4 Functional CRISPR dissection of gene networks controlling human regulatory T cell identity]
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[https://www.sciencedirect.com/science/article/pii/S0092867420306887 Molecular Pathways of Colon Inflammation Induced by Cancer Immunotherapy]
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[https://www.nature.com/articles/s41588-020-00721-x Single-cell epigenomic analyses implicate candidate causal variants at inherited risk loci for Alzheimer’s and Parkinson’s diseases]
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[https://www.nature.com/articles/s41467-020-14766-3 Trajectory-based differential expression analysis for single-cell sequencing data]
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[https://www.nature.com/articles/s41588-018-0156-2 Genetic determinants of co-accessible chromatin regions in activated T cells across humans]
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[https://www.sciencedirect.com/science/article/pii/S009286742030341X Single-Cell Analyses Inform Mechanisms of Myeloid-Targeted Therapies in Colon Cancer]
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[https://www.sciencedirect.com/science/article/pii/S0092867419308967?via%3Dihub Single-Cell Analysis of Crohn's Disease Lesions Identifies a Pathogenic Cellular Module Associated with Resistance to Anti-TNF Therapy]
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[https://www.nature.com/articles/s41467-020-15956-9 Multi-omic single-cell snapshots reveal multiple independent trajectories to drug tolerance in a melanoma cell line]
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[https://pubmed.ncbi.nlm.nih.gov/32393797/ Multi-omic single-cell snapshots reveal multiple independent trajectories to drug tolerance in a melanoma cell line]
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[https://pubmed.ncbi.nlm.nih.gov/31974247/ Single-cell Transcriptional Diversity Is a Hallmark of Developmental Potential]
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[https://pubmed.ncbi.nlm.nih.gov/30388456/ Defining T Cell States Associated With Response to Checkpoint Immunotherapy in Melanoma]
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[https://pubmed.ncbi.nlm.nih.gov/31915379/ Rapid Non-Uniform Adaptation to Conformation-Specific KRAS(G12C) Inhibition]
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[https://pubmed.ncbi.nlm.nih.gov/31959990/ Targeted Therapy Guided by Single-Cell Transcriptomic Analysis in Drug-Induced Hypersensitivity Syndrome: A Case Report]
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[https://pubmed.ncbi.nlm.nih.gov/32066951/ Distinct Microbial and Immune Niches of the Human Colon]
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[https://pubmed.ncbi.nlm.nih.gov/29434354/ Single-cell Gene Expression Reveals a Landscape of Regulatory T Cell Phenotypes Shaped by the TCR]
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[https://pubmed.ncbi.nlm.nih.gov/30078704/ A Single-Cell Atlas of In Vivo Mammalian Chromatin Accessibility]
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[https://pubmed.ncbi.nlm.nih.gov/31792411/ Single-cell Multiomic Analysis Identifies Regulatory Programs in Mixed-Phenotype Acute Leukemia]
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[https://www.sciencedirect.com/science/article/pii/S1931312819303488 Obese Individuals with and without Type 2 Diabetes Show Different Gut Microbial Functional Capacity and Composition]
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[https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-019-0722-6 Clustering co-abundant genes identifies components of the gut microbiome that are reproducibly associated with colorectal cancer and inflammatory bowel disease]
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[https://www.nature.com/articles/s41587-019-0206-z Massively parallel single-cell chromatin landscapes of human immune cell development and intratumoral T cell exhaustion]
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|style="padding:.4em;"|SB Baek
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[https://www.biorxiv.org/content/10.1101/739011v1 Assessment of computational methods for the analysis of single-cell ATAC-seq data]
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|style="padding:.4em;" rowspan=2|2019/10/01
|style="padding:.4em;" rowspan=2|Microbiome
|style="padding:.4em;"|19-47
|style="padding:.4em;"|MY Lee
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[https://www.sciencedirect.com/science/article/pii/S1931312819303026 Meta-Analysis Reveals Reproducible Gut Microbiome Alterations in Response to a High-Fat Diet]
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|style="padding:.4em;"|19-46
|style="padding:.4em;"|NY Kim
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[https://www.sciencedirect.com/science/article/pii/S0092867419307731 Tumor Microbiome Diversity and Composition Influence Pancreatic Cancer Outcomes]
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|style="padding:.4em;" rowspan=2|2019/09/24
|style="padding:.4em;" rowspan=2|Single-cell
|style="padding:.4em;"|19-45
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://genome.cshlp.org/content/early/2019/04/01/gr.243725.118 The accessible chromatin landscape of the murine hippocampus at single-cell resolution]
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|style="padding:.4em;"|19-44
|style="padding:.4em;"|SB Baek
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[https://www.sciencedirect.com/science/article/pii/S009286741830446X Integrated Single-Cell Analysis Maps the Continuous Regulatory Landscape of Human Hematopoietic Differentiation]
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|style="padding:.4em;" rowspan=2|2019/09/17
|style="padding:.4em;" rowspan=2|Microbiome
|style="padding:.4em;"|19-43
|style="padding:.4em;"|SH Lee
|style="padding:.4em;text-align:left"|
[https://science.sciencemag.org/content/365/6449/eaau4735 A sparse covarying unit that describes healthy and impaired human gut microbiota development]
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|style="padding:.4em;"|19-42
|style="padding:.4em;"|CY Kim
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[https://www.sciencedirect.com/science/article/pii/S0092867419307810 Large-Scale Analyses of Human Microbiomes Reveal Thousands of Small, Novel Genes]
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|style="padding:.4em;" rowspan=2|2019/09/10
|style="padding:.4em;" rowspan=2|Single-cell
|style="padding:.4em;"|19-41
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S0092867419307329?via%3Dihub Intra- and Inter-cellular Rewiring of the Human Colon during Ulcerative Colitis]
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|style="padding:.4em;"|19-40
|style="padding:.4em;"|IS Choi
|style="padding:.4em;text-align:left"|
[https://www.nature.com/articles/nbt.4042 Multiplexed droplet single-cell RNA-sequencing using natural genetic variation]
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|style="padding:.4em;" rowspan=2|2019/09/03
|style="padding:.4em;" rowspan=2|Microbiome
|style="padding:.4em;"|19-39
|style="padding:.4em;"|NY Kim
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S193131281930352X?via%3Dihub The Landscape of Genetic Content in the Gut and Oral Human Microbiome]
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|style="padding:.4em;"|19-38
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S0092867419307755?via%3Dihub Benchmarking Metagenomics Tools for Taxonomic Classification]
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{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2019
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|style="padding:.4em;" rowspan=2|2019/08/20
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|style="padding:.4em;"|JH Cha
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[https://www.biorxiv.org/content/10.1101/719088v1 Coexpression uncovers a unified single-cell transcriptomic landscape]
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|style="padding:.4em;"|19-36
|style="padding:.4em;"|MY Lee
|style="padding:.4em;text-align:left"|
[https://www.biorxiv.org/content/10.1101/586859v1 Single-cell interactomes of the human brain reveal cell-type specific convergence of brain disorders]
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|style="padding:.4em;" rowspan=3|2019/08/14
|style="padding:.4em;"|19-35
|style="padding:.4em;"|SH Lee
|style="padding:.4em;text-align:left"|
[https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004075 Proportionality: a valid alternative to correlation for relative data]
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|style="padding:.4em;"|19-34
|style="padding:.4em;"|SH Lee
|style="padding:.4em;text-align:left"|
[https://www.nature.com/articles/s41598-017-16520-0 propr: An R-package for Identifying Proportionally Abundant Features Using Compositional Data Analysis]
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|style="padding:.4em;"|19-33
|style="padding:.4em;"|HJ Han
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[https://www.nature.com/articles/s41591-018-0157-9 Robust prediction of response to immune checkpoint blockade therapy in metastatic melanoma]
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|style="padding:.4em;" rowspan=2|2019/08/13
|style="padding:.4em;"|19-32
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[https://www.nature.com/articles/s41592-018-0254-1 A test metric for assessing single-cell RNA-seq batch correction]
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|style="padding:.4em;"|19-31
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S0092867419305598 Comprehensive Integration of Single-Cell Data]
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|style="padding:.4em;" rowspan=2|2019/08/08
|style="padding:.4em;"|19-30
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S0092867418313941 Defining T Cell States Associated with Response to Checkpoint Immunotherapy in Melanoma]
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|style="padding:.4em;"|19-29
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S009286741831242X High-Dimensional Analysis Delineates Myeloid and Lymphoid Compartment Remodeling during Successful Immune-Checkpoint Cancer Therapy]
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|style="padding:.4em;" rowspan=2|2019/08/07
|style="padding:.4em;"|19-28
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[https://www.biorxiv.org/content/10.1101/555557v1 A single-cell reference map for human blood and tissue T cell activation reveals functional states in health and disease]
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|style="padding:.4em;"|19-27
|style="padding:.4em;"|IS Choi
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S009286741831568X Dysfunctional CD8 T Cells Form a Proliferative, Dynamically Regulated Compartment within Human Melanoma]
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|style="padding:.4em;" rowspan=2|2019/07/30
|style="padding:.4em;"|19-26
|style="padding:.4em;"|IS Choi
|style="padding:.4em;text-align:left"|
[https://www.nature.com/articles/nm.4466 High-dimensional single-cell analysis predicts response to anti-PD-1 immunotherapy]
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|style="padding:.4em;"|19-25
|style="padding:.4em;"|JW Cho
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S0092867418311784 A Cancer Cell Program Promotes T Cell Exclusion and Resistance to Checkpoint Blockade]
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|style="padding:.4em;" rowspan=2|2019/07/23
|style="padding:.4em;"|19-24
|style="padding:.4em;"|HJ Han
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[https://www.ncbi.nlm.nih.gov/pubmed/26006008 COMPASS identifies T-cell subsets correlated with clinical outcomes.]
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|style="padding:.4em;"|19-23
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[https://www.nature.com/articles/ncomms14825 Sensitive detection of rare disease-associated cell subsets via representation learning]
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|style="padding:.4em;" rowspan=3|2019/05/30
|style="padding:.4em;"|19-22
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[https://www.ncbi.nlm.nih.gov/pubmed/30936547 Meta-analysis of fecal metagenomes reveals global microbial signatures that are specific for colorectal cancer]
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|style="padding:.4em;"|19-21
|style="padding:.4em;"|
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[https://www.ncbi.nlm.nih.gov/pubmed/30936548 Metagenomic analysis of colorectal cancer datasets identifies cross-cohort microbial diagnostic signatures and a link with choline degradation]
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|style="padding:.4em;"|19-20
|style="padding:.4em;"|
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[https://www.ncbi.nlm.nih.gov/pubmed/30664783 Microbial network disturbances in relapsing refractory Crohn's disease.]
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|style="padding:.4em;" rowspan=3|2019/05/23
|style="padding:.4em;"|19-19
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[https://www.ncbi.nlm.nih.gov/pubmed/30867587 New insights from uncultivated genomes of the global human gut microbiome]
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|style="padding:.4em;"|19-18
|style="padding:.4em;"|
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[https://www.ncbi.nlm.nih.gov/pubmed/30745586 A new genomic blueprint of the human gut microbiota]
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|style="padding:.4em;"|19-17
|style="padding:.4em;"|
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[https://www.ncbi.nlm.nih.gov/pubmed/30661755 Extensive Unexplored Human Microbiome Diversity Revealed by Over 150,000 Genomes from Metagenomes Spanning Age, Geography, and Lifestyle.]
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|style="padding:.4em;" rowspan=2|2019/05/16
|style="padding:.4em;"|19-16
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[https://www.ncbi.nlm.nih.gov/pubmed/29311644 Dynamics of metatranscription in the inflammatory bowel disease gut microbiome.]
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|style="padding:.4em;"|19-15
|style="padding:.4em;"|
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[https://www.ncbi.nlm.nih.gov/pubmed/29335555 Metatranscriptome of human faecal microbial communities in a cohort of adult men.]
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|style="padding:.4em;" rowspan=2|2019/05/09
|style="padding:.4em;"|19-14
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[https://www.ncbi.nlm.nih.gov/pubmed/30193113 Post-Antibiotic Gut Mucosal Microbiome Reconstitution Is Impaired by Probiotics and Improved by Autologous FMT.]
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|style="padding:.4em;"|19-13
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[https://www.ncbi.nlm.nih.gov/pubmed/30193112 Personalized Gut Mucosal Colonization Resistance to Empiric Probiotics Is Associated with Unique Host and Microbiome Features.]
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|style="padding:.4em;" rowspan=2|2019/05/02
|style="padding:.4em;"|19-12
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[https://www.ncbi.nlm.nih.gov/pubmed/30753825 Distinct Immune Cell Populations Define Response to Anti-PD-1 Monotherapy and Anti-PD-1/Anti-CTLA-4 Combined Therapy.]
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[https://www.ncbi.nlm.nih.gov/pubmed/30778252 Subsets of exhausted CD8+ T cells differentially mediate tumor control and respond to checkpoint blockade.]
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|style="padding:.4em;" rowspan=2|2019/4/11
|style="padding:.4em;"|19-10
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[https://www.ncbi.nlm.nih.gov/pubmed/30479382 Lineage tracking reveals dynamic relationships of T cells in colorectal cancer.]
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[https://www.ncbi.nlm.nih.gov/pubmed/30523328 Single cell dissection of plasma cell heterogeneity in symptomatic and asymptomatic myeloma.]
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|style="padding:.4em;" rowspan=2|2019/4/4
|style="padding:.4em;"|19-8
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[https://www.ncbi.nlm.nih.gov/pubmed/29942092 Single-cell profiling of breast cancer T cells reveals a tissue-resident memory subset associated with improved prognosis.]
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[https://www.ncbi.nlm.nih.gov/pubmed/29942094 Global characterization of T cells in non-small-cell lung cancer by single-cell sequencing]
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|style="padding:.4em;" rowspan=2|2019/3/28
|style="padding:.4em;"|19-6
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[https://www.ncbi.nlm.nih.gov/pubmed/28319088 Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors.]
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|style="padding:.4em;"|19-5
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[https://www.ncbi.nlm.nih.gov/pubmed/28622514 Landscape of Infiltrating T Cells in Liver Cancer Revealed by Single-Cell Sequencing.]
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|style="padding:.4em;" rowspan=3|2019/3/21
|style="padding:.4em;"|19-4
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[https://www.ncbi.nlm.nih.gov/pubmed/29988129 Phenotype molding of stromal cells in the lung tumor microenvironment.]
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|style="padding:.4em;"|19-3-1
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[https://www.ncbi.nlm.nih.gov/pubmed/30787436 A single-cell molecular map of mouse gastrulation and early organogenesis]
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|style="padding:.4em;"|19-3
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[https://www.ncbi.nlm.nih.gov/pubmed/30787437 The single-cell transcriptional landscape of mammalian organogenesis]
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|style="padding:.4em;" rowspan=2|2019/3/14
|style="padding:.4em;"|19-2
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[https://www.ncbi.nlm.nih.gov/pubmed/29961579 Single-Cell Map of Diverse Immune Phenotypes in the Breast Tumor Microenvironment]
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|style="padding:.4em;"|19-1
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[https://www.ncbi.nlm.nih.gov/pubmed/29198524 Single-Cell Transcriptomic Analysis of Primary and Metastatic Tumor Ecosystems in Head and Neck Cancer]
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{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2018
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|style="padding:.4em;" rowspan=2|2018/06/14
|style="padding:.4em;"|18-12
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=A+pan-cancer+analysis+of+enhancer+expression+in+nearly+9000+patient+samples A Pan-Cancer Analysis of Enhancer Expression in Nearly 9000 Patient Samples.]
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|style="padding:.4em;"|18-11
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=Machine+learning+identifies+stemness+features+associated+with+oncogenic+dedifferentiation Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation.]
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|style="padding:.4em;" rowspan=2|2018/06/07
|style="padding:.4em;"|18-10
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=Developmental+and+oncogenic+programs+in+H3K27M+gliomas+dissected+by+single-cell+RNA-seq Developmental and oncogenic programs in H3K27M gliomas dissected by single-cell RNA-seq.]
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|style="padding:.4em;"|18-9
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=Chemoresistance+evolution+in+triple-negative+breast+cancer+delineated+by+single-cell+sequencing Chemoresistance Evolution in Triple-Negative Breast Cancer Delineated by Single-Cell Sequencing.]
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|style="padding:.4em;" rowspan=2|2018/05/31
|style="padding:.4em;"|18-8
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=Mapping+human+pluripotent+stem+cell+differentiation+pathways+using+high+throughput+single-cell+RNA-sequencing Mapping human pluripotent stem cell differentiation pathways using high throughput single-cell RNA-sequencing.]
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=A+single-cell+RNA-seq+survey+of+the+developmental+landscape+of+the+human+prefrontal+cortex A single-cell RNA-seq survey of the developmental landscape of the human prefrontal cortex.]
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|style="padding:.4em;" rowspan=2|2018/05/24
|style="padding:.4em;"|18-6
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=Single-cell+RNA+sequencing+identifies+celltype-specific+cis-eQTLs+and+co-expression+QTLs Single-cell RNA sequencing identifies celltype-specific cis-eQTLs and co-expression QTLs.]
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|style="padding:.4em;"|18-5
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=FOCS%3A+a+novel+method+for+analyzing+enhancer+and+gene+activity+patterns+infers+an+extensive+enhancer-promoter+map FOCS: a novel method for analyzing enhancer and gene activity patterns infers an extensive enhancer-promoter map.]
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|style="padding:.4em;" rowspan=2|2018/05/17
|style="padding:.4em;"|18-4
|style="padding:.4em;"|KS Kim
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[https://www.ncbi.nlm.nih.gov/pubmed/29149608 A Distinct Gene Module for Dysfunction Uncoupled from Activation in Tumor-Infiltrating T Cells.]
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|style="padding:.4em;"|18-3
|style="padding:.4em;"|SH Lee
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[https://www.ncbi.nlm.nih.gov/pubmed/29610481 A global transcriptional network connecting noncoding mutations to changes in tumor gene expression.]
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|style="padding:.4em;" rowspan=2|2018/05/10
|style="padding:.4em;"|18-2
|style="padding:.4em;"|JW Cho
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=Inferring+regulatory+element+landscapes+and+transcription+factor+networks+from+cancer+methylomes Inferring regulatory element landscapes and transcription factor networks from cancer methylomes.]
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|style="padding:.4em;"|18-1
|style="padding:.4em;"|DS Bae
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[https://www.ncbi.nlm.nih.gov/pubmed/28129544 Genomic and Transcriptomic Features of Response to Anti-PD-1 Therapy in Metastatic Melanoma.]
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{|class=wikitable style="text-align:center;"
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|style="padding:.4em;" rowspan=2|2017/06/28
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|style="padding:.4em;"|HJ Han
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[https://www.ncbi.nlm.nih.gov/pubmed/28104840 Tumor aneuploidy correlates with markers of immune evasion and with reduced response to immunotherapy.]
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|style="padding:.4em;"|17-35
|style="padding:.4em;"|JW Cho
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[https://www.ncbi.nlm.nih.gov/pubmed/27240091 Landscape of tumor-infiltrating T cell repertoire of human cancers.]
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|style="padding:.4em;" rowspan=2|2017/06/14
|style="padding:.4em;"|17-34
|style="padding:.4em;"|JW Cho
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[http://biorxiv.org/content/early/2015/09/01/025908 The landscape of T cell infiltration in human cancer and its association with antigen presenting gene expression.]
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|style="padding:.4em;"|17-33
|style="padding:.4em;"|MY Lee
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.08.052 A Distinct Gene Module for Dysfunction Uncoupled from Activation in Tumor-Infiltrating T Cells.]
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|style="padding:.4em;" rowspan=2|2017/06/07
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[http://www.tandfonline.com/doi/full/10.1080/2162402X.2016.1253654 Identification of genetic determinants of breast cancer immune phenotypes by integrative genome-scale analysis.]
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|style="padding:.4em;"|17-31
|style="padding:.4em;"|MY Lee
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.celrep.2016.03.075 Genomic Correlates of Immune-Cell Infiltrates in Colorectal Carcinoma.]
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|style="padding:.4em;" rowspan=2|2017/05/31
|style="padding:.4em;"|17-30
|style="padding:.4em;"|DS Bae
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.02.065 Genomic and Transcriptomic Features of Response to Anti-PD-1 Therapy in Metastatic Melanoma.]
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|style="padding:.4em;"|17-29
|style="padding:.4em;"|JW Cho
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.celrep.2016.12.019 Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade.]
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|style="padding:.4em;" rowspan=2|2017/05/24
|style="padding:.4em;"|17-28
|style="padding:.4em;"|EB Kim
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.12.022 Systemic Immunity Is Required for Effective Cencer Immunotherapy.]
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|style="padding:.4em;"|17-27
|style="padding:.4em;"|CY Kim
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.10.020 Linking the Human Gut Microbiome to Inflammatory Cytokine Production Capacity.]
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|style="padding:.4em;" rowspan=2|2017/05/17
|style="padding:.4em;"|17-26
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.10.018 Host and Environmental Factors Influencing Individual Human Cytokine Responses.]
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.10.017 A Functional Genomics Approach to Understand Variation in Cytokine Production in Humans.]
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|style="padding:.4em;" rowspan=2|2017/04/26
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1038%2FNMETH.4177 Pooled CRISPR screening with single-cell transcriptome readout.]
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.11.039 Dissecting Immune Circuits by Linking CRISPR-Pooled Screens with Single-Cell RNA-Seq.]
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|style="padding:.4em;" rowspan=2|2017/04/12
|style="padding:.4em;"|17-22
|style="padding:.4em;"|SH Lee
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.11.038 Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens.]
|-
|style="padding:.4em;"|17-21
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1038%2Fnbt.3569 Wishbone identifies bifurcating developmental trajectories from single-cell data.]
|-
|style="padding:.4em;" rowspan=2|2017/04/05
|style="padding:.4em;"|17-20
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[http://biorxiv.org/content/early/2017/02/21/110668 Reversed graph embedding resolves complex single-cell developmental trajectories.]
|-
|style="padding:.4em;"|17-19
|style="padding:.4em;"|SH Lee
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1038%2Fnmeth.4150 Single-cell mRNA quantification and differential analysis with Census.]
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|style="padding:.4em;" rowspan=2|2017/03/29
|style="padding:.4em;"|17-18
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.celrep.2016.12.060 Single-Cell Transcriptomic Analysis Defines Heterogeneity and Transcriptional Dynamics in the Adult Neural Stem Cell Lineage.]
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|style="padding:.4em;"|17-17
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/26051941 Single-Cell Network Analysis Identifies DDIT3 as a Nodal Lineage Regulator in Hematopoiesis.]
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|style="padding:.4em;" rowspan=2|2017/03/22
|style="padding:.4em;"|17-16
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/27580035 Single-cell analysis of mixed-lineage states leading to a binary cell fate choice.]
|-
|style="padding:.4em;"|17-15
|style="padding:.4em;"|EB Kim
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/27281220 Dissecting direct reprogramming from fibroblast to neuron using single-cell RNA-seq.]
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|style="padding:.4em;" rowspan=2|2017/03/15
|style="padding:.4em;"|17-14
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1126%2Fscience.aad0501 Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq.]
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|style="padding:.4em;"|17-13
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/26084335 Single-cell mRNA sequencing identifies subclonal heterogeneity in anti-cancer drug responses of lung adenocarcinoma cells.]
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|style="padding:.4em;" rowspan=2|2017/02/28
|style="padding:.4em;"|17-12
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/27824113 A Comprehensive Characterization of the Function of LincRNAs in Transcriptional Regulation Through Long-Range Chromatin Interactions.]
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|style="padding:.4em;"|17-11
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[//pubmed.gov/27851969 Epigenomic Deconvolution of Breast Tumors Reveals Metabolic Coupling between Constituent Cell Types.]
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|style="padding:.4em;" rowspan=2|2017/02/21
|style="padding:.4em;"|17-10
|style="padding:.4em;"|EB Kim
|style="padding:.4em;text-align:left"|
[http://biorxiv.org/content/early/2016/12/30/097451 Convergence of dispersed regulatory mutations predicts driver genes in prostate cancer.]
|-
|style="padding:.4em;"|17-09
|style="padding:.4em;"|EB Kim
|style="padding:.4em;text-align:left"|
[//pubmed.gov/27723759 Chromatin structure-based prediction of recurrent noncoding mutations in cancer.]
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|style="padding:.4em;" rowspan=2|2017/02/07
|style="padding:.4em;"|17-08
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[http://biorxiv.org/content/early/2016/11/17/088286 Somatic Mutations and Neoepitope Homology in Melanomas Treated with CTLA-4 Blockade.]
|-
|style="padding:.4em;"|17-07
|style="padding:.4em;"|MY Lee
|style="padding:.4em;text-align:left"|
[http://biorxiv.org/content/early/2016/11/28/090134 Chemotherapy weakly contributes to predicted neoantigen expression in ovarian cancer.]
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|style="padding:.4em;" rowspan=1|2017/01/31
|style="padding:.4em;"|17-06
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[//www.ncbi.nlm.nih.gov/pubmed/27912059 Microbiota Diurnal Rhythmicity Programs Host Transcriptome Oscillations.]
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|style="padding:.4em;" rowspan=1|2017/01/24
|style="padding:.4em;"|17-05
|style="padding:.4em;"|JW Cho
|style="padding:.4em;text-align:left"|
[//www.ncbi.nlm.nih.gov/pubmed/27851914 Transcriptional Landscape of Human Tissue Lymphocytes Unveils Uniqueness of Tumor-Infiltrating T Regulatory Cells.]
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|style="padding:.4em;" rowspan=2|2017/01/17
|style="padding:.4em;"|17-04
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[//www.ncbi.nlm.nih.gov/pubmed/25938943 Mapping long-range promoter contacts in human cells with high-resolution capture Hi-C]
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|style="padding:.4em;"|17-03
|style="padding:.4em;"|EB Kim
|style="padding:.4em;text-align:left"|
[//www.ncbi.nlm.nih.gov/pubmed/27306882 CHiCAGO: robust detection of DNA looping interactions in Capture Hi-C data.]
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|style="padding:.4em;" rowspan=1|2017/01/10
|style="padding:.4em;"|17-02
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[//www.ncbi.nlm.nih.gov/pubmed/26527291 ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis]
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|style="padding:.4em;" rowspan=1|2017/01/03
|style="padding:.4em;"|17-01
|style="padding:.4em;"|SH Lee
|style="padding:.4em;text-align:left"|
[//www.ncbi.nlm.nih.gov/pubmed/26287467 Single-cell messenger RNA sequencing reveals rare intestinal cell types]
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{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2016
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!scope="col" style="padding:.4em" |Date
!scope="col" style="padding:.4em" | Paper index
!scope="col" style="padding:.4em" | Presenter
!scope="col" style="padding:.4em" | Paper title
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|style="padding:.4em;" rowspan=1|2016/12/27
|style="padding:.4em;"|2016-31
|style="padding:.4em;"|EB Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/25664528 Decoding the regulatory network of early blood development from single-cell gene expression measurements.]
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|style="padding:.4em;" rowspan=1|2016/12/6
|style="padding:.4em;"|2016-30
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26299571 Single-Cell RNA-Seq with Waterfall Reveals Molecular Cascades underlying Adult Neurogenesis.]
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|style="padding:.4em;" rowspan=1|2016/11/29
|style="padding:.4em;"|2016-29
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/24658644 The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells.]
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|style="padding:.4em;" rowspan=1|2016/11/22
|style="padding:.4em;"|2016-28
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26887813 Classification of low quality cells from single-cell RNA-seq data.]
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|style="padding:.4em;" rowspan=1|2016/11/15
|style="padding:.4em;"|2016-27
|style="padding:.4em;"|MY Lee
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26000487 Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells.]
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|style="padding:.4em;" rowspan=1|2016/11/8
|style="padding:.4em;"|2016-26
|style="padding:.4em;"|JW Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26000488 Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets.]
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|style="padding:.4em;" rowspan=1|2016/11/1
|style="padding:.4em;"|2016-25
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27426982 Causal Mechanistic Regulatory Network for Glioblastoma Deciphered Using Systems Genetics Network Analysis.]
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|style="padding:.4em;" rowspan=1|2016/10/25
|style="padding:.4em;"|2016-24
|style="padding:.4em;"|MY Lee
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27549193 Comprehensive analyses of tumor immunity: implications for cancer immunotherapy.]
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|style="padding:.4em;" rowspan=1|2016/10/11
|style="padding:.4em;"|2016-23
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=sidespread+parainflammation Widespread parainflammation in human cancer]
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|style="padding:.4em;" rowspan=1|2016/9/27
|style="padding:.4em;"|2016-22
|style="padding:.4em;"|ER Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27535533 Analysis of protein-coding genetic variation in 60,706 humans]
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|style="padding:.4em;" rowspan=1|2016/9/20
|style="padding:.4em;"|2016-21
|style="padding:.4em;"|SM Yang
|style="padding:.4em;text-align:left"|
Functional characterization of somatic mutations in cancer using network-based inference of protein activity
[http://www.ncbi.nlm.nih.gov/pubmed/27322546 pubmed]
[http://www.nature.com/ng/journal/v48/n8/full/ng.3593.html fulltext]
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|style="padding:.4em;" rowspan=1|2016/9/13
|style="padding:.4em;"|2016-20
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=exploiting+single-cell+expression+to+characterize Exploiting single-cell expression to characterize co-expression replicability.]
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|style="padding:.4em;" rowspan=1|2016/9/6
|style="padding:.4em;"|2016-19
|style="padding:.4em;"|T Lee
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26940869 Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade]
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|style="padding:.4em;" rowspan=1|2016/8/31
|style="padding:.4em;"|2016-18
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27264179 A Computational Drug Repositioning Approach for Targeting Oncogenic Transcription Factors]
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|style="padding:.4em;" rowspan=1|2016/8/16
|style="padding:.4em;"|2016-17
|style="padding:.4em;"|JW Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27309802 The landscape of accessible chromatin in mammalian preimplantation embryos]
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|style="padding:.4em;" rowspan=1|2016/8/8
|style="padding:.4em;"|2016-16
|style="padding:.4em;"|EB Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27064255 Enhancer-promoter interactions are encoded by complex genomic signatures on looping chromatin]
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|style="padding:.4em;" rowspan=1|2016/8/1
|style="padding:.4em;"|2016-15
|style="padding:.4em;"|MY Lee
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27040498 Personalized Immunomonitoring Uncovers Molecular Networks that Stratify Lupus Patients]
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|style="padding:.4em;" rowspan=1|2016/7/25
|style="padding:.4em;"|2016-14
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=integration+of+summary+data+from+gwas+and+eqtl+studies Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets]
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|style="padding:.4em;" rowspan=1|2016/7/18
|style="padding:.4em;"|2016-13
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/23624555 Identification of transcriptional regulators in the mouse immune system]
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|style="padding:.4em;" rowspan=2|2016/6/8
|style="padding:.4em;"|2016-12
|style="padding:.4em;"|DS Bae, CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26619012 Mapping the effects of drugs on the immune system]
|-
|style="padding:.4em;"| 2016-11
|style="padding:.4em;"|DS Bae, CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26186195 Elucidating compound mechanism of action by network perturbation analysis]
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|style="padding:.4em;" rowspan=2|2016/6/1
|style="padding:.4em;"|2016-10
|style="padding:.4em;"|MY Lee,SM Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26854917 Integrative approaches for large-scale transcriptome-wide association studies]
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|style="padding:.4em;"| 2016-9
|style="padding:.4em;"|MY Lee,SM Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26950747 Tissue-specific regulatory circuits reveal variable modular perturbations across complex diseases]
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|style="padding:.4em;" rowspan=2|2016/5/18
|style="padding:.4em;"|2016-8
|style="padding:.4em;"|CY Kim,SJ Kwon
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27013732 Survey of variation in human transcription factors reveals prevalent DNA binding changes]
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|style="padding:.4em;"| 2016-7
|style="padding:.4em;"| CY Kim,SJ Kwon
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26502339 Large-scale identification of sequence variants influencing human transcription factor occupancy in vivo]
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|style="padding:.4em;" rowspan=2|2016/5/11
|style="padding:.4em;"|2016-6
|style="padding:.4em;"|DS Bae, CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/25171417 Predicting Cancer-specific vulnerability via data-driven detection of synthetic lethality]
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|style="padding:.4em;"| 2016-5
|style="padding:.4em;"|DS Bae, CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/23467089 Dynamic regulatory network controlling Th17 cell differentiation]
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|style="padding:.4em;" rowspan=2|2016/5/4
|style="padding:.4em;"|2016-4
|style="padding:.4em;"|MY Lee,SM Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/25853550 Characterization of the immunophenotypes and antigenomes of colorectal cancers reveals distinct tumor escape mechanisms and novel targets for immunotherapy]
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|style="padding:.4em;"| 2016-3
|style="padding:.4em;"|MY Lee,SM Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26618344 Regulators of genetic risk of breast cancer identified by integrative network analysis]
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|style="padding:.4em;" rowspan=2|2016/4/27
|style="padding:.4em;"|2016-2
|style="padding:.4em;"|CY Kim,SJ Kwon
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26780608 A predictive computational framework for direct reprogramming between human cell types]
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|style="padding:.4em;"| 2016-1
|style="padding:.4em;"| CY Kim,SJ Kwon
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/25126793 CellNet: Network biology applied to stem cell engineering]
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{|class=wikitable style="text-align:center;"
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!scope="col" style="padding:.4em" | Date
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!scope="col" style="padding:.4em" | Presenter
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|style="padding:.4em;" rowspan=2|2015/06/11
|style="padding:.4em;"|2015-55
|style="padding:.4em;"|
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[http://genome.cshlp.org/content/24/2/340.long Improved exome prioritization of disease genes through cross-species phenotype comparison.]
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|style="padding:.4em;"|2015-54
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|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0002929714001128 Phevor combines multiple biomedical ontologies for accurate identification of disease-causing alleles in single individuals and small nuclear families.]
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|style="padding:.4em;" rowspan=2|2015/06/04
|style="padding:.4em;"|2015-53
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[http://www.nature.com/nmeth/journal/v10/n11/full/nmeth.2656.html eXtasy: variant prioritization by genomic data fusion.]
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|style="padding:.4em;"|2015-52
|style="padding:.4em;"|
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[http://genome.cshlp.org/content/21/9/1529.long A probabilistic disease-gene finder for personal genomes.]
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|style="padding:.4em;" rowspan=2|2015/05/28
|style="padding:.4em;"|2015-51
|style="padding:.4em;"|
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[http://www.nature.com/ng/journal/v46/n12/full/ng.3141.html Systematic analysis of noncoding somatic mutations and gene expression alterations across 14 tumor types.]
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|style="padding:.4em;"|2015-50
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nbt/journal/v28/n5/full/nbt.1630.html GREAT improves functional interpretation of cis-regulatory regions.]
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|style="padding:.4em;" rowspan=2|2015/05/21
|style="padding:.4em;"|2015-49
|style="padding:.4em;"|
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[http://www.nature.com/nmeth/journal/v11/n3/full/nmeth.2832.html Functional annotation of noncoding sequence variants.]
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|style="padding:.4em;"|2015-48
|style="padding:.4em;"|
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[http://genomebiology.com/content/15/10/480 FunSeq2: A framework for prioritizing noncoding regulatory variants in cancer.]
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|style="padding:.4em;" rowspan=2|2015/05/14
|style="padding:.4em;"|2015-47
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nmeth/journal/v12/n2/full/nmeth.3215.html Selecting causal genes from genome-wide association studies via functionally coherent subnetworks.]
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|style="padding:.4em;"|2015-46
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/ncomms/2015/150119/ncomms6890/full/ncomms6890.html Biological interpretation of genome-wide association studies using predicted gene functions.]
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|style="padding:.4em;" rowspan=3|2015/05/07
|style="padding:.4em;"|2015-45
|style="padding:.4em;"|
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[http://www.nature.com/ncomms/2014/140626/ncomms5212/full/ncomms5212.html Human symptoms-disease network.]
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|style="padding:.4em;"|2015-44
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867413010246 A nondegenerate code of deleterious variants in Mendelian loci contributes to complex disease risk.]
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|style="padding:.4em;"|2015-43
|style="padding:.4em;"|
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[http://www.sciencemag.org/content/347/6224/1257601.long Uncovering disease-disease relationships through the incomplete interactome.]

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|style="padding:.4em;" rowspan=2|2015/04/30
|style="padding:.4em;"|2015-42
|style="padding:.4em;"|
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[http://genome.cshlp.org/content/25/1/142.long The discovery of integrated gene networks for autism and related disorders.]
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|style="padding:.4em;"|2015-41
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[http://msb.embopress.org/content/10/12/774.long Integrated systems analysis reveals a molecular network underlying autism spectrum disorders.]
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|style="padding:.4em;" rowspan=3|2015/04/23
|style="padding:.4em;"|2015-40
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v518/n7539/full/nature13990.html Dissecting neural differentiation regulatory networks through epigenetic footprinting.]
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|style="padding:.4em;"|2015-39
|style="padding:.4em;"|
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[http://www.nature.com/nature/journal/v518/n7539/full/nature14221.html Cell-of-origin chromatin organization shapes the mutational landscape of cancer.]
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|style="padding:.4em;"|2015-38
|style="padding:.4em;"|
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[http://www.nature.com/nature/journal/v518/n7539/full/nature14248.html Integrative analysis of 111 reference human epigenomes.]
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|style="padding:.4em;" rowspan=2|2015/04/09
|style="padding:.4em;"|2015-37
|style="padding:.4em;"|
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[http://genome.cshlp.org/content/25/2/246.long Genome-wide analysis of local chromatin packing in Arabidopsis thaliana.]
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|style="padding:.4em;"|2015-36
|style="padding:.4em;"|
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[http://www.sciencedirect.com/science/article/pii/S0092867414014974 A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping.]
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|style="padding:.4em;" rowspan=2|2015/04/02
|style="padding:.4em;"|2015-35
|style="padding:.4em;"|
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[http://www.nature.com/nrg/journal/v14/n6/full/nrg3454.html Exploring the three-dimensional organization of genomes: interpreting chromatin interaction data.]
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|style="padding:.4em;"|2015-34
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[http://www.sciencemag.org/content/347/6225/1010.long Transcribed enhancers lead waves of coordinated transcription in transitioning mammalian cells.]
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|style="padding:.4em;" rowspan=2|2015/03/26
|style="padding:.4em;"|2015-33
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[http://genome.cshlp.org/content/25/2/257.long Cupid: simultaneous reconstruction of microRNA-target and ceRNA networks.]
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|style="padding:.4em;"|2015-32
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[http://genome.cshlp.org/content/25/1/41.long Characterization of the neural stem cell gene regulatory network identifies OLIG2 as a multifunctional regulator of self-renewal.]
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|style="padding:.4em;" rowspan=2|2015/03/19
|style="padding:.4em;"|2015-31
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nbt/journal/vaop/ncurrent/full/nbt.3154.html Decoding the regulatory network of early blood development from single-cell gene expression measurements.]
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|style="padding:.4em;"|2015-30
|style="padding:.4em;"|
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[http://www.nature.com/nrg/journal/v16/n3/full/nrg3833.html Computational and analytical challenges in single-cell transcriptomics.]
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|style="padding:.4em;" rowspan=3|2015/03/12
|style="padding:.4em;"|2015-29
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[http://www.sciencedirect.com/science/article/pii/S0092867415000136 Extensive Strain-Level Copy-Number Variation across Human Gut Microbiome Species.]
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|style="padding:.4em;"|2015-28
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v500/n7464/full/nature12506.html Richness of human gut microbiome correlates with metabolic markers.]
|-
|style="padding:.4em;"|2015-27
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v490/n7418/full/nature11450.html A metagenome-wide association study of gut microbiota in type 2 diabetes.]
|-
|style="padding:.4em;" rowspan=3|2015/03/09
|style="padding:.4em;"|2015-26
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003326 Practical guidelines for the comprehensive analysis of ChIP-seq data.]
|-
|style="padding:.4em;"|2015-25
|style="padding:.4em;"|T Lee
|style="padding:.4em;text-align:left"|
[http://genome.cshlp.org/content/23/5/777.long Patterns of regulatory activity across diverse human cell types predict tissue identity, transcription factor binding, and long-range interactions.]
|-
|style="padding:.4em;"|2015-24
|style="padding:.4em;"|ER Kim
|style="padding:.4em;text-align:left"|
[http://msb.embopress.org/content/10/11/760.long Rapid neurogenesis through transcriptional activation in human stem cells.]
|-
|style="padding:.4em;" rowspan=3|2015/03/02
|style="padding:.4em;"|2015-23
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414011787 Genome-Scale CRISPR-Mediated Control of Gene Repression and Activation.]
|-
|style="padding:.4em;"|2015-22
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1004237 Integrating multiple genomic data to predict disease-causing nonsynonymous single nucleotide variants in exome sequencing studies.]
|-
|style="padding:.4em;"|2015-21
|style="padding:.4em;"|ER Kim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/ng/journal/v45/n6/full/ng.2653.html The Genotype-Tissue Expression (GTEx) project.]
|-
|style="padding:.4em;" rowspan=3|2015/02/24
|style="padding:.4em;"|2015-20
|style="padding:.4em;"|ER Kim
|style="padding:.4em;text-align:left"|
[http://www.sciencemag.org/content/346/6212/1007.long Mouse regulatory DNA landscapes reveal global principles of cis-regulatory evolution.]
|-
|style="padding:.4em;"|2015-19
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v515/n7527/full/nature13985.html Principles of regulatory information conservation between mouse and human.]
|-
|style="padding:.4em;"|2015-18
|style="padding:.4em;"|BH Kang
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v515/n7527/full/nature13972.html Conservation of trans-acting circuitry during mammalian regulatory evolution.]
|-
|style="padding:.4em;" rowspan=3|2015/02/16
|style="padding:.4em;"|2015-17
|style="padding:.4em;"|T Lee
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature13835.html Genetic and epigenetic fine mapping of causal autoimmune disease variants.]
|-
|style="padding:.4em;"|2015-16
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/ng/journal/v46/n3/full/ng.2892.html A general framework for estimating the relative pathogenicity of human genetic variants.]
|-
|style="padding:.4em;"|2015-15
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003825 A probabilistic model to predict clinical phenotypic traits from genome sequencing.]
|-
|style="padding:.4em;" rowspan=4|2015/02/02
|style="padding:.4em;"|2015-14
|style="padding:.4em;"|BH Kang
|style="padding:.4em;text-align:left"|
[http://www.pnas.org/content/111/22/E2329.long Relating the metatranscriptome and metagenome of the human gut.]
|-
|style="padding:.4em;"|2015-13
|style="padding:.4em;"|BH Kang
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v513/n7516/full/nature13568.html Alterations of the human gut microbiome in liver cirrhosis.]
|-
|style="padding:.4em;"|2015-12
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nbt/journal/v32/n8/full/nbt.2942.html An integrated catalog of reference genes in the human gut microbiome.]
|-
|style="padding:.4em;"|2015-11
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nbt/journal/v32/n8/full/nbt.2939.html Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes.]
|-
|style="padding:.4em;" rowspan=5|2015/01/26
|style="padding:.4em;"|2015-10
|style="padding:.4em;"|HH Kim
|style="padding:.4em;text-align:left"|
[http://msb.embopress.org/content/9/1/666.long Computational meta'omics for microbial community studies.]
|-
|style="padding:.4em;"|2015-09
|style="padding:.4em;"|HH Kim
|style="padding:.4em;text-align:left"|
[http://genomemedicine.com/content/5/7/65 Functional profiling of the gut microbiome in disease-associated inflammation.]
|-
|style="padding:.4em;"|2015-08
|style="padding:.4em;"|BH Kang
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S016895251200145X Biodiversity and functional genomics in the human microbiome.]
|-
|style="padding:.4em;"|2015-07
|style="padding:.4em;"|BH Kang
|style="padding:.4em;text-align:left"|
[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002808 Chapter 12: Human Microbiome Analysis.]
|-
|style="padding:.4em;"|2015-06
|style="padding:.4em;"|BH Kang
|style="padding:.4em;text-align:left"|
[http://www.cell.com/cell/abstract/S0092-8674%2814%2900864-2 Conducting a Microbiome Study.]
|-
|style="padding:.4em;" rowspan=3|2015/01/12
|style="padding:.4em;"|2015-05
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/ncomms/2014/141210/ncomms6522/full/ncomms6522.html Small RNA changes en route to distinct cellular states of induced pluripotency.]
|-
|style="padding:.4em;"|2015-04
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v516/n7530/full/nature14046.html Genome-wide characterization of the routes to pluripotency.]
|-
|style="padding:.4em;"|2015-03
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v516/n7530/full/nature14047.html Divergent reprogramming routes lead to alternative stem-cell states.]
|-
|style="padding:.4em;" rowspan=2|2015/01/05
|style="padding:.4em;"|2015-02
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.pnas.org/content/111/21/E2191.long Global view of enhancer-promoter interactome in human cells.]
|-
|style="padding:.4em;"|2015-01
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://nar.oxfordjournals.org/content/41/22/10391.long Combining Hi-C data with phylogenetic correlation to predict the target genes of distal regulatory elements in human genome.]
|}
{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2014
|-
!scope="col" style="padding:.4em" | Date
!scope="col" style="padding:.4em" | Paper index
!scope="col" style="padding:.4em" | Presenter
!scope="col" style="padding:.4em" | Paper title
|-
|style="padding:.4em;" rowspan=2|2014/12/23
|style="padding:.4em;"|2014-41
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867413012270 Super-enhancers in the control of cell identity and disease.]
|-
|style="padding:.4em;"|2014-40
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867413003929 Master transcription factors and mediator establish super-enhancers at key cell identity genes.]
|-
|style="padding:.4em;" rowspan=4|2014/12/09
|style="padding:.4em;"|2014-39
|style="padding:.4em;"|HH Kim
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414013713 Unraveling the biology of a fungal meningitis pathogen using chemical genetics.]
|-
|style="padding:.4em;"|2014-38
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414014226 A proteome-scale map of the human interactome network.]
|-
|style="padding:.4em;"|2014-37
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[http://msb.embopress.org/content/10/9/752.long The role of the interactome in the maintenance of deleterious variability in human populations.]
|-
|style="padding:.4em;"|2014-36
|style="padding:.4em;"|HS Shim
|style="padding:.4em;text-align:left"|
[http://www.sciencemag.org/content/342/6154/1235587.long Integrative annotation of variants from 1092 humans: application to cancer genomics.]
|-
|style="padding:.4em;" rowspan=2|2014/12/02
|style="padding:.4em;"|2014-35
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://genome.cshlp.org/content/23/8/1319.long Mapping functional transcription factor networks from gene expression data.]
|-
|style="padding:.4em;"|2014-34
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nrg/journal/v15/n7/full/nrg3684.html In pursuit of design principles of regulatory sequences.]
|-
|style="padding:.4em;" rowspan=2|2014/11/25
|style="padding:.4em;"|2014-33
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[http://genomemedicine.com/content/3/6/36 Epigenomics of human embryonic stem cells and induced pluripotent stem cells: insights into pluripotency and implications for disease.]
|-
|style="padding:.4em;"|2014-32
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S009286741300891X Developmental fate and cellular maturity encoded in human regulatory DNA landscapes.]
|-
|style="padding:.4em;" rowspan=2|2014/11/18
|style="padding:.4em;"|2014-31
|style="padding:.4em;"|SM Yang
|style="padding:.4em;text-align:left"|
[http://genomemedicine.com/content/6/8/64 The 'dnet' approach promotes emerging research on cancer patient survival.]
|-
|style="padding:.4em;"|2014-30
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414010368 Determination and inference of eukaryotic transcription factor sequence specificity.]
|-
|style="padding:.4em;" rowspan=3|2014/11/11
|style="padding:.4em;"|2014-29
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[http://www.pnas.org/content/110/16/6412.long Transcription factors interfering with dedifferentiation induce cell type-specific transcriptional profiles.]
|-
|style="padding:.4em;"|2014-28
|style="padding:.4em;"|HH Kim
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414009350 Dissecting engineered cell types and enhancing cell fate conversion via CellNet.]
|-
|style="padding:.4em;"|2014-27
|style="padding:.4em;"|HH Kim
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414009349 CellNet: network biology applied to stem cell engineering.]
|-
|style="padding:.4em;" rowspan=2|2014/11/04
|style="padding:.4em;"|2014-26
|style="padding:.4em;"|HS Shim
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414009775 Predicting Cancer-Specific Vulnerability via Data-Driven Detection of Synthetic Lethality.]
|-
|style="padding:.4em;"|2014-25
|style="padding:.4em;"|JH Shin
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nmeth/journal/v11/n9/full/nmeth.3046.html Phen-Gen: combining phenotype and genotype to analyze rare disorders.]
|-
|style="padding:.4em;" rowspan=2|2014/10/28
|style="padding:.4em;"|2014-24
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nbt/journal/vaop/ncurrent/full/nbt.2877.html A community effort to assess and improve drug sensitivity prediction algorithms.]
|-
|style="padding:.4em;"|2014-23
|style="padding:.4em;"|HS Shim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/ng/journal/v46/n9/full/ng.3051.html Multi-tiered genomic analysis of head and neck cancer ties TP53 mutation to 3p loss.]
|-
|style="padding:.4em;" rowspan=2|2014/09/30
|style="padding:.4em;"|2014-22
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nmeth/journal/v10/n11/full/nmeth.2651.html Network-based stratification of tumor mutations.]
|-
|style="padding:.4em;"|2014-21
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414001457 Synonymous mutations frequently act as driver mutations in human cancers.]
|-
|style="padding:.4em;" rowspan=2|2014/09/23
|style="padding:.4em;"|2014-20
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003460 VarWalker: Personalized Mutation Network Analysis of Putative Cancer Genes from Next-Generation Sequencing Data.]
|-
|style="padding:.4em;"|2014-19
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://genomebiology.com/content/13/12/R124 DriverNet: uncovering the impact of somatic driver mutations on transcriptional networks in cancer.]
|-
|style="padding:.4em;" rowspan=2|2014/09/16
|style="padding:.4em;"|2014-18
|style="padding:.4em;"|JH Shin
|style="padding:.4em;text-align:left"|
[http://genomebiology.com/content/14/5/R52 Integrated analysis of recurrent properties of cancer genes to identify novel drivers.]
|-
|style="padding:.4em;"|2014-17
|style="padding:.4em;"|AR Cho
|style="padding:.4em;text-align:left"|
[http://genomemedicine.com/content/4/11/89 Improving the prediction of the functional impact of cancer mutations by baseline tolerance transformation.]
|-
|style="padding:.4em;" rowspan=2|2014/09/02
|style="padding:.4em;"|2014-16
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=23228031 A network module-based method for identifying cancer prognostic signatures.]
|-
|style="padding:.4em;"|2014-15
|style="padding:.4em;"|AR Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=24429628 Realizing the promise of cancer predisposition genes.]
|-
|style="padding:.4em;" rowspan=2|2014/08/19
|style="padding:.4em;"|2014-14
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=24952901 Assessing the clinical utility of cancer genomic and proteomic data across tumor types.]
|-
|style="padding:.4em;"|2014-13
|style="padding:.4em;"|HS Shim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=24132290 Mutational landscape and significance across 12 major cancer types.]
|-
|style="padding:.4em;" rowspan=2|2014/08/12
|style="padding:.4em;"|2014-12
|style="padding:.4em;"|HS Shim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/23945592 Signatures of mutational processes in human cancer.]
|-
|style="padding:.4em;"|2014-11
|style="padding:.4em;"|HJ Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/24390350 Discovery and saturation analysis of cancer genes across 21 tumour types.]
|-
|style="padding:.4em;" rowspan=2|2014/08/05
|style="padding:.4em;"|2014-10
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=24084849 Comprehensive identification of mutational cancer driver genes across 12 tumor types.]
|-
|style="padding:.4em;"|2014-9
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=24037244 IntOGen-mutations identifies cancer drivers across tumor types.]
|-
|style="padding:.4em;" rowspan=3|2014/07/29
|style="padding:.4em;"|2014-8
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/23900255 Computational approaches to identify functional genetic variants in cancer genomes.]
|-
|style="padding:.4em;"|2014-7
|style="padding:.4em;"|AR Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=23770567 Mutational heterogeneity in cancer and the search for new cancer-associated genes.]
|-
|style="padding:.4em;"|2014-6
|style="padding:.4em;"|AR Cho
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nmeth/journal/v11/n4/abs/nmeth.2891.html Cancer genomes: discerning drivers from passengers]
|-
|style="padding:.4em;" rowspan=3|2014/07/22
|style="padding:.4em;"|2014-5
|style="padding:.4em;"|AR Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/24071849 The Cancer Genome Atlas Pan-Cancer analysis project.]
|-
|style="padding:.4em;"|2014-4
|style="padding:.4em;"|AR Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/23539594 Cancer genome landscapes.]
|-
|style="padding:.4em;"|2014-3
|style="padding:.4em;"|AR Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=21900272 Distinguishing driver and passenger mutations in an evolutionary history categorized by interference.]
|-
|style="padding:.4em;" rowspan=2|2014/07/15
|style="padding:.4em;"|2014-2
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=24670764 A promoter-level mammalian expression atlas.]
|-
|style="padding:.4em;"|2014-1
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=24670763 An atlas of active enhancers across human cell types and tissues.]
|}
{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2013
|-
!scope="col" style="padding:.4em" | Date
!scope="col" style="padding:.4em" | Paper index
!scope="col" style="padding:.4em" | Presenter
!scope="col" style="padding:.4em" | Paper title
|-
|style="padding:.4em;" rowspan=2|2013/06/26
|style="padding:.4em;"|2013-31
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|[http://nar.oxfordjournals.org/content/40/16/7690.long Integration of Hi-C and ChIP-seq data reveals distinct types of chromatin linkages]
|-
|style="padding:.4em;"|2013-30
|style="padding:.4em;"|YH Ko
|style="padding:.4em;text-align:left"|[http://www.nature.com/nbt/journal/v31/n4/full/nbt.2519.html Deciphering molecular circuits from genetic variation underlying transcriptional responsiveness to stimuli]
|-
|style="padding:.4em;" rowspan=2|2013/06/04
|style="padding:.4em;"|2013-29
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|[https://www.cell.com/abstract/S0092-8674(13)00439-X Mapping the Human miRNA Interactome by CLASH Reveals Frequent Noncanonical Binding]
|-
|style="padding:.4em;"|2013-28
|style="padding:.4em;"|ER Kim
|style="padding:.4em;text-align:left"|[http://www.nature.com/nbt/journal/v31/n3/full/nbt.2514.html Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples]
|-
|style="padding:.4em;" rowspan=2|2013/05/28
|style="padding:.4em;"|2013-27
|style="padding:.4em;"|YH Ko
|style="padding:.4em;text-align:left"|[http://www.pnas.org/content/102/38/13544.long Discovering statistically significant pathways in expression profiling studies]
|-
|style="padding:.4em;"|2013-26
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|[http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0058977 Prediction and Validation of Gene-Disease Associations Using Methods Inspired by Social Network Analyses]
|-
|style="padding:.4em;" rowspan=2|2013/05/21
|style="padding:.4em;"|2013-25
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|[http://www.nature.com/nature/journal/v496/n7446/full/nature11981.html Dynamic regulatory network controlling TH17 cell differentiation]
|-
|style="padding:.4em;"|2013-24
|style="padding:.4em;"|T Lee
|style="padding:.4em;text-align:left"|[http://www.sciencedirect.com/science/article/pii/S0092867412013529 Deciphering and Prediction of Transcriptome Dynamics under Fluctuating Field Conditions]

|-
|style="padding:.4em;" rowspan=2|2013/05/14
|style="padding:.4em;"|2013-23
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|[http://www.sciencedirect.com/science/article/pii/S0092867412015565 Integrative eQTL-Based Analyses Reveal the Biology of Breast Cancer Risk Loci]

|-
|style="padding:.4em;"|2013-22
|style="padding:.4em;"|HJ Kim
|style="padding:.4em;text-align:left"|[http://www.nature.com/ng/journal/v45/n2/full/ng.2504.html Chromatin marks identify critical cell types for fine mapping complex trait variants]
|-
|style="padding:.4em;" rowspan=2|'''2013/05/07'''
|style="padding:.4em;"|2013-21
|style="padding:.4em;"|ER Kim
|style="padding:.4em;text-align:left"|[http://www.cell.com/abstract/S0092-8674(12)01555-3 Genome-wide Chromatin State Transitions Associated with Developmental and Environmental Cues]
|-
|style="padding:.4em;"|2013-20
|style="padding:.4em;"|HS Shim
|style="padding:.4em;text-align:left"|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1003201 Human Disease-Associated Genetic Variation Impacts Large Intergenic Non-Coding RNA Expression]
|-
|style="padding:.4em;"|2013/04/09
|style="padding:.4em;"|2013-19
|style="padding:.4em;"|HJ Kim
|style="padding:.4em;text-align:left"|[http://genome.cshlp.org/content/22/9/1790.long Annotation of functional variation in personal genomes using RegulomeDB]
|-
|style="padding:.4em;"|2013/04/02
|style="padding:.4em;"|2013-18
|style="padding:.4em;"|HJ Kim
|style="padding:.4em;text-align:left"|[http://genome.cshlp.org/content/22/9/1775.long The GENCODE v7 catalog of human long noncoding RNAs: Analysis of their gene structure, evolution, and expression]
|-
|style="padding:.4em;" |2013/03/12
|style="padding:.4em;"|2013-17
|style="padding:.4em;"| ER Kim
|style="padding:.4em;text-align:left"|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002311 Global Mapping of Cell Type–Specific Open Chromatin by FAIRE-seq Reveals the Regulatory Role of the NFI Family in Adipocyte Differentiation]
|-
|style="padding:.4em;" rowspan=1|2013/03/05
|style="padding:.4em;"|2013-16
|style="padding:.4em;"| ER Kim
|style="padding:.4em;text-align:left"|[http://www.nature.com/nrg/journal/v10/n10/full/nrg2641.html ChIP–seq: advantages and challenges of a maturing technology]
|-
|style="padding:.4em;" rowspan=3|2013/02/21
|style="padding:.4em;"|2013-15
|style="padding:.4em;"| KS Kim
|style="padding:.4em;text-align:left"|[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002830 Novel Modeling of Combinatorial miRNA Targeting Identifies SNP with Potential Role in Bone Density]
|-
|style="padding:.4em;"|2013-14
|style="padding:.4em;"| KS Kim
|style="padding:.4em;text-align:left"|[http://www.cell.com/abstract/S0092-8674(12)01424-9 A Molecular Roadmap of Reprogramming Somatic Cells into iPS Cells]
|-
|style="padding:.4em;"|2013-13
|style="padding:.4em;"| HJ Han
|style="padding:.4em;text-align:left"|[http://genome.cshlp.org/content/22/6/1015.long Differential DNase I hypersensitivity reveals factor-dependent chromatin dynamics.]
|-
|style="padding:.4em;" rowspan=3|2013/02/15
|style="padding:.4em;"|2013-12
|style="padding:.4em;"| HJ Han
|style="padding:.4em;text-align:left"|[http://www.nature.com/ng/journal/v43/n3/full/ng.759.html Chromatin accessibility pre-determines glucocorticoid receptor binding patterns]
|-
|style="padding:.4em;"|2013-11
|style="padding:.4em;"| JE Shim, CY Kim
|style="padding:.4em;text-align:left"|[http://www.nature.com/nbt/journal/v30/n11/full/nbt.2422.html Interpreting noncoding genetic variation in complex traits and human disease]
|-
|style="padding:.4em;"|2013-10
|style="padding:.4em;"|HJ Han, JH Kim
|style="padding:.4em;text-align:left"|[http://genome.cshlp.org/content/21/3/447.full.pdf+html Accurate inference of transcription factor binding from DNA sequence and chromatin accessibility data]
|-
|style="padding:.4em;" rowspan=3|2013/02/08
|style="padding:.4em;"|2013-09
|style="padding:.4em;"|HJ Kim
|style="padding:.4em;text-align:left"|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002599 Widespread Site-Dependent Buffering of Human Regulatory Polymorphism]
|-
|style="padding:.4em;"|2013-08
|style="padding:.4em;"|JE Shim, CY Kim
|style="padding:.4em;text-align:left;"|[http://genome.cshlp.org/content/22/9/1748.long Linking disease associations with regulatory information in the human genome]
|-
|style="padding:.4em;"|2013-07
|style="padding:.4em;"|HJ Han, JH Kim
|style="padding:.4em;text-align:left"|[http://genome.cshlp.org/content/22/9/1658.long Understanding transcriptional regulation by integrative analysis of transcription factor binding data]
|-
|style="padding:.4em;" rowspan=3|2013/01/25
|style="padding:.4em;"|2013-06
|style="padding:.4em;"|HJ Han, '''JH Kim'''
|style="padding:.4em;text-align:left;"|[http://www.nature.com/nature/journal/v489/n7414/full/nature11279.html The long-range interaction landscape of gene promoters]
|-
|style="padding:.4em;"|2013-05
|style="padding:.4em;"|'''ER Kim''', HS Shim
|style="padding:.4em;text-align:left;"|[http://www.nature.com/nature/journal/v489/n7414/full/nature11233.html Landscape of transcription in human cells]
|-
|style="padding:.4em;"|2013-04
|style="padding:.4em;"|'''HJ Han''', JH Kim
|style="padding:.4em;text-align:left;"|[http://www.nature.com/nature/journal/v489/n7414/full/nature11245.html Architecture of the human regulatory network derived from ENCODE data]

|-
|style="padding:.4em;" rowspan=2|2013/01/18
|style="padding:.4em;"|2013-03
|style="padding:.4em;"|KS Kim, '''TH Kim'''
|style="padding:.4em;text-align:left;"|[http://www.nature.com/nature/journal/v489/n7414/full/nature11212.html An expansive human regulatory lexicon encoded in transcription factor footprints]
|-
|style="padding:.4em;"|2013-02
|style="padding:.4em;"|'''HJ Han''', JH Kim
|style="padding:.4em;text-align:left;"|[http://www.nature.com/nature/journal/v489/n7414/full/nature11232.html The accessible chromatin landscape of the human genome]
|-
|style="padding:.4em;" rowspan=1|2013/01/11
|style="padding:.4em;"|2013-01
|style="padding:.4em;"| '''JE Shim''', CY Kim
|style="padding:.4em;text-align:left"|[http://www.nature.com/nature/journal/v489/n7414/full/nature11247.html An integrated encyclopedia of DNA elements in the human genome]
|}

2012
{|border ="1"
|style="color:black; background-color:#dcdcdc; padding:5px;" align="center" |Date
|style="color:black; background-color:#dcdcdc;" align="center"|Paper index
|style="color:black; background-color:#dcdcdc;" align="center"|Paper title
|-
|rowspan = "1"|2013/01/11
|align ="center"|2012-81
|[http://genome.cshlp.org/content/22/8/1589.full (TH Kim) MuSiC: identifying mutational significance in cancer genomes.]
|-
|rowspan = "1"|2012/12/04
|align ="center"|2012-80
|[http://genome.cshlp.org/content/22/8/1383 (CY KIM) Human genomic disease variants: A neutral evolutionary explanation]
|-
|rowspan = "2"|2012/11/20
| align="center"|2012-79
|[http://www.cell.com/abstract/S0092-8674(12)00639-3 (HS Shim) Circuitry and Dynamics of Human Transcription Factor Regulatory Networks]
|-
| align="center"|2012-78
|[http://www.nature.com/nature/journal/v487/n7408/full/nature11288.html (HJ Kim) Interpreting cancer genomes using systematic host network perturbations by tumour virus proteins]
|-
|rowspan = "2"|2012/11/06
| align="center"|2012-77
|[http://www.sciencemag.org/content/337/6099/1190.short (HJ Han) Systematic Localization of Common Disease-Associated Variation in Regulatory DNA]
|-
| align="center"|2012-76
|[http://www.pnas.org/cgi/doi/10.1073/pnas.1201904109 (KS Kim) A public resource facilitating clinical use of genomes]
|-
|rowspan = "6" |2012/07/19
| align="center"|2012-75
|[http://genome.cshlp.org/content/22/7/1334 (HJ Han & YH Ko) Predictive regulatory models in Drosophila melanogaster by integrative inference of transcriptional networks]
|-
| align="center"|2012-74
|[http://www.sciencemag.org/content/337/6090/100.full (JE Shim) An Abundance of Rare Functional Variants in 202 Drug Target Genes Sequenced in 14,002 People]
|-
| align="center"|2012-73
|[http://www.sciencemag.org/content/337/6090/64.abstract (JE Shim) Evolution and Functional Impact of Rare Coding Variation from Deep Sequencing of Human Exomes]
|-
| align="center"|2012-72
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2063581/ (SH Hwang) Network-based classification of breast cancer metastasis]
|-
| align="center"|2012-71
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002707 (T Lee&CY Kim)Brain Expression Genome-Wide Association Study (eGWAS) Identifies Human Disease-Associated Variants]
|-
| align="center"|2012-70
|[http://genome.cshlp.org/content/20/9/1297 (ER Kim&TH Kim)The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data]
|-
|rowspan = "8" |2012/07/16
| align="center"|2012-69
|[http://www.nature.com/ng/journal/v43/n5/full/ng.806.html (ER Kim&TH Kim)A framework for variation discovery and genotyping using next-generation DNA sequencing data]
|-
| align="center"|2012-66
|[http://bioinformatics.oxfordjournals.org/content/25/16/2078.long (ER Kim&TH Kim)The Sequence Alignment/Map format and SAMtools]
|-
| align="center"|2012-65
|[http://bioinformatics.oxfordjournals.org/content/early/2011/06/07/bioinformatics.btr330 (ER Kim&TH Kim)The Variant Call Format and VCFtools]
|-
| align="center"|2012-64
|[http://www.cell.com/abstract/S0092-8674(12)00104-3 (YH Go&HJ Han)The Impact of the Gut Microbiota on Human Health: An Integrative View]
|-
|align="center"|2012-63
|[http://www.sciencemag.org/content/336/6086/1262.abstract (T Lee&CY Kim)Host-Gut Microbiota Metabolic Interactions]
|-
|align="center"|2012-62
|[http://www.sciencemag.org/content/336/6086/1268.full (AR Cho,JH Ju)Interactions Between the Microbiota and the Immune System]
|-
|align="center"|2012-61
|[http://www.sciencemag.org/content/336/6086/1255.abstract (SH Hwang&HJ Cho)The Application of Ecological Theory Toward an Understanding of the Human Microbiome]
|-
|align="center"|2012-60
|[http://stm.sciencemag.org/content/4/137/137rv5 (SH Hwang&HJ Cho)Microbiota-Targeted Therapies: An Ecological Perspective]
|-
|rowspan = "3" |2012/07/13
|align="center"|2012-59
|[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002358 (JH Shin&HJ Kim)Metabolic Reconstruction for Metagenomic Data and Its Application to the Human Microbiome]
|-
| align="center"|2012-58
|[http://www.nature.com/nature/journal/v486/n7402/full/nature11209.html (JH Shin&HJ Kim)A framework for human microbiome research]
|-
|align="center"|2012-57
|[http://www.nature.com/nature/journal/v486/n7402/full/nature11234.html (JH Shin&HJ Kim)Structure, function and diversity of the healthy human microbiome]
|-
|rowspan = "4" |2012/07/12
|align="center"|2012-56
|[http://nar.oxfordjournals.org/content/40/D1/D957.long (AR Cho&JH Ju)COLT-Cancer: functional genetic screening resource for essential genes in human cancer cell lines]
|-
|align="center"|2012-55
|[http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002511 (YH Go&HJ Han)Google Goes Cancer: Improving Outcome Prediction for Cancer Patients by Network-Based Ranking of Marker Genes]
|-
| align="center"|2012-54
|[http://www.nature.com/msb/journal/v7/n1/full/msb201147.html (YH Go&HJ Han)A pharmacogenomic method for individualized prediction of drug sensitivity]
|-
|align="center"|2012-53
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature10983.html (JH Soh)The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups]
|-
|rowspan = "6" |2012/07/09
|align="center"|2012-52
|[http://www.nature.com/nature/journal/v483/n7391/full/nature11003.html (ER Kim&TH Kim)The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity]
|-
|align="center"|2012-51
|[http://www.nature.com/nature/journal/v483/n7391/full/nature11005.html (ER Kim&TH Kim)Systematic identification of genomic markers of drug sensitivity in cancer cells]
|-
| align="center"|2012-50
|[http://www.pnas.org/content/early/2011/10/13/1018854108.abstract (ER Kim&TH Kim)Subtype and pathway specific responses to anticancer compounds in breast cancer]
|-
|align="center"|2012-49
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature10945.html (JE Shim&KS Kim)De novo mutations revealed by whole-exome sequencing are strongly associated with autism]
|-
|align="center"|2012-48
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature11011.html (JE Shim&KS Kim)Patterns and rates of exonic de novo mutations in autism spectrum disorders]
|-
|align="center"|2012-47
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature10989.html (JE Shim&KS Kim)Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations]
|-
|rowspan = "6" |2012/07/06
| align="center"|2012-46
|[http://www.g3journal.org/content/1/3/233.full (T Lee&CY Kim)Integrating Rare-Variant Testing, Function Prediction, and Gene Network in Composite Resequencing-Based Genome-Wide Association Studies (CR-GWAS)]
|-
|align="center"|2012-45
|[http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002621 (T Lee&CY Kim)Type 2 Diabetes Risk Alleles Demonstrate Extreme Directional Differentiation among Human Populations, Compared to Other Diseases]
|-
|align="center"|2012-44
|[http://www.nature.com/nature/journal/v480/n7376/full/nature10665.html (AR Cho&JH Ju)Predicting mutation outcome from early stochastic variation in genetic interaction partners]
|-
|align="center"|2012-43
|[http://www.sciencemag.org/content/335/6064/82 (AR Cho&JH Ju)Fitness Trade-Offs and Environmentally Induced Mutation Buffering in Isogenic C. elegans]
|-
| align="center"|2012-42
|[http://genome.cshlp.org/content/22/6/1163 (JE Shim&KS Kim)Identification of microRNA-regulated gene networks by expression analysis of target genes]
|-
|align="center"|2012-41
|[http://www.nature.com/ng/journal/v44/n6/full/ng.2303.html (JE Shim&KS Kim)Exome sequencing and the genetic basis of complex traits]
|-
|rowspan = "6" |2012/07/02
|align="center"|2012-40
|[http://www.cell.com/abstract/S0092-8674(12)00573-9 (JH Soh)Functional Repurposing Revealed by Comparing S. pombe and S. cerevisiae Genetic Interactions]
|-
|align="center"|2012-39
|[http://genome.cshlp.org/content/22/2/375.long (ER Kim&TH Kim)De novo discovery of mutated driver pathways in cancer]
|-
| align="center"|2012-38
|[http://www.nature.com/msb/journal/v4/n1/full/msb20082.html (YH Go&HJ Han)A systems biology approach to prediction of oncogenes and molecular perturbation targets in B-cell lymphomas]
|-
|align="center"|2012-37
|[http://www.nature.com/msb/journal/v7/n1/full/msb201126.html (SH Hwang&HJ Cho)PREDICT: a method for inferring novel drug indications with application to personalized medicine]
|-
|align="center"|2012-36
|[http://www.nature.com/nature/journal/v453/n7198/full/nature06973.html (SH Hwang&HJ Cho)Synergistic response to oncogenic mutations defines gene class critical to cancer phenotype]
|-
|align="center"|2012-35
|[http://www.sciencemag.org/content/334/6062/1518.full (JH Shin&HJ Kim)Detecting Novel Associations in Large Data Sets]
|-
|rowspan = "3" |2012/03/05
| align="center"|2012-34
|[http://www.ncbi.nlm.nih.gov/pubmed/18516045 (8,HH Kim)Mapping and quantifying mammalian transcriptomes by RNA-Seq.]
|-
|align="center"|2012-33
|[http://genomebiology.com/2010/11/10/R106 (11,Go&Ju)Differential expression analysis for sequence count data]
|-
|align="center"|2012-32
|[http://bioinformatics.oxfordjournals.org/content/26/1/139.short (12,Go&Ju)edgeR: a Bioconductor package for differential expression analysis of digital gene expression data ]
|-
|rowspan = "7" |2012/02/27 2012/02/28
| align="center"|2012-31
|[http://www.nature.com/nrg/journal/v10/n1/full/nrg2484.html (1,JW Song)RNA-Seq: a revolutionary tool for transcriptomics]
|-
|align="center"|2012-30
|[http://www.nature.com/nmeth/journal/v8/n6/full/nmeth.1613.html (2,JW Song)Computational methods for transcriptome annotation and quantification using RNA-seq]
|-
|align="center"|2012-29
|[http://genomebiology.com/2010/11/12/220 (3,HJ Han)From RNA-seq reads to differential expression results]
|-
|align="center"|2012-28
|[http://www.nature.com/nmeth/journal/v7/n9/full/nmeth.1491.html (4,AR Cho)Comprehensive comparative analysis of strand-specific RNA sequencing methods]
|-
|align="center"|2012-27
|[http://www.plosone.org/article/info:doi%2F10.1371%2Fjournal.pone.0026426 (5,T Lee)A Low-Cost Library Construction Protocol and Data Analysis Pipeline for Illumina-Based Strand-Specific Multiplex RNA-Seq]
|-
|align="center"|2012-26
|[http://www.nature.com/nbt/journal/v28/n5/abs/nbt.1621.html (6,So&Shin)Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation]
|-
|align="center"|2012-25
|[http://bioinformatics.oxfordjournals.org/content/25/9/1105.abstract (7,So&Shin)TopHat: discovering splice junctions with RNA-Seq]
|-
|rowspan = "5" |2012/02/06
| align="center"|2012-24
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3125733/ mirConnX: condition-specific mRNA-microRNA network integrator]
|-
|align="center"|2012-23
|[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002190 Construction and Analysis of an Integrated Regulatory Network Derived from High-Throughput Sequencing Data]
|-
|align="center"|2012-22
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002415 A Densely Interconnected Genome-Wide Network of MicroRNAs and Oncogenic Pathways Revealed Using Gene Expression Signatures]
|-
|align="center"|2012-21
|[http://genome.cshlp.org/content/20/5/589.short Reprogramming of miRNA networks in cancer and leukemia]
|-
|align="center"|2012-20
|[http://www.cell.com/abstract/S0092-8674(11)01152-4 An Extensive MicroRNA-Mediated Network of RNA-RNA Interactions Regulates Established Oncogenic Pathways in Glioblastoma]
|-
|rowspan = "5" |2012/01/30
|align="center"|2012-19
|[http://www.cell.com/abstract/S0092-8674(11)00237-6 Principles and Strategies for Developing Network Models in Cancer]
|-
|align="center"|2012-18
|[http://www.nature.com/ng/journal/v37/n4/full/ng1532.html Reverse engineering of regulatory networks in human B cells]
|-
|align="center"|2012-17
|[http://www.nature.com/nature/journal/v452/n7186/abs/nature06757.html Variations in DNA elucidate molecular networks that cause disease]
|-
|align="center"|2012-16
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2779083/ Harnessing gene expression to identify the genetic basis of drug resistance]
|-
|align="center"|2012-15
|[http://www.sciencedirect.com/science/article/pii/S0092867410012936 An Integrated Approach to Uncover Drivers of Cancer]
|-
|rowspan = "3" |2012/01/09
|align="center"|2012-14
|[http://www.ncbi.nlm.nih.gov/pubmed/18704161 Genetic variation in an individual human exome.]
|-
|align="center"|2012-13
|[http://www.ncbi.nlm.nih.gov/pubmed/22081227 Predicting phenotypic variation in yeast from individual genome sequences.]
|-
|align="center"|2012-12
|[http://www.ncbi.nlm.nih.gov/pubmed/20435227 Clinical assessment incorporating a personal genome.]
|-
|rowspan = "6" |2012/01/09 2012/01/16
| align="center"|2012-11
|[http://www.ncbi.nlm.nih.gov/pubmed/20399638 Human allelic variation: perspective from protein function, structure, and evolution.]
|-
|align="center"|2012-10
|[http://www.ncbi.nlm.nih.gov/pubmed/19561590 Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm.]
|-
|align="center"|2012-09
|[http://www.ncbi.nlm.nih.gov/pubmed/11230178 Prediction of deleterious human alleles.]
|-
|align="center"|2012-08
|[http://www.ncbi.nlm.nih.gov/pubmed/12202775 Human non-synonymous SNPs: server and survey.]
|-
|align="center"|2012-07
|[http://www.ncbi.nlm.nih.gov/pubmed/20354512 A method and server for predicting damaging missense mutations.]
|-
|align="center"|2012-06
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1920242/ SNAP: predict effect of non-synonymous polymorphisms on function]
|-
|rowspan = "3" |2012/01/09 2012/01/16
|align="center"|2012-05
|[http://www.ncbi.nlm.nih.gov/pubmed/21920052 Computational and statistical approaches to analyzing variants identified by exome sequencing.]
|-
|align="center"|2012-04
|[http://www.ncbi.nlm.nih.gov/pubmed/18179889 Shifting paradigm of association studies: value of rare single-nucleotide polymorphisms.]
|-
|align="center"|2012-03
|[http://www.ncbi.nlm.nih.gov/pubmed/19684571 Targeted capture and massively parallel sequencing of 12 human exomes.]
|-
|rowspan = "2" |2012/01/09 2012/01/16
|align="center"|2012-02
|[http://www.ncbi.nlm.nih.gov/pubmed/17637733 The distribution of fitness effects of new mutations.]
|-
|align="center"|2012-01
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1852724/ Most Rare Missense Alleles Are Deleterious in Humans: Implications for Complex Disease and Association Studies]
|}
2011
{|border ="1"
|style="color:black; background-color:#dcdcdc;" align="center" |Date
|style="color:black; background-color:#dcdcdc;" align="center"|Paper_index
|style="color:black; background-color:#dcdcdc;" align="center"|Paper_title
|-
|rowspan = "6" |2011/11/28
|align="center"|2011-49
|[http://bmir.stanford.edu/file_asset/index.php/1407/BMIR-2009-1355.pdf (Shin)Data-Driven Methods to Discover Molecular Determinants of Serious Adverse Drug Events]
|-
|align="center"|2011-48
|[http://www.nature.com/nchembio/journal/v4/n11/abs/nchembio.118.html (Shin)Network pharmacology: the next paradigm in drug discovery]
|-
|align="center"|2011-47
|[http://www.nature.com/msb/journal/v7/n1/full/msb201171.html (Oh)Systematic exploration of synergistic drug pairs]
|-
|align="center"|2011-46
|[http://www.nature.com/msb/journal/v5/n1/full/msb200995.html (Shim)Chemogenomic profiling predicts antifungal synergies]
|-
|align="center"|2011-45
|[http://www.pnas.org/content/104/32/13086 (Beck)A strategy for predicting the chemosensitivity of human cancers and its application to drug discovery]
|-
|align="center"|2011-44
|[http://www.nature.com/nchembio/journal/v1/n7/full/nchembio747.html (Hwang)Analysis of drug-induced effect patterns to link structure and side effects of medicines]
|-
|rowspan = "3" |2011/11/14
|align="center"|2011-43
|[http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000662 Network-Based Elucidation of Human Disease Similarities Reveals Common Functional Modules Enriched for Pluripotent Drug Targets]
|-
|align="center"|2011-42
|[http://www.nature.com/msb/journal/v7/n1/full/msb201131.html Cross-species discovery of syncretic drug combinations that potentiate the antifungal fluconazole]
|-
|align="center"|2011-41
|[http://www.nature.com/msb/journal/v7/n1/full/msb20115.html Analysis of multiple compound–protein interactions reveals novel bioactive molecules]
|-
|rowspan = "4" |2011/11/07
|align="center"|2011-40
|[http://www.nature.com/nbt/journal/v25/n10/abs/nbt1338.html Drug—target network]
|-
|align="center"|2011-39
|[http://www.nature.com/msb/journal/v6/n1/full/msb200998.html A side effect resource to capture phenotypic effects of drugs]
|-
|align="center"|2011-38
|[http://genome.cshlp.org/content/18/2/206.long Quantitative systems-level determinants of human genes targeted by successful drugs]
|-
|align="center"|2011-37
|[http://www.sciencemag.org/content/321/5886/263.short Drug Target Identification Using Side-Effect Similarity]
|-
|rowspan = "3" |2011/11/07
|align="center"|2011-36
|[http://stm.sciencemag.org/content/3/96/96ra77.short Discovery and Preclinical Validation of Drug Indications Using Compendia of Public Gene Expression Data]
|-
|align="center"|2011-35
|[http://bib.oxfordjournals.org/content/12/4/303.short Exploiting drug–disease relationships for computational drug repositioning]
|-
|align="center"|2011-34
|[http://www.springerlink.com/content/4489r051nu2t0ul1/ Drug Discovery in a Multidimensional World: Systems, Patterns, and Networks]
|-
|rowspan = "3" |2011/10/05
|align="center"|2011-33
|[http://genome.cshlp.org/content/20/7/960.full Analysis of membrane proteins in metagenomics: Networks of correlated environmental features and protein families]
|-
|align="center"|2011-32
|[http://www.pnas.org/content/106/5/1374.full Quantifying environmental adaptation of metabolic pathways in metagenomics]
|-
|align="center"|2011-31
|[http://www.pnas.org/content/104/35/13913.abstract Quantitative assessment of protein function prediction from metagenomics shotgun sequences]
|-
|rowspan = "4" |2011/10/04
|align="center"|2011-30
|[http://www.nature.com/nature/journal/v464/n7285/full/nature08821.html A human gut microbial gene catalogue established by metagenomic sequencing]
|-
|align="center"|2011-29
|[http://www.nature.com/nrmicro/journal/v6/n9/abs/nrmicro1935.html Molecular eco-systems biology: towards an understanding of community function]
|-
|align="center"|2011-28
|[http://genome.cshlp.org/content/early/2009/04/20/gr.085464.108 Microbial community profiling for human microbiome projects: Tools, techniques, and challenges]
|-
|align="center"|2011-27
|[http://www.nature.com/nrmicro/journal/v9/n4/full/nrmicro2540.html Unravelling the effects of the environment and host genotype on the gut microbiome]
|-
|rowspan = "2" |2011/09/19
|align="center"|2011-26
|[http://www.sciencemag.org/content/333/6042/596.full independently evolved virulence effectors converge onto hubs in a plant immune system Network]
|-
|align="center"|2011-25
|[http://www.sciencemag.org/content/333/6042/601.full Evidence for network evolution in an arabidopsis interactome Map]
|-
|rowspan = "2" |2011/09/05
|align="center"|2011-24
|[http://www.sciencedirect.com/science/article/pii/S0092867411005861 Exome Sequencing of Ion Channel Genes Reveals Complex Profiles Confounding Personal Risk Assessment in Epilepsy]
|-
|align="center"|2011-23
|[http://www.cell.com/abstract/S0092-8674(11)00543-5 Pluripotency factors in Embryonic stem cells Regulate Differentiation into Germ Layers]
|-
|rowspan = "2" |2011/09/22
|align="center"|2011-22
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002077 Integrated Genome-scale predition of Detrimental Mutations in Transcription Networks]
|-
|align="center"|2011-21
|[http://www.nature.com/nrg/journal/v12/n4/abs/nrg2969.html From expression QTLs to personalized transcriptomics]
|-
|2011/06/20
|align="center"|2011-20
|[http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001046 a user's guide to the encyclopedia of DNA elements(ENCODE)]
|-
|rowspan = "2" |2011/03/30
|align="center"|2011-19
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature09944.html enterotypes of the human gut microbiome]
|-
|align="center"|2011-18
|[http://www.nature.com/msb/journal/v7/n1/full/msb20116.html toward molecular trait-based ecology, through intergration of biogeochemical, geographical and metagenomic data]
|-
|rowspan = "2" |2011/03/16
|align="center"|2011-17
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002047 variable pathogenicity determines individual lifespan in ''caenorhabditis elegans'']
|-
|align="center"|2011-16
|[http://www.cell.com/abstract/S0092-8674(11)00371-0 a high-resolution ''c.elegans'' essential gene network based on phenotypic profiling of a complex tissue]
|-
|rowspan = "3" |2011/04/25
|align="center"|2011-15
|[http://www.ncbi.nlm.nih.gov/pubmed/21376230 Hallmarks of Cancer : The next generation]
|-
|align="center"|2011-14
|[http://www.cell.com/abstract/S0092-8674(11)00296-0 Mapping Cancer Origins]
|-
|align="center"|2011-13
|[http://www.cell.com/abstract/S0092-8674(11)00297-2 Genetic Interactions in Cancer Progression and Treatment]
|-
|rowspan = "2" |2011/04/11
|align="center"|2011-12
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1001273 Proteins encoded in genomic regions associated with immune-mediated disease physically interact and suggest underlying biology]
|-
|align="center"|2011-11
|***Changed!***
[http://www.ncbi.nlm.nih.gov/pubmed/19557189 Identifying relationships among genomic disease regions: predicting genes at pathogenic SNP associations and rare deletions]
|-
|rowspan = "2"|2011/03/28
|align="center"|2011-10
|[http://www.pnas.org/content/106/44/18843.long profiling translatomes of discrete cell populations resolves altered cellular priorities during hypoxia in Arabidopsis]
|-
|align="center"|2011-09
|[http://www.nature.com/msb/journal/v6/n1/full/msb201076.html cell-type specific analysis of translating RNAs in developing flowers reveals new levels of control]
|-
|rowspan = "2"|2011/03/14
|align="center"|2011-08
|[http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6WSN-51SFHJD-1&_user=44062&_coverDate=01%2F07%2F2011&_rdoc=1&_fmt=high&_orig=search&_origin=search&_sort=d&_docanchor=&view=c&_acct=C000004738&_version=1&_urlVersion=0&_userid=44062&md5=2f6017aab4c794ed7e78fbbd8447077a&searchtype=a phenotypic landscape of a bacterial cell]
|-
|align="center"|2011-07
|[http://www.nature.com/msb/journal/v6/n1/full/msb2010107.html cross-species chemogenomic profiling reveals evolutionarily conserved drug mode of action]
|-
|rowspan = "2"|2011/02/28
|align="center"|2011-06
|[http://www.pnas.org/content/early/2010/09/23/1004666107.abstract genomic patterns of pleiotropy and the evolution of complexity]
|-
|align="center"|2011-05
|[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1001009 simultaneous genome-wide inference of physical, genetic, regulatory, and functional pathway]
|-
|2011/02/21
|align="center"|2011-04
|[http://www.nature.com/msb/journal/v6/n1/full/msb201071.html dynamic interaction networks in a hierarchically organized tissue]
|-
|2011/02/14
|align="center"|2011-03
|[http://www.sciencemag.org/content/330/6009/1385.abstract rewiring of genetic networks in response to DNA damage]
|-
|rowspan = "2"|2011/01/31
|align="center"|2011-02
|[http://www.nature.com/nrg/journal/v10/n9/abs/nrg2633.html Applying mass spectrometry-based proteomics to genetics, genomics and network biology]
|-
|align="center"|2011-01
|[http://physiolgenomics.physiology.org/content/33/1/18.long Data analysis and bioinformatics tools for tandem mass spectrometry in proteomics]
|}
2010
{|border ="1"
|style="color:black; background-color:#dcdcdc;" align="center" |Date
|style="color:black; background-color:#dcdcdc;" align="center"|Paper_index
|style="color:black; background-color:#dcdcdc;" align="center"|Paper_title
|-
|rowspan = "2"|2010/12/27
|align="center"|2010-29
|[http://www.nature.com/nature/journal/v467/n7312/full/nature09326.html Functional_roles_fornoise_in_genetic_circuits]
|-
|align="center"|2010-28
|[http://www.nature.com/ng/journal/v42/n7/full/ng.610.html Estimation_of_effect_size_distribution_from_genome-wide_association_studies_and_implications_for_future_discoveries]
|-
|rowspan = "2"|2010/11/15
|align="center"|2010-27
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1000932 Liver_and_Adipose_Expression_associated_SNPs_are_enriched_for_association_to_type_2_diabetes]
|-
|align="center"|2010-26
|[http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B8JDC-50J4MRJ-2&_user=44062&_coverDate=10%2F10%2F2010&_rdoc=1&_fmt=high&_orig=search&_origin=search&_sort=d&_docanchor=&view=c&_acct=C000004738&_version=1&_urlVersion=0&_userid=44062&md5=7667a67cf8ba3f68117b8d0ff85a8887&searchtype=a It's_the_machine_that_matters:_predicting_gene_function_and_phenotype_from_protein_networks]
|-
|rowspan = "2"|2010/11/01
|align="center"|2010-25
|[http://genome.cshlp.org/content/early/2010/06/15/gr.104216.109 A_genome-wide_map_of_human_genetic_interactions_inferred_from_radiation_hybrid_genotypes]
|-
|align="center"|2010-24
|[http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0012139 A_Genome-Wide_Gene_Function_Prediction_Resource_for_Drosophila_melanogaster]
|-
|2010/10/11
|align="center"|2010-23
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2913399/?tool=pubmed Dissecting_spatio-temporal_protein_networks_driving_human_heart_development_and_related_disorders]
|-
|rowspan = "2"|2010/09/13
|align="center"|2010-22
|[http://www.nature.com/ng/journal/v42/n7/full/ng.600.html Transposable_elements_have_rewired_the_core_regulatory_network_of_human_embryonic_stem_cells]
|-
|align="center"|2010-21
|[http://www.nature.com/ng/journal/v42/n7/full/ng0710-557.html Limits_of_sequence_and_functional_conservation]
|-
|2010/05/26
|align="center"|2010-20
|[[media:100428_network_based_elucidation_of_human_disease_similarities_reveals_common_functional_modules_enriched_for_pluripotent_drug_targets.pdf|network_based_elucidation_of_human_disease_similarities_reveals_common_functional_modules_enriched_for_pluripotent_drug_targets.pdf]]
|-
|2010/05/19
|align="center"|2010-19
|[[media:100421_interpreting_metabolomic_profiles_using_unbiased_pathway_models.pdf|interpreting_metabolomic_profiles_using_unbiased_pathway_models.pdf]]
|-
|2010/04/21
|align="center"|2010-18
|[[media:100414_identification_of_networks_of_co_occurring_tumor_related_dna_copy_number_changes_using_a_genome_wide_scoringapproach.pdf|identification_of_networks_of_co_occurring_tumor_related_dna_copy_number_changes_using_a_genome_wide_scoringapproach.pdf]]
|-
|2010/04/14
|align="center"|2010-17
|[http://www.cell.com/retrieve/pii/S0092867410000796 an_atlas_of_combinatorial_transcriptional_regulation_in_mouse_and_man]
|-
|rowspan = "2" |2010/04/07
|align="center"|2010-16
|[http://www.pnas.org/content/early/2010/03/11/0910200107.long systematic_discovery_of_nonobvious_human_disease_models_through_orthologous_phenotypes]
|-
|align="center"|2010-15
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature08800.html genome_side_association_study_of_107_phenotypes_in_Arabidopsis_thaliana_inbred_lines]
|-
|rowspan="2"|2010/03/31
|align="center"|2010-14
|[[media:100331_toward_stem_cell_systems_biology_from_molecules_to_networks_and_landscapes.pdf|toward_stem_cell_systems_biology_from_molecules_to_networks_and_landscapes.pdf]]
|-
|align="center"|2010-13
|[http://www.nature.com/nrm/journal/v10/n10/abs/nrm2766.html systems_biology_of_stem_cell_fate_and_cellular_reprogramming]
|-
|2010/03/24
|align="center"|2010-12
|[http://www.nature.com/nbt/journal/v27/n2/abs/nbt.1522.html dynamic_modularity_in_protein_interaction_networks_predicts_breast_cancer_outcome]
|-
|2010/01/18
|align="center"|2010-11
|JournalClub_100118_a_tutorial_on_statistical_methods_for_population_association_studies
|-
|2010/01/16
|align="center"|2010-10
|systems-level_dinamic_analyses_of_fate_change_in_murine_embryonic_stem_cells
|-
|2010/01/15
|align="center"|2010-09
|distinguishing_direct_versus_indirect_transcription_factor-DNA-interactions
|-
|2010/01/14
|align="center"|2010-08
|chemogenomic_profiling_predicts_antifungal_synergies
|-
|2010/01/13
|align="center"|2010-07
|edgetic_perturbation_models_of_human_inherited_disorders
|-
|2010/01/12
|align="center"|2010-06
|analysis_of_cell_fate_from_single-cell_gene_expression_profiles_in_C.elegans
|-
|2010/01/11
|align="center"|2010-05
|predicting_new_molecular_targets_for_known_drugs.pdf
Reference:SEA(Similarity Ensemble Approach)
|-
|2010/01/09
|align="center"|2010-04
|harnessing_gene_expression_to_identify_the_genetic_basis_of_drug_resistance
|-
|2010/01/08
|align="center"|2010-03
|an_integrative_approach_to_reveal_driver_gene_fusions_from_paired_end_sequencing_data_in_cancer
|-
|2010/01/07
|align="center"|2010-02
|a_phenotypic_profile_of_the_candida_albicans_regulatory_network
|-
|2010/01/06
|align="center"|2010-01
|a_global_view_of_protein_expression_in_human_cells_tissues_and_organs
|}

People

2024-01-25T00:57:53Z

Bsb0613: /* Scientists & Research Staff */

__NOTOC__
=='''Scientists & Research Staff'''==
{|border="0" width="1050"
|align="center"|[[File:이인석교수님-상체.jpg|100px|link=people:IS_Lee]]
|align="center"|[[File:Smyang.jpg|70px|link=people:SM_Yang]]
|align="center"|[[File:Shlee thumbnail.jpg|70px|link=People:S_Lee]]
|align="center"|[[File:Junha_Cha_profile2.png|70px|link=People:JH_Cha]]
|align="center"|[[File:나연.png|70px|link=People:NY_Kim]]
|align="center"|[[File:SeungbynBaek AACR.jpg|80px|link=People:SB_Baek]]
|align="center"|[[File:KakaoTalk 20200914 132021053.jpg|80px|link=People:IS_Choi]]
|align="center"|[[File:chajh profile.jpg|70px|link=People:Junhyeong_Cha]]
|-

|width=160px; align="center"|[[people:IS_Lee|<big>'''Insuk Lee'''</big>]]
|width=160px; align="center"|[[people:SM_Yang|<big>'''Sunmo Yang'''</big>]]
|width=160px; align="center"|[[People:S_Lee|<big>'''Sungho Lee'''</big>]]
|width=160px; align="center"|[[People:JH_Cha|<big>'''Junha Cha'''</big>]]
|width=160px; align="center"|[[People:NY_Kim|<big>'''Nayeon Kim'''</big>]]
|width=160px; align="center"|[[People:SB_Baek|<big>'''Seungbyn Baek'''</big>]]
|width=160px; align="center"|[[People:IS_Choi|<big>'''Ilseok Choi'''</big>]]
|width=160px; align="center"|[[People:Junhyeong_Cha|<big>'''Junhyeong Cha'''</big>]]
|-

|align="center"|[[people:IS_Lee|Principal Investigator Professor]]
|align="center"|[[people:SM_Yang|Bioinformatics Programmer & Sys. Admin]]
|align="center"|[[People:S_Lee|Ph.D candidate (Microbiome) (2017.3 - )]]
|align="center"|[[People:JH_Cha|Ph.D candidate (Single-cell Biology) (2019.9 - )]]
|align="center"|[[People:NY_Kim|Ph.D candidate (Microbiome) (2019.9 - )]]
|align="center"|[[People:SB_Baek|Ph.D candidate (Single-cell Biology) (2019.9 - )]]
|align="center"|[[People:IS_Choi|Ph.D candidate (Single-cell Biology) (2020.9 - )]]
|align="center"|[[People:Junhyeong_Cha|Ph.D candidate (Microbiome) (2020.9 - )]]
|}
{|border="0" width="1050"
|align="center"|[[File:KakaoTalk 20210225 184422243.jpg|70px|link=People:Junyeong_Ma]]
|align="center"|[[File:profile_front_resized.jpg|70px|link=People:Han-June_KIM]]
|align="center"|[[File:사진.jpg|70px|link=People:Jiwon_Yu]]
|align="center"|[[File:KakaoTalk_20210825_174338962.jpg|70px|link=People:EuiJeong_Sung]]
|align="center"|[[File:Hunsei.PNG|70px|link=People:Sehun_Ahn]]
|align="center"|[[File:고건.jpg|70px|link=People:Geon_Koh]]
|align="center"|[[File:원종.jpg|70px|link=People:WonJong_Kim]]
|align="center"|[[File:정연.jpg|70px|link=People:Jungyeon_Kim]]
|-
|width=160px; align="center"|[[People:Junyeong_Ma|<big>'''Junyeong Ma'''</big>]]
|width=160px; align="center"|[[People:Hanjune_Kim|<big>'''Hanjune Kim'''</big>]]
|width=160px; align="center"|[[People:Jiwon_Yu|<big>'''Jiwon Yu'''</big>]]
|width=160px; align="center"|[[People:Euijeong_Sung|<big>'''Euijeong Sung'''</big>]]
|width=160px; align="center"|[[People:Sehun_Ahn|<big>'''Sehun Ahn'''</big>]]
|width=160px; align="center"|[[People:Geon Koh|<big>'''Geon Koh'''</big>]]
|width=160px; align="center"|[[People:WonJong_Kim|<big>'''Wonjong Kim'''</big>]]
|width=160px; align="center"|[[People:Jungyeon_Kim|<big>'''Jungyeon Kim'''</big>]]
|-
|align="center"|[[People:Junyeong_Ma|Ph.D candidate (Microbiome) (2021.3 - )]]
|align="center"|[[People:Hanjune_Kim|Integrated Ph.D course (Microbiome) (2021.3 - )]]
|align="center"|[[People:Jiwon_Yu|Research Staff (Single-cell Biology) ]]
|align="center"|[[People:Euijeong_Sung|Integrated Ph.D course (Single-cell Biology) (2021.9 - )]]
|align="center"|[[People:Sehun_Ahn|Integrated Ph.D course (Microbiome) (2022.3 - )]]
|align="center"|[[People:Geon_Koh|Integrated Ph.D course (Microbiome) (2022.9 - )]]
|align="center"|[[People:WonJong_Kim|Integrated Ph.D course (Microbiome) (2023.3 - )]]
|align="center"|[[People:Jungyeon_Kim|Integrated Ph.D course (Microbiome) (2023.3 - )]]
|}

=='''Administrative Assistant'''==
*Eunjung Choi
=='''Undergraduate Students'''==
*Yerin Kim
*Sang Min Park
*Minsun Seo
*Jaeryun Sim
*Sun Min Lim
*Yurim Jung

=='''Alumni'''==
*'''Former Post-docs and Staff Scientists'''
**1. Taeyun Oh (2009.5-2011.12), currently Senior Researcher at Cowellbiogim, Korea
**2. Sohyun Hwang (2010.3-2015.8), currently Associate Professor (차의과대학 의과학과 교수), [https://grad.cha.ac.kr/%ed%99%a9%ec%86%8c%ed%98%84/ Department of Biomedical Science, CHA University], Korea [https://scholar.google.com/citations?user=z5yC9cwAAAAJ&hl=en Google Scholar]
**3. Samuel Beck (2011.2-2011.12), currently Associate Professor, Boston University, USA (미 보스턴 대학교 부교수), [https://beck310.wixsite.com/becklab Lab Homepage]
**4. Jawon Song (2011.1-2012.5), currently Research Associate (미국 텍사스슈퍼컴퓨팅센터 선임연구원), [https://www.tacc.utexas.edu/about/directory/jawon-song Texas Advanced Computing Center], Austin, TX, USA [https://scholar.google.com/citations?user=wv61wGoAAAAJ&hl=en Google Scholar]
**5. Yoonhee Ko (2012.8-2014.2), currently Associate Professor (한국외국어대 바이오공학과 교수), [https://hufsbiomed.weebly.com/ Department of Biomedical Engineering, Hankuk University of Foreign Studies], Korea [https://scholar.google.com/citations?user=GBxCYX8AAAAJ&hl=en Google Scholar]
**6. Jonghoon Lee (2013.10-2014.2), currently Associate Professor (가천대학교 식품생명공학과 교수), [https://www.gachon.ac.kr/foodbiotech/6142/subview.do Department of Food Biotechnology, Gachon University], Korea
**7. Jung Eun Shim (2009.8-2017.11), currently Staff Scientist at Yonsei Genomics Center Bioinformatics Data Analysis Core (연세의료원 유전체센터 생명정보분석코어 책임연구원)

*'''Former Graduate Students (Ph.D)'''
**1. Junha Shin (2008.9-2014.2), PhD; currently Postdoc Fellow, University of Wisconsin-Madison (미국 위스콘신주립대 박사후연구원), USA [https://roylab.discovery.wisc.edu/ Roy Lab]
**2. Hanhae Kim (2009.9-2015.2), PhD; currently Research Fellow (한국과학기술기획평가원 연구위원), [http://www.kistep.re.kr Korea Institute of S&T Evaluation and Planning (KISTEP)]
**3. Ara Cho (2009.9-2015.2), PhD; currently Research Fellow (한국과학기술기획평가원 연구위원), [http://www.kistep.re.kr Korea Institute of S&T Evaluation and Planning (KISTEP)]
**4. Eiru Kim (2010.9-2016.8), PhD; currently Senior Expert in Data Science, [https://www.gnf.org/ The Genomics Institute of the Novartis Research Foundation], San Diego, USA (노바티스 선임데이터분석연구원)
**5. Tak Lee (2011.3-2017.2), PhD; currently Senior Bioinformatician, [https://www.mpipz.mpg.de/en Max Planck Institute for Plant Breeding Research] (독일 막스플랭크 연구소 선임연구원)
**6. Heonjong Han (2013.3~2019.8), PhD; currently Bioinformatics engineer at [https://3billion.io/ 3billion] (바이오기업 데이터사이언티스트)
**7. Kyungsoo Kim (2013.3~2020.2), PhD; currently Research Professor at Severance Hospital (강남세브란스의료원 유방암센터 연구교수)
**8. Jae-Won Cho (2016.9~2021.2), PhD; currently Postdoc Fellow at Harvard Medical School (하버드의대 박사후 연구원) [https://scholar.google.com/citations?user=H4jO_DQAAAAJ&hl=en Hemberg Lab]
**9. Chan Yeong Kim (2015.3~2021.2), PhD; currently Postdoc Fellow at European Molecular Biology Laboratory in Heidelberg (유럽연합분자생물학연구소 박사후 연구원) [http://www.bork.embl.de/j/ Bork Group]
**10. Sungho Lee (2017.3-2024.2), PhD; currently Data Scientist at [http://www.mogam.re.kr/kor/ MOCAM Institute for Biomedical Research] (목암생명과학연구소, (주)녹십자 출연 연구소, 선임연구원)

*'''Former Graduate Students (Master's degree)'''
**1. Sun-Gou Ji (2009.9-2011.8), M.Eng.; PhD from University of Cambridge (Sanger Genome center); currently Vice President, Computational Genetics at [https://bridgebio.com/ BridgeBio Pharma], Boston, USA (미 바이오기업 임원)
**2. Hyojin Kim (2013.3-2015.2), M.Eng.; PhD from University of Aachen; currently Computational Scientist, Uniklinikum Aachen (the university hospital of the city of Aachen), Germany (독일 아헨대학병원 데이터사이언티스트)
**3. Hongseok Shim (2013.9-2015.8), M.Eng.; currently PhD student, Arizona State University, Biomedical Informatics program (애리조나주립대 생물정보학 박사과정)
**4. Sunmo Yang (2014.9-2017.2), M.Eng. currently Bioinformatics programmer, Yonsei University (연세대 네트워크생명공학 연구실 책임데이터연구원)
**5. Muyoung Lee (2016.3-2018.2), M.Eng. currently PhD student, University of Texas at Austin, Cellular and Molecular Biology Program (텍사스주립대 생물정보학 박사과정)
**6. Jiwon Yu (2021.9-2023.8), M.Eng. currently Data Scientist at [https://www.samsungbioepis.com/kr/index.do SAMSUNG BIOEPIS] (삼성바이오에피스 데이터사이언티스트)

*'''Former Visiting Scientists/Students'''
**1. Minkyung Shin (2012.3-2012.7), went to University of Southern California graduate school
**2. Paul Chung (2012 summer), currently E-Commerce Analyst/Coordinator at [http://www.riotgames.com/ Riot Games], USA
**3. Hyunjin Cho (2014 summer), currently Bioinformatics research assistant at [http://www.libd.org/ The Lieber Institute for Brain Development | LIBD], USA
**4. Michael Chung (2016.8-2016.12), currently work at [https://about.meta.com/ Meta], USA

*'''Former Undergraduate Students'''
**1. Ila Shin, went to medical school (Yonsei Univ.) Currently Professor, Kyunghee University School of Medicine (경희대학교 의과대학 교수)
**2. Yoonkyung Ko
**3. Sangyo Park, went to law school (Kyounghee Univ.)
**4. Jayoon Shin, went to medical school (Pusan National Univ.)
**5. Dongjoo Sun, went to dental school (Wongwang Univ.)
**6. Eulsoo Kim
**7. Myungwhan Lee, went to medical school (Yonsei Univ.)
**8. Moonhee Lee, went to graduate school (University of Amsterdam, Netherlands)
**9. Jinhyun Ju, went to graduate school (Cornell Medical School, Computational Systems Biology); Currently Principal Computational Biologist, Cancer Genomics at [https://bridgebio.com/ BridgeBio Pharma], Seattle, USA (미 바이오기업 선임연구원)
**10. Haeyoung Shin, went to graduate school (Brown University, Computational Neuroscience); Currently Professor, Seoul National University (서울대학교 생명과학부 교수)
**11. Joonghee Soh, went to graduate school (KAIST, Culture Technology)
**12. Hee Jung Cho, went to graduate school (UC Berkeley)
**13. TaeHwan Kim, went to medical school
**14. Joohyung Kim, went to medical school (Kyungpook National Univ.)
**15. Donghyun Shin, went to medical school (Yonsei Univ.)
**16. Byunghee Kang (2015 spring - 2016 summer), went to graduate school (PosTech)
**17. Sunphil Kim (2016 winter)
**18. Jaeho Shim (2016 winter)
**19. Seunghyun Shin (2016 spring)
**20. Aejoo Hong (2016 spring)
**21. Changbae Bang (2016 summer), went to medical school (Yonsei Univ.)
**22. Seoyoung Choi (2016 summer)
**23. Dabin Jung (2016 summer)
**24. Boreum Nam (2016 summer)
**25. Seungun Lee (2016 summer)
**26. Yeaji Kim (2016 summer and fall)
**27. Sangyoung Lee (2017 winter)
**28. Heejun Jang (2017 winter & spring), went to KIST as Research Assistant
**29. Jungha Lee (2017 summer), went to graduate school (Seoul National Univ.)
**30. Seunghyun Wang (2017 summer), went to graduate school (KAIST)
**31. Dong-Min Yang (2017 fall)
**32. Doo-Hee Lee (2017 fall)
**33. Suk-Jae Han (2018 winter)
**34. Ji-Eun Han (2018 winter)
**35. Yuri Ko (2018 spring)
**36. Sung-Woo Kim (2018 spring)
**37. Woosung Kwon (2018 summer)
**38. Soyun Kong (2018 summer)
**39. Hojeong Keum (2018 summer and fall)
**40. Sungjun Lim (2018 summer and fall)
**41. Jiyoon Lee (2018 summer and fall)
**42. Ilseok Choi(2019 winter, spring, summer and fall, 2020 winter, spring)
**43. Junik Park (2019 winter)
**44. Lee Yoo (2019 winter & spring)
**45. Yeonwha Kim (2019 winter & spring)
**46. Sohee Kim (2019 spring)
**47. Juseong Lee (2019 spring)
**48. Junyoung Yang (2019 spring & summer)
**49. Junyoung Ma (2019 summer, 2020 winter, summer, fall, 2021 winter)
**50. Dayun Jung (2019 summer)
**51. Hanjoon Kim (2019 summer, 2021 winter)
**52. Hyungjin Kim (2020 winter, spring)
**53. Sujin Hyun (2020 winter)
**54. Hyuki Lee (2020 winter)
**55. Donghwan Lee (2020 winter)
**56. Junhyung Cha (2020 spring, summer)
**57. Jiwon Yu (2020 summer, 2021 Spring, summer)
**58. Dong Seok Kim (2020 summer)
**59. Donggeun Lee (2020 summer)
**60. Sehun Ahn (2020 summer, 2021 winter, spring, summer, 2022 winter)
**61. Jiho Kim (2020 summer)
**62. Euijung Seong (2020 fall, 2021 spring, summer)
**63. Junhan Kim (2021 winter & spring)
**64. Suyong Choi (2021 winter)
**65. Sujin Pyeon (2021 Spring)
**66. Hyeon Hee Ji (2021 Spring)
**67. Semin Kim (2021 Summer)
**68. Su Hwan Kim (2021 Summer)
**69. Yongbin Kim (2021 Summer)
**70. Nan Park (2021 Summer)
**71. Hoyong Jung (2021 Summer)
**72. Ingyeong Koh (2021 Summer)
**73. Jongbin Jeong (2022 Winter)
**74. Sungchul Yang (2022 Winter, Spring, Summer)
**75. Hyojung Lee (2022 Winter, Spring)
**76. Jun Hyeong Kim (2022 Winter, Spring)
**77. Young Ji Roh (2022 Spring, Summer)
**78. Geon Koh (2022 Spring, Summer)
**79. Hayoung Kang (2022 Summer, 2023 Spring)
**80. Won Jong Kim (2022 Summer, Fall, 2023 Winter)
**81. Christian Lee (2022 Summer)
**82. Je Yun Kang (2022 Summer)
**83. Sumin Lee (2022 Fall)
**84. Jungyeon Kim (2022 Fall, 2023 Winter)
**85. Seol Song (2023 Winter)
**86. Soyeon Kim (2023 Summer)
**87. Sieun Park (2023 Summer)
**88. Gunhyeong Lee (2023 Summer)
**89. Yunseo Han (2023 Summer)
**90. Jae Hyun Kim (2023 Summer)
**91. Jeong-min Seo (2023 Summer)
**92. Yerin Kim (2023 Fall)

File:SeungbynBaek AACR.jpg

2024-01-25T00:53:46Z

Bsb0613:

Journal Club

2023-11-05T07:34:27Z

Bsb0613:

{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2023-2 scOmics
|-
!scope="col" style="padding:.4em" | Date
!scope="col" style="padding:.4em" | Team
!scope="col" style="padding:.4em" | Paper index
!scope="col" style="padding:.4em" | Presenter
!scope="col" style="padding:.4em" | Paper title
|-
|style="padding:.4em;" rowspan=1|2023/12/05
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-33
|style="padding:.4em;"|EJ Sung
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41588-022-01273-y MHC II immunogenicity shapes the neoepitope landscape in human tumors]
|-
|style="padding:.4em;" rowspan=1|2023/11/28
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-32
|style="padding:.4em;"|IS Choi
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41586-023-06130-4 Hallmarks of transcriptional intratumour heterogeneity across a thousand tumours]
|-
|style="padding:.4em;" rowspan=1|2023/11/21
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-31
|style="padding:.4em;"|JW Yu
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41467-023-37353-8 Pan-cancer classification of single cells in the tumour microenvironment]
|-
|style="padding:.4em;" rowspan=1|2023/10/31
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-30
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41587-023-01686-y Single-cell mapping of combinatorial target antigens for CAR switches using logic gates]
|-
|style="padding:.4em;" rowspan=1|2023/10/24
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-29
|style="padding:.4em;"|SB Baek
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41587-023-01782-z Comparative analysis of cell–cell communication at single-cell resolution]
|-
|style="padding:.4em;" rowspan=1|2023/09/26
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-28
|style="padding:.4em;"|EJ Sung
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s43018-023-00566-3 Phenotypic diversity of T cells in human primary and metastatic brain tumors revealed by multiomic interrogation]
|-
|style="padding:.4em;" rowspan=1|2023/09/19
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-27
|style="padding:.4em;"|IS Choi
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41591-023-02324-5 An integrated tumor, immune and microbiome atlas of colon cancer]
|-
|style="padding:.4em;" rowspan=1|2023/09/12
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-26
|style="padding:.4em;"|SB Baek
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41587-022-01476-y Multi-omic single-cell velocity models epigenome–transcriptome interactions and improves cell fate prediction]
|-
|style="padding:.4em;" rowspan=1|2023/09/05
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-25
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.immuni.2023.04.010 Recruitment of epitope-specific T cell clones with a low-avidity threshold supports efficacy against mutational escape upon re-infection]
|}

{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2023-2 Microbiome
|-
!scope="col" style="padding:.4em" | Date
!scope="col" style="padding:.4em" | Team
!scope="col" style="padding:.4em" | Paper index
!scope="col" style="padding:.4em" | Presenter
!scope="col" style="padding:.4em" | Paper title
|-
|style="padding:.4em;" rowspan=1|2023/12/06
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-48
|style="padding:.4em;"|SH Ahn
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41467-023-39264-0 A data-driven approach for predicting the impact of drugs on the human microbiome]
|-
|style="padding:.4em;" rowspan=1|2023/12/06
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-47
|style="padding:.4em;"|HJ Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2023.04.06.535777 Activation of programmed cell death and counter-defense functions of phage accessory genes]
|-
|style="padding:.4em;" rowspan=1|2023/11/29
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-46
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41467-023-39459-5 Top-down identification of keystone taxa in the microbiome]
|-
|style="padding:.4em;" rowspan=1|2023/11/29
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-45
|style="padding:.4em;"|JY Ma
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.cels.2022.12.007 Pitfalls of genotyping microbial communities with rapidly growing genome collections]
|-
|style="padding:.4em;" rowspan=1|2023/11/22
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-44
|style="padding:.4em;"|NY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1186/s13059-023-03028-2 Reconstruction of the last bacterial common ancestor from 183 pangenomes reveals a versatile ancient core genome]
|-
|style="padding:.4em;" rowspan=1|2023/11/22
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-43
|style="padding:.4em;"|SH Lee
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41587-023-01868-8 Generation of accurate, expandable phylogenomic trees with uDance]
|-
|style="padding:.4em;" rowspan=1|2023/11/08
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-42
|style="padding:.4em;"|WJ Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.immuni.2023.04.003 Phage display sequencing reveals that genetic, environmental, and intrinsic factors influence variation of human antibody epitope repertoire]
|-
|style="padding:.4em;" rowspan=1|2023/11/08
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-41
|style="padding:.4em;"|JY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.immuni.2023.04.017 Phage-display immunoprecipitation sequencing of the antibody epitope repertoire in inflammatory bowel disease reveals distinct antibody signatures]
|-
|style="padding:.4em;" rowspan=1|2023/11/01
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-40
|style="padding:.4em;"|G Koh
|style="padding:.4em;text-align:left"|
[https://doi.org/10.15252/msb.202311525 Consistency across multi-omics layers in a drug-perturbed gut microbial community]
|-
|style="padding:.4em;" rowspan=1|2023/11/01
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-39
|style="padding:.4em;"|HJ Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41587-023-01953-y Identification of mobile genetic elements with geNomad]
|-
|style="padding:.4em;" rowspan=1|2023/10/25
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-38
|style="padding:.4em;"|SH Ahn
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41591-023-02407-3 Microbiome-derived cobalamin and succinyl-CoA as biomarkers for improved screening of anal cancer]
|-
|style="padding:.4em;" rowspan=1|2023/10/11
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-37
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41591-023-02424-2 The airway microbiome mediates the interaction between environmental exposure and respiratory health in humans]
|-
|style="padding:.4em;" rowspan=1|2023/10/11
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-36
|style="padding:.4em;"|JY Ma
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1080/19490976.2023.2224474 Ordering taxa in image convolution networks improves microbiome-based machine learning accuracy]
|-
|style="padding:.4em;" rowspan=1|2023/09/27
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-35
|style="padding:.4em;"|NY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2023.08.12.553040 The defensome of complex bacterial communities]
|-
|style="padding:.4em;" rowspan=1|2023/09/27
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-34
|style="padding:.4em;"|SH Lee
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.cell.2023.03.011 Dietary tryptophan metabolite released by intratumoral Lactobacillus reuteri facilitates immune checkpoint inhibitor treatment]
|-
|style="padding:.4em;" rowspan=1|2023/09/20
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-33
|style="padding:.4em;"|WJ Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s43587-022-00306-9 Toward an improved definition of a healthy microbiome for healthy aging]
|-
|style="padding:.4em;" rowspan=1|2023/09/20
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-32
|style="padding:.4em;"|JY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s43587-022-00287-9 Associations of the skin, oral and gut microbiome with aging, frailty and infection risk reservoirs in older adults]
|-
|style="padding:.4em;" rowspan=1|2023/09/13
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-31
|style="padding:.4em;"|G Koh
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1186/s40168-023-01614-x Statistical modeling of gut microbiota for personalized health status monitoring]
|-
|style="padding:.4em;" rowspan=1|2023/09/13
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-30
|style="padding:.4em;"|SH Ahn
|style="padding:.4em;text-align:left"|
[https://doi.org/10.7554/eLife.50240 Adjusting for age improves identification of gut microbiome alterations in multiple diseases]
|-
|style="padding:.4em;" rowspan=1|2023/09/06
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-29
|style="padding:.4em;"|HJ Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41564-023-01370-6 Centenarians have a diverse gut virome with the potential to modulate metabolism and promote healthy lifespan]
|-
|style="padding:.4em;" rowspan=1|2023/09/06
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-28
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.chom.2023.01.003 Longitudinal comparison of the developing gut virome in infants and their mothers]
|}

{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2023-1 ADVANCED MICROBIOME DATA ANALYSIS
|-
!scope="col" style="padding:.4em" | Date
!scope="col" style="padding:.4em" | Team
!scope="col" style="padding:.4em" | Paper index
!scope="col" style="padding:.4em" | Presenter
!scope="col" style="padding:.4em" | Paper title
|-
|style="padding:.4em;" rowspan=1|2023/06/13
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-24
|style="padding:.4em;"|JY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.chom.2020.03.005 Structure of the Mucosal and Stool Microbiome in Lynch Syndrome]
|-
|style="padding:.4em;" rowspan=1|2023/06/13
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-23
|style="padding:.4em;"|WJ Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41586-019-1237-9 Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases]
|-
|style="padding:.4em;" rowspan=1|2023/06/13
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-22
|style="padding:.4em;"|SH Ahn
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.jare.2022.03.007 Roles of oral microbiota and oral-gut microbial transmission in hypertension]
|-
|style="padding:.4em;" rowspan=1|2023/05/30
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-21
|style="padding:.4em;"|SY Lim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.chom.2022.08.009 Human gut microbiota stimulate defined innate immune responses that vary from phylum to strain]
|-
|style="padding:.4em;" rowspan=1|2023/05/30
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-20
|style="padding:.4em;"|BS Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41591-023-02217-7 Gut microbial metabolism of 5-ASA diminishes its clinical efficacy in inflammatory bowel disease]
|-
|style="padding:.4em;" rowspan=1|2023/05/30
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-19
|style="padding:.4em;"|JY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.chom.2023.01.004 Deficient butyrate-producing capacity in the gut microbiome is associated with bacterial network disturbances and fatigue symptoms in ME/CFS]
|-
|style="padding:.4em;" rowspan=1|2023/05/23
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-18
|style="padding:.4em;"|EJ Sung
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.ccell.2022.11.013 Gut microbiota-mediated nucleotide synthesis attenuates the response to neoadjuvant chemoradiotherapy in rectal cancer]
|-
|style="padding:.4em;" rowspan=1|2023/05/23
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-17
|style="padding:.4em;"|G Koh
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.chom.2021.06.019 Multi-omics reveal microbial determinants impacting responses to biologic therapies in inflammatory bowel disease]
|-
|style="padding:.4em;" rowspan=1|2023/05/23
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-16
|style="padding:.4em;"|SH Lee
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41586-022-05181-3 Identification of trypsin-degrading commensals in the large intestine]
|-
|style="padding:.4em;" rowspan=1|2023/05/16
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-15
|style="padding:.4em;"|JP Hong
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41586-022-05546-8 Questioning the fetal microbiome illustrates pitfalls of low-biomass microbial studies]
|-
|style="padding:.4em;" rowspan=1|2023/05/16
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-14
|style="padding:.4em;"|MR Jang
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.chom.2023.01.013 Tissue-resident Lachnospiraceae family bacteria protect against colorectal carcinogenesis by promoting tumor immune surveillance]
|-
|style="padding:.4em;" rowspan=1|2023/05/16
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-13
|style="padding:.4em;"|JW Yu
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.cell.2022.09.005 Pan-cancer analyses reveal cancer-type-specific fungal ecologies and bacteriome interactions]
|-
|style="padding:.4em;" rowspan=1|2023/05/09
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-12
|style="padding:.4em;"|NY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.cell.2022.09.015 A pan-cancer mycobiome analysis reveals fungal involvement in gastrointestinal and lung tumors]
|-
|style="padding:.4em;" rowspan=1|2023/05/09
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-11
|style="padding:.4em;"|HR Shin
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41564-021-01030-7 Multi-kingdom microbiota analyses identify bacterial–fungal interactions and biomarkers of colorectal cancer across cohorts]
|-
|style="padding:.4em;" rowspan=1|2023/05/09
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-10
|style="padding:.4em;"|SG Oh
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s42255-022-00716-4 The antitumour effects of caloric restriction are mediated by the gut microbiome]
|-
|style="padding:.4em;" rowspan=1|2023/05/02
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-9
|style="padding:.4em;"|WJ Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41591-022-01964-3 Variability of strain engraftment and predictability of microbiome composition after fecal microbiota transplantation across different diseases]
|-
|style="padding:.4em;" rowspan=1|2023/05/02
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-8
|style="padding:.4em;"|SM Han
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41591-022-01913-0 Drivers and determinants of strain dynamics following fecal microbiota transplantation]
|-
|style="padding:.4em;" rowspan=1|2023/05/02
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-7
|style="padding:.4em;"|YY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.cell.2022.11.023 Mobile genetic elements from the maternal microbiome shape infant gut microbial assembly and metabolism]
|-
|style="padding:.4em;" rowspan=1|2023/04/18
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-6
|style="padding:.4em;"|SH Heo
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.chom.2023.01.018 Mother-to-infant microbiota transmission and infant microbiota development across multiple body sites]
|-
|style="padding:.4em;" rowspan=1|2023/04/18
|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|SY Yang
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[https://doi.org/10.1038/s41467-023-36633-7 Population-level impacts of antibiotic usage on the human gut microbiome]
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|style="padding:.4em;"|YH Yoon
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[https://doi.org/10.1002/imt2.61 Targeting keystone species helps restore the dysbiosis of butyrate‐producing bacteria in nonalcoholic fatty liver disease]
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[https://doi.org/10.1002/advs.202203115 Differential Oral Microbial Input Determines Two Microbiota Pneumo-Types Associated with Health Status]
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|style="padding:.4em;"|SH Ahn
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[https://doi.org/10.1016/j.cell.2022.08.021 Gut microbiome of multiple sclerosis patients and paired household healthy controls reveal associations with disease risk and course]
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[https://doi.org/10.1101/2023.01.17.524482 Decoupling the correlation between cytotoxic and exhausted T lymphocyte transcriptomic signatures enhances melanoma immunotherapy response prediction from tumor expression]
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|style="padding:.4em;" rowspan=1|Single-cell
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[https://doi.org/10.1101/2023.07.28.550993 Major data analysis errors invalidate cancer microbiome findings]
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|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|G Koh
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[https://doi.org/10.1038/s43018-022-00433-7 Single-cell meta-analyses reveal responses of tumor-reactive CXCL13+ T cells to immune-checkpoint blockade]
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[https://doi.org/10.1038/s41587-022-01342-x Estimation of tumor cell total mRNA expression in 15 cancer types predicts disease progression]
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|style="padding:.4em;" rowspan=1|2023/07/12
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-19
|style="padding:.4em;"|JH Cha
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[https://doi.org/10.1038/s41587-022-01288-0 DIALOGUE maps multicellular programs in tissue from single-cell or spatial transcriptomics data]
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|style="padding:.4em;" rowspan=1|2023/07/05
|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|SB Baek
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[https://doi.org/10.1038/s41591-023-02371-y Pan-cancer T cell atlas links a cellular stress response state to immunotherapy resistance]
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|style="padding:.4em;" rowspan=1|2023/06/28
|style="padding:.4em;" rowspan=1|Single-cell
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[https://doi.org/10.1016/j.patter.2022.100651 Self-supervised graph representation learning integrates multiple molecular networks and decodes gene-disease relationships]
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|style="padding:.4em;" rowspan=1|2023/06/21
|style="padding:.4em;" rowspan=1|Single-cell
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[https://doi.org/10.1016/j.ccell.2022.10.008 High-resolution single-cell atlas reveals diversity and plasticity of tissue-resident neutrophils in non-small cell lung cancer]
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|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-15
|style="padding:.4em;"|G Koh
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[https://doi.org/10.1186/s13059-022-02828-2 Parallel single-cell and bulk transcriptome analyses reveal key features of the gastric tumor microenvironment]
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|style="padding:.4em;" rowspan=1|2023/05/31
|style="padding:.4em;" rowspan=1|Single-cell
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[https://doi.org/10.1038/s41467-022-32838-4 Mutated processes predict immune checkpoint inhibitor therapy benefit in metastatic melanoma]
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|style="padding:.4em;"|JH Cha
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[https://doi.org/10.1038/s43018-021-00292-8 Temporal single-cell tracing reveals clonal revival and expansion of precursor exhausted T cells during anti-PD-1 therapy in lung cancer]
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[https://doi.org/10.1016/j.cell.2022.11.028 Integrative single-cell analysis of cardiogenesis indentifies developmental trajectories and non-conding mutations in congenital heart disease]
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[https://doi.org/10.1101/2022.12.20.521311 Supervised discovery of interpretable gene programs from single-cell data]
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[https://doi.org/10.1038/s41586-022-05435-0 Effect of the intratumoral microbiota on spatial and cellular heterogeneity in cancer]
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|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|G Koh
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[https://www.nature.com/articles/s41588-022-01141-9 Cancer cell states recur across tumor types and form specific interactions with the tumor microenvironment]
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[https://www.biorxiv.org/content/10.1101/2022.08.05.502989v1 MetaTiME: Meta-components of the Tumor Immune Microenvironment]
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[https://www.nature.com/articles/s41590-022-01262-7 Pre-encoded responsiveness to type I interferon in the peripheral immune system defines outcome of PD1 blockade therapy]
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|style="padding:.4em;"|SB Baek
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[https://www.biorxiv.org/content/10.1101/2022.03.16.484513v1 Integrated single-cell profiling dissects cell-state-specific enhancer landscapes of human tumor-infiltrating T cells]
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|style="padding:.4em;"|EJ Sung
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[https://www.nature.com/articles/s41591-022-01799-y A T cell resilience model associated with response to immunotherapy in multiple tumor types]
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|style="padding:.4em;"|IS Choi
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[https://www.nature.com/articles/s41588-022-01134-8 Single-nucleus and spatial transcriptome profiling of pancreatic cancer identifies multicellular dynamics associated with neoadjuvant treatment]
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|style="padding:.4em;"|G Koh
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[https://www.sciencedirect.com/science/article/pii/S1535610822003178 Pan-cancer integrative histology-genomic analysis via multimodal deep learning]
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|style="padding:.4em;"|JY Ma
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[https://doi.org/10.1016/j.chom.2023.05.024 Enterosignatures define common bacterial guilds in the human gut microbiome]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|NY Kim
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-25
|style="padding:.4em;"|SH Lee
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[https://doi.org/10.1016/j.chom.2023.05.027 The TaxUMAP atlas: Efficient display of large clinical microbiome data reveals ecological competition in protection against bacteremia]
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|style="padding:.4em;"|WJ Kim
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[http://dx.doi.org/10.1038/nbt.3704 Measurement of bacterial replication rates in microbial communities]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-23
|style="padding:.4em;"|JY Kim
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[https://doi.org/10.1186/s40168-023-01564-4 Skin microbiome diferentiates into distinct cutotypes with unique metabolic functions upon exposure to polycyclic aromatic hydrocarbons]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-22
|style="padding:.4em;"|SH Ahn
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[https://doi.org/10.1016/j.xcrm.2023.100920 Enrichment of oral-derived bacteria in inflamed colorectal tumors and distinct associations of Fusobacterium in the mesenchymal subtype]
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|style="padding:.4em;" rowspan=1|2023/08/04
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[https://doi.org/10.1038/s41586-023-05989-7 Profiling the human intestinal environment under physiological conditions]
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|style="padding:.4em;"|JH Cha
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[https://doi.org/10.1186/s40168-023-01494-1 Genome-centric metagenomics reveals the host-driven dynamics and ecological role of CPR bacteria in an activated sludge system]
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[https://doi.org/10.1016/j.patter.2022.100658 Enhanced metagenomic deep learning for disease prediction and consistent signature recognition by restructured microbiome 2D representations]
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[https://doi.org/10.1186/s13059-022-02809-5 Gene fow and introgression are pervasive forces shaping the evolution of bacterial species]
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[https://doi.org/10.1186/s40168-022-01435-4 Alterations of oral microbiota and impact on the gut microbiome in type 1 diabetes mellitus revealed by integrated multi‑omic analyses]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|JY Ma
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[https://doi.org/10.1016/j.immuni.2022.08.016 The CD4+ T cell response to a commensal-derived epitope transitions from a tolerant to an inflammatory state in Crohn’s disease]
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|style="padding:.4em;" rowspan=1|2023/05/26
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|style="padding:.4em;"|NY Kim
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|SH Lee
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[https://doi.org/10.1101/2022.10.11.511790 Single Cell Transcriptomics Reveals the Hidden Microbiomes of Human Tissues]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|JY Kim
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[https://doi.org/10.1038/s41564-017-0096-0 Stability of the human faecal microbiome in a cohort of adult men]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|WJ Kim
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[https://doi.org/10.1038/s41564-017-0084-4 Metatranscriptome of human faecal microbial communities in a cohort of adult men]
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|style="padding:.4em;" rowspan=1|2023/03/24
|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|SH Ahn
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[https://doi.org/10.1016/j.ccell.2022.09.009 Tumor microbiome links cellular programs and immunity in pancreatic cancer]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|HJ Kim
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|style="padding:.4em;" rowspan=1|2023/03/10
|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|JH Cha
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[https://doi.org/10.1038/s41586-022-05620-1 The person-to-person transmission landscape of the gut and oral microbiomes]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|JY Ma
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[https://doi.org/10.1101/2023.01.30.526328 BIRDMAn: A Bayesian differential abundance framework that enables robust inference of host-microbe associations]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|NY Kim
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[https://www.nature.com/articles/s41564-022-01157-1 Phage–host coevolution in natural populations]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|SH Lee
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[https://www.nature.com/articles/s41467-022-29968-0 A randomized controlled trial for response of microbiome network to exercise and diet intervention in patients with nonalcoholic fatty liver disease]
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|style="padding:.4em;"|SH Ahn
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[https://www.biorxiv.org/content/10.1101/2022.05.19.492684v1 Scalable power analysis and effect size exploration of microbiome community differences with Evident]
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|style="padding:.4em;" rowspan=1|Microbiome
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[https://www.biorxiv.org/content/10.1101/2022.08.19.504505v1 SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks]
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[https://www.pnas.org/doi/10.1073/pnas.2105859118 Representation learning of RNA velocity reveals robust cell transitions]
[https://www.nature.com/articles/s41467-022-34188-7 UniTVelo: temporally unified RNA velocity reinforces single-cell trajectory inference]
[https://www.sciencedirect.com/science/article/pii/S0092867421015774 Mapping transcriptomic vector fields of single cells]
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[https://pubmed.ncbi.nlm.nih.gov/29347966/ ReprDB and panDB: minimalist databases with maximal microbial representation]
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[https://www.biorxiv.org/content/10.1101/2022.07.06.499075v1 Maast: genotyping thousands of microbial strains efficiently]
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[https://www.biorxiv.org/content/10.1101/2022.06.16.496510v2 MIDAS2: Metagenomic Intra-species Diversity Analysis System]
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[https://www.biorxiv.org/content/10.1101/2022.02.01.478746v2 Scalable microbial strain inference in metagenomic data using StrainFacts]
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[https://www.biorxiv.org/content/10.1101/2022.02.15.480535v1 StrainPanDA: linked reconstruction of strain composition and gene content profiles via pangenome-based decomposition of metagenomic data]
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[https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-022-01251-w Metagenomic strain detection with SameStr: identification of a persisting core gut microbiota transferable by fecal transplantation]
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[https://www.nature.com/articles/s41587-021-01102-3 Fast and accurate metagenotyping of the human gut microbiome with GT-Pro]
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[https://www.nature.com/articles/s41587-020-00797-0 inStrain profiles population microdiversity from metagenomic data and sensitively detects shared microbial strains]
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[https://genome.cshlp.org/content/early/2021/07/22/gr.265058.120 Longitudinal linked-read sequencing reveals ecological and evolutionary responses of a human gut microbiome during antibiotic treatment]
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[https://www.sciencedirect.com/science/article/pii/S1931312821002365 Dispersal strategies shape persistence and evolution of human gut bacteria]
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[https://www.sciencedirect.com/science/article/pii/S0092867421003524 The long-term genetic stability and individual specificity of the human gut microbiome]
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[https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02042-y Analysis of 1321 Eubacterium rectale genomes from metagenomes uncovers complex phylogeographic population structure and subspecies functional adaptations]
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[https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3000102 Evolutionary dynamics of bacteria in the gut microbiome within and across hosts]
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[https://elifesciences.org/articles/42693 Extensive transmission of microbes along the gastrointestinal tract]
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[https://www.cell.com/cell-host-microbe/fulltext/S1931-3128(19)30041-1?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS1931312819300411%3Fshowall%3Dtrue Distinct Genetic and Functional Traits of Human Intestinal Prevotella copri Strains Are Associated with Different Habitual Diets]
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[https://www.nature.com/articles/nmeth.3802 Strain-level microbial epidemiology and population genomics from shotgun metagenomics]
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[https://www.cell.com/cell/fulltext/S0092-8674(21)00942-9#secsectitle0025 Chromatin and gene-regulatory dynamics of the developing human cerebral cortex at single-cell resolution]
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[https://www.biorxiv.org/content/10.1101/2021.02.09.430114v2 Single-cell ATAC and RNA sequencing reveal pre-existing and persistent subpopulations of cells associated with relapse of prostate cancer]
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[https://www.biorxiv.org/content/10.1101/2021.03.16.435578v1 Integrated single-cell transcriptomics and epigenomics reveals strong germinal center-associated etiology of autoimmune risk loci]
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[https://www.biorxiv.org/content/10.1101/2021.07.28.453784v1 Functional Inference of Gene Regulation using Single-Cell Multi-Omics]
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[https://www.biorxiv.org/content/10.1101/2021.03.24.436532v1 Single-cell analyses reveal a continuum of cell state and composition changes in the malignant transformation of polyps to colorectal cancer]
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[https://www.cell.com/cancer-cell/fulltext/S1535-6108(21)00165-3 Single-cell sequencing links multiregional immune landscapes and tissue-resident T cells in ccRCC to tumor topology and therapy efficacy]
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[https://www.sciencedirect.com/science/article/pii/S1535610821001173 Tumor and immune reprogramming during immunotherapy in advanced renal cell carcinoma]
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[https://www.sciencedirect.com/science/article/pii/S1074761321001199 Single-cell chromatin accessibility landscape identifies tissue repair program in human regulatory T cells]
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[https://www.nature.com/articles/s41591-021-01232-w Identification of resistance pathways and therapeutic targets in relapsed multiple myeloma patients through single-cell sequencing]
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[https://www.nature.com/articles/s41591-021-01323-8 A single-cell map of intratumoral changes during anti-PD1 treatment of patients with breast cancer]
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[https://www.sciencedirect.com/science/article/abs/pii/S0092867420316135 Single-cell landscape of the ecosystem in early-relapse hepatocellular carcinoma]
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[https://www.nature.com/articles/s41586-021-03552-w Interpreting type 1 diabetes risk with genetics and single-cell epigenomics]
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[https://www.sciencedirect.com/science/article/pii/S0092867421000726 Massive expansion of human gut bacteriophage diversity]
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[https://www.sciencedirect.com/science/article/pii/S1931312821001451 The infant gut resistome associates with E. coli, environmental exposures, gut microbiome maturity, and asthma-associated bacterial composition]
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[https://www.sciencedirect.com/science/article/pii/S1931312820306703?dgcid=rss_sd_all Methotrexate impacts conserved pathways in diverse human gut bacteria leading to decreased host immune activation]
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[https://science.sciencemag.org/content/366/6471/eaax9176 A metagenomic strategy for harnessing the chemical repertoire of the human microbiome]
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[https://www.nature.com/articles/s41467-019-10927-1 Predictive metabolomic profiling of microbial communities using amplicon or metagenomic sequences]
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[https://www.nature.com/articles/s41564-018-0306-4 Gut microbiome structure and metabolic activity in inflammatory bowel disease]
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[https://www.nature.com/articles/s41591-020-01183-8 Microbiome connections with host metabolism and habitual diet from 1,098 deeply phenotyped individuals]
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[https://www.nature.com/articles/s41467-020-18476-8 A predictive index for health status using species-level gut microbiome profiling]
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[https://www.nature.com/articles/s41586-019-1237-9 Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases]
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[https://www.nature.com/articles/s41467-021-21475-y Gastrointestinal microbiota composition predicts peripheral inflammatory state during treatment of human tuberculosis]
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[https://www.sciencedirect.com/science/article/pii/S1931312820301694 Structure of the Mucosal and Stool Microbiome in Lynch Syndrome]
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[https://science.sciencemag.org/content/369/6506/936 Cross-reactivity between tumor MHC class 1-restricted antigens and an enterococcal bacteriophage]
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[https://www.nature.com/articles/s41564-020-00831-6 Bifidobacterium bifidum strains synergize with immune checkpoint inhibitors to reduce tumour burden in mice]
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[https://www.nature.com/articles/s41591-020-01223-3 The gut microbiome modulates the protective association between a Mediterranean diet and cardiometabolic disease risk]
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[https://www.nature.com/articles/s41467-019-14177-z Impact of commonly used drugs on the composition and metabolic function of the gut microbiota]
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[https://www.sciencedirect.com/science/article/pii/S0092867420305638 Personalized Mapping of Drug Metabolism by the Human Gut Microbiome]
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[https://www.cell.com/fulltext/S0092-8674(17)30107-1 Mining the Human Gut Microbiota for Immunomodulatory Organisms]
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[https://www.nature.com/articles/s41586-020-2095-1 Microbiome analyses of blood and tissues suggest cancer diagnostic approach]
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[https://science.sciencemag.org/content/368/6494/973 The human tumor microbiome is composed of tumor type-specific intracellular bacteria]
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[https://www.nature.com/articles/s41590-020-0784-4 Functional CRISPR dissection of gene networks controlling human regulatory T cell identity]
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[https://www.sciencedirect.com/science/article/pii/S0092867420306887 Molecular Pathways of Colon Inflammation Induced by Cancer Immunotherapy]
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[https://www.nature.com/articles/s41588-020-00721-x Single-cell epigenomic analyses implicate candidate causal variants at inherited risk loci for Alzheimer’s and Parkinson’s diseases]
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[https://www.nature.com/articles/s41467-020-14766-3 Trajectory-based differential expression analysis for single-cell sequencing data]
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[https://www.sciencedirect.com/science/article/pii/S0092867419308967?via%3Dihub Single-Cell Analysis of Crohn's Disease Lesions Identifies a Pathogenic Cellular Module Associated with Resistance to Anti-TNF Therapy]
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|style="padding:.4em;"|JY Seong
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[https://pubmed.ncbi.nlm.nih.gov/31915379/ Rapid Non-Uniform Adaptation to Conformation-Specific KRAS(G12C) Inhibition]
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|style="padding:.4em;"|20-7
|style="padding:.4em;"|OY Min
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[https://pubmed.ncbi.nlm.nih.gov/31959990/ Targeted Therapy Guided by Single-Cell Transcriptomic Analysis in Drug-Induced Hypersensitivity Syndrome: A Case Report]
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|style="padding:.4em;" rowspan=2|2020/05/19
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|style="padding:.4em;"|20-6
|style="padding:.4em;"|SN Lee
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[https://pubmed.ncbi.nlm.nih.gov/32066951/ Distinct Microbial and Immune Niches of the Human Colon]
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|style="padding:.4em;"|20-5
|style="padding:.4em;"|DJ Park
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[https://pubmed.ncbi.nlm.nih.gov/31375813/ Massively Parallel Single-Cell Chromatin Landscapes of Human Immune Cell Development and Intratumoral T Cell Exhaustion]
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|style="padding:.4em;" rowspan=2|2020/05/12
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|style="padding:.4em;"|20-4
|style="padding:.4em;"|SY Park
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[https://pubmed.ncbi.nlm.nih.gov/29434354/ Single-cell Gene Expression Reveals a Landscape of Regulatory T Cell Phenotypes Shaped by the TCR]
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|style="padding:.4em;"|20-3
|style="padding:.4em;"|NY Kim
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[https://pubmed.ncbi.nlm.nih.gov/30078704/ A Single-Cell Atlas of In Vivo Mammalian Chromatin Accessibility]
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|style="padding:.4em;" rowspan=2|2020/04/28
|style="padding:.4em;" rowspan=2|Single-cell
|style="padding:.4em;"|20-2
|style="padding:.4em;"|SB Baek
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[https://pubmed.ncbi.nlm.nih.gov/32014031/ scAI: An Unsupervised Approach for the Integrative Analysis of Parallel Single-Cell Transcriptomic and Epigenomic Profiles]
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|style="padding:.4em;"|20-1
|style="padding:.4em;"|JH Cha
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[https://pubmed.ncbi.nlm.nih.gov/31792411/ Single-cell Multiomic Analysis Identifies Regulatory Programs in Mixed-Phenotype Acute Leukemia]
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{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2019-2nd semester
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|style="padding:.4em;" rowspan=2|2019/10/15
|style="padding:.4em;" rowspan=2|Microbiome
|style="padding:.4em;"|19-51
|style="padding:.4em;"|CY Kim
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[https://www.sciencedirect.com/science/article/pii/S1931312819303488 Obese Individuals with and without Type 2 Diabetes Show Different Gut Microbial Functional Capacity and Composition]
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|style="padding:.4em;"|19-50
|style="padding:.4em;"|SH Lee
|style="padding:.4em;text-align:left"|
[https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-019-0722-6 Clustering co-abundant genes identifies components of the gut microbiome that are reproducibly associated with colorectal cancer and inflammatory bowel disease]
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|style="padding:.4em;" rowspan=2|2019/10/08
|style="padding:.4em;" rowspan=2|Single-cell
|style="padding:.4em;"|19-49
|style="padding:.4em;"|SB Baek
|style="padding:.4em;text-align:left"|
[https://www.nature.com/articles/s41587-019-0206-z Massively parallel single-cell chromatin landscapes of human immune cell development and intratumoral T cell exhaustion]
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|style="padding:.4em;"|19-48
|style="padding:.4em;"|SB Baek
|style="padding:.4em;text-align:left"|
[https://www.biorxiv.org/content/10.1101/739011v1 Assessment of computational methods for the analysis of single-cell ATAC-seq data]
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|style="padding:.4em;" rowspan=2|2019/10/01
|style="padding:.4em;" rowspan=2|Microbiome
|style="padding:.4em;"|19-47
|style="padding:.4em;"|MY Lee
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S1931312819303026 Meta-Analysis Reveals Reproducible Gut Microbiome Alterations in Response to a High-Fat Diet]
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|style="padding:.4em;"|19-46
|style="padding:.4em;"|NY Kim
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S0092867419307731 Tumor Microbiome Diversity and Composition Influence Pancreatic Cancer Outcomes]
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|style="padding:.4em;" rowspan=2|2019/09/24
|style="padding:.4em;" rowspan=2|Single-cell
|style="padding:.4em;"|19-45
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://genome.cshlp.org/content/early/2019/04/01/gr.243725.118 The accessible chromatin landscape of the murine hippocampus at single-cell resolution]
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|style="padding:.4em;"|19-44
|style="padding:.4em;"|SB Baek
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S009286741830446X Integrated Single-Cell Analysis Maps the Continuous Regulatory Landscape of Human Hematopoietic Differentiation]
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|style="padding:.4em;" rowspan=2|2019/09/17
|style="padding:.4em;" rowspan=2|Microbiome
|style="padding:.4em;"|19-43
|style="padding:.4em;"|SH Lee
|style="padding:.4em;text-align:left"|
[https://science.sciencemag.org/content/365/6449/eaau4735 A sparse covarying unit that describes healthy and impaired human gut microbiota development]
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|style="padding:.4em;"|19-42
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S0092867419307810 Large-Scale Analyses of Human Microbiomes Reveal Thousands of Small, Novel Genes]
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|style="padding:.4em;" rowspan=2|2019/09/10
|style="padding:.4em;" rowspan=2|Single-cell
|style="padding:.4em;"|19-41
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S0092867419307329?via%3Dihub Intra- and Inter-cellular Rewiring of the Human Colon during Ulcerative Colitis]
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|style="padding:.4em;"|19-40
|style="padding:.4em;"|IS Choi
|style="padding:.4em;text-align:left"|
[https://www.nature.com/articles/nbt.4042 Multiplexed droplet single-cell RNA-sequencing using natural genetic variation]
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|style="padding:.4em;" rowspan=2|2019/09/03
|style="padding:.4em;" rowspan=2|Microbiome
|style="padding:.4em;"|19-39
|style="padding:.4em;"|NY Kim
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S193131281930352X?via%3Dihub The Landscape of Genetic Content in the Gut and Oral Human Microbiome]
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|style="padding:.4em;"|19-38
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S0092867419307755?via%3Dihub Benchmarking Metagenomics Tools for Taxonomic Classification]
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{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2019
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!scope="col" style="padding:.4em" |Date
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|style="padding:.4em;" rowspan=2|2019/08/20
|style="padding:.4em;"|19-37
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://www.biorxiv.org/content/10.1101/719088v1 Coexpression uncovers a unified single-cell transcriptomic landscape]
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|style="padding:.4em;"|19-36
|style="padding:.4em;"|MY Lee
|style="padding:.4em;text-align:left"|
[https://www.biorxiv.org/content/10.1101/586859v1 Single-cell interactomes of the human brain reveal cell-type specific convergence of brain disorders]
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|style="padding:.4em;" rowspan=3|2019/08/14
|style="padding:.4em;"|19-35
|style="padding:.4em;"|SH Lee
|style="padding:.4em;text-align:left"|
[https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004075 Proportionality: a valid alternative to correlation for relative data]
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|style="padding:.4em;"|19-34
|style="padding:.4em;"|SH Lee
|style="padding:.4em;text-align:left"|
[https://www.nature.com/articles/s41598-017-16520-0 propr: An R-package for Identifying Proportionally Abundant Features Using Compositional Data Analysis]
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|style="padding:.4em;"|19-33
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[https://www.nature.com/articles/s41591-018-0157-9 Robust prediction of response to immune checkpoint blockade therapy in metastatic melanoma]
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|style="padding:.4em;" rowspan=2|2019/08/13
|style="padding:.4em;"|19-32
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[https://www.nature.com/articles/s41592-018-0254-1 A test metric for assessing single-cell RNA-seq batch correction]
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|style="padding:.4em;"|19-31
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S0092867419305598 Comprehensive Integration of Single-Cell Data]
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|style="padding:.4em;" rowspan=2|2019/08/08
|style="padding:.4em;"|19-30
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S0092867418313941 Defining T Cell States Associated with Response to Checkpoint Immunotherapy in Melanoma]
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|style="padding:.4em;"|19-29
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S009286741831242X High-Dimensional Analysis Delineates Myeloid and Lymphoid Compartment Remodeling during Successful Immune-Checkpoint Cancer Therapy]
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|style="padding:.4em;" rowspan=2|2019/08/07
|style="padding:.4em;"|19-28
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[https://www.biorxiv.org/content/10.1101/555557v1 A single-cell reference map for human blood and tissue T cell activation reveals functional states in health and disease]
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|style="padding:.4em;"|19-27
|style="padding:.4em;"|IS Choi
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S009286741831568X Dysfunctional CD8 T Cells Form a Proliferative, Dynamically Regulated Compartment within Human Melanoma]
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|style="padding:.4em;" rowspan=2|2019/07/30
|style="padding:.4em;"|19-26
|style="padding:.4em;"|IS Choi
|style="padding:.4em;text-align:left"|
[https://www.nature.com/articles/nm.4466 High-dimensional single-cell analysis predicts response to anti-PD-1 immunotherapy]
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|style="padding:.4em;"|19-25
|style="padding:.4em;"|JW Cho
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S0092867418311784 A Cancer Cell Program Promotes T Cell Exclusion and Resistance to Checkpoint Blockade]
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|style="padding:.4em;" rowspan=2|2019/07/23
|style="padding:.4em;"|19-24
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/26006008 COMPASS identifies T-cell subsets correlated with clinical outcomes.]
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|style="padding:.4em;"|19-23
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[https://www.nature.com/articles/ncomms14825 Sensitive detection of rare disease-associated cell subsets via representation learning]
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|style="padding:.4em;" rowspan=3|2019/05/30
|style="padding:.4em;"|19-22
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[https://www.ncbi.nlm.nih.gov/pubmed/30936547 Meta-analysis of fecal metagenomes reveals global microbial signatures that are specific for colorectal cancer]
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|style="padding:.4em;"|19-21
|style="padding:.4em;"|
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[https://www.ncbi.nlm.nih.gov/pubmed/30936548 Metagenomic analysis of colorectal cancer datasets identifies cross-cohort microbial diagnostic signatures and a link with choline degradation]
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|style="padding:.4em;"|19-20
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/30664783 Microbial network disturbances in relapsing refractory Crohn's disease.]
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|style="padding:.4em;" rowspan=3|2019/05/23
|style="padding:.4em;"|19-19
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[https://www.ncbi.nlm.nih.gov/pubmed/30867587 New insights from uncultivated genomes of the global human gut microbiome]
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|style="padding:.4em;"|19-18
|style="padding:.4em;"|
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[https://www.ncbi.nlm.nih.gov/pubmed/30745586 A new genomic blueprint of the human gut microbiota]
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|style="padding:.4em;"|19-17
|style="padding:.4em;"|
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[https://www.ncbi.nlm.nih.gov/pubmed/30661755 Extensive Unexplored Human Microbiome Diversity Revealed by Over 150,000 Genomes from Metagenomes Spanning Age, Geography, and Lifestyle.]
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|style="padding:.4em;" rowspan=2|2019/05/16
|style="padding:.4em;"|19-16
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[https://www.ncbi.nlm.nih.gov/pubmed/29311644 Dynamics of metatranscription in the inflammatory bowel disease gut microbiome.]
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|style="padding:.4em;"|19-15
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/29335555 Metatranscriptome of human faecal microbial communities in a cohort of adult men.]
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|style="padding:.4em;" rowspan=2|2019/05/09
|style="padding:.4em;"|19-14
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[https://www.ncbi.nlm.nih.gov/pubmed/30193113 Post-Antibiotic Gut Mucosal Microbiome Reconstitution Is Impaired by Probiotics and Improved by Autologous FMT.]
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|style="padding:.4em;"|19-13
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[https://www.ncbi.nlm.nih.gov/pubmed/30193112 Personalized Gut Mucosal Colonization Resistance to Empiric Probiotics Is Associated with Unique Host and Microbiome Features.]
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|style="padding:.4em;" rowspan=2|2019/05/02
|style="padding:.4em;"|19-12
|style="padding:.4em;"|
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[https://www.ncbi.nlm.nih.gov/pubmed/30753825 Distinct Immune Cell Populations Define Response to Anti-PD-1 Monotherapy and Anti-PD-1/Anti-CTLA-4 Combined Therapy.]
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[https://www.ncbi.nlm.nih.gov/pubmed/30778252 Subsets of exhausted CD8+ T cells differentially mediate tumor control and respond to checkpoint blockade.]
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|style="padding:.4em;" rowspan=2|2019/4/11
|style="padding:.4em;"|19-10
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[https://www.ncbi.nlm.nih.gov/pubmed/30479382 Lineage tracking reveals dynamic relationships of T cells in colorectal cancer.]
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[https://www.ncbi.nlm.nih.gov/pubmed/30523328 Single cell dissection of plasma cell heterogeneity in symptomatic and asymptomatic myeloma.]
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|style="padding:.4em;"|19-8
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[https://www.ncbi.nlm.nih.gov/pubmed/29942092 Single-cell profiling of breast cancer T cells reveals a tissue-resident memory subset associated with improved prognosis.]
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|style="padding:.4em;"|19-7
|style="padding:.4em;"|
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[https://www.ncbi.nlm.nih.gov/pubmed/29942094 Global characterization of T cells in non-small-cell lung cancer by single-cell sequencing]
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|style="padding:.4em;" rowspan=2|2019/3/28
|style="padding:.4em;"|19-6
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[https://www.ncbi.nlm.nih.gov/pubmed/28319088 Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors.]
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|style="padding:.4em;"|19-5
|style="padding:.4em;"|
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[https://www.ncbi.nlm.nih.gov/pubmed/28622514 Landscape of Infiltrating T Cells in Liver Cancer Revealed by Single-Cell Sequencing.]
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|style="padding:.4em;" rowspan=3|2019/3/21
|style="padding:.4em;"|19-4
|style="padding:.4em;"|
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[https://www.ncbi.nlm.nih.gov/pubmed/29988129 Phenotype molding of stromal cells in the lung tumor microenvironment.]
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|style="padding:.4em;"|19-3-1
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[https://www.ncbi.nlm.nih.gov/pubmed/30787436 A single-cell molecular map of mouse gastrulation and early organogenesis]
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|style="padding:.4em;"|19-3
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[https://www.ncbi.nlm.nih.gov/pubmed/30787437 The single-cell transcriptional landscape of mammalian organogenesis]
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|style="padding:.4em;" rowspan=2|2019/3/14
|style="padding:.4em;"|19-2
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[https://www.ncbi.nlm.nih.gov/pubmed/29961579 Single-Cell Map of Diverse Immune Phenotypes in the Breast Tumor Microenvironment]
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[https://www.ncbi.nlm.nih.gov/pubmed/29198524 Single-Cell Transcriptomic Analysis of Primary and Metastatic Tumor Ecosystems in Head and Neck Cancer]
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{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2018
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|style="padding:.4em;"|18-12
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=A+pan-cancer+analysis+of+enhancer+expression+in+nearly+9000+patient+samples A Pan-Cancer Analysis of Enhancer Expression in Nearly 9000 Patient Samples.]
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=Machine+learning+identifies+stemness+features+associated+with+oncogenic+dedifferentiation Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation.]
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|style="padding:.4em;" rowspan=2|2018/06/07
|style="padding:.4em;"|18-10
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=Developmental+and+oncogenic+programs+in+H3K27M+gliomas+dissected+by+single-cell+RNA-seq Developmental and oncogenic programs in H3K27M gliomas dissected by single-cell RNA-seq.]
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=Chemoresistance+evolution+in+triple-negative+breast+cancer+delineated+by+single-cell+sequencing Chemoresistance Evolution in Triple-Negative Breast Cancer Delineated by Single-Cell Sequencing.]
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|style="padding:.4em;" rowspan=2|2018/05/31
|style="padding:.4em;"|18-8
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=Mapping+human+pluripotent+stem+cell+differentiation+pathways+using+high+throughput+single-cell+RNA-sequencing Mapping human pluripotent stem cell differentiation pathways using high throughput single-cell RNA-sequencing.]
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=A+single-cell+RNA-seq+survey+of+the+developmental+landscape+of+the+human+prefrontal+cortex A single-cell RNA-seq survey of the developmental landscape of the human prefrontal cortex.]
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|style="padding:.4em;" rowspan=2|2018/05/24
|style="padding:.4em;"|18-6
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=Single-cell+RNA+sequencing+identifies+celltype-specific+cis-eQTLs+and+co-expression+QTLs Single-cell RNA sequencing identifies celltype-specific cis-eQTLs and co-expression QTLs.]
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|style="padding:.4em;"|18-5
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=FOCS%3A+a+novel+method+for+analyzing+enhancer+and+gene+activity+patterns+infers+an+extensive+enhancer-promoter+map FOCS: a novel method for analyzing enhancer and gene activity patterns infers an extensive enhancer-promoter map.]
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|style="padding:.4em;" rowspan=2|2018/05/17
|style="padding:.4em;"|18-4
|style="padding:.4em;"|KS Kim
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[https://www.ncbi.nlm.nih.gov/pubmed/29149608 A Distinct Gene Module for Dysfunction Uncoupled from Activation in Tumor-Infiltrating T Cells.]
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|style="padding:.4em;"|18-3
|style="padding:.4em;"|SH Lee
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[https://www.ncbi.nlm.nih.gov/pubmed/29610481 A global transcriptional network connecting noncoding mutations to changes in tumor gene expression.]
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|style="padding:.4em;" rowspan=2|2018/05/10
|style="padding:.4em;"|18-2
|style="padding:.4em;"|JW Cho
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=Inferring+regulatory+element+landscapes+and+transcription+factor+networks+from+cancer+methylomes Inferring regulatory element landscapes and transcription factor networks from cancer methylomes.]
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|style="padding:.4em;"|DS Bae
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[https://www.ncbi.nlm.nih.gov/pubmed/28129544 Genomic and Transcriptomic Features of Response to Anti-PD-1 Therapy in Metastatic Melanoma.]
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{|class=wikitable style="text-align:center;"
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[https://www.ncbi.nlm.nih.gov/pubmed/28104840 Tumor aneuploidy correlates with markers of immune evasion and with reduced response to immunotherapy.]
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|style="padding:.4em;"|17-35
|style="padding:.4em;"|JW Cho
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[https://www.ncbi.nlm.nih.gov/pubmed/27240091 Landscape of tumor-infiltrating T cell repertoire of human cancers.]
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|style="padding:.4em;"|17-34
|style="padding:.4em;"|JW Cho
|style="padding:.4em;text-align:left"|
[http://biorxiv.org/content/early/2015/09/01/025908 The landscape of T cell infiltration in human cancer and its association with antigen presenting gene expression.]
|-
|style="padding:.4em;"|17-33
|style="padding:.4em;"|MY Lee
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.08.052 A Distinct Gene Module for Dysfunction Uncoupled from Activation in Tumor-Infiltrating T Cells.]
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|style="padding:.4em;" rowspan=2|2017/06/07
|style="padding:.4em;"|17-32
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.tandfonline.com/doi/full/10.1080/2162402X.2016.1253654 Identification of genetic determinants of breast cancer immune phenotypes by integrative genome-scale analysis.]
|-
|style="padding:.4em;"|17-31
|style="padding:.4em;"|MY Lee
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.celrep.2016.03.075 Genomic Correlates of Immune-Cell Infiltrates in Colorectal Carcinoma.]
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|style="padding:.4em;" rowspan=2|2017/05/31
|style="padding:.4em;"|17-30
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.02.065 Genomic and Transcriptomic Features of Response to Anti-PD-1 Therapy in Metastatic Melanoma.]
|-
|style="padding:.4em;"|17-29
|style="padding:.4em;"|JW Cho
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.celrep.2016.12.019 Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade.]
|-
|style="padding:.4em;" rowspan=2|2017/05/24
|style="padding:.4em;"|17-28
|style="padding:.4em;"|EB Kim
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.12.022 Systemic Immunity Is Required for Effective Cencer Immunotherapy.]
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|style="padding:.4em;"|17-27
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.10.020 Linking the Human Gut Microbiome to Inflammatory Cytokine Production Capacity.]
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|style="padding:.4em;" rowspan=2|2017/05/17
|style="padding:.4em;"|17-26
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.10.018 Host and Environmental Factors Influencing Individual Human Cytokine Responses.]
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|style="padding:.4em;"|17-25
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.10.017 A Functional Genomics Approach to Understand Variation in Cytokine Production in Humans.]
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|style="padding:.4em;" rowspan=2|2017/04/26
|style="padding:.4em;"|17-24
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1038%2FNMETH.4177 Pooled CRISPR screening with single-cell transcriptome readout.]
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|style="padding:.4em;"|17-23
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.11.039 Dissecting Immune Circuits by Linking CRISPR-Pooled Screens with Single-Cell RNA-Seq.]
|-
|style="padding:.4em;" rowspan=2|2017/04/12
|style="padding:.4em;"|17-22
|style="padding:.4em;"|SH Lee
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.11.038 Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens.]
|-
|style="padding:.4em;"|17-21
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1038%2Fnbt.3569 Wishbone identifies bifurcating developmental trajectories from single-cell data.]
|-
|style="padding:.4em;" rowspan=2|2017/04/05
|style="padding:.4em;"|17-20
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[http://biorxiv.org/content/early/2017/02/21/110668 Reversed graph embedding resolves complex single-cell developmental trajectories.]
|-
|style="padding:.4em;"|17-19
|style="padding:.4em;"|SH Lee
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1038%2Fnmeth.4150 Single-cell mRNA quantification and differential analysis with Census.]
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|style="padding:.4em;" rowspan=2|2017/03/29
|style="padding:.4em;"|17-18
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.celrep.2016.12.060 Single-Cell Transcriptomic Analysis Defines Heterogeneity and Transcriptional Dynamics in the Adult Neural Stem Cell Lineage.]
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|style="padding:.4em;"|17-17
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/26051941 Single-Cell Network Analysis Identifies DDIT3 as a Nodal Lineage Regulator in Hematopoiesis.]
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|style="padding:.4em;" rowspan=2|2017/03/22
|style="padding:.4em;"|17-16
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/27580035 Single-cell analysis of mixed-lineage states leading to a binary cell fate choice.]
|-
|style="padding:.4em;"|17-15
|style="padding:.4em;"|EB Kim
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/27281220 Dissecting direct reprogramming from fibroblast to neuron using single-cell RNA-seq.]
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|style="padding:.4em;" rowspan=2|2017/03/15
|style="padding:.4em;"|17-14
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1126%2Fscience.aad0501 Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq.]
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|style="padding:.4em;"|17-13
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/26084335 Single-cell mRNA sequencing identifies subclonal heterogeneity in anti-cancer drug responses of lung adenocarcinoma cells.]
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|style="padding:.4em;" rowspan=2|2017/02/28
|style="padding:.4em;"|17-12
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/27824113 A Comprehensive Characterization of the Function of LincRNAs in Transcriptional Regulation Through Long-Range Chromatin Interactions.]
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|style="padding:.4em;"|17-11
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[//pubmed.gov/27851969 Epigenomic Deconvolution of Breast Tumors Reveals Metabolic Coupling between Constituent Cell Types.]
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|style="padding:.4em;" rowspan=2|2017/02/21
|style="padding:.4em;"|17-10
|style="padding:.4em;"|EB Kim
|style="padding:.4em;text-align:left"|
[http://biorxiv.org/content/early/2016/12/30/097451 Convergence of dispersed regulatory mutations predicts driver genes in prostate cancer.]
|-
|style="padding:.4em;"|17-09
|style="padding:.4em;"|EB Kim
|style="padding:.4em;text-align:left"|
[//pubmed.gov/27723759 Chromatin structure-based prediction of recurrent noncoding mutations in cancer.]
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|style="padding:.4em;" rowspan=2|2017/02/07
|style="padding:.4em;"|17-08
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[http://biorxiv.org/content/early/2016/11/17/088286 Somatic Mutations and Neoepitope Homology in Melanomas Treated with CTLA-4 Blockade.]
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|style="padding:.4em;"|17-07
|style="padding:.4em;"|MY Lee
|style="padding:.4em;text-align:left"|
[http://biorxiv.org/content/early/2016/11/28/090134 Chemotherapy weakly contributes to predicted neoantigen expression in ovarian cancer.]
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|style="padding:.4em;" rowspan=1|2017/01/31
|style="padding:.4em;"|17-06
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[//www.ncbi.nlm.nih.gov/pubmed/27912059 Microbiota Diurnal Rhythmicity Programs Host Transcriptome Oscillations.]
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|style="padding:.4em;" rowspan=1|2017/01/24
|style="padding:.4em;"|17-05
|style="padding:.4em;"|JW Cho
|style="padding:.4em;text-align:left"|
[//www.ncbi.nlm.nih.gov/pubmed/27851914 Transcriptional Landscape of Human Tissue Lymphocytes Unveils Uniqueness of Tumor-Infiltrating T Regulatory Cells.]
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|style="padding:.4em;" rowspan=2|2017/01/17
|style="padding:.4em;"|17-04
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[//www.ncbi.nlm.nih.gov/pubmed/25938943 Mapping long-range promoter contacts in human cells with high-resolution capture Hi-C]
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|style="padding:.4em;"|17-03
|style="padding:.4em;"|EB Kim
|style="padding:.4em;text-align:left"|
[//www.ncbi.nlm.nih.gov/pubmed/27306882 CHiCAGO: robust detection of DNA looping interactions in Capture Hi-C data.]
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|style="padding:.4em;" rowspan=1|2017/01/10
|style="padding:.4em;"|17-02
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[//www.ncbi.nlm.nih.gov/pubmed/26527291 ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis]
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|style="padding:.4em;" rowspan=1|2017/01/03
|style="padding:.4em;"|17-01
|style="padding:.4em;"|SH Lee
|style="padding:.4em;text-align:left"|
[//www.ncbi.nlm.nih.gov/pubmed/26287467 Single-cell messenger RNA sequencing reveals rare intestinal cell types]
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{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2016
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!scope="col" style="padding:.4em" |Date
!scope="col" style="padding:.4em" | Paper index
!scope="col" style="padding:.4em" | Presenter
!scope="col" style="padding:.4em" | Paper title
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|style="padding:.4em;" rowspan=1|2016/12/27
|style="padding:.4em;"|2016-31
|style="padding:.4em;"|EB Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/25664528 Decoding the regulatory network of early blood development from single-cell gene expression measurements.]
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|style="padding:.4em;" rowspan=1|2016/12/6
|style="padding:.4em;"|2016-30
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26299571 Single-Cell RNA-Seq with Waterfall Reveals Molecular Cascades underlying Adult Neurogenesis.]
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|style="padding:.4em;" rowspan=1|2016/11/29
|style="padding:.4em;"|2016-29
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/24658644 The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells.]
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|style="padding:.4em;" rowspan=1|2016/11/22
|style="padding:.4em;"|2016-28
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26887813 Classification of low quality cells from single-cell RNA-seq data.]
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|style="padding:.4em;" rowspan=1|2016/11/15
|style="padding:.4em;"|2016-27
|style="padding:.4em;"|MY Lee
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26000487 Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells.]
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|style="padding:.4em;" rowspan=1|2016/11/8
|style="padding:.4em;"|2016-26
|style="padding:.4em;"|JW Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26000488 Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets.]
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|style="padding:.4em;" rowspan=1|2016/11/1
|style="padding:.4em;"|2016-25
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27426982 Causal Mechanistic Regulatory Network for Glioblastoma Deciphered Using Systems Genetics Network Analysis.]
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|style="padding:.4em;" rowspan=1|2016/10/25
|style="padding:.4em;"|2016-24
|style="padding:.4em;"|MY Lee
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27549193 Comprehensive analyses of tumor immunity: implications for cancer immunotherapy.]
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|style="padding:.4em;" rowspan=1|2016/10/11
|style="padding:.4em;"|2016-23
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=sidespread+parainflammation Widespread parainflammation in human cancer]
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|style="padding:.4em;" rowspan=1|2016/9/27
|style="padding:.4em;"|2016-22
|style="padding:.4em;"|ER Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27535533 Analysis of protein-coding genetic variation in 60,706 humans]
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|style="padding:.4em;" rowspan=1|2016/9/20
|style="padding:.4em;"|2016-21
|style="padding:.4em;"|SM Yang
|style="padding:.4em;text-align:left"|
Functional characterization of somatic mutations in cancer using network-based inference of protein activity
[http://www.ncbi.nlm.nih.gov/pubmed/27322546 pubmed]
[http://www.nature.com/ng/journal/v48/n8/full/ng.3593.html fulltext]
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|style="padding:.4em;" rowspan=1|2016/9/13
|style="padding:.4em;"|2016-20
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=exploiting+single-cell+expression+to+characterize Exploiting single-cell expression to characterize co-expression replicability.]
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|style="padding:.4em;" rowspan=1|2016/9/6
|style="padding:.4em;"|2016-19
|style="padding:.4em;"|T Lee
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26940869 Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade]
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|style="padding:.4em;" rowspan=1|2016/8/31
|style="padding:.4em;"|2016-18
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27264179 A Computational Drug Repositioning Approach for Targeting Oncogenic Transcription Factors]
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|style="padding:.4em;" rowspan=1|2016/8/16
|style="padding:.4em;"|2016-17
|style="padding:.4em;"|JW Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27309802 The landscape of accessible chromatin in mammalian preimplantation embryos]
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|style="padding:.4em;" rowspan=1|2016/8/8
|style="padding:.4em;"|2016-16
|style="padding:.4em;"|EB Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27064255 Enhancer-promoter interactions are encoded by complex genomic signatures on looping chromatin]
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|style="padding:.4em;" rowspan=1|2016/8/1
|style="padding:.4em;"|2016-15
|style="padding:.4em;"|MY Lee
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27040498 Personalized Immunomonitoring Uncovers Molecular Networks that Stratify Lupus Patients]
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|style="padding:.4em;" rowspan=1|2016/7/25
|style="padding:.4em;"|2016-14
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=integration+of+summary+data+from+gwas+and+eqtl+studies Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets]
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|style="padding:.4em;" rowspan=1|2016/7/18
|style="padding:.4em;"|2016-13
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/23624555 Identification of transcriptional regulators in the mouse immune system]
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|style="padding:.4em;" rowspan=2|2016/6/8
|style="padding:.4em;"|2016-12
|style="padding:.4em;"|DS Bae, CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26619012 Mapping the effects of drugs on the immune system]
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|style="padding:.4em;"| 2016-11
|style="padding:.4em;"|DS Bae, CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26186195 Elucidating compound mechanism of action by network perturbation analysis]
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|style="padding:.4em;" rowspan=2|2016/6/1
|style="padding:.4em;"|2016-10
|style="padding:.4em;"|MY Lee,SM Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26854917 Integrative approaches for large-scale transcriptome-wide association studies]
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|style="padding:.4em;"| 2016-9
|style="padding:.4em;"|MY Lee,SM Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26950747 Tissue-specific regulatory circuits reveal variable modular perturbations across complex diseases]
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|style="padding:.4em;" rowspan=2|2016/5/18
|style="padding:.4em;"|2016-8
|style="padding:.4em;"|CY Kim,SJ Kwon
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27013732 Survey of variation in human transcription factors reveals prevalent DNA binding changes]
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|style="padding:.4em;"| 2016-7
|style="padding:.4em;"| CY Kim,SJ Kwon
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26502339 Large-scale identification of sequence variants influencing human transcription factor occupancy in vivo]
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|style="padding:.4em;" rowspan=2|2016/5/11
|style="padding:.4em;"|2016-6
|style="padding:.4em;"|DS Bae, CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/25171417 Predicting Cancer-specific vulnerability via data-driven detection of synthetic lethality]
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|style="padding:.4em;"| 2016-5
|style="padding:.4em;"|DS Bae, CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/23467089 Dynamic regulatory network controlling Th17 cell differentiation]
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|style="padding:.4em;" rowspan=2|2016/5/4
|style="padding:.4em;"|2016-4
|style="padding:.4em;"|MY Lee,SM Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/25853550 Characterization of the immunophenotypes and antigenomes of colorectal cancers reveals distinct tumor escape mechanisms and novel targets for immunotherapy]
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|style="padding:.4em;"| 2016-3
|style="padding:.4em;"|MY Lee,SM Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26618344 Regulators of genetic risk of breast cancer identified by integrative network analysis]
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|style="padding:.4em;" rowspan=2|2016/4/27
|style="padding:.4em;"|2016-2
|style="padding:.4em;"|CY Kim,SJ Kwon
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26780608 A predictive computational framework for direct reprogramming between human cell types]
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|style="padding:.4em;"| 2016-1
|style="padding:.4em;"| CY Kim,SJ Kwon
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/25126793 CellNet: Network biology applied to stem cell engineering]
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{|class=wikitable style="text-align:center;"
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|style="padding:.4em;" rowspan=2|2015/06/11
|style="padding:.4em;"|2015-55
|style="padding:.4em;"|
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[http://genome.cshlp.org/content/24/2/340.long Improved exome prioritization of disease genes through cross-species phenotype comparison.]
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|style="padding:.4em;"|2015-54
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|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0002929714001128 Phevor combines multiple biomedical ontologies for accurate identification of disease-causing alleles in single individuals and small nuclear families.]
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|style="padding:.4em;" rowspan=2|2015/06/04
|style="padding:.4em;"|2015-53
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[http://www.nature.com/nmeth/journal/v10/n11/full/nmeth.2656.html eXtasy: variant prioritization by genomic data fusion.]
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|style="padding:.4em;"|2015-52
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[http://genome.cshlp.org/content/21/9/1529.long A probabilistic disease-gene finder for personal genomes.]
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|style="padding:.4em;" rowspan=2|2015/05/28
|style="padding:.4em;"|2015-51
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[http://www.nature.com/ng/journal/v46/n12/full/ng.3141.html Systematic analysis of noncoding somatic mutations and gene expression alterations across 14 tumor types.]
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|style="padding:.4em;"|2015-50
|style="padding:.4em;"|
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[http://www.nature.com/nbt/journal/v28/n5/full/nbt.1630.html GREAT improves functional interpretation of cis-regulatory regions.]
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|style="padding:.4em;" rowspan=2|2015/05/21
|style="padding:.4em;"|2015-49
|style="padding:.4em;"|
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[http://www.nature.com/nmeth/journal/v11/n3/full/nmeth.2832.html Functional annotation of noncoding sequence variants.]
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|style="padding:.4em;"|2015-48
|style="padding:.4em;"|
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[http://genomebiology.com/content/15/10/480 FunSeq2: A framework for prioritizing noncoding regulatory variants in cancer.]
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|style="padding:.4em;" rowspan=2|2015/05/14
|style="padding:.4em;"|2015-47
|style="padding:.4em;"|
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[http://www.nature.com/nmeth/journal/v12/n2/full/nmeth.3215.html Selecting causal genes from genome-wide association studies via functionally coherent subnetworks.]
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|style="padding:.4em;"|2015-46
|style="padding:.4em;"|
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[http://www.nature.com/ncomms/2015/150119/ncomms6890/full/ncomms6890.html Biological interpretation of genome-wide association studies using predicted gene functions.]
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|style="padding:.4em;" rowspan=3|2015/05/07
|style="padding:.4em;"|2015-45
|style="padding:.4em;"|
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[http://www.nature.com/ncomms/2014/140626/ncomms5212/full/ncomms5212.html Human symptoms-disease network.]
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|style="padding:.4em;"|2015-44
|style="padding:.4em;"|
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[http://www.sciencedirect.com/science/article/pii/S0092867413010246 A nondegenerate code of deleterious variants in Mendelian loci contributes to complex disease risk.]
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|style="padding:.4em;"|2015-43
|style="padding:.4em;"|
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[http://www.sciencemag.org/content/347/6224/1257601.long Uncovering disease-disease relationships through the incomplete interactome.]

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|style="padding:.4em;" rowspan=2|2015/04/30
|style="padding:.4em;"|2015-42
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[http://genome.cshlp.org/content/25/1/142.long The discovery of integrated gene networks for autism and related disorders.]
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|style="padding:.4em;"|2015-41
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://msb.embopress.org/content/10/12/774.long Integrated systems analysis reveals a molecular network underlying autism spectrum disorders.]
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|style="padding:.4em;" rowspan=3|2015/04/23
|style="padding:.4em;"|2015-40
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v518/n7539/full/nature13990.html Dissecting neural differentiation regulatory networks through epigenetic footprinting.]
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|style="padding:.4em;"|2015-39
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v518/n7539/full/nature14221.html Cell-of-origin chromatin organization shapes the mutational landscape of cancer.]
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|style="padding:.4em;"|2015-38
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v518/n7539/full/nature14248.html Integrative analysis of 111 reference human epigenomes.]
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|style="padding:.4em;" rowspan=2|2015/04/09
|style="padding:.4em;"|2015-37
|style="padding:.4em;"|
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[http://genome.cshlp.org/content/25/2/246.long Genome-wide analysis of local chromatin packing in Arabidopsis thaliana.]
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|style="padding:.4em;"|2015-36
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414014974 A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping.]
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|style="padding:.4em;" rowspan=2|2015/04/02
|style="padding:.4em;"|2015-35
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nrg/journal/v14/n6/full/nrg3454.html Exploring the three-dimensional organization of genomes: interpreting chromatin interaction data.]
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|style="padding:.4em;"|2015-34
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.sciencemag.org/content/347/6225/1010.long Transcribed enhancers lead waves of coordinated transcription in transitioning mammalian cells.]
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|style="padding:.4em;" rowspan=2|2015/03/26
|style="padding:.4em;"|2015-33
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://genome.cshlp.org/content/25/2/257.long Cupid: simultaneous reconstruction of microRNA-target and ceRNA networks.]
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|style="padding:.4em;"|2015-32
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://genome.cshlp.org/content/25/1/41.long Characterization of the neural stem cell gene regulatory network identifies OLIG2 as a multifunctional regulator of self-renewal.]
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|style="padding:.4em;" rowspan=2|2015/03/19
|style="padding:.4em;"|2015-31
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nbt/journal/vaop/ncurrent/full/nbt.3154.html Decoding the regulatory network of early blood development from single-cell gene expression measurements.]
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|style="padding:.4em;"|2015-30
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nrg/journal/v16/n3/full/nrg3833.html Computational and analytical challenges in single-cell transcriptomics.]
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|style="padding:.4em;" rowspan=3|2015/03/12
|style="padding:.4em;"|2015-29
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867415000136 Extensive Strain-Level Copy-Number Variation across Human Gut Microbiome Species.]
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|style="padding:.4em;"|2015-28
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v500/n7464/full/nature12506.html Richness of human gut microbiome correlates with metabolic markers.]
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|style="padding:.4em;"|2015-27
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v490/n7418/full/nature11450.html A metagenome-wide association study of gut microbiota in type 2 diabetes.]
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|style="padding:.4em;" rowspan=3|2015/03/09
|style="padding:.4em;"|2015-26
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003326 Practical guidelines for the comprehensive analysis of ChIP-seq data.]
|-
|style="padding:.4em;"|2015-25
|style="padding:.4em;"|T Lee
|style="padding:.4em;text-align:left"|
[http://genome.cshlp.org/content/23/5/777.long Patterns of regulatory activity across diverse human cell types predict tissue identity, transcription factor binding, and long-range interactions.]
|-
|style="padding:.4em;"|2015-24
|style="padding:.4em;"|ER Kim
|style="padding:.4em;text-align:left"|
[http://msb.embopress.org/content/10/11/760.long Rapid neurogenesis through transcriptional activation in human stem cells.]
|-
|style="padding:.4em;" rowspan=3|2015/03/02
|style="padding:.4em;"|2015-23
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414011787 Genome-Scale CRISPR-Mediated Control of Gene Repression and Activation.]
|-
|style="padding:.4em;"|2015-22
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1004237 Integrating multiple genomic data to predict disease-causing nonsynonymous single nucleotide variants in exome sequencing studies.]
|-
|style="padding:.4em;"|2015-21
|style="padding:.4em;"|ER Kim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/ng/journal/v45/n6/full/ng.2653.html The Genotype-Tissue Expression (GTEx) project.]
|-
|style="padding:.4em;" rowspan=3|2015/02/24
|style="padding:.4em;"|2015-20
|style="padding:.4em;"|ER Kim
|style="padding:.4em;text-align:left"|
[http://www.sciencemag.org/content/346/6212/1007.long Mouse regulatory DNA landscapes reveal global principles of cis-regulatory evolution.]
|-
|style="padding:.4em;"|2015-19
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v515/n7527/full/nature13985.html Principles of regulatory information conservation between mouse and human.]
|-
|style="padding:.4em;"|2015-18
|style="padding:.4em;"|BH Kang
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v515/n7527/full/nature13972.html Conservation of trans-acting circuitry during mammalian regulatory evolution.]
|-
|style="padding:.4em;" rowspan=3|2015/02/16
|style="padding:.4em;"|2015-17
|style="padding:.4em;"|T Lee
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature13835.html Genetic and epigenetic fine mapping of causal autoimmune disease variants.]
|-
|style="padding:.4em;"|2015-16
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/ng/journal/v46/n3/full/ng.2892.html A general framework for estimating the relative pathogenicity of human genetic variants.]
|-
|style="padding:.4em;"|2015-15
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003825 A probabilistic model to predict clinical phenotypic traits from genome sequencing.]
|-
|style="padding:.4em;" rowspan=4|2015/02/02
|style="padding:.4em;"|2015-14
|style="padding:.4em;"|BH Kang
|style="padding:.4em;text-align:left"|
[http://www.pnas.org/content/111/22/E2329.long Relating the metatranscriptome and metagenome of the human gut.]
|-
|style="padding:.4em;"|2015-13
|style="padding:.4em;"|BH Kang
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v513/n7516/full/nature13568.html Alterations of the human gut microbiome in liver cirrhosis.]
|-
|style="padding:.4em;"|2015-12
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nbt/journal/v32/n8/full/nbt.2942.html An integrated catalog of reference genes in the human gut microbiome.]
|-
|style="padding:.4em;"|2015-11
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nbt/journal/v32/n8/full/nbt.2939.html Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes.]
|-
|style="padding:.4em;" rowspan=5|2015/01/26
|style="padding:.4em;"|2015-10
|style="padding:.4em;"|HH Kim
|style="padding:.4em;text-align:left"|
[http://msb.embopress.org/content/9/1/666.long Computational meta'omics for microbial community studies.]
|-
|style="padding:.4em;"|2015-09
|style="padding:.4em;"|HH Kim
|style="padding:.4em;text-align:left"|
[http://genomemedicine.com/content/5/7/65 Functional profiling of the gut microbiome in disease-associated inflammation.]
|-
|style="padding:.4em;"|2015-08
|style="padding:.4em;"|BH Kang
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S016895251200145X Biodiversity and functional genomics in the human microbiome.]
|-
|style="padding:.4em;"|2015-07
|style="padding:.4em;"|BH Kang
|style="padding:.4em;text-align:left"|
[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002808 Chapter 12: Human Microbiome Analysis.]
|-
|style="padding:.4em;"|2015-06
|style="padding:.4em;"|BH Kang
|style="padding:.4em;text-align:left"|
[http://www.cell.com/cell/abstract/S0092-8674%2814%2900864-2 Conducting a Microbiome Study.]
|-
|style="padding:.4em;" rowspan=3|2015/01/12
|style="padding:.4em;"|2015-05
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/ncomms/2014/141210/ncomms6522/full/ncomms6522.html Small RNA changes en route to distinct cellular states of induced pluripotency.]
|-
|style="padding:.4em;"|2015-04
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v516/n7530/full/nature14046.html Genome-wide characterization of the routes to pluripotency.]
|-
|style="padding:.4em;"|2015-03
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v516/n7530/full/nature14047.html Divergent reprogramming routes lead to alternative stem-cell states.]
|-
|style="padding:.4em;" rowspan=2|2015/01/05
|style="padding:.4em;"|2015-02
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.pnas.org/content/111/21/E2191.long Global view of enhancer-promoter interactome in human cells.]
|-
|style="padding:.4em;"|2015-01
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://nar.oxfordjournals.org/content/41/22/10391.long Combining Hi-C data with phylogenetic correlation to predict the target genes of distal regulatory elements in human genome.]
|}
{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2014
|-
!scope="col" style="padding:.4em" | Date
!scope="col" style="padding:.4em" | Paper index
!scope="col" style="padding:.4em" | Presenter
!scope="col" style="padding:.4em" | Paper title
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|style="padding:.4em;" rowspan=2|2014/12/23
|style="padding:.4em;"|2014-41
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867413012270 Super-enhancers in the control of cell identity and disease.]
|-
|style="padding:.4em;"|2014-40
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867413003929 Master transcription factors and mediator establish super-enhancers at key cell identity genes.]
|-
|style="padding:.4em;" rowspan=4|2014/12/09
|style="padding:.4em;"|2014-39
|style="padding:.4em;"|HH Kim
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414013713 Unraveling the biology of a fungal meningitis pathogen using chemical genetics.]
|-
|style="padding:.4em;"|2014-38
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414014226 A proteome-scale map of the human interactome network.]
|-
|style="padding:.4em;"|2014-37
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[http://msb.embopress.org/content/10/9/752.long The role of the interactome in the maintenance of deleterious variability in human populations.]
|-
|style="padding:.4em;"|2014-36
|style="padding:.4em;"|HS Shim
|style="padding:.4em;text-align:left"|
[http://www.sciencemag.org/content/342/6154/1235587.long Integrative annotation of variants from 1092 humans: application to cancer genomics.]
|-
|style="padding:.4em;" rowspan=2|2014/12/02
|style="padding:.4em;"|2014-35
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://genome.cshlp.org/content/23/8/1319.long Mapping functional transcription factor networks from gene expression data.]
|-
|style="padding:.4em;"|2014-34
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nrg/journal/v15/n7/full/nrg3684.html In pursuit of design principles of regulatory sequences.]
|-
|style="padding:.4em;" rowspan=2|2014/11/25
|style="padding:.4em;"|2014-33
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[http://genomemedicine.com/content/3/6/36 Epigenomics of human embryonic stem cells and induced pluripotent stem cells: insights into pluripotency and implications for disease.]
|-
|style="padding:.4em;"|2014-32
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S009286741300891X Developmental fate and cellular maturity encoded in human regulatory DNA landscapes.]
|-
|style="padding:.4em;" rowspan=2|2014/11/18
|style="padding:.4em;"|2014-31
|style="padding:.4em;"|SM Yang
|style="padding:.4em;text-align:left"|
[http://genomemedicine.com/content/6/8/64 The 'dnet' approach promotes emerging research on cancer patient survival.]
|-
|style="padding:.4em;"|2014-30
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414010368 Determination and inference of eukaryotic transcription factor sequence specificity.]
|-
|style="padding:.4em;" rowspan=3|2014/11/11
|style="padding:.4em;"|2014-29
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[http://www.pnas.org/content/110/16/6412.long Transcription factors interfering with dedifferentiation induce cell type-specific transcriptional profiles.]
|-
|style="padding:.4em;"|2014-28
|style="padding:.4em;"|HH Kim
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414009350 Dissecting engineered cell types and enhancing cell fate conversion via CellNet.]
|-
|style="padding:.4em;"|2014-27
|style="padding:.4em;"|HH Kim
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414009349 CellNet: network biology applied to stem cell engineering.]
|-
|style="padding:.4em;" rowspan=2|2014/11/04
|style="padding:.4em;"|2014-26
|style="padding:.4em;"|HS Shim
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414009775 Predicting Cancer-Specific Vulnerability via Data-Driven Detection of Synthetic Lethality.]
|-
|style="padding:.4em;"|2014-25
|style="padding:.4em;"|JH Shin
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nmeth/journal/v11/n9/full/nmeth.3046.html Phen-Gen: combining phenotype and genotype to analyze rare disorders.]
|-
|style="padding:.4em;" rowspan=2|2014/10/28
|style="padding:.4em;"|2014-24
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nbt/journal/vaop/ncurrent/full/nbt.2877.html A community effort to assess and improve drug sensitivity prediction algorithms.]
|-
|style="padding:.4em;"|2014-23
|style="padding:.4em;"|HS Shim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/ng/journal/v46/n9/full/ng.3051.html Multi-tiered genomic analysis of head and neck cancer ties TP53 mutation to 3p loss.]
|-
|style="padding:.4em;" rowspan=2|2014/09/30
|style="padding:.4em;"|2014-22
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nmeth/journal/v10/n11/full/nmeth.2651.html Network-based stratification of tumor mutations.]
|-
|style="padding:.4em;"|2014-21
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414001457 Synonymous mutations frequently act as driver mutations in human cancers.]
|-
|style="padding:.4em;" rowspan=2|2014/09/23
|style="padding:.4em;"|2014-20
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003460 VarWalker: Personalized Mutation Network Analysis of Putative Cancer Genes from Next-Generation Sequencing Data.]
|-
|style="padding:.4em;"|2014-19
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://genomebiology.com/content/13/12/R124 DriverNet: uncovering the impact of somatic driver mutations on transcriptional networks in cancer.]
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|style="padding:.4em;" rowspan=2|2014/09/16
|style="padding:.4em;"|2014-18
|style="padding:.4em;"|JH Shin
|style="padding:.4em;text-align:left"|
[http://genomebiology.com/content/14/5/R52 Integrated analysis of recurrent properties of cancer genes to identify novel drivers.]
|-
|style="padding:.4em;"|2014-17
|style="padding:.4em;"|AR Cho
|style="padding:.4em;text-align:left"|
[http://genomemedicine.com/content/4/11/89 Improving the prediction of the functional impact of cancer mutations by baseline tolerance transformation.]
|-
|style="padding:.4em;" rowspan=2|2014/09/02
|style="padding:.4em;"|2014-16
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=23228031 A network module-based method for identifying cancer prognostic signatures.]
|-
|style="padding:.4em;"|2014-15
|style="padding:.4em;"|AR Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=24429628 Realizing the promise of cancer predisposition genes.]
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|style="padding:.4em;" rowspan=2|2014/08/19
|style="padding:.4em;"|2014-14
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=24952901 Assessing the clinical utility of cancer genomic and proteomic data across tumor types.]
|-
|style="padding:.4em;"|2014-13
|style="padding:.4em;"|HS Shim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=24132290 Mutational landscape and significance across 12 major cancer types.]
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|style="padding:.4em;" rowspan=2|2014/08/12
|style="padding:.4em;"|2014-12
|style="padding:.4em;"|HS Shim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/23945592 Signatures of mutational processes in human cancer.]
|-
|style="padding:.4em;"|2014-11
|style="padding:.4em;"|HJ Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/24390350 Discovery and saturation analysis of cancer genes across 21 tumour types.]
|-
|style="padding:.4em;" rowspan=2|2014/08/05
|style="padding:.4em;"|2014-10
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=24084849 Comprehensive identification of mutational cancer driver genes across 12 tumor types.]
|-
|style="padding:.4em;"|2014-9
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=24037244 IntOGen-mutations identifies cancer drivers across tumor types.]
|-
|style="padding:.4em;" rowspan=3|2014/07/29
|style="padding:.4em;"|2014-8
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/23900255 Computational approaches to identify functional genetic variants in cancer genomes.]
|-
|style="padding:.4em;"|2014-7
|style="padding:.4em;"|AR Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=23770567 Mutational heterogeneity in cancer and the search for new cancer-associated genes.]
|-
|style="padding:.4em;"|2014-6
|style="padding:.4em;"|AR Cho
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nmeth/journal/v11/n4/abs/nmeth.2891.html Cancer genomes: discerning drivers from passengers]
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|style="padding:.4em;" rowspan=3|2014/07/22
|style="padding:.4em;"|2014-5
|style="padding:.4em;"|AR Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/24071849 The Cancer Genome Atlas Pan-Cancer analysis project.]
|-
|style="padding:.4em;"|2014-4
|style="padding:.4em;"|AR Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/23539594 Cancer genome landscapes.]
|-
|style="padding:.4em;"|2014-3
|style="padding:.4em;"|AR Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=21900272 Distinguishing driver and passenger mutations in an evolutionary history categorized by interference.]
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|style="padding:.4em;" rowspan=2|2014/07/15
|style="padding:.4em;"|2014-2
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=24670764 A promoter-level mammalian expression atlas.]
|-
|style="padding:.4em;"|2014-1
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=24670763 An atlas of active enhancers across human cell types and tissues.]
|}
{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2013
|-
!scope="col" style="padding:.4em" | Date
!scope="col" style="padding:.4em" | Paper index
!scope="col" style="padding:.4em" | Presenter
!scope="col" style="padding:.4em" | Paper title
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|style="padding:.4em;" rowspan=2|2013/06/26
|style="padding:.4em;"|2013-31
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|[http://nar.oxfordjournals.org/content/40/16/7690.long Integration of Hi-C and ChIP-seq data reveals distinct types of chromatin linkages]
|-
|style="padding:.4em;"|2013-30
|style="padding:.4em;"|YH Ko
|style="padding:.4em;text-align:left"|[http://www.nature.com/nbt/journal/v31/n4/full/nbt.2519.html Deciphering molecular circuits from genetic variation underlying transcriptional responsiveness to stimuli]
|-
|style="padding:.4em;" rowspan=2|2013/06/04
|style="padding:.4em;"|2013-29
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|[https://www.cell.com/abstract/S0092-8674(13)00439-X Mapping the Human miRNA Interactome by CLASH Reveals Frequent Noncanonical Binding]
|-
|style="padding:.4em;"|2013-28
|style="padding:.4em;"|ER Kim
|style="padding:.4em;text-align:left"|[http://www.nature.com/nbt/journal/v31/n3/full/nbt.2514.html Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples]
|-
|style="padding:.4em;" rowspan=2|2013/05/28
|style="padding:.4em;"|2013-27
|style="padding:.4em;"|YH Ko
|style="padding:.4em;text-align:left"|[http://www.pnas.org/content/102/38/13544.long Discovering statistically significant pathways in expression profiling studies]
|-
|style="padding:.4em;"|2013-26
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|[http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0058977 Prediction and Validation of Gene-Disease Associations Using Methods Inspired by Social Network Analyses]
|-
|style="padding:.4em;" rowspan=2|2013/05/21
|style="padding:.4em;"|2013-25
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|[http://www.nature.com/nature/journal/v496/n7446/full/nature11981.html Dynamic regulatory network controlling TH17 cell differentiation]
|-
|style="padding:.4em;"|2013-24
|style="padding:.4em;"|T Lee
|style="padding:.4em;text-align:left"|[http://www.sciencedirect.com/science/article/pii/S0092867412013529 Deciphering and Prediction of Transcriptome Dynamics under Fluctuating Field Conditions]

|-
|style="padding:.4em;" rowspan=2|2013/05/14
|style="padding:.4em;"|2013-23
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|[http://www.sciencedirect.com/science/article/pii/S0092867412015565 Integrative eQTL-Based Analyses Reveal the Biology of Breast Cancer Risk Loci]

|-
|style="padding:.4em;"|2013-22
|style="padding:.4em;"|HJ Kim
|style="padding:.4em;text-align:left"|[http://www.nature.com/ng/journal/v45/n2/full/ng.2504.html Chromatin marks identify critical cell types for fine mapping complex trait variants]
|-
|style="padding:.4em;" rowspan=2|'''2013/05/07'''
|style="padding:.4em;"|2013-21
|style="padding:.4em;"|ER Kim
|style="padding:.4em;text-align:left"|[http://www.cell.com/abstract/S0092-8674(12)01555-3 Genome-wide Chromatin State Transitions Associated with Developmental and Environmental Cues]
|-
|style="padding:.4em;"|2013-20
|style="padding:.4em;"|HS Shim
|style="padding:.4em;text-align:left"|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1003201 Human Disease-Associated Genetic Variation Impacts Large Intergenic Non-Coding RNA Expression]
|-
|style="padding:.4em;"|2013/04/09
|style="padding:.4em;"|2013-19
|style="padding:.4em;"|HJ Kim
|style="padding:.4em;text-align:left"|[http://genome.cshlp.org/content/22/9/1790.long Annotation of functional variation in personal genomes using RegulomeDB]
|-
|style="padding:.4em;"|2013/04/02
|style="padding:.4em;"|2013-18
|style="padding:.4em;"|HJ Kim
|style="padding:.4em;text-align:left"|[http://genome.cshlp.org/content/22/9/1775.long The GENCODE v7 catalog of human long noncoding RNAs: Analysis of their gene structure, evolution, and expression]
|-
|style="padding:.4em;" |2013/03/12
|style="padding:.4em;"|2013-17
|style="padding:.4em;"| ER Kim
|style="padding:.4em;text-align:left"|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002311 Global Mapping of Cell Type–Specific Open Chromatin by FAIRE-seq Reveals the Regulatory Role of the NFI Family in Adipocyte Differentiation]
|-
|style="padding:.4em;" rowspan=1|2013/03/05
|style="padding:.4em;"|2013-16
|style="padding:.4em;"| ER Kim
|style="padding:.4em;text-align:left"|[http://www.nature.com/nrg/journal/v10/n10/full/nrg2641.html ChIP–seq: advantages and challenges of a maturing technology]
|-
|style="padding:.4em;" rowspan=3|2013/02/21
|style="padding:.4em;"|2013-15
|style="padding:.4em;"| KS Kim
|style="padding:.4em;text-align:left"|[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002830 Novel Modeling of Combinatorial miRNA Targeting Identifies SNP with Potential Role in Bone Density]
|-
|style="padding:.4em;"|2013-14
|style="padding:.4em;"| KS Kim
|style="padding:.4em;text-align:left"|[http://www.cell.com/abstract/S0092-8674(12)01424-9 A Molecular Roadmap of Reprogramming Somatic Cells into iPS Cells]
|-
|style="padding:.4em;"|2013-13
|style="padding:.4em;"| HJ Han
|style="padding:.4em;text-align:left"|[http://genome.cshlp.org/content/22/6/1015.long Differential DNase I hypersensitivity reveals factor-dependent chromatin dynamics.]
|-
|style="padding:.4em;" rowspan=3|2013/02/15
|style="padding:.4em;"|2013-12
|style="padding:.4em;"| HJ Han
|style="padding:.4em;text-align:left"|[http://www.nature.com/ng/journal/v43/n3/full/ng.759.html Chromatin accessibility pre-determines glucocorticoid receptor binding patterns]
|-
|style="padding:.4em;"|2013-11
|style="padding:.4em;"| JE Shim, CY Kim
|style="padding:.4em;text-align:left"|[http://www.nature.com/nbt/journal/v30/n11/full/nbt.2422.html Interpreting noncoding genetic variation in complex traits and human disease]
|-
|style="padding:.4em;"|2013-10
|style="padding:.4em;"|HJ Han, JH Kim
|style="padding:.4em;text-align:left"|[http://genome.cshlp.org/content/21/3/447.full.pdf+html Accurate inference of transcription factor binding from DNA sequence and chromatin accessibility data]
|-
|style="padding:.4em;" rowspan=3|2013/02/08
|style="padding:.4em;"|2013-09
|style="padding:.4em;"|HJ Kim
|style="padding:.4em;text-align:left"|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002599 Widespread Site-Dependent Buffering of Human Regulatory Polymorphism]
|-
|style="padding:.4em;"|2013-08
|style="padding:.4em;"|JE Shim, CY Kim
|style="padding:.4em;text-align:left;"|[http://genome.cshlp.org/content/22/9/1748.long Linking disease associations with regulatory information in the human genome]
|-
|style="padding:.4em;"|2013-07
|style="padding:.4em;"|HJ Han, JH Kim
|style="padding:.4em;text-align:left"|[http://genome.cshlp.org/content/22/9/1658.long Understanding transcriptional regulation by integrative analysis of transcription factor binding data]
|-
|style="padding:.4em;" rowspan=3|2013/01/25
|style="padding:.4em;"|2013-06
|style="padding:.4em;"|HJ Han, '''JH Kim'''
|style="padding:.4em;text-align:left;"|[http://www.nature.com/nature/journal/v489/n7414/full/nature11279.html The long-range interaction landscape of gene promoters]
|-
|style="padding:.4em;"|2013-05
|style="padding:.4em;"|'''ER Kim''', HS Shim
|style="padding:.4em;text-align:left;"|[http://www.nature.com/nature/journal/v489/n7414/full/nature11233.html Landscape of transcription in human cells]
|-
|style="padding:.4em;"|2013-04
|style="padding:.4em;"|'''HJ Han''', JH Kim
|style="padding:.4em;text-align:left;"|[http://www.nature.com/nature/journal/v489/n7414/full/nature11245.html Architecture of the human regulatory network derived from ENCODE data]

|-
|style="padding:.4em;" rowspan=2|2013/01/18
|style="padding:.4em;"|2013-03
|style="padding:.4em;"|KS Kim, '''TH Kim'''
|style="padding:.4em;text-align:left;"|[http://www.nature.com/nature/journal/v489/n7414/full/nature11212.html An expansive human regulatory lexicon encoded in transcription factor footprints]
|-
|style="padding:.4em;"|2013-02
|style="padding:.4em;"|'''HJ Han''', JH Kim
|style="padding:.4em;text-align:left;"|[http://www.nature.com/nature/journal/v489/n7414/full/nature11232.html The accessible chromatin landscape of the human genome]
|-
|style="padding:.4em;" rowspan=1|2013/01/11
|style="padding:.4em;"|2013-01
|style="padding:.4em;"| '''JE Shim''', CY Kim
|style="padding:.4em;text-align:left"|[http://www.nature.com/nature/journal/v489/n7414/full/nature11247.html An integrated encyclopedia of DNA elements in the human genome]
|}

2012
{|border ="1"
|style="color:black; background-color:#dcdcdc; padding:5px;" align="center" |Date
|style="color:black; background-color:#dcdcdc;" align="center"|Paper index
|style="color:black; background-color:#dcdcdc;" align="center"|Paper title
|-
|rowspan = "1"|2013/01/11
|align ="center"|2012-81
|[http://genome.cshlp.org/content/22/8/1589.full (TH Kim) MuSiC: identifying mutational significance in cancer genomes.]
|-
|rowspan = "1"|2012/12/04
|align ="center"|2012-80
|[http://genome.cshlp.org/content/22/8/1383 (CY KIM) Human genomic disease variants: A neutral evolutionary explanation]
|-
|rowspan = "2"|2012/11/20
| align="center"|2012-79
|[http://www.cell.com/abstract/S0092-8674(12)00639-3 (HS Shim) Circuitry and Dynamics of Human Transcription Factor Regulatory Networks]
|-
| align="center"|2012-78
|[http://www.nature.com/nature/journal/v487/n7408/full/nature11288.html (HJ Kim) Interpreting cancer genomes using systematic host network perturbations by tumour virus proteins]
|-
|rowspan = "2"|2012/11/06
| align="center"|2012-77
|[http://www.sciencemag.org/content/337/6099/1190.short (HJ Han) Systematic Localization of Common Disease-Associated Variation in Regulatory DNA]
|-
| align="center"|2012-76
|[http://www.pnas.org/cgi/doi/10.1073/pnas.1201904109 (KS Kim) A public resource facilitating clinical use of genomes]
|-
|rowspan = "6" |2012/07/19
| align="center"|2012-75
|[http://genome.cshlp.org/content/22/7/1334 (HJ Han & YH Ko) Predictive regulatory models in Drosophila melanogaster by integrative inference of transcriptional networks]
|-
| align="center"|2012-74
|[http://www.sciencemag.org/content/337/6090/100.full (JE Shim) An Abundance of Rare Functional Variants in 202 Drug Target Genes Sequenced in 14,002 People]
|-
| align="center"|2012-73
|[http://www.sciencemag.org/content/337/6090/64.abstract (JE Shim) Evolution and Functional Impact of Rare Coding Variation from Deep Sequencing of Human Exomes]
|-
| align="center"|2012-72
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2063581/ (SH Hwang) Network-based classification of breast cancer metastasis]
|-
| align="center"|2012-71
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002707 (T Lee&CY Kim)Brain Expression Genome-Wide Association Study (eGWAS) Identifies Human Disease-Associated Variants]
|-
| align="center"|2012-70
|[http://genome.cshlp.org/content/20/9/1297 (ER Kim&TH Kim)The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data]
|-
|rowspan = "8" |2012/07/16
| align="center"|2012-69
|[http://www.nature.com/ng/journal/v43/n5/full/ng.806.html (ER Kim&TH Kim)A framework for variation discovery and genotyping using next-generation DNA sequencing data]
|-
| align="center"|2012-66
|[http://bioinformatics.oxfordjournals.org/content/25/16/2078.long (ER Kim&TH Kim)The Sequence Alignment/Map format and SAMtools]
|-
| align="center"|2012-65
|[http://bioinformatics.oxfordjournals.org/content/early/2011/06/07/bioinformatics.btr330 (ER Kim&TH Kim)The Variant Call Format and VCFtools]
|-
| align="center"|2012-64
|[http://www.cell.com/abstract/S0092-8674(12)00104-3 (YH Go&HJ Han)The Impact of the Gut Microbiota on Human Health: An Integrative View]
|-
|align="center"|2012-63
|[http://www.sciencemag.org/content/336/6086/1262.abstract (T Lee&CY Kim)Host-Gut Microbiota Metabolic Interactions]
|-
|align="center"|2012-62
|[http://www.sciencemag.org/content/336/6086/1268.full (AR Cho,JH Ju)Interactions Between the Microbiota and the Immune System]
|-
|align="center"|2012-61
|[http://www.sciencemag.org/content/336/6086/1255.abstract (SH Hwang&HJ Cho)The Application of Ecological Theory Toward an Understanding of the Human Microbiome]
|-
|align="center"|2012-60
|[http://stm.sciencemag.org/content/4/137/137rv5 (SH Hwang&HJ Cho)Microbiota-Targeted Therapies: An Ecological Perspective]
|-
|rowspan = "3" |2012/07/13
|align="center"|2012-59
|[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002358 (JH Shin&HJ Kim)Metabolic Reconstruction for Metagenomic Data and Its Application to the Human Microbiome]
|-
| align="center"|2012-58
|[http://www.nature.com/nature/journal/v486/n7402/full/nature11209.html (JH Shin&HJ Kim)A framework for human microbiome research]
|-
|align="center"|2012-57
|[http://www.nature.com/nature/journal/v486/n7402/full/nature11234.html (JH Shin&HJ Kim)Structure, function and diversity of the healthy human microbiome]
|-
|rowspan = "4" |2012/07/12
|align="center"|2012-56
|[http://nar.oxfordjournals.org/content/40/D1/D957.long (AR Cho&JH Ju)COLT-Cancer: functional genetic screening resource for essential genes in human cancer cell lines]
|-
|align="center"|2012-55
|[http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002511 (YH Go&HJ Han)Google Goes Cancer: Improving Outcome Prediction for Cancer Patients by Network-Based Ranking of Marker Genes]
|-
| align="center"|2012-54
|[http://www.nature.com/msb/journal/v7/n1/full/msb201147.html (YH Go&HJ Han)A pharmacogenomic method for individualized prediction of drug sensitivity]
|-
|align="center"|2012-53
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature10983.html (JH Soh)The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups]
|-
|rowspan = "6" |2012/07/09
|align="center"|2012-52
|[http://www.nature.com/nature/journal/v483/n7391/full/nature11003.html (ER Kim&TH Kim)The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity]
|-
|align="center"|2012-51
|[http://www.nature.com/nature/journal/v483/n7391/full/nature11005.html (ER Kim&TH Kim)Systematic identification of genomic markers of drug sensitivity in cancer cells]
|-
| align="center"|2012-50
|[http://www.pnas.org/content/early/2011/10/13/1018854108.abstract (ER Kim&TH Kim)Subtype and pathway specific responses to anticancer compounds in breast cancer]
|-
|align="center"|2012-49
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature10945.html (JE Shim&KS Kim)De novo mutations revealed by whole-exome sequencing are strongly associated with autism]
|-
|align="center"|2012-48
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature11011.html (JE Shim&KS Kim)Patterns and rates of exonic de novo mutations in autism spectrum disorders]
|-
|align="center"|2012-47
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature10989.html (JE Shim&KS Kim)Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations]
|-
|rowspan = "6" |2012/07/06
| align="center"|2012-46
|[http://www.g3journal.org/content/1/3/233.full (T Lee&CY Kim)Integrating Rare-Variant Testing, Function Prediction, and Gene Network in Composite Resequencing-Based Genome-Wide Association Studies (CR-GWAS)]
|-
|align="center"|2012-45
|[http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002621 (T Lee&CY Kim)Type 2 Diabetes Risk Alleles Demonstrate Extreme Directional Differentiation among Human Populations, Compared to Other Diseases]
|-
|align="center"|2012-44
|[http://www.nature.com/nature/journal/v480/n7376/full/nature10665.html (AR Cho&JH Ju)Predicting mutation outcome from early stochastic variation in genetic interaction partners]
|-
|align="center"|2012-43
|[http://www.sciencemag.org/content/335/6064/82 (AR Cho&JH Ju)Fitness Trade-Offs and Environmentally Induced Mutation Buffering in Isogenic C. elegans]
|-
| align="center"|2012-42
|[http://genome.cshlp.org/content/22/6/1163 (JE Shim&KS Kim)Identification of microRNA-regulated gene networks by expression analysis of target genes]
|-
|align="center"|2012-41
|[http://www.nature.com/ng/journal/v44/n6/full/ng.2303.html (JE Shim&KS Kim)Exome sequencing and the genetic basis of complex traits]
|-
|rowspan = "6" |2012/07/02
|align="center"|2012-40
|[http://www.cell.com/abstract/S0092-8674(12)00573-9 (JH Soh)Functional Repurposing Revealed by Comparing S. pombe and S. cerevisiae Genetic Interactions]
|-
|align="center"|2012-39
|[http://genome.cshlp.org/content/22/2/375.long (ER Kim&TH Kim)De novo discovery of mutated driver pathways in cancer]
|-
| align="center"|2012-38
|[http://www.nature.com/msb/journal/v4/n1/full/msb20082.html (YH Go&HJ Han)A systems biology approach to prediction of oncogenes and molecular perturbation targets in B-cell lymphomas]
|-
|align="center"|2012-37
|[http://www.nature.com/msb/journal/v7/n1/full/msb201126.html (SH Hwang&HJ Cho)PREDICT: a method for inferring novel drug indications with application to personalized medicine]
|-
|align="center"|2012-36
|[http://www.nature.com/nature/journal/v453/n7198/full/nature06973.html (SH Hwang&HJ Cho)Synergistic response to oncogenic mutations defines gene class critical to cancer phenotype]
|-
|align="center"|2012-35
|[http://www.sciencemag.org/content/334/6062/1518.full (JH Shin&HJ Kim)Detecting Novel Associations in Large Data Sets]
|-
|rowspan = "3" |2012/03/05
| align="center"|2012-34
|[http://www.ncbi.nlm.nih.gov/pubmed/18516045 (8,HH Kim)Mapping and quantifying mammalian transcriptomes by RNA-Seq.]
|-
|align="center"|2012-33
|[http://genomebiology.com/2010/11/10/R106 (11,Go&Ju)Differential expression analysis for sequence count data]
|-
|align="center"|2012-32
|[http://bioinformatics.oxfordjournals.org/content/26/1/139.short (12,Go&Ju)edgeR: a Bioconductor package for differential expression analysis of digital gene expression data ]
|-
|rowspan = "7" |2012/02/27 2012/02/28
| align="center"|2012-31
|[http://www.nature.com/nrg/journal/v10/n1/full/nrg2484.html (1,JW Song)RNA-Seq: a revolutionary tool for transcriptomics]
|-
|align="center"|2012-30
|[http://www.nature.com/nmeth/journal/v8/n6/full/nmeth.1613.html (2,JW Song)Computational methods for transcriptome annotation and quantification using RNA-seq]
|-
|align="center"|2012-29
|[http://genomebiology.com/2010/11/12/220 (3,HJ Han)From RNA-seq reads to differential expression results]
|-
|align="center"|2012-28
|[http://www.nature.com/nmeth/journal/v7/n9/full/nmeth.1491.html (4,AR Cho)Comprehensive comparative analysis of strand-specific RNA sequencing methods]
|-
|align="center"|2012-27
|[http://www.plosone.org/article/info:doi%2F10.1371%2Fjournal.pone.0026426 (5,T Lee)A Low-Cost Library Construction Protocol and Data Analysis Pipeline for Illumina-Based Strand-Specific Multiplex RNA-Seq]
|-
|align="center"|2012-26
|[http://www.nature.com/nbt/journal/v28/n5/abs/nbt.1621.html (6,So&Shin)Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation]
|-
|align="center"|2012-25
|[http://bioinformatics.oxfordjournals.org/content/25/9/1105.abstract (7,So&Shin)TopHat: discovering splice junctions with RNA-Seq]
|-
|rowspan = "5" |2012/02/06
| align="center"|2012-24
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3125733/ mirConnX: condition-specific mRNA-microRNA network integrator]
|-
|align="center"|2012-23
|[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002190 Construction and Analysis of an Integrated Regulatory Network Derived from High-Throughput Sequencing Data]
|-
|align="center"|2012-22
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002415 A Densely Interconnected Genome-Wide Network of MicroRNAs and Oncogenic Pathways Revealed Using Gene Expression Signatures]
|-
|align="center"|2012-21
|[http://genome.cshlp.org/content/20/5/589.short Reprogramming of miRNA networks in cancer and leukemia]
|-
|align="center"|2012-20
|[http://www.cell.com/abstract/S0092-8674(11)01152-4 An Extensive MicroRNA-Mediated Network of RNA-RNA Interactions Regulates Established Oncogenic Pathways in Glioblastoma]
|-
|rowspan = "5" |2012/01/30
|align="center"|2012-19
|[http://www.cell.com/abstract/S0092-8674(11)00237-6 Principles and Strategies for Developing Network Models in Cancer]
|-
|align="center"|2012-18
|[http://www.nature.com/ng/journal/v37/n4/full/ng1532.html Reverse engineering of regulatory networks in human B cells]
|-
|align="center"|2012-17
|[http://www.nature.com/nature/journal/v452/n7186/abs/nature06757.html Variations in DNA elucidate molecular networks that cause disease]
|-
|align="center"|2012-16
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2779083/ Harnessing gene expression to identify the genetic basis of drug resistance]
|-
|align="center"|2012-15
|[http://www.sciencedirect.com/science/article/pii/S0092867410012936 An Integrated Approach to Uncover Drivers of Cancer]
|-
|rowspan = "3" |2012/01/09
|align="center"|2012-14
|[http://www.ncbi.nlm.nih.gov/pubmed/18704161 Genetic variation in an individual human exome.]
|-
|align="center"|2012-13
|[http://www.ncbi.nlm.nih.gov/pubmed/22081227 Predicting phenotypic variation in yeast from individual genome sequences.]
|-
|align="center"|2012-12
|[http://www.ncbi.nlm.nih.gov/pubmed/20435227 Clinical assessment incorporating a personal genome.]
|-
|rowspan = "6" |2012/01/09 2012/01/16
| align="center"|2012-11
|[http://www.ncbi.nlm.nih.gov/pubmed/20399638 Human allelic variation: perspective from protein function, structure, and evolution.]
|-
|align="center"|2012-10
|[http://www.ncbi.nlm.nih.gov/pubmed/19561590 Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm.]
|-
|align="center"|2012-09
|[http://www.ncbi.nlm.nih.gov/pubmed/11230178 Prediction of deleterious human alleles.]
|-
|align="center"|2012-08
|[http://www.ncbi.nlm.nih.gov/pubmed/12202775 Human non-synonymous SNPs: server and survey.]
|-
|align="center"|2012-07
|[http://www.ncbi.nlm.nih.gov/pubmed/20354512 A method and server for predicting damaging missense mutations.]
|-
|align="center"|2012-06
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1920242/ SNAP: predict effect of non-synonymous polymorphisms on function]
|-
|rowspan = "3" |2012/01/09 2012/01/16
|align="center"|2012-05
|[http://www.ncbi.nlm.nih.gov/pubmed/21920052 Computational and statistical approaches to analyzing variants identified by exome sequencing.]
|-
|align="center"|2012-04
|[http://www.ncbi.nlm.nih.gov/pubmed/18179889 Shifting paradigm of association studies: value of rare single-nucleotide polymorphisms.]
|-
|align="center"|2012-03
|[http://www.ncbi.nlm.nih.gov/pubmed/19684571 Targeted capture and massively parallel sequencing of 12 human exomes.]
|-
|rowspan = "2" |2012/01/09 2012/01/16
|align="center"|2012-02
|[http://www.ncbi.nlm.nih.gov/pubmed/17637733 The distribution of fitness effects of new mutations.]
|-
|align="center"|2012-01
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1852724/ Most Rare Missense Alleles Are Deleterious in Humans: Implications for Complex Disease and Association Studies]
|}
2011
{|border ="1"
|style="color:black; background-color:#dcdcdc;" align="center" |Date
|style="color:black; background-color:#dcdcdc;" align="center"|Paper_index
|style="color:black; background-color:#dcdcdc;" align="center"|Paper_title
|-
|rowspan = "6" |2011/11/28
|align="center"|2011-49
|[http://bmir.stanford.edu/file_asset/index.php/1407/BMIR-2009-1355.pdf (Shin)Data-Driven Methods to Discover Molecular Determinants of Serious Adverse Drug Events]
|-
|align="center"|2011-48
|[http://www.nature.com/nchembio/journal/v4/n11/abs/nchembio.118.html (Shin)Network pharmacology: the next paradigm in drug discovery]
|-
|align="center"|2011-47
|[http://www.nature.com/msb/journal/v7/n1/full/msb201171.html (Oh)Systematic exploration of synergistic drug pairs]
|-
|align="center"|2011-46
|[http://www.nature.com/msb/journal/v5/n1/full/msb200995.html (Shim)Chemogenomic profiling predicts antifungal synergies]
|-
|align="center"|2011-45
|[http://www.pnas.org/content/104/32/13086 (Beck)A strategy for predicting the chemosensitivity of human cancers and its application to drug discovery]
|-
|align="center"|2011-44
|[http://www.nature.com/nchembio/journal/v1/n7/full/nchembio747.html (Hwang)Analysis of drug-induced effect patterns to link structure and side effects of medicines]
|-
|rowspan = "3" |2011/11/14
|align="center"|2011-43
|[http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000662 Network-Based Elucidation of Human Disease Similarities Reveals Common Functional Modules Enriched for Pluripotent Drug Targets]
|-
|align="center"|2011-42
|[http://www.nature.com/msb/journal/v7/n1/full/msb201131.html Cross-species discovery of syncretic drug combinations that potentiate the antifungal fluconazole]
|-
|align="center"|2011-41
|[http://www.nature.com/msb/journal/v7/n1/full/msb20115.html Analysis of multiple compound–protein interactions reveals novel bioactive molecules]
|-
|rowspan = "4" |2011/11/07
|align="center"|2011-40
|[http://www.nature.com/nbt/journal/v25/n10/abs/nbt1338.html Drug—target network]
|-
|align="center"|2011-39
|[http://www.nature.com/msb/journal/v6/n1/full/msb200998.html A side effect resource to capture phenotypic effects of drugs]
|-
|align="center"|2011-38
|[http://genome.cshlp.org/content/18/2/206.long Quantitative systems-level determinants of human genes targeted by successful drugs]
|-
|align="center"|2011-37
|[http://www.sciencemag.org/content/321/5886/263.short Drug Target Identification Using Side-Effect Similarity]
|-
|rowspan = "3" |2011/11/07
|align="center"|2011-36
|[http://stm.sciencemag.org/content/3/96/96ra77.short Discovery and Preclinical Validation of Drug Indications Using Compendia of Public Gene Expression Data]
|-
|align="center"|2011-35
|[http://bib.oxfordjournals.org/content/12/4/303.short Exploiting drug–disease relationships for computational drug repositioning]
|-
|align="center"|2011-34
|[http://www.springerlink.com/content/4489r051nu2t0ul1/ Drug Discovery in a Multidimensional World: Systems, Patterns, and Networks]
|-
|rowspan = "3" |2011/10/05
|align="center"|2011-33
|[http://genome.cshlp.org/content/20/7/960.full Analysis of membrane proteins in metagenomics: Networks of correlated environmental features and protein families]
|-
|align="center"|2011-32
|[http://www.pnas.org/content/106/5/1374.full Quantifying environmental adaptation of metabolic pathways in metagenomics]
|-
|align="center"|2011-31
|[http://www.pnas.org/content/104/35/13913.abstract Quantitative assessment of protein function prediction from metagenomics shotgun sequences]
|-
|rowspan = "4" |2011/10/04
|align="center"|2011-30
|[http://www.nature.com/nature/journal/v464/n7285/full/nature08821.html A human gut microbial gene catalogue established by metagenomic sequencing]
|-
|align="center"|2011-29
|[http://www.nature.com/nrmicro/journal/v6/n9/abs/nrmicro1935.html Molecular eco-systems biology: towards an understanding of community function]
|-
|align="center"|2011-28
|[http://genome.cshlp.org/content/early/2009/04/20/gr.085464.108 Microbial community profiling for human microbiome projects: Tools, techniques, and challenges]
|-
|align="center"|2011-27
|[http://www.nature.com/nrmicro/journal/v9/n4/full/nrmicro2540.html Unravelling the effects of the environment and host genotype on the gut microbiome]
|-
|rowspan = "2" |2011/09/19
|align="center"|2011-26
|[http://www.sciencemag.org/content/333/6042/596.full independently evolved virulence effectors converge onto hubs in a plant immune system Network]
|-
|align="center"|2011-25
|[http://www.sciencemag.org/content/333/6042/601.full Evidence for network evolution in an arabidopsis interactome Map]
|-
|rowspan = "2" |2011/09/05
|align="center"|2011-24
|[http://www.sciencedirect.com/science/article/pii/S0092867411005861 Exome Sequencing of Ion Channel Genes Reveals Complex Profiles Confounding Personal Risk Assessment in Epilepsy]
|-
|align="center"|2011-23
|[http://www.cell.com/abstract/S0092-8674(11)00543-5 Pluripotency factors in Embryonic stem cells Regulate Differentiation into Germ Layers]
|-
|rowspan = "2" |2011/09/22
|align="center"|2011-22
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002077 Integrated Genome-scale predition of Detrimental Mutations in Transcription Networks]
|-
|align="center"|2011-21
|[http://www.nature.com/nrg/journal/v12/n4/abs/nrg2969.html From expression QTLs to personalized transcriptomics]
|-
|2011/06/20
|align="center"|2011-20
|[http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001046 a user's guide to the encyclopedia of DNA elements(ENCODE)]
|-
|rowspan = "2" |2011/03/30
|align="center"|2011-19
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature09944.html enterotypes of the human gut microbiome]
|-
|align="center"|2011-18
|[http://www.nature.com/msb/journal/v7/n1/full/msb20116.html toward molecular trait-based ecology, through intergration of biogeochemical, geographical and metagenomic data]
|-
|rowspan = "2" |2011/03/16
|align="center"|2011-17
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002047 variable pathogenicity determines individual lifespan in ''caenorhabditis elegans'']
|-
|align="center"|2011-16
|[http://www.cell.com/abstract/S0092-8674(11)00371-0 a high-resolution ''c.elegans'' essential gene network based on phenotypic profiling of a complex tissue]
|-
|rowspan = "3" |2011/04/25
|align="center"|2011-15
|[http://www.ncbi.nlm.nih.gov/pubmed/21376230 Hallmarks of Cancer : The next generation]
|-
|align="center"|2011-14
|[http://www.cell.com/abstract/S0092-8674(11)00296-0 Mapping Cancer Origins]
|-
|align="center"|2011-13
|[http://www.cell.com/abstract/S0092-8674(11)00297-2 Genetic Interactions in Cancer Progression and Treatment]
|-
|rowspan = "2" |2011/04/11
|align="center"|2011-12
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1001273 Proteins encoded in genomic regions associated with immune-mediated disease physically interact and suggest underlying biology]
|-
|align="center"|2011-11
|***Changed!***
[http://www.ncbi.nlm.nih.gov/pubmed/19557189 Identifying relationships among genomic disease regions: predicting genes at pathogenic SNP associations and rare deletions]
|-
|rowspan = "2"|2011/03/28
|align="center"|2011-10
|[http://www.pnas.org/content/106/44/18843.long profiling translatomes of discrete cell populations resolves altered cellular priorities during hypoxia in Arabidopsis]
|-
|align="center"|2011-09
|[http://www.nature.com/msb/journal/v6/n1/full/msb201076.html cell-type specific analysis of translating RNAs in developing flowers reveals new levels of control]
|-
|rowspan = "2"|2011/03/14
|align="center"|2011-08
|[http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6WSN-51SFHJD-1&_user=44062&_coverDate=01%2F07%2F2011&_rdoc=1&_fmt=high&_orig=search&_origin=search&_sort=d&_docanchor=&view=c&_acct=C000004738&_version=1&_urlVersion=0&_userid=44062&md5=2f6017aab4c794ed7e78fbbd8447077a&searchtype=a phenotypic landscape of a bacterial cell]
|-
|align="center"|2011-07
|[http://www.nature.com/msb/journal/v6/n1/full/msb2010107.html cross-species chemogenomic profiling reveals evolutionarily conserved drug mode of action]
|-
|rowspan = "2"|2011/02/28
|align="center"|2011-06
|[http://www.pnas.org/content/early/2010/09/23/1004666107.abstract genomic patterns of pleiotropy and the evolution of complexity]
|-
|align="center"|2011-05
|[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1001009 simultaneous genome-wide inference of physical, genetic, regulatory, and functional pathway]
|-
|2011/02/21
|align="center"|2011-04
|[http://www.nature.com/msb/journal/v6/n1/full/msb201071.html dynamic interaction networks in a hierarchically organized tissue]
|-
|2011/02/14
|align="center"|2011-03
|[http://www.sciencemag.org/content/330/6009/1385.abstract rewiring of genetic networks in response to DNA damage]
|-
|rowspan = "2"|2011/01/31
|align="center"|2011-02
|[http://www.nature.com/nrg/journal/v10/n9/abs/nrg2633.html Applying mass spectrometry-based proteomics to genetics, genomics and network biology]
|-
|align="center"|2011-01
|[http://physiolgenomics.physiology.org/content/33/1/18.long Data analysis and bioinformatics tools for tandem mass spectrometry in proteomics]
|}
2010
{|border ="1"
|style="color:black; background-color:#dcdcdc;" align="center" |Date
|style="color:black; background-color:#dcdcdc;" align="center"|Paper_index
|style="color:black; background-color:#dcdcdc;" align="center"|Paper_title
|-
|rowspan = "2"|2010/12/27
|align="center"|2010-29
|[http://www.nature.com/nature/journal/v467/n7312/full/nature09326.html Functional_roles_fornoise_in_genetic_circuits]
|-
|align="center"|2010-28
|[http://www.nature.com/ng/journal/v42/n7/full/ng.610.html Estimation_of_effect_size_distribution_from_genome-wide_association_studies_and_implications_for_future_discoveries]
|-
|rowspan = "2"|2010/11/15
|align="center"|2010-27
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1000932 Liver_and_Adipose_Expression_associated_SNPs_are_enriched_for_association_to_type_2_diabetes]
|-
|align="center"|2010-26
|[http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B8JDC-50J4MRJ-2&_user=44062&_coverDate=10%2F10%2F2010&_rdoc=1&_fmt=high&_orig=search&_origin=search&_sort=d&_docanchor=&view=c&_acct=C000004738&_version=1&_urlVersion=0&_userid=44062&md5=7667a67cf8ba3f68117b8d0ff85a8887&searchtype=a It's_the_machine_that_matters:_predicting_gene_function_and_phenotype_from_protein_networks]
|-
|rowspan = "2"|2010/11/01
|align="center"|2010-25
|[http://genome.cshlp.org/content/early/2010/06/15/gr.104216.109 A_genome-wide_map_of_human_genetic_interactions_inferred_from_radiation_hybrid_genotypes]
|-
|align="center"|2010-24
|[http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0012139 A_Genome-Wide_Gene_Function_Prediction_Resource_for_Drosophila_melanogaster]
|-
|2010/10/11
|align="center"|2010-23
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2913399/?tool=pubmed Dissecting_spatio-temporal_protein_networks_driving_human_heart_development_and_related_disorders]
|-
|rowspan = "2"|2010/09/13
|align="center"|2010-22
|[http://www.nature.com/ng/journal/v42/n7/full/ng.600.html Transposable_elements_have_rewired_the_core_regulatory_network_of_human_embryonic_stem_cells]
|-
|align="center"|2010-21
|[http://www.nature.com/ng/journal/v42/n7/full/ng0710-557.html Limits_of_sequence_and_functional_conservation]
|-
|2010/05/26
|align="center"|2010-20
|[[media:100428_network_based_elucidation_of_human_disease_similarities_reveals_common_functional_modules_enriched_for_pluripotent_drug_targets.pdf|network_based_elucidation_of_human_disease_similarities_reveals_common_functional_modules_enriched_for_pluripotent_drug_targets.pdf]]
|-
|2010/05/19
|align="center"|2010-19
|[[media:100421_interpreting_metabolomic_profiles_using_unbiased_pathway_models.pdf|interpreting_metabolomic_profiles_using_unbiased_pathway_models.pdf]]
|-
|2010/04/21
|align="center"|2010-18
|[[media:100414_identification_of_networks_of_co_occurring_tumor_related_dna_copy_number_changes_using_a_genome_wide_scoringapproach.pdf|identification_of_networks_of_co_occurring_tumor_related_dna_copy_number_changes_using_a_genome_wide_scoringapproach.pdf]]
|-
|2010/04/14
|align="center"|2010-17
|[http://www.cell.com/retrieve/pii/S0092867410000796 an_atlas_of_combinatorial_transcriptional_regulation_in_mouse_and_man]
|-
|rowspan = "2" |2010/04/07
|align="center"|2010-16
|[http://www.pnas.org/content/early/2010/03/11/0910200107.long systematic_discovery_of_nonobvious_human_disease_models_through_orthologous_phenotypes]
|-
|align="center"|2010-15
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature08800.html genome_side_association_study_of_107_phenotypes_in_Arabidopsis_thaliana_inbred_lines]
|-
|rowspan="2"|2010/03/31
|align="center"|2010-14
|[[media:100331_toward_stem_cell_systems_biology_from_molecules_to_networks_and_landscapes.pdf|toward_stem_cell_systems_biology_from_molecules_to_networks_and_landscapes.pdf]]
|-
|align="center"|2010-13
|[http://www.nature.com/nrm/journal/v10/n10/abs/nrm2766.html systems_biology_of_stem_cell_fate_and_cellular_reprogramming]
|-
|2010/03/24
|align="center"|2010-12
|[http://www.nature.com/nbt/journal/v27/n2/abs/nbt.1522.html dynamic_modularity_in_protein_interaction_networks_predicts_breast_cancer_outcome]
|-
|2010/01/18
|align="center"|2010-11
|JournalClub_100118_a_tutorial_on_statistical_methods_for_population_association_studies
|-
|2010/01/16
|align="center"|2010-10
|systems-level_dinamic_analyses_of_fate_change_in_murine_embryonic_stem_cells
|-
|2010/01/15
|align="center"|2010-09
|distinguishing_direct_versus_indirect_transcription_factor-DNA-interactions
|-
|2010/01/14
|align="center"|2010-08
|chemogenomic_profiling_predicts_antifungal_synergies
|-
|2010/01/13
|align="center"|2010-07
|edgetic_perturbation_models_of_human_inherited_disorders
|-
|2010/01/12
|align="center"|2010-06
|analysis_of_cell_fate_from_single-cell_gene_expression_profiles_in_C.elegans
|-
|2010/01/11
|align="center"|2010-05
|predicting_new_molecular_targets_for_known_drugs.pdf
Reference:SEA(Similarity Ensemble Approach)
|-
|2010/01/09
|align="center"|2010-04
|harnessing_gene_expression_to_identify_the_genetic_basis_of_drug_resistance
|-
|2010/01/08
|align="center"|2010-03
|an_integrative_approach_to_reveal_driver_gene_fusions_from_paired_end_sequencing_data_in_cancer
|-
|2010/01/07
|align="center"|2010-02
|a_phenotypic_profile_of_the_candida_albicans_regulatory_network
|-
|2010/01/06
|align="center"|2010-01
|a_global_view_of_protein_expression_in_human_cells_tissues_and_organs
|}

Journal Club

2023-09-18T05:10:39Z

Bsb0613:

{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2023-2 scOmics
|-
!scope="col" style="padding:.4em" | Date
!scope="col" style="padding:.4em" | Team
!scope="col" style="padding:.4em" | Paper index
!scope="col" style="padding:.4em" | Presenter
!scope="col" style="padding:.4em" | Paper title
|-
|style="padding:.4em;" rowspan=1|2023/11/14
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-33
|style="padding:.4em;"|EJ Sung
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41588-022-01273-y MHC II immunogenicity shapes the neoepitope landscape in human tumors]
|-
|style="padding:.4em;" rowspan=1|2023/11/07
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-32
|style="padding:.4em;"|IS Choi
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41586-023-06130-4 Hallmarks of transcriptional intratumour heterogeneity across a thousand tumours]
|-
|style="padding:.4em;" rowspan=1|2023/10/31
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-31
|style="padding:.4em;"|JW Yu
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41467-023-37353-8 Pan-cancer classification of single cells in the tumour microenvironment]
|-
|style="padding:.4em;" rowspan=1|2023/10/24
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-30
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41587-023-01686-y Single-cell mapping of combinatorial target antigens for CAR switches using logic gates]
|-
|style="padding:.4em;" rowspan=1|2023/10/17
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-29
|style="padding:.4em;"|SB Baek
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41587-023-01782-z Comparative analysis of cell–cell communication at single-cell resolution]
|-
|style="padding:.4em;" rowspan=1|2023/09/26
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-28
|style="padding:.4em;"|EJ Sung
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s43018-023-00566-3 Phenotypic diversity of T cells in human primary and metastatic brain tumors revealed by multiomic interrogation]
|-
|style="padding:.4em;" rowspan=1|2023/09/19
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-27
|style="padding:.4em;"|IS Choi
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41591-023-02324-5 An integrated tumor, immune and microbiome atlas of colon cancer]
|-
|style="padding:.4em;" rowspan=1|2023/09/12
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-26
|style="padding:.4em;"|SB Baek
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41587-022-01476-y Multi-omic single-cell velocity models epigenome–transcriptome interactions and improves cell fate prediction]
|-
|style="padding:.4em;" rowspan=1|2023/09/05
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-25
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.immuni.2023.04.010 Recruitment of epitope-specific T cell clones with a low-avidity threshold supports efficacy against mutational escape upon re-infection]
|}

{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2023-2 Microbiome
|-
!scope="col" style="padding:.4em" | Date
!scope="col" style="padding:.4em" | Team
!scope="col" style="padding:.4em" | Paper index
!scope="col" style="padding:.4em" | Presenter
!scope="col" style="padding:.4em" | Paper title
|-
|style="padding:.4em;" rowspan=1|2023/11/22
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-48
|style="padding:.4em;"|SH Ahn
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41467-023-39264-0 A data-driven approach for predicting the impact of drugs on the human microbiome]
|-
|style="padding:.4em;" rowspan=1|2023/11/08
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-47
|style="padding:.4em;"|HJ Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2023.04.06.535777 Activation of programmed cell death and counter-defense functions of phage accessory genes]
|-
|style="padding:.4em;" rowspan=1|2023/11/08
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-46
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41467-023-39459-5 Top-down identification of keystone taxa in the microbiome]
|-
|style="padding:.4em;" rowspan=1|2023/11/01
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-45
|style="padding:.4em;"|JY Ma
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.cels.2022.12.007 Pitfalls of genotyping microbial communities with rapidly growing genome collections]
|-
|style="padding:.4em;" rowspan=1|2023/11/01
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-44
|style="padding:.4em;"|NY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1186/s13059-023-03028-2 Reconstruction of the last bacterial common ancestor from 183 pangenomes reveals a versatile ancient core genome]
|-
|style="padding:.4em;" rowspan=1|2023/10/25
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-43
|style="padding:.4em;"|SH Lee
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41587-023-01868-8 Generation of accurate, expandable phylogenomic trees with uDance]
|-
|style="padding:.4em;" rowspan=1|2023/10/25
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-42
|style="padding:.4em;"|WJ Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.immuni.2023.04.003 Phage display sequencing reveals that genetic, environmental, and intrinsic factors influence variation of human antibody epitope repertoire]
|-
|style="padding:.4em;" rowspan=1|2023/10/18
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-41
|style="padding:.4em;"|JY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.immuni.2023.04.017 Phage-display immunoprecipitation sequencing of the antibody epitope repertoire in inflammatory bowel disease reveals distinct antibody signatures]
|-
|style="padding:.4em;" rowspan=1|2023/10/18
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-40
|style="padding:.4em;"|G Koh
|style="padding:.4em;text-align:left"|
[https://doi.org/10.15252/msb.202311525 Consistency across multi-omics layers in a drug-perturbed gut microbial community]
|-
|style="padding:.4em;" rowspan=1|2023/10/11
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-39
|style="padding:.4em;"|SH Ahn
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41591-023-02407-3 Microbiome-derived cobalamin and succinyl-CoA as biomarkers for improved screening of anal cancer]
|-
|style="padding:.4em;" rowspan=1|2023/10/11
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-38
|style="padding:.4em;"|HJ Kim
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[https://doi.org/10.1101/2023.03.05.531206 You can move, but you can’t hide: identification of mobile genetic elements with geNomad]
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|style="padding:.4em;"|JH Cha
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[https://doi.org/10.1038/s41591-023-02424-2 The airway microbiome mediates the interaction between environmental exposure and respiratory health in humans]
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[https://doi.org/10.1080/19490976.2023.2224474 Ordering taxa in image convolution networks improves microbiome-based machine learning accuracy]
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[https://doi.org/10.1101/2023.08.12.553040 The defensome of complex bacterial communities]
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|style="padding:.4em;"|SH Lee
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[https://doi.org/10.1016/j.cell.2023.03.011 Dietary tryptophan metabolite released by intratumoral Lactobacillus reuteri facilitates immune checkpoint inhibitor treatment]
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[https://doi.org/10.1038/s43587-022-00306-9 Toward an improved definition of a healthy microbiome for healthy aging]
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|style="padding:.4em;"|JY Kim
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[https://doi.org/10.1038/s43587-022-00287-9 Associations of the skin, oral and gut microbiome with aging, frailty and infection risk reservoirs in older adults]
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|style="padding:.4em;"|G Koh
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[https://doi.org/10.1186/s40168-023-01614-x Statistical modeling of gut microbiota for personalized health status monitoring]
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[https://doi.org/10.7554/eLife.50240 Adjusting for age improves identification of gut microbiome alterations in multiple diseases]
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[https://doi.org/10.1038/s41564-023-01370-6 Centenarians have a diverse gut virome with the potential to modulate metabolism and promote healthy lifespan]
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[https://doi.org/10.1016/j.chom.2023.01.003 Longitudinal comparison of the developing gut virome in infants and their mothers]
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[https://doi.org/10.1016/j.chom.2020.03.005 Structure of the Mucosal and Stool Microbiome in Lynch Syndrome]
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[https://doi.org/10.1038/s41586-019-1237-9 Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases]
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[https://doi.org/10.1016/j.jare.2022.03.007 Roles of oral microbiota and oral-gut microbial transmission in hypertension]
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[https://doi.org/10.1016/j.chom.2022.08.009 Human gut microbiota stimulate defined innate immune responses that vary from phylum to strain]
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[https://doi.org/10.1038/s41591-023-02217-7 Gut microbial metabolism of 5-ASA diminishes its clinical efficacy in inflammatory bowel disease]
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[https://doi.org/10.1016/j.chom.2023.01.004 Deficient butyrate-producing capacity in the gut microbiome is associated with bacterial network disturbances and fatigue symptoms in ME/CFS]
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[https://doi.org/10.1016/j.ccell.2022.11.013 Gut microbiota-mediated nucleotide synthesis attenuates the response to neoadjuvant chemoradiotherapy in rectal cancer]
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[https://doi.org/10.1016/j.chom.2021.06.019 Multi-omics reveal microbial determinants impacting responses to biologic therapies in inflammatory bowel disease]
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|style="padding:.4em;"|SH Lee
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[https://doi.org/10.1038/s41586-022-05181-3 Identification of trypsin-degrading commensals in the large intestine]
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|style="padding:.4em;"|JP Hong
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[https://doi.org/10.1038/s41586-022-05546-8 Questioning the fetal microbiome illustrates pitfalls of low-biomass microbial studies]
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|style="padding:.4em;"|23-14
|style="padding:.4em;"|MR Jang
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[https://doi.org/10.1016/j.chom.2023.01.013 Tissue-resident Lachnospiraceae family bacteria protect against colorectal carcinogenesis by promoting tumor immune surveillance]
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|style="padding:.4em;"|JW Yu
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[https://doi.org/10.1016/j.cell.2022.09.005 Pan-cancer analyses reveal cancer-type-specific fungal ecologies and bacteriome interactions]
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[https://doi.org/10.1016/j.cell.2022.09.015 A pan-cancer mycobiome analysis reveals fungal involvement in gastrointestinal and lung tumors]
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[https://doi.org/10.1038/s41564-021-01030-7 Multi-kingdom microbiota analyses identify bacterial–fungal interactions and biomarkers of colorectal cancer across cohorts]
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[https://doi.org/10.1038/s42255-022-00716-4 The antitumour effects of caloric restriction are mediated by the gut microbiome]
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[https://doi.org/10.1038/s41591-022-01964-3 Variability of strain engraftment and predictability of microbiome composition after fecal microbiota transplantation across different diseases]
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[https://doi.org/10.1038/s41591-022-01913-0 Drivers and determinants of strain dynamics following fecal microbiota transplantation]
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[https://doi.org/10.1016/j.cell.2022.11.023 Mobile genetic elements from the maternal microbiome shape infant gut microbial assembly and metabolism]
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[https://doi.org/10.1016/j.chom.2023.01.018 Mother-to-infant microbiota transmission and infant microbiota development across multiple body sites]
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[https://doi.org/10.1038/s41467-023-36633-7 Population-level impacts of antibiotic usage on the human gut microbiome]
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[https://doi.org/10.1038/s41586-022-05438-x Enterococci enhance Clostridioides difficile pathogenesis]
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[https://doi.org/10.1002/imt2.61 Targeting keystone species helps restore the dysbiosis of butyrate‐producing bacteria in nonalcoholic fatty liver disease]
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[https://doi.org/10.1002/advs.202203115 Differential Oral Microbial Input Determines Two Microbiota Pneumo-Types Associated with Health Status]
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[https://doi.org/10.1016/j.cell.2022.08.021 Gut microbiome of multiple sclerosis patients and paired household healthy controls reveal associations with disease risk and course]
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[https://doi.org/10.1101/2023.01.17.524482 Decoupling the correlation between cytotoxic and exhausted T lymphocyte transcriptomic signatures enhances melanoma immunotherapy response prediction from tumor expression]
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[https://doi.org/10.1101/2023.07.28.550993 Major data analysis errors invalidate cancer microbiome findings]
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[https://doi.org/10.1038/s43018-022-00475-x A single-cell atlas of glioblastoma evolution under therapy reveals cell-intrinsic and cell-extrinsic therapeutic targets]
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[https://doi.org/10.1038/s43018-022-00433-7 Single-cell meta-analyses reveal responses of tumor-reactive CXCL13+ T cells to immune-checkpoint blockade]
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[https://doi.org/10.1038/s41587-022-01342-x Estimation of tumor cell total mRNA expression in 15 cancer types predicts disease progression]
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|style="padding:.4em;"|SB Baek
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[https://doi.org/10.1038/s41591-023-02371-y Pan-cancer T cell atlas links a cellular stress response state to immunotherapy resistance]
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[https://doi.org/10.1016/j.patter.2022.100651 Self-supervised graph representation learning integrates multiple molecular networks and decodes gene-disease relationships]
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[https://doi.org/10.1016/j.ccell.2022.10.008 High-resolution single-cell atlas reveals diversity and plasticity of tissue-resident neutrophils in non-small cell lung cancer]
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[https://doi.org/10.1186/s13059-022-02828-2 Parallel single-cell and bulk transcriptome analyses reveal key features of the gastric tumor microenvironment]
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[https://doi.org/10.1038/s41467-022-32838-4 Mutated processes predict immune checkpoint inhibitor therapy benefit in metastatic melanoma]
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[https://doi.org/10.1038/s43018-021-00292-8 Temporal single-cell tracing reveals clonal revival and expansion of precursor exhausted T cells during anti-PD-1 therapy in lung cancer]
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[https://doi.org/10.1016/j.cell.2022.11.028 Integrative single-cell analysis of cardiogenesis indentifies developmental trajectories and non-conding mutations in congenital heart disease]
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[https://doi.org/10.1101/2022.12.20.521311 Supervised discovery of interpretable gene programs from single-cell data]
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[https://doi.org/10.1038/s41586-022-05435-0 Effect of the intratumoral microbiota on spatial and cellular heterogeneity in cancer]
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[https://www.nature.com/articles/s41588-022-01141-9 Cancer cell states recur across tumor types and form specific interactions with the tumor microenvironment]
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[https://www.biorxiv.org/content/10.1101/2022.08.05.502989v1 MetaTiME: Meta-components of the Tumor Immune Microenvironment]
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[https://www.nature.com/articles/s41590-022-01262-7 Pre-encoded responsiveness to type I interferon in the peripheral immune system defines outcome of PD1 blockade therapy]
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[https://www.biorxiv.org/content/10.1101/2022.03.16.484513v1 Integrated single-cell profiling dissects cell-state-specific enhancer landscapes of human tumor-infiltrating T cells]
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[https://www.nature.com/articles/s41591-022-01799-y A T cell resilience model associated with response to immunotherapy in multiple tumor types]
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[https://www.nature.com/articles/s41588-022-01134-8 Single-nucleus and spatial transcriptome profiling of pancreatic cancer identifies multicellular dynamics associated with neoadjuvant treatment]
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[https://pubmed.ncbi.nlm.nih.gov/35649411/ Cross-tissue, single-cell stromal atlas identifies shared pathological fibroblast phenotypes in four chronic inflammatory diseases]
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[https://www.sciencedirect.com/science/article/pii/S1535610822003178 Pan-cancer integrative histology-genomic analysis via multimodal deep learning]
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[https://doi.org/10.1016/j.chom.2023.05.024 Enterosignatures define common bacterial guilds in the human gut microbiome]
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[https://doi.org/10.1186/s13059-023-02902-3 PhyloMed: a phylogeny-based test of mediation effect in microbiome]
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|style="padding:.4em;"|SH Lee
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[https://doi.org/10.1016/j.chom.2023.05.027 The TaxUMAP atlas: Efficient display of large clinical microbiome data reveals ecological competition in protection against bacteremia]
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[http://dx.doi.org/10.1038/nbt.3704 Measurement of bacterial replication rates in microbial communities]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|JY Kim
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[https://doi.org/10.1186/s40168-023-01564-4 Skin microbiome diferentiates into distinct cutotypes with unique metabolic functions upon exposure to polycyclic aromatic hydrocarbons]
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|style="padding:.4em;" rowspan=1|2023/08/11
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|style="padding:.4em;"|SH Ahn
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[https://doi.org/10.1016/j.xcrm.2023.100920 Enrichment of oral-derived bacteria in inflamed colorectal tumors and distinct associations of Fusobacterium in the mesenchymal subtype]
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|style="padding:.4em;"|HJ Kim
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[https://doi.org/10.1038/s41586-023-05989-7 Profiling the human intestinal environment under physiological conditions]
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|style="padding:.4em;"|JH Cha
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[https://doi.org/10.1186/s40168-023-01494-1 Genome-centric metagenomics reveals the host-driven dynamics and ecological role of CPR bacteria in an activated sludge system]
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|style="padding:.4em;"|JY Ma
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[https://doi.org/10.1016/j.patter.2022.100658 Enhanced metagenomic deep learning for disease prediction and consistent signature recognition by restructured microbiome 2D representations]
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|style="padding:.4em;"|NY Kim
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[https://doi.org/10.1186/s13059-022-02809-5 Gene fow and introgression are pervasive forces shaping the evolution of bacterial species]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1186/s40168-022-01435-4 Alterations of oral microbiota and impact on the gut microbiome in type 1 diabetes mellitus revealed by integrated multi‑omic analyses]
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[https://doi.org/10.1038/s41467-022-33397-4 Deciphering microbial gene function using natural language processing]
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|style="padding:.4em;"|SH Lee
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[https://doi.org/10.1101/2022.11.28.518265 Rethinking bacterial relationships in light of their molecular abilities]
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|style="padding:.4em;" rowspan=1|2023/06/02
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|style="padding:.4em;"|JY Ma
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[https://doi.org/10.1016/j.immuni.2022.08.016 The CD4+ T cell response to a commensal-derived epitope transitions from a tolerant to an inflammatory state in Crohn’s disease]
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|style="padding:.4em;"|NY Kim
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[https://doi.org/10.1038/s41591-018-0203-7 Antigen discovery and specification of immunodominance hierarchies for MHCIIrestricted epitopes]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|SH Lee
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[https://doi.org/10.1101/2022.10.11.511790 Single Cell Transcriptomics Reveals the Hidden Microbiomes of Human Tissues]
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|style="padding:.4em;"|JY Kim
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[https://doi.org/10.1038/s41564-017-0096-0 Stability of the human faecal microbiome in a cohort of adult men]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|WJ Kim
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[https://doi.org/10.1038/s41564-017-0084-4 Metatranscriptome of human faecal microbial communities in a cohort of adult men]
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|style="padding:.4em;"|SH Ahn
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[https://doi.org/10.1016/j.ccell.2022.09.009 Tumor microbiome links cellular programs and immunity in pancreatic cancer]
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|style="padding:.4em;"|HJ Kim
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[https://doi.org/10.1038/s41467-022-32832-w Extensive gut virome variation and its associations with host and environmental factors in a population-level cohort]
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|style="padding:.4em;" rowspan=1|2023/03/10
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-7
|style="padding:.4em;"|JH Cha
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[https://doi.org/10.1038/s41586-022-05620-1 The person-to-person transmission landscape of the gut and oral microbiomes]
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|style="padding:.4em;"|JY Ma
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[https://doi.org/10.1101/2023.01.30.526328 BIRDMAn: A Bayesian differential abundance framework that enables robust inference of host-microbe associations]
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|style="padding:.4em;"|NY Kim
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[https://www.nature.com/articles/s41564-022-01157-1 Phage–host coevolution in natural populations]
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|style="padding:.4em;"|SH Lee
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[https://www.nature.com/articles/s41467-022-29968-0 A randomized controlled trial for response of microbiome network to exercise and diet intervention in patients with nonalcoholic fatty liver disease]
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|style="padding:.4em;"|SH Ahn
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[https://www.biorxiv.org/content/10.1101/2022.05.19.492684v1 Scalable power analysis and effect size exploration of microbiome community differences with Evident]
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[https://www.biorxiv.org/content/10.1101/2022.08.05.502982v1 Phanta: Phage-inclusive profiling of human gut metagenomes]
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[https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1010373 Computational approach to modeling microbiome landscapes associated with chronic human disease progression]
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[https://www.biorxiv.org/content/10.1101/2022.08.19.504505v1 SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks]
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|style="padding:.4em;"|22-31
|style="padding:.4em;"|JH Cha, SB Baek, IS Choi
|style="padding:.4em;text-align:left"|
[https://www.pnas.org/doi/10.1073/pnas.2105859118 Representation learning of RNA velocity reveals robust cell transitions]
[https://www.nature.com/articles/s41467-022-34188-7 UniTVelo: temporally unified RNA velocity reinforces single-cell trajectory inference]
[https://www.sciencedirect.com/science/article/pii/S0092867421015774 Mapping transcriptomic vector fields of single cells]
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[https://www.nature.com/articles/s41467-022-31535-6 Network-based machine learning approach to predict immunotherapy response in cancer patients]
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|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|SB Baek
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[https://www.biorxiv.org/content/10.1101/2022.05.04.490536v1 Modeling fragment counts improves single-cell ATAC-seq analysis]
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|style="padding:.4em;"|G Koh
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[https://www.nature.com/articles/s41586-022-04718-w Extricating human tumour immune alterations from tissue inflammation]
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|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|JW Yu
|style="padding:.4em;text-align:left"|
[https://www.biorxiv.org/content/10.1101/2022.06.15.495325v1 T cell receptor convergence is an indicator of antigen-specific T cell response in cancer immunotherapies]
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|style="padding:.4em;" rowspan=1|2022/09/06
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|style="padding:.4em;"|JH Cha
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[https://www.nature.com/articles/s41587-021-01091-3 Identifying phenotype-associated subpopulations by integrating bulk and single-cell sequencing data]
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|style="padding:.4em;" rowspan=1|2022/09/01
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|22-27
|style="padding:.4em;"|SB Baek
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[https://www.biorxiv.org/content/10.1101/2021.12.06.471401v1 MIRA: Joint regulatory modeling of multimodal expression and chromatin accessibility in single cells]
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[https://www.sciencedirect.com/science/article/pii/S1535610822000654 Immune phenotypic linkage between colorectal cancer and liver metastasis]
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|style="padding:.4em;"|IS Choi
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[https://doi.org/10.1101/2022.02.05.479217 Biologically informed deep learning to infer gene program activity in single cells]
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[https://www.nature.com/articles/s43018-022-00356-3 Cell type and gene expression deconvolution with BayesPrism enables Bayesian integrative analysis across bulk and single-cell RNA sequencing in oncology]
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|style="padding:.4em;"|JH Cha
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[https://pubmed.ncbi.nlm.nih.gov/34462589/ Mapping single-cell data to reference atlases by transfer learning]
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|style="padding:.4em;"|JW Yu
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[https://doi.org/10.1101/2021.10.31.466532 Pan-cancer mapping of single T cell profiles reveals a TCF1:CXCR6-CXCL16 regulatory axis essential for effective anti-tumor immunity]
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|style="padding:.4em;"|IS Choi
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[https://pubmed.ncbi.nlm.nih.gov/34845454/ Identifying disease-critical cell types and cellular processes across the human body by integration of single-cell profiles and human genetics]
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[https://www.biorxiv.org/content/10.1101/2021.06.07.447430v2 Metacells untangle large and complex single-cell transcriptome networks]
[https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1812-2 MetaCell: analysis of single-cell RNA-seq data using K-nn graph partitions]
[https://genomebiology.biomedcentral.com/articles/10.1186/s13059-022-02667-1 Metacell‑2: a divide‑and‑conquer metacell algorithm for scalable scRNA‑seq analysis]
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[https://pubmed.ncbi.nlm.nih.gov/34675423/ Co-varying neighborhood analysis identifies cell populations associated with phenotypes of interest from single-cell transcriptomics]
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[https://pubmed.ncbi.nlm.nih.gov/34426704/ Integrating T cell receptor sequences and transcriptional profiles by clonotype neighbor graph analysis (CoNGA)]
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[https://pubmed.ncbi.nlm.nih.gov/34986867/ Hepatocellular carcinoma patients with high circulating cytotoxic T cells and intra-tumoral immune signature benefit from pembrolizumab: results from a single-arm phase 2 trial]
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[https://pubmed.ncbi.nlm.nih.gov/35199064/ Effect of imputation on gene network reconstruction from single-cell RNA-seq data]
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|style="padding:.4em;"|IS Choi
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[https://pubmed.ncbi.nlm.nih.gov/35105355/ Diagnostic Evidence GAuge of Single cells (DEGAS): a flexible deep transfer learning framework for prioritizing cells in relation to disease]
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[https://pubmed.ncbi.nlm.nih.gov/35172892/ Integrating single-cell sequencing data with GWAS summary statistics reveals CD16+monocytes and memory CD8+T cells involved in severe COVID-19]
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[https://pubmed.ncbi.nlm.nih.gov/34663807/ Single cell T cell landscape and T cell receptor repertoire profiling of AML in context of PD-1 blockade therapy]
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[https://pubmed.ncbi.nlm.nih.gov/34594031/ Systematic investigation of cytokine signaling activity at the tissue and single-cell levels]
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[https://pubmed.ncbi.nlm.nih.gov/34852236/ Neoantigen-driven B cell and CD4 T follicular helper cell collaboration promotes anti-tumor CD8 T cell responses]
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[https://pubmed.ncbi.nlm.nih.gov/34930412/ MultiMAP: dimensionality reduction and integration of multimodal data]
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[https://www.biorxiv.org/content/10.1101/2020.10.19.345983v2 CellRank for directed single-cell fate mapping]
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[https://pubmed.ncbi.nlm.nih.gov/34653365/ Single-cell analyses reveal key immune cell subsets associated with response to PD-L1 blockade in triple-negative breast cancer]
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|style="padding:.4em;"|IS Choi
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[https://pubmed.ncbi.nlm.nih.gov/34390642/ Chromatin and gene-regulatory dynamics of the developing human cerebral cortex at single-cell resolution]
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[https://pubmed.ncbi.nlm.nih.gov/34767762/ Single-cell analysis of human non-small cell lung cancer lesions refines tumor classification and patient stratification]
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|style="padding:.4em;"|JH Cha
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[https://pubmed.ncbi.nlm.nih.gov/34914499/ Pan-cancer single-cell landscape of tumor-infiltrating T cells]
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[https://pubmed.ncbi.nlm.nih.gov/34597583/ Atlas of clinically distinct cell states and ecosystems across human solid tumors]
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[https://pubmed.ncbi.nlm.nih.gov/34489465/ Single-cell ATAC and RNA sequencing reveal pre-existing and persistent cells associated with prostate cancer relapse]
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[https://www.biorxiv.org/content/10.1101/2022.08.02.502504v1 A novel in silico method employs chemical and protein similarity algorithms to accurately identify chemical transformations in the human gut microbiome]
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[https://www.biorxiv.org/content/10.1101/2021.09.13.460160v3 Inference of disease-associated microbial biomarkers based on metagenomic and metatranscriptomic data]
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[https://www.sciencedirect.com/science/article/pii/S0092867422009199?via%3Dihub Personalized microbiome-driven effects of non-nutritive sweeteners on human glucose tolerance]
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[https://www.nature.com/articles/s41564-022-01121-z Identification of shared and disease-specific host gene–microbiome associations across human diseases using multi-omic integration]
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[https://www.sciencedirect.com/science/article/pii/S193131282200049X Caudovirales bacteriophages are associated with improved executive function and memory in flies, mice, and humans]
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[https://www.biorxiv.org/content/10.1101/2021.10.06.463341v2.full SynTracker: a synteny based tool for tracking microbial strains]
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[https://www.nature.com/articles/s41587-022-01226-0 Identification of antimicrobial peptides from the human gut microbiome using deep learning]
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[https://www.nature.com/articles/s41586-022-04648-7 Discovery of bioactive microbial gene products in inflammatory bowel disease]
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[https://www.nature.com/articles/s43588-022-00247-8 Large-scale microbiome data integration enables robust biomarker identification]
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[https://www.nature.com/articles/s41467-022-30512-3 Predicting cancer prognosis and drug response from the tumor microbiome]
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[https://www.cell.com/cell-reports/pdf/S2211-1247(22)00770-7.pdf Thousands of small, novel genes predicted in global phage genomes]
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[https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-022-01231-0 MetaPop: a pipeline for macro- and microdiversity analyses and visualization of microbial and viral metagenome-derived populations]
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[https://www.nature.com/articles/s41586-022-04862-3 Biosynthetic potential of the global ocean microbiome]
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[https://journals.asm.org/doi/10.1128/msystems.00050-22 Compositionally Aware Phylogenetic Beta-Diversity Measures Better Resolve Microbiomes Associated with Phenotype]
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[https://www.nature.com/articles/s41596-020-00480-3 Tutorial: assessing metagenomics software with the CAMI benchmarking toolkit disease]
[https://www.nature.com/articles/s41592-022-01431-4 Critical Assessment of Metagenome Interpretation: the second round of challenges]
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[https://www.sciencedirect.com/science/article/pii/S2666379121002561 Identification of Faecalibacterium prausnitzii strains for gut microbiome-based intervention in Alzheimer’s-type dementia]
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[https://www.nature.com/articles/s41591-022-01688-4 Microbiome and metabolome features of the cardiometabolic disease spectrum]
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[https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02195-w Functional and genetic markers of niche partitioning among enigmatic members of the human oral microbiome]
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[https://www.biorxiv.org/content/10.1101/2022.02.21.480893v1 Integrating phylogenetic and functional data in microbiome studies]
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[https://genomebiology.biomedcentral.com/articles/10.1186/s13059-021-02473-1 Pandora: nucleotide-resolution bacterial pan-genomics with reference graphs]
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[https://journals.asm.org/doi/10.1128/mSystems.00252-19 Comprehensive Analysis Reveals the Evolution and Pathogenicity of Aeromonas, Viewed from Both Single Isolated Species and Microbial Communities]
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[https://genomebiology.biomedcentral.com/articles/10.1186/s13059-022-02610-4 AGAMEMNON: an Accurate metaGenomics And MEtatranscriptoMics quaNtificatiON analysis suite]
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[https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02200-2 Metapangenomics of the oral microbiome provides insights into habitat adaptation and cultivar diversity]
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[https://genomemedicine.biomedcentral.com/articles/10.1186/s13073-022-01011-3 Microbiota of the prostate tumor environment investigated by whole-transcriptome profiling]
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[https://genomebiology.biomedcentral.com/articles/10.1186/s13059-021-02576-9 Species-resolved sequencing of low-biomass or degraded microbiomes using 2bRAD-M]
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[https://pubmed.ncbi.nlm.nih.gov/34517888/ Gut microbial determinants of clinically important improvement in patients with rheumatoid arthritis]
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[https://pubmed.ncbi.nlm.nih.gov/34618582/ Commensal bacteria promote endocrine resistance in prostate cancer through androgen biosynthesis]
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[https://pubmed.ncbi.nlm.nih.gov/34551799/ The influence of the gut microbiome on BCG-induced trained immunity]
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[https://pubmed.ncbi.nlm.nih.gov/34912116/ Towards the biogeography of prokaryotic genes]
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[https://www.biorxiv.org/content/10.1101/2022.07.06.499075v1 Maast: genotyping thousands of microbial strains efficiently]
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[https://www.biorxiv.org/content/10.1101/2022.02.01.478746v2 Scalable microbial strain inference in metagenomic data using StrainFacts]
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[https://www.biorxiv.org/content/10.1101/2022.02.15.480535v1 StrainPanDA: linked reconstruction of strain composition and gene content profiles via pangenome-based decomposition of metagenomic data]
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[https://www.nature.com/articles/s41587-021-01102-3 Fast and accurate metagenotyping of the human gut microbiome with GT-Pro]
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[https://www.nature.com/articles/s41587-020-00797-0 inStrain profiles population microdiversity from metagenomic data and sensitively detects shared microbial strains]
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[https://genome.cshlp.org/content/early/2021/07/22/gr.265058.120 Longitudinal linked-read sequencing reveals ecological and evolutionary responses of a human gut microbiome during antibiotic treatment]
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[https://www.cell.com/cell-host-microbe/fulltext/S1931-3128(19)30041-1?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS1931312819300411%3Fshowall%3Dtrue Distinct Genetic and Functional Traits of Human Intestinal Prevotella copri Strains Are Associated with Different Habitual Diets]
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[https://www.nature.com/articles/nmeth.3802 Strain-level microbial epidemiology and population genomics from shotgun metagenomics]
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[https://www.biorxiv.org/content/10.1101/2021.02.09.430114v2 Single-cell ATAC and RNA sequencing reveal pre-existing and persistent subpopulations of cells associated with relapse of prostate cancer]
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[https://www.sciencedirect.com/science/article/pii/S1931312821001451 The infant gut resistome associates with E. coli, environmental exposures, gut microbiome maturity, and asthma-associated bacterial composition]
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[https://www.sciencedirect.com/science/article/pii/S1931312820306703?dgcid=rss_sd_all Methotrexate impacts conserved pathways in diverse human gut bacteria leading to decreased host immune activation]
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[https://science.sciencemag.org/content/366/6471/eaax9176 A metagenomic strategy for harnessing the chemical repertoire of the human microbiome]
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[https://www.nature.com/articles/s41467-019-10927-1 Predictive metabolomic profiling of microbial communities using amplicon or metagenomic sequences]
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[https://www.nature.com/articles/s41564-018-0306-4 Gut microbiome structure and metabolic activity in inflammatory bowel disease]
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[https://www.nature.com/articles/s41591-020-01183-8 Microbiome connections with host metabolism and habitual diet from 1,098 deeply phenotyped individuals]
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[https://www.nature.com/articles/s41467-020-18476-8 A predictive index for health status using species-level gut microbiome profiling]
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[https://www.nature.com/articles/s41467-021-21475-y Gastrointestinal microbiota composition predicts peripheral inflammatory state during treatment of human tuberculosis]
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[https://www.sciencedirect.com/science/article/pii/S1931312820301694 Structure of the Mucosal and Stool Microbiome in Lynch Syndrome]
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[https://science.sciencemag.org/content/369/6506/936 Cross-reactivity between tumor MHC class 1-restricted antigens and an enterococcal bacteriophage]
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[https://www.nature.com/articles/s41564-020-00831-6 Bifidobacterium bifidum strains synergize with immune checkpoint inhibitors to reduce tumour burden in mice]
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[https://www.nature.com/articles/s41591-020-01223-3 The gut microbiome modulates the protective association between a Mediterranean diet and cardiometabolic disease risk]
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[https://www.nature.com/articles/s41467-019-14177-z Impact of commonly used drugs on the composition and metabolic function of the gut microbiota]
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[https://www.sciencedirect.com/science/article/pii/S0092867420305638 Personalized Mapping of Drug Metabolism by the Human Gut Microbiome]
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[https://www.cell.com/fulltext/S0092-8674(17)30107-1 Mining the Human Gut Microbiota for Immunomodulatory Organisms]
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[https://www.nature.com/articles/s41586-020-2095-1 Microbiome analyses of blood and tissues suggest cancer diagnostic approach]
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[https://science.sciencemag.org/content/368/6494/973 The human tumor microbiome is composed of tumor type-specific intracellular bacteria]
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[https://www.nature.com/articles/s41590-020-0784-4 Functional CRISPR dissection of gene networks controlling human regulatory T cell identity]
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[https://www.sciencedirect.com/science/article/pii/S0092867420306887 Molecular Pathways of Colon Inflammation Induced by Cancer Immunotherapy]
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[https://www.nature.com/articles/s41588-020-00721-x Single-cell epigenomic analyses implicate candidate causal variants at inherited risk loci for Alzheimer’s and Parkinson’s diseases]
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[https://www.nature.com/articles/s41467-020-14766-3 Trajectory-based differential expression analysis for single-cell sequencing data]
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[https://www.nature.com/articles/s41588-018-0156-2 Genetic determinants of co-accessible chromatin regions in activated T cells across humans]
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[https://www.sciencedirect.com/science/article/pii/S009286742030341X Single-Cell Analyses Inform Mechanisms of Myeloid-Targeted Therapies in Colon Cancer]
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[https://www.sciencedirect.com/science/article/pii/S0092867419308967?via%3Dihub Single-Cell Analysis of Crohn's Disease Lesions Identifies a Pathogenic Cellular Module Associated with Resistance to Anti-TNF Therapy]
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[https://pubmed.ncbi.nlm.nih.gov/32393797/ Multi-omic single-cell snapshots reveal multiple independent trajectories to drug tolerance in a melanoma cell line]
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|style="padding:.4em;"|20-14
|style="padding:.4em;"|JW Choi
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[https://pubmed.ncbi.nlm.nih.gov/30595452/ Dysfunctional CD8 T Cells Form a Proliferative, Dynamically Regulated Compartment Within Human Melanoma]
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[https://pubmed.ncbi.nlm.nih.gov/30388455/ A Cancer Cell Program Promotes T Cell Exclusion and Resistance to Checkpoint Blockade]
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[https://pubmed.ncbi.nlm.nih.gov/31974247/ Single-cell Transcriptional Diversity Is a Hallmark of Developmental Potential]
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[https://pubmed.ncbi.nlm.nih.gov/31675496/ Landscape and Dynamics of Single Immune Cells in Hepatocellular Carcinoma]
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[https://pubmed.ncbi.nlm.nih.gov/32103181/ Peripheral T Cell Expansion Predicts Tumour Infiltration and Clinical Response]
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[https://pubmed.ncbi.nlm.nih.gov/30388456/ Defining T Cell States Associated With Response to Checkpoint Immunotherapy in Melanoma]
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[https://pubmed.ncbi.nlm.nih.gov/31915379/ Rapid Non-Uniform Adaptation to Conformation-Specific KRAS(G12C) Inhibition]
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[https://pubmed.ncbi.nlm.nih.gov/31959990/ Targeted Therapy Guided by Single-Cell Transcriptomic Analysis in Drug-Induced Hypersensitivity Syndrome: A Case Report]
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[https://pubmed.ncbi.nlm.nih.gov/32066951/ Distinct Microbial and Immune Niches of the Human Colon]
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[https://pubmed.ncbi.nlm.nih.gov/29434354/ Single-cell Gene Expression Reveals a Landscape of Regulatory T Cell Phenotypes Shaped by the TCR]
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|style="padding:.4em;"|20-3
|style="padding:.4em;"|NY Kim
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[https://pubmed.ncbi.nlm.nih.gov/30078704/ A Single-Cell Atlas of In Vivo Mammalian Chromatin Accessibility]
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[https://pubmed.ncbi.nlm.nih.gov/32014031/ scAI: An Unsupervised Approach for the Integrative Analysis of Parallel Single-Cell Transcriptomic and Epigenomic Profiles]
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[https://pubmed.ncbi.nlm.nih.gov/31792411/ Single-cell Multiomic Analysis Identifies Regulatory Programs in Mixed-Phenotype Acute Leukemia]
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[https://www.sciencedirect.com/science/article/pii/S1931312819303488 Obese Individuals with and without Type 2 Diabetes Show Different Gut Microbial Functional Capacity and Composition]
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|style="padding:.4em;"|19-50
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[https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-019-0722-6 Clustering co-abundant genes identifies components of the gut microbiome that are reproducibly associated with colorectal cancer and inflammatory bowel disease]
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[https://www.nature.com/articles/s41587-019-0206-z Massively parallel single-cell chromatin landscapes of human immune cell development and intratumoral T cell exhaustion]
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[https://www.biorxiv.org/content/10.1101/739011v1 Assessment of computational methods for the analysis of single-cell ATAC-seq data]
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[https://www.sciencedirect.com/science/article/pii/S1931312819303026 Meta-Analysis Reveals Reproducible Gut Microbiome Alterations in Response to a High-Fat Diet]
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|style="padding:.4em;"|19-46
|style="padding:.4em;"|NY Kim
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[https://www.sciencedirect.com/science/article/pii/S0092867419307731 Tumor Microbiome Diversity and Composition Influence Pancreatic Cancer Outcomes]
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[https://genome.cshlp.org/content/early/2019/04/01/gr.243725.118 The accessible chromatin landscape of the murine hippocampus at single-cell resolution]
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[https://www.sciencedirect.com/science/article/pii/S009286741830446X Integrated Single-Cell Analysis Maps the Continuous Regulatory Landscape of Human Hematopoietic Differentiation]
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|style="padding:.4em;" rowspan=2|2019/09/17
|style="padding:.4em;" rowspan=2|Microbiome
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[https://science.sciencemag.org/content/365/6449/eaau4735 A sparse covarying unit that describes healthy and impaired human gut microbiota development]
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|style="padding:.4em;"|19-42
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[https://www.sciencedirect.com/science/article/pii/S0092867419307810 Large-Scale Analyses of Human Microbiomes Reveal Thousands of Small, Novel Genes]
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[https://www.sciencedirect.com/science/article/pii/S0092867419307329?via%3Dihub Intra- and Inter-cellular Rewiring of the Human Colon during Ulcerative Colitis]
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[https://www.nature.com/articles/nbt.4042 Multiplexed droplet single-cell RNA-sequencing using natural genetic variation]
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[https://www.sciencedirect.com/science/article/pii/S193131281930352X?via%3Dihub The Landscape of Genetic Content in the Gut and Oral Human Microbiome]
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[https://www.sciencedirect.com/science/article/pii/S0092867419307755?via%3Dihub Benchmarking Metagenomics Tools for Taxonomic Classification]
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[https://www.biorxiv.org/content/10.1101/719088v1 Coexpression uncovers a unified single-cell transcriptomic landscape]
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[https://www.biorxiv.org/content/10.1101/586859v1 Single-cell interactomes of the human brain reveal cell-type specific convergence of brain disorders]
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|style="padding:.4em;" rowspan=3|2019/08/14
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[https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004075 Proportionality: a valid alternative to correlation for relative data]
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[https://www.nature.com/articles/s41592-018-0254-1 A test metric for assessing single-cell RNA-seq batch correction]
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[https://www.sciencedirect.com/science/article/pii/S0092867419305598 Comprehensive Integration of Single-Cell Data]
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[https://www.sciencedirect.com/science/article/pii/S0092867418313941 Defining T Cell States Associated with Response to Checkpoint Immunotherapy in Melanoma]
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[https://www.sciencedirect.com/science/article/pii/S009286741831242X High-Dimensional Analysis Delineates Myeloid and Lymphoid Compartment Remodeling during Successful Immune-Checkpoint Cancer Therapy]
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[https://www.sciencedirect.com/science/article/pii/S009286741831568X Dysfunctional CD8 T Cells Form a Proliferative, Dynamically Regulated Compartment within Human Melanoma]
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[https://www.nature.com/articles/nm.4466 High-dimensional single-cell analysis predicts response to anti-PD-1 immunotherapy]
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[https://www.sciencedirect.com/science/article/pii/S0092867418311784 A Cancer Cell Program Promotes T Cell Exclusion and Resistance to Checkpoint Blockade]
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[https://www.ncbi.nlm.nih.gov/pubmed/26006008 COMPASS identifies T-cell subsets correlated with clinical outcomes.]
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[https://www.ncbi.nlm.nih.gov/pubmed/30936547 Meta-analysis of fecal metagenomes reveals global microbial signatures that are specific for colorectal cancer]
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[https://www.ncbi.nlm.nih.gov/pubmed/30936548 Metagenomic analysis of colorectal cancer datasets identifies cross-cohort microbial diagnostic signatures and a link with choline degradation]
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[https://www.ncbi.nlm.nih.gov/pubmed/30664783 Microbial network disturbances in relapsing refractory Crohn's disease.]
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|style="padding:.4em;" rowspan=3|2019/05/23
|style="padding:.4em;"|19-19
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[https://www.ncbi.nlm.nih.gov/pubmed/30867587 New insights from uncultivated genomes of the global human gut microbiome]
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|style="padding:.4em;"|19-18
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[https://www.ncbi.nlm.nih.gov/pubmed/30745586 A new genomic blueprint of the human gut microbiota]
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|style="padding:.4em;"|19-17
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[https://www.ncbi.nlm.nih.gov/pubmed/30661755 Extensive Unexplored Human Microbiome Diversity Revealed by Over 150,000 Genomes from Metagenomes Spanning Age, Geography, and Lifestyle.]
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|style="padding:.4em;" rowspan=2|2019/05/16
|style="padding:.4em;"|19-16
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[https://www.ncbi.nlm.nih.gov/pubmed/29311644 Dynamics of metatranscription in the inflammatory bowel disease gut microbiome.]
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|style="padding:.4em;"|19-15
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[https://www.ncbi.nlm.nih.gov/pubmed/29335555 Metatranscriptome of human faecal microbial communities in a cohort of adult men.]
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|style="padding:.4em;" rowspan=2|2019/05/09
|style="padding:.4em;"|19-14
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[https://www.ncbi.nlm.nih.gov/pubmed/30193113 Post-Antibiotic Gut Mucosal Microbiome Reconstitution Is Impaired by Probiotics and Improved by Autologous FMT.]
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|style="padding:.4em;"|19-13
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[https://www.ncbi.nlm.nih.gov/pubmed/30193112 Personalized Gut Mucosal Colonization Resistance to Empiric Probiotics Is Associated with Unique Host and Microbiome Features.]
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|style="padding:.4em;" rowspan=2|2019/05/02
|style="padding:.4em;"|19-12
|style="padding:.4em;"|
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[https://www.ncbi.nlm.nih.gov/pubmed/30753825 Distinct Immune Cell Populations Define Response to Anti-PD-1 Monotherapy and Anti-PD-1/Anti-CTLA-4 Combined Therapy.]
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|style="padding:.4em;"|19-11
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[https://www.ncbi.nlm.nih.gov/pubmed/30778252 Subsets of exhausted CD8+ T cells differentially mediate tumor control and respond to checkpoint blockade.]
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|style="padding:.4em;" rowspan=2|2019/4/11
|style="padding:.4em;"|19-10
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[https://www.ncbi.nlm.nih.gov/pubmed/30479382 Lineage tracking reveals dynamic relationships of T cells in colorectal cancer.]
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|style="padding:.4em;"|19-9
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[https://www.ncbi.nlm.nih.gov/pubmed/30523328 Single cell dissection of plasma cell heterogeneity in symptomatic and asymptomatic myeloma.]
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|style="padding:.4em;" rowspan=2|2019/4/4
|style="padding:.4em;"|19-8
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[https://www.ncbi.nlm.nih.gov/pubmed/29942092 Single-cell profiling of breast cancer T cells reveals a tissue-resident memory subset associated with improved prognosis.]
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|style="padding:.4em;"|19-7
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[https://www.ncbi.nlm.nih.gov/pubmed/29942094 Global characterization of T cells in non-small-cell lung cancer by single-cell sequencing]
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|style="padding:.4em;" rowspan=2|2019/3/28
|style="padding:.4em;"|19-6
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[https://www.ncbi.nlm.nih.gov/pubmed/28319088 Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors.]
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|style="padding:.4em;"|19-5
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[https://www.ncbi.nlm.nih.gov/pubmed/28622514 Landscape of Infiltrating T Cells in Liver Cancer Revealed by Single-Cell Sequencing.]
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|style="padding:.4em;" rowspan=3|2019/3/21
|style="padding:.4em;"|19-4
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[https://www.ncbi.nlm.nih.gov/pubmed/29988129 Phenotype molding of stromal cells in the lung tumor microenvironment.]
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|style="padding:.4em;"|19-3-1
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[https://www.ncbi.nlm.nih.gov/pubmed/30787436 A single-cell molecular map of mouse gastrulation and early organogenesis]
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|style="padding:.4em;"|19-3
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[https://www.ncbi.nlm.nih.gov/pubmed/30787437 The single-cell transcriptional landscape of mammalian organogenesis]
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|style="padding:.4em;" rowspan=2|2019/3/14
|style="padding:.4em;"|19-2
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[https://www.ncbi.nlm.nih.gov/pubmed/29961579 Single-Cell Map of Diverse Immune Phenotypes in the Breast Tumor Microenvironment]
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|style="padding:.4em;"|19-1
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[https://www.ncbi.nlm.nih.gov/pubmed/29198524 Single-Cell Transcriptomic Analysis of Primary and Metastatic Tumor Ecosystems in Head and Neck Cancer]
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{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2018
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!scope="col" style="padding:.4em" |Date
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|style="padding:.4em;" rowspan=2|2018/06/14
|style="padding:.4em;"|18-12
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=A+pan-cancer+analysis+of+enhancer+expression+in+nearly+9000+patient+samples A Pan-Cancer Analysis of Enhancer Expression in Nearly 9000 Patient Samples.]
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|style="padding:.4em;"|18-11
|style="padding:.4em;"|
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=Machine+learning+identifies+stemness+features+associated+with+oncogenic+dedifferentiation Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation.]
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|style="padding:.4em;" rowspan=2|2018/06/07
|style="padding:.4em;"|18-10
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=Developmental+and+oncogenic+programs+in+H3K27M+gliomas+dissected+by+single-cell+RNA-seq Developmental and oncogenic programs in H3K27M gliomas dissected by single-cell RNA-seq.]
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|style="padding:.4em;"|18-9
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=Chemoresistance+evolution+in+triple-negative+breast+cancer+delineated+by+single-cell+sequencing Chemoresistance Evolution in Triple-Negative Breast Cancer Delineated by Single-Cell Sequencing.]
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|style="padding:.4em;" rowspan=2|2018/05/31
|style="padding:.4em;"|18-8
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=Mapping+human+pluripotent+stem+cell+differentiation+pathways+using+high+throughput+single-cell+RNA-sequencing Mapping human pluripotent stem cell differentiation pathways using high throughput single-cell RNA-sequencing.]
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|style="padding:.4em;"|18-7
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=A+single-cell+RNA-seq+survey+of+the+developmental+landscape+of+the+human+prefrontal+cortex A single-cell RNA-seq survey of the developmental landscape of the human prefrontal cortex.]
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|style="padding:.4em;" rowspan=2|2018/05/24
|style="padding:.4em;"|18-6
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=Single-cell+RNA+sequencing+identifies+celltype-specific+cis-eQTLs+and+co-expression+QTLs Single-cell RNA sequencing identifies celltype-specific cis-eQTLs and co-expression QTLs.]
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|style="padding:.4em;"|18-5
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=FOCS%3A+a+novel+method+for+analyzing+enhancer+and+gene+activity+patterns+infers+an+extensive+enhancer-promoter+map FOCS: a novel method for analyzing enhancer and gene activity patterns infers an extensive enhancer-promoter map.]
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|style="padding:.4em;" rowspan=2|2018/05/17
|style="padding:.4em;"|18-4
|style="padding:.4em;"|KS Kim
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[https://www.ncbi.nlm.nih.gov/pubmed/29149608 A Distinct Gene Module for Dysfunction Uncoupled from Activation in Tumor-Infiltrating T Cells.]
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|style="padding:.4em;"|18-3
|style="padding:.4em;"|SH Lee
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[https://www.ncbi.nlm.nih.gov/pubmed/29610481 A global transcriptional network connecting noncoding mutations to changes in tumor gene expression.]
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|style="padding:.4em;" rowspan=2|2018/05/10
|style="padding:.4em;"|18-2
|style="padding:.4em;"|JW Cho
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=Inferring+regulatory+element+landscapes+and+transcription+factor+networks+from+cancer+methylomes Inferring regulatory element landscapes and transcription factor networks from cancer methylomes.]
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|style="padding:.4em;"|18-1
|style="padding:.4em;"|DS Bae
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[https://www.ncbi.nlm.nih.gov/pubmed/28129544 Genomic and Transcriptomic Features of Response to Anti-PD-1 Therapy in Metastatic Melanoma.]
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{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2017
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!scope="col" style="padding:.4em" |Date
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|style="padding:.4em;" rowspan=2|2017/06/28
|style="padding:.4em;"|17-36
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/28104840 Tumor aneuploidy correlates with markers of immune evasion and with reduced response to immunotherapy.]
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|style="padding:.4em;"|17-35
|style="padding:.4em;"|JW Cho
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/27240091 Landscape of tumor-infiltrating T cell repertoire of human cancers.]
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|style="padding:.4em;" rowspan=2|2017/06/14
|style="padding:.4em;"|17-34
|style="padding:.4em;"|JW Cho
|style="padding:.4em;text-align:left"|
[http://biorxiv.org/content/early/2015/09/01/025908 The landscape of T cell infiltration in human cancer and its association with antigen presenting gene expression.]
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|style="padding:.4em;"|17-33
|style="padding:.4em;"|MY Lee
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.08.052 A Distinct Gene Module for Dysfunction Uncoupled from Activation in Tumor-Infiltrating T Cells.]
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|style="padding:.4em;" rowspan=2|2017/06/07
|style="padding:.4em;"|17-32
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[http://www.tandfonline.com/doi/full/10.1080/2162402X.2016.1253654 Identification of genetic determinants of breast cancer immune phenotypes by integrative genome-scale analysis.]
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|style="padding:.4em;"|17-31
|style="padding:.4em;"|MY Lee
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.celrep.2016.03.075 Genomic Correlates of Immune-Cell Infiltrates in Colorectal Carcinoma.]
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|style="padding:.4em;" rowspan=2|2017/05/31
|style="padding:.4em;"|17-30
|style="padding:.4em;"|DS Bae
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.02.065 Genomic and Transcriptomic Features of Response to Anti-PD-1 Therapy in Metastatic Melanoma.]
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|style="padding:.4em;"|17-29
|style="padding:.4em;"|JW Cho
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.celrep.2016.12.019 Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade.]
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|style="padding:.4em;" rowspan=2|2017/05/24
|style="padding:.4em;"|17-28
|style="padding:.4em;"|EB Kim
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.12.022 Systemic Immunity Is Required for Effective Cencer Immunotherapy.]
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|style="padding:.4em;"|17-27
|style="padding:.4em;"|CY Kim
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.10.020 Linking the Human Gut Microbiome to Inflammatory Cytokine Production Capacity.]
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|style="padding:.4em;" rowspan=2|2017/05/17
|style="padding:.4em;"|17-26
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.10.018 Host and Environmental Factors Influencing Individual Human Cytokine Responses.]
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|style="padding:.4em;"|17-25
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.10.017 A Functional Genomics Approach to Understand Variation in Cytokine Production in Humans.]
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|style="padding:.4em;" rowspan=2|2017/04/26
|style="padding:.4em;"|17-24
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1038%2FNMETH.4177 Pooled CRISPR screening with single-cell transcriptome readout.]
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|style="padding:.4em;"|17-23
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.11.039 Dissecting Immune Circuits by Linking CRISPR-Pooled Screens with Single-Cell RNA-Seq.]
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|style="padding:.4em;" rowspan=2|2017/04/12
|style="padding:.4em;"|17-22
|style="padding:.4em;"|SH Lee
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.11.038 Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens.]
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|style="padding:.4em;"|17-21
|style="padding:.4em;"|CY Kim
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1038%2Fnbt.3569 Wishbone identifies bifurcating developmental trajectories from single-cell data.]
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|style="padding:.4em;" rowspan=2|2017/04/05
|style="padding:.4em;"|17-20
|style="padding:.4em;"|KS Kim
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[http://biorxiv.org/content/early/2017/02/21/110668 Reversed graph embedding resolves complex single-cell developmental trajectories.]
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|style="padding:.4em;"|17-19
|style="padding:.4em;"|SH Lee
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1038%2Fnmeth.4150 Single-cell mRNA quantification and differential analysis with Census.]
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|style="padding:.4em;" rowspan=2|2017/03/29
|style="padding:.4em;"|17-18
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.celrep.2016.12.060 Single-Cell Transcriptomic Analysis Defines Heterogeneity and Transcriptional Dynamics in the Adult Neural Stem Cell Lineage.]
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|style="padding:.4em;"|17-17
|style="padding:.4em;"|DS Bae
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[https://www.ncbi.nlm.nih.gov/pubmed/26051941 Single-Cell Network Analysis Identifies DDIT3 as a Nodal Lineage Regulator in Hematopoiesis.]
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|style="padding:.4em;" rowspan=2|2017/03/22
|style="padding:.4em;"|17-16
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[https://www.ncbi.nlm.nih.gov/pubmed/27580035 Single-cell analysis of mixed-lineage states leading to a binary cell fate choice.]
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|style="padding:.4em;"|17-15
|style="padding:.4em;"|EB Kim
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[https://www.ncbi.nlm.nih.gov/pubmed/27281220 Dissecting direct reprogramming from fibroblast to neuron using single-cell RNA-seq.]
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|style="padding:.4em;" rowspan=2|2017/03/15
|style="padding:.4em;"|17-14
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1126%2Fscience.aad0501 Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq.]
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|style="padding:.4em;"|17-13
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[https://www.ncbi.nlm.nih.gov/pubmed/26084335 Single-cell mRNA sequencing identifies subclonal heterogeneity in anti-cancer drug responses of lung adenocarcinoma cells.]
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|style="padding:.4em;" rowspan=2|2017/02/28
|style="padding:.4em;"|17-12
|style="padding:.4em;"|KS Kim
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[https://www.ncbi.nlm.nih.gov/pubmed/27824113 A Comprehensive Characterization of the Function of LincRNAs in Transcriptional Regulation Through Long-Range Chromatin Interactions.]
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|style="padding:.4em;"|17-11
|style="padding:.4em;"|JE Shim
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[//pubmed.gov/27851969 Epigenomic Deconvolution of Breast Tumors Reveals Metabolic Coupling between Constituent Cell Types.]
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|style="padding:.4em;" rowspan=2|2017/02/21
|style="padding:.4em;"|17-10
|style="padding:.4em;"|EB Kim
|style="padding:.4em;text-align:left"|
[http://biorxiv.org/content/early/2016/12/30/097451 Convergence of dispersed regulatory mutations predicts driver genes in prostate cancer.]
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|style="padding:.4em;"|17-09
|style="padding:.4em;"|EB Kim
|style="padding:.4em;text-align:left"|
[//pubmed.gov/27723759 Chromatin structure-based prediction of recurrent noncoding mutations in cancer.]
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|style="padding:.4em;" rowspan=2|2017/02/07
|style="padding:.4em;"|17-08
|style="padding:.4em;"|DS Bae
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[http://biorxiv.org/content/early/2016/11/17/088286 Somatic Mutations and Neoepitope Homology in Melanomas Treated with CTLA-4 Blockade.]
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|style="padding:.4em;"|17-07
|style="padding:.4em;"|MY Lee
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[http://biorxiv.org/content/early/2016/11/28/090134 Chemotherapy weakly contributes to predicted neoantigen expression in ovarian cancer.]
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|style="padding:.4em;" rowspan=1|2017/01/31
|style="padding:.4em;"|17-06
|style="padding:.4em;"|CY Kim
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[//www.ncbi.nlm.nih.gov/pubmed/27912059 Microbiota Diurnal Rhythmicity Programs Host Transcriptome Oscillations.]
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|style="padding:.4em;" rowspan=1|2017/01/24
|style="padding:.4em;"|17-05
|style="padding:.4em;"|JW Cho
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[//www.ncbi.nlm.nih.gov/pubmed/27851914 Transcriptional Landscape of Human Tissue Lymphocytes Unveils Uniqueness of Tumor-Infiltrating T Regulatory Cells.]
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|style="padding:.4em;" rowspan=2|2017/01/17
|style="padding:.4em;"|17-04
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[//www.ncbi.nlm.nih.gov/pubmed/25938943 Mapping long-range promoter contacts in human cells with high-resolution capture Hi-C]
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|style="padding:.4em;"|17-03
|style="padding:.4em;"|EB Kim
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[//www.ncbi.nlm.nih.gov/pubmed/27306882 CHiCAGO: robust detection of DNA looping interactions in Capture Hi-C data.]
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|style="padding:.4em;" rowspan=1|2017/01/10
|style="padding:.4em;"|17-02
|style="padding:.4em;"|JE Shim
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[//www.ncbi.nlm.nih.gov/pubmed/26527291 ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis]
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|style="padding:.4em;" rowspan=1|2017/01/03
|style="padding:.4em;"|17-01
|style="padding:.4em;"|SH Lee
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[//www.ncbi.nlm.nih.gov/pubmed/26287467 Single-cell messenger RNA sequencing reveals rare intestinal cell types]
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{|class=wikitable style="text-align:center;"
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|style="padding:.4em;" rowspan=1|2016/12/27
|style="padding:.4em;"|2016-31
|style="padding:.4em;"|EB Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/25664528 Decoding the regulatory network of early blood development from single-cell gene expression measurements.]
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|style="padding:.4em;" rowspan=1|2016/12/6
|style="padding:.4em;"|2016-30
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26299571 Single-Cell RNA-Seq with Waterfall Reveals Molecular Cascades underlying Adult Neurogenesis.]
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|style="padding:.4em;" rowspan=1|2016/11/29
|style="padding:.4em;"|2016-29
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/24658644 The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells.]
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|style="padding:.4em;" rowspan=1|2016/11/22
|style="padding:.4em;"|2016-28
|style="padding:.4em;"|CY Kim
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[http://www.ncbi.nlm.nih.gov/pubmed/26887813 Classification of low quality cells from single-cell RNA-seq data.]
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|style="padding:.4em;" rowspan=1|2016/11/15
|style="padding:.4em;"|2016-27
|style="padding:.4em;"|MY Lee
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26000487 Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells.]
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|style="padding:.4em;" rowspan=1|2016/11/8
|style="padding:.4em;"|2016-26
|style="padding:.4em;"|JW Cho
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[http://www.ncbi.nlm.nih.gov/pubmed/26000488 Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets.]
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|style="padding:.4em;" rowspan=1|2016/11/1
|style="padding:.4em;"|2016-25
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27426982 Causal Mechanistic Regulatory Network for Glioblastoma Deciphered Using Systems Genetics Network Analysis.]
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|style="padding:.4em;" rowspan=1|2016/10/25
|style="padding:.4em;"|2016-24
|style="padding:.4em;"|MY Lee
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27549193 Comprehensive analyses of tumor immunity: implications for cancer immunotherapy.]
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|style="padding:.4em;" rowspan=1|2016/10/11
|style="padding:.4em;"|2016-23
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=sidespread+parainflammation Widespread parainflammation in human cancer]
|-
|style="padding:.4em;" rowspan=1|2016/9/27
|style="padding:.4em;"|2016-22
|style="padding:.4em;"|ER Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27535533 Analysis of protein-coding genetic variation in 60,706 humans]
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|style="padding:.4em;" rowspan=1|2016/9/20
|style="padding:.4em;"|2016-21
|style="padding:.4em;"|SM Yang
|style="padding:.4em;text-align:left"|
Functional characterization of somatic mutations in cancer using network-based inference of protein activity
[http://www.ncbi.nlm.nih.gov/pubmed/27322546 pubmed]
[http://www.nature.com/ng/journal/v48/n8/full/ng.3593.html fulltext]
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|style="padding:.4em;" rowspan=1|2016/9/13
|style="padding:.4em;"|2016-20
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=exploiting+single-cell+expression+to+characterize Exploiting single-cell expression to characterize co-expression replicability.]
|-
|style="padding:.4em;" rowspan=1|2016/9/6
|style="padding:.4em;"|2016-19
|style="padding:.4em;"|T Lee
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26940869 Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade]
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|style="padding:.4em;" rowspan=1|2016/8/31
|style="padding:.4em;"|2016-18
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27264179 A Computational Drug Repositioning Approach for Targeting Oncogenic Transcription Factors]
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|style="padding:.4em;" rowspan=1|2016/8/16
|style="padding:.4em;"|2016-17
|style="padding:.4em;"|JW Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27309802 The landscape of accessible chromatin in mammalian preimplantation embryos]
|-
|style="padding:.4em;" rowspan=1|2016/8/8
|style="padding:.4em;"|2016-16
|style="padding:.4em;"|EB Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27064255 Enhancer-promoter interactions are encoded by complex genomic signatures on looping chromatin]
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|style="padding:.4em;" rowspan=1|2016/8/1
|style="padding:.4em;"|2016-15
|style="padding:.4em;"|MY Lee
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27040498 Personalized Immunomonitoring Uncovers Molecular Networks that Stratify Lupus Patients]
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|style="padding:.4em;" rowspan=1|2016/7/25
|style="padding:.4em;"|2016-14
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=integration+of+summary+data+from+gwas+and+eqtl+studies Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets]
|-
|style="padding:.4em;" rowspan=1|2016/7/18
|style="padding:.4em;"|2016-13
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/23624555 Identification of transcriptional regulators in the mouse immune system]
|-
|style="padding:.4em;" rowspan=2|2016/6/8
|style="padding:.4em;"|2016-12
|style="padding:.4em;"|DS Bae, CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26619012 Mapping the effects of drugs on the immune system]
|-
|style="padding:.4em;"| 2016-11
|style="padding:.4em;"|DS Bae, CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26186195 Elucidating compound mechanism of action by network perturbation analysis]
|-
|style="padding:.4em;" rowspan=2|2016/6/1
|style="padding:.4em;"|2016-10
|style="padding:.4em;"|MY Lee,SM Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26854917 Integrative approaches for large-scale transcriptome-wide association studies]
|-
|style="padding:.4em;"| 2016-9
|style="padding:.4em;"|MY Lee,SM Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26950747 Tissue-specific regulatory circuits reveal variable modular perturbations across complex diseases]
|-
|style="padding:.4em;" rowspan=2|2016/5/18
|style="padding:.4em;"|2016-8
|style="padding:.4em;"|CY Kim,SJ Kwon
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27013732 Survey of variation in human transcription factors reveals prevalent DNA binding changes]
|-
|style="padding:.4em;"| 2016-7
|style="padding:.4em;"| CY Kim,SJ Kwon
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26502339 Large-scale identification of sequence variants influencing human transcription factor occupancy in vivo]
|-
|style="padding:.4em;" rowspan=2|2016/5/11
|style="padding:.4em;"|2016-6
|style="padding:.4em;"|DS Bae, CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/25171417 Predicting Cancer-specific vulnerability via data-driven detection of synthetic lethality]
|-
|style="padding:.4em;"| 2016-5
|style="padding:.4em;"|DS Bae, CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/23467089 Dynamic regulatory network controlling Th17 cell differentiation]
|-
|style="padding:.4em;" rowspan=2|2016/5/4
|style="padding:.4em;"|2016-4
|style="padding:.4em;"|MY Lee,SM Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/25853550 Characterization of the immunophenotypes and antigenomes of colorectal cancers reveals distinct tumor escape mechanisms and novel targets for immunotherapy]
|-
|style="padding:.4em;"| 2016-3
|style="padding:.4em;"|MY Lee,SM Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26618344 Regulators of genetic risk of breast cancer identified by integrative network analysis]
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|style="padding:.4em;" rowspan=2|2016/4/27
|style="padding:.4em;"|2016-2
|style="padding:.4em;"|CY Kim,SJ Kwon
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26780608 A predictive computational framework for direct reprogramming between human cell types]
|-
|style="padding:.4em;"| 2016-1
|style="padding:.4em;"| CY Kim,SJ Kwon
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/25126793 CellNet: Network biology applied to stem cell engineering]
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{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2015
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!scope="col" style="padding:.4em" | Date
!scope="col" style="padding:.4em" | Paper index
!scope="col" style="padding:.4em" | Presenter
!scope="col" style="padding:.4em" | Paper title
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|style="padding:.4em;" rowspan=2|2015/06/11
|style="padding:.4em;"|2015-55
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://genome.cshlp.org/content/24/2/340.long Improved exome prioritization of disease genes through cross-species phenotype comparison.]
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|style="padding:.4em;"|2015-54
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|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0002929714001128 Phevor combines multiple biomedical ontologies for accurate identification of disease-causing alleles in single individuals and small nuclear families.]
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|style="padding:.4em;" rowspan=2|2015/06/04
|style="padding:.4em;"|2015-53
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nmeth/journal/v10/n11/full/nmeth.2656.html eXtasy: variant prioritization by genomic data fusion.]
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|style="padding:.4em;"|2015-52
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://genome.cshlp.org/content/21/9/1529.long A probabilistic disease-gene finder for personal genomes.]
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|style="padding:.4em;" rowspan=2|2015/05/28
|style="padding:.4em;"|2015-51
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/ng/journal/v46/n12/full/ng.3141.html Systematic analysis of noncoding somatic mutations and gene expression alterations across 14 tumor types.]
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|style="padding:.4em;"|2015-50
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nbt/journal/v28/n5/full/nbt.1630.html GREAT improves functional interpretation of cis-regulatory regions.]
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|style="padding:.4em;" rowspan=2|2015/05/21
|style="padding:.4em;"|2015-49
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nmeth/journal/v11/n3/full/nmeth.2832.html Functional annotation of noncoding sequence variants.]
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|style="padding:.4em;"|2015-48
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://genomebiology.com/content/15/10/480 FunSeq2: A framework for prioritizing noncoding regulatory variants in cancer.]
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|style="padding:.4em;" rowspan=2|2015/05/14
|style="padding:.4em;"|2015-47
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nmeth/journal/v12/n2/full/nmeth.3215.html Selecting causal genes from genome-wide association studies via functionally coherent subnetworks.]
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|style="padding:.4em;"|2015-46
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/ncomms/2015/150119/ncomms6890/full/ncomms6890.html Biological interpretation of genome-wide association studies using predicted gene functions.]
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|style="padding:.4em;" rowspan=3|2015/05/07
|style="padding:.4em;"|2015-45
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/ncomms/2014/140626/ncomms5212/full/ncomms5212.html Human symptoms-disease network.]
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|style="padding:.4em;"|2015-44
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867413010246 A nondegenerate code of deleterious variants in Mendelian loci contributes to complex disease risk.]
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|style="padding:.4em;"|2015-43
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.sciencemag.org/content/347/6224/1257601.long Uncovering disease-disease relationships through the incomplete interactome.]

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|style="padding:.4em;" rowspan=2|2015/04/30
|style="padding:.4em;"|2015-42
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://genome.cshlp.org/content/25/1/142.long The discovery of integrated gene networks for autism and related disorders.]
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|style="padding:.4em;"|2015-41
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://msb.embopress.org/content/10/12/774.long Integrated systems analysis reveals a molecular network underlying autism spectrum disorders.]
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|style="padding:.4em;" rowspan=3|2015/04/23
|style="padding:.4em;"|2015-40
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v518/n7539/full/nature13990.html Dissecting neural differentiation regulatory networks through epigenetic footprinting.]
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|style="padding:.4em;"|2015-39
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v518/n7539/full/nature14221.html Cell-of-origin chromatin organization shapes the mutational landscape of cancer.]
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|style="padding:.4em;"|2015-38
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v518/n7539/full/nature14248.html Integrative analysis of 111 reference human epigenomes.]
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|style="padding:.4em;" rowspan=2|2015/04/09
|style="padding:.4em;"|2015-37
|style="padding:.4em;"|
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[http://genome.cshlp.org/content/25/2/246.long Genome-wide analysis of local chromatin packing in Arabidopsis thaliana.]
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|style="padding:.4em;"|2015-36
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414014974 A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping.]
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|style="padding:.4em;" rowspan=2|2015/04/02
|style="padding:.4em;"|2015-35
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nrg/journal/v14/n6/full/nrg3454.html Exploring the three-dimensional organization of genomes: interpreting chromatin interaction data.]
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|style="padding:.4em;"|2015-34
|style="padding:.4em;"|
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[http://www.sciencemag.org/content/347/6225/1010.long Transcribed enhancers lead waves of coordinated transcription in transitioning mammalian cells.]
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|style="padding:.4em;" rowspan=2|2015/03/26
|style="padding:.4em;"|2015-33
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://genome.cshlp.org/content/25/2/257.long Cupid: simultaneous reconstruction of microRNA-target and ceRNA networks.]
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|style="padding:.4em;"|2015-32
|style="padding:.4em;"|
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[http://genome.cshlp.org/content/25/1/41.long Characterization of the neural stem cell gene regulatory network identifies OLIG2 as a multifunctional regulator of self-renewal.]
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|style="padding:.4em;" rowspan=2|2015/03/19
|style="padding:.4em;"|2015-31
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nbt/journal/vaop/ncurrent/full/nbt.3154.html Decoding the regulatory network of early blood development from single-cell gene expression measurements.]
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|style="padding:.4em;"|2015-30
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nrg/journal/v16/n3/full/nrg3833.html Computational and analytical challenges in single-cell transcriptomics.]
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|style="padding:.4em;" rowspan=3|2015/03/12
|style="padding:.4em;"|2015-29
|style="padding:.4em;"|
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[http://www.sciencedirect.com/science/article/pii/S0092867415000136 Extensive Strain-Level Copy-Number Variation across Human Gut Microbiome Species.]
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|style="padding:.4em;"|2015-28
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v500/n7464/full/nature12506.html Richness of human gut microbiome correlates with metabolic markers.]
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|style="padding:.4em;"|2015-27
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v490/n7418/full/nature11450.html A metagenome-wide association study of gut microbiota in type 2 diabetes.]
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|style="padding:.4em;" rowspan=3|2015/03/09
|style="padding:.4em;"|2015-26
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003326 Practical guidelines for the comprehensive analysis of ChIP-seq data.]
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|style="padding:.4em;"|2015-25
|style="padding:.4em;"|T Lee
|style="padding:.4em;text-align:left"|
[http://genome.cshlp.org/content/23/5/777.long Patterns of regulatory activity across diverse human cell types predict tissue identity, transcription factor binding, and long-range interactions.]
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|style="padding:.4em;"|2015-24
|style="padding:.4em;"|ER Kim
|style="padding:.4em;text-align:left"|
[http://msb.embopress.org/content/10/11/760.long Rapid neurogenesis through transcriptional activation in human stem cells.]
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|style="padding:.4em;" rowspan=3|2015/03/02
|style="padding:.4em;"|2015-23
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414011787 Genome-Scale CRISPR-Mediated Control of Gene Repression and Activation.]
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|style="padding:.4em;"|2015-22
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1004237 Integrating multiple genomic data to predict disease-causing nonsynonymous single nucleotide variants in exome sequencing studies.]
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|style="padding:.4em;"|2015-21
|style="padding:.4em;"|ER Kim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/ng/journal/v45/n6/full/ng.2653.html The Genotype-Tissue Expression (GTEx) project.]
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|style="padding:.4em;" rowspan=3|2015/02/24
|style="padding:.4em;"|2015-20
|style="padding:.4em;"|ER Kim
|style="padding:.4em;text-align:left"|
[http://www.sciencemag.org/content/346/6212/1007.long Mouse regulatory DNA landscapes reveal global principles of cis-regulatory evolution.]
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|style="padding:.4em;"|2015-19
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v515/n7527/full/nature13985.html Principles of regulatory information conservation between mouse and human.]
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|style="padding:.4em;"|2015-18
|style="padding:.4em;"|BH Kang
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v515/n7527/full/nature13972.html Conservation of trans-acting circuitry during mammalian regulatory evolution.]
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|style="padding:.4em;" rowspan=3|2015/02/16
|style="padding:.4em;"|2015-17
|style="padding:.4em;"|T Lee
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature13835.html Genetic and epigenetic fine mapping of causal autoimmune disease variants.]
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|style="padding:.4em;"|2015-16
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/ng/journal/v46/n3/full/ng.2892.html A general framework for estimating the relative pathogenicity of human genetic variants.]
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|style="padding:.4em;"|2015-15
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003825 A probabilistic model to predict clinical phenotypic traits from genome sequencing.]
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|style="padding:.4em;" rowspan=4|2015/02/02
|style="padding:.4em;"|2015-14
|style="padding:.4em;"|BH Kang
|style="padding:.4em;text-align:left"|
[http://www.pnas.org/content/111/22/E2329.long Relating the metatranscriptome and metagenome of the human gut.]
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|style="padding:.4em;"|2015-13
|style="padding:.4em;"|BH Kang
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v513/n7516/full/nature13568.html Alterations of the human gut microbiome in liver cirrhosis.]
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|style="padding:.4em;"|2015-12
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nbt/journal/v32/n8/full/nbt.2942.html An integrated catalog of reference genes in the human gut microbiome.]
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|style="padding:.4em;"|2015-11
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nbt/journal/v32/n8/full/nbt.2939.html Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes.]
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|style="padding:.4em;" rowspan=5|2015/01/26
|style="padding:.4em;"|2015-10
|style="padding:.4em;"|HH Kim
|style="padding:.4em;text-align:left"|
[http://msb.embopress.org/content/9/1/666.long Computational meta'omics for microbial community studies.]
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|style="padding:.4em;"|2015-09
|style="padding:.4em;"|HH Kim
|style="padding:.4em;text-align:left"|
[http://genomemedicine.com/content/5/7/65 Functional profiling of the gut microbiome in disease-associated inflammation.]
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|style="padding:.4em;"|2015-08
|style="padding:.4em;"|BH Kang
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S016895251200145X Biodiversity and functional genomics in the human microbiome.]
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|style="padding:.4em;"|2015-07
|style="padding:.4em;"|BH Kang
|style="padding:.4em;text-align:left"|
[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002808 Chapter 12: Human Microbiome Analysis.]
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|style="padding:.4em;"|2015-06
|style="padding:.4em;"|BH Kang
|style="padding:.4em;text-align:left"|
[http://www.cell.com/cell/abstract/S0092-8674%2814%2900864-2 Conducting a Microbiome Study.]
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|style="padding:.4em;" rowspan=3|2015/01/12
|style="padding:.4em;"|2015-05
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/ncomms/2014/141210/ncomms6522/full/ncomms6522.html Small RNA changes en route to distinct cellular states of induced pluripotency.]
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|style="padding:.4em;"|2015-04
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v516/n7530/full/nature14046.html Genome-wide characterization of the routes to pluripotency.]
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|style="padding:.4em;"|2015-03
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v516/n7530/full/nature14047.html Divergent reprogramming routes lead to alternative stem-cell states.]
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|style="padding:.4em;" rowspan=2|2015/01/05
|style="padding:.4em;"|2015-02
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.pnas.org/content/111/21/E2191.long Global view of enhancer-promoter interactome in human cells.]
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|style="padding:.4em;"|2015-01
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://nar.oxfordjournals.org/content/41/22/10391.long Combining Hi-C data with phylogenetic correlation to predict the target genes of distal regulatory elements in human genome.]
|}
{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2014
|-
!scope="col" style="padding:.4em" | Date
!scope="col" style="padding:.4em" | Paper index
!scope="col" style="padding:.4em" | Presenter
!scope="col" style="padding:.4em" | Paper title
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|style="padding:.4em;" rowspan=2|2014/12/23
|style="padding:.4em;"|2014-41
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867413012270 Super-enhancers in the control of cell identity and disease.]
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|style="padding:.4em;"|2014-40
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867413003929 Master transcription factors and mediator establish super-enhancers at key cell identity genes.]
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|style="padding:.4em;" rowspan=4|2014/12/09
|style="padding:.4em;"|2014-39
|style="padding:.4em;"|HH Kim
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414013713 Unraveling the biology of a fungal meningitis pathogen using chemical genetics.]
|-
|style="padding:.4em;"|2014-38
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414014226 A proteome-scale map of the human interactome network.]
|-
|style="padding:.4em;"|2014-37
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[http://msb.embopress.org/content/10/9/752.long The role of the interactome in the maintenance of deleterious variability in human populations.]
|-
|style="padding:.4em;"|2014-36
|style="padding:.4em;"|HS Shim
|style="padding:.4em;text-align:left"|
[http://www.sciencemag.org/content/342/6154/1235587.long Integrative annotation of variants from 1092 humans: application to cancer genomics.]
|-
|style="padding:.4em;" rowspan=2|2014/12/02
|style="padding:.4em;"|2014-35
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://genome.cshlp.org/content/23/8/1319.long Mapping functional transcription factor networks from gene expression data.]
|-
|style="padding:.4em;"|2014-34
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nrg/journal/v15/n7/full/nrg3684.html In pursuit of design principles of regulatory sequences.]
|-
|style="padding:.4em;" rowspan=2|2014/11/25
|style="padding:.4em;"|2014-33
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[http://genomemedicine.com/content/3/6/36 Epigenomics of human embryonic stem cells and induced pluripotent stem cells: insights into pluripotency and implications for disease.]
|-
|style="padding:.4em;"|2014-32
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S009286741300891X Developmental fate and cellular maturity encoded in human regulatory DNA landscapes.]
|-
|style="padding:.4em;" rowspan=2|2014/11/18
|style="padding:.4em;"|2014-31
|style="padding:.4em;"|SM Yang
|style="padding:.4em;text-align:left"|
[http://genomemedicine.com/content/6/8/64 The 'dnet' approach promotes emerging research on cancer patient survival.]
|-
|style="padding:.4em;"|2014-30
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414010368 Determination and inference of eukaryotic transcription factor sequence specificity.]
|-
|style="padding:.4em;" rowspan=3|2014/11/11
|style="padding:.4em;"|2014-29
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[http://www.pnas.org/content/110/16/6412.long Transcription factors interfering with dedifferentiation induce cell type-specific transcriptional profiles.]
|-
|style="padding:.4em;"|2014-28
|style="padding:.4em;"|HH Kim
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414009350 Dissecting engineered cell types and enhancing cell fate conversion via CellNet.]
|-
|style="padding:.4em;"|2014-27
|style="padding:.4em;"|HH Kim
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414009349 CellNet: network biology applied to stem cell engineering.]
|-
|style="padding:.4em;" rowspan=2|2014/11/04
|style="padding:.4em;"|2014-26
|style="padding:.4em;"|HS Shim
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414009775 Predicting Cancer-Specific Vulnerability via Data-Driven Detection of Synthetic Lethality.]
|-
|style="padding:.4em;"|2014-25
|style="padding:.4em;"|JH Shin
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nmeth/journal/v11/n9/full/nmeth.3046.html Phen-Gen: combining phenotype and genotype to analyze rare disorders.]
|-
|style="padding:.4em;" rowspan=2|2014/10/28
|style="padding:.4em;"|2014-24
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nbt/journal/vaop/ncurrent/full/nbt.2877.html A community effort to assess and improve drug sensitivity prediction algorithms.]
|-
|style="padding:.4em;"|2014-23
|style="padding:.4em;"|HS Shim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/ng/journal/v46/n9/full/ng.3051.html Multi-tiered genomic analysis of head and neck cancer ties TP53 mutation to 3p loss.]
|-
|style="padding:.4em;" rowspan=2|2014/09/30
|style="padding:.4em;"|2014-22
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nmeth/journal/v10/n11/full/nmeth.2651.html Network-based stratification of tumor mutations.]
|-
|style="padding:.4em;"|2014-21
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414001457 Synonymous mutations frequently act as driver mutations in human cancers.]
|-
|style="padding:.4em;" rowspan=2|2014/09/23
|style="padding:.4em;"|2014-20
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003460 VarWalker: Personalized Mutation Network Analysis of Putative Cancer Genes from Next-Generation Sequencing Data.]
|-
|style="padding:.4em;"|2014-19
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://genomebiology.com/content/13/12/R124 DriverNet: uncovering the impact of somatic driver mutations on transcriptional networks in cancer.]
|-
|style="padding:.4em;" rowspan=2|2014/09/16
|style="padding:.4em;"|2014-18
|style="padding:.4em;"|JH Shin
|style="padding:.4em;text-align:left"|
[http://genomebiology.com/content/14/5/R52 Integrated analysis of recurrent properties of cancer genes to identify novel drivers.]
|-
|style="padding:.4em;"|2014-17
|style="padding:.4em;"|AR Cho
|style="padding:.4em;text-align:left"|
[http://genomemedicine.com/content/4/11/89 Improving the prediction of the functional impact of cancer mutations by baseline tolerance transformation.]
|-
|style="padding:.4em;" rowspan=2|2014/09/02
|style="padding:.4em;"|2014-16
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=23228031 A network module-based method for identifying cancer prognostic signatures.]
|-
|style="padding:.4em;"|2014-15
|style="padding:.4em;"|AR Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=24429628 Realizing the promise of cancer predisposition genes.]
|-
|style="padding:.4em;" rowspan=2|2014/08/19
|style="padding:.4em;"|2014-14
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=24952901 Assessing the clinical utility of cancer genomic and proteomic data across tumor types.]
|-
|style="padding:.4em;"|2014-13
|style="padding:.4em;"|HS Shim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=24132290 Mutational landscape and significance across 12 major cancer types.]
|-
|style="padding:.4em;" rowspan=2|2014/08/12
|style="padding:.4em;"|2014-12
|style="padding:.4em;"|HS Shim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/23945592 Signatures of mutational processes in human cancer.]
|-
|style="padding:.4em;"|2014-11
|style="padding:.4em;"|HJ Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/24390350 Discovery and saturation analysis of cancer genes across 21 tumour types.]
|-
|style="padding:.4em;" rowspan=2|2014/08/05
|style="padding:.4em;"|2014-10
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=24084849 Comprehensive identification of mutational cancer driver genes across 12 tumor types.]
|-
|style="padding:.4em;"|2014-9
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=24037244 IntOGen-mutations identifies cancer drivers across tumor types.]
|-
|style="padding:.4em;" rowspan=3|2014/07/29
|style="padding:.4em;"|2014-8
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/23900255 Computational approaches to identify functional genetic variants in cancer genomes.]
|-
|style="padding:.4em;"|2014-7
|style="padding:.4em;"|AR Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=23770567 Mutational heterogeneity in cancer and the search for new cancer-associated genes.]
|-
|style="padding:.4em;"|2014-6
|style="padding:.4em;"|AR Cho
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nmeth/journal/v11/n4/abs/nmeth.2891.html Cancer genomes: discerning drivers from passengers]
|-
|style="padding:.4em;" rowspan=3|2014/07/22
|style="padding:.4em;"|2014-5
|style="padding:.4em;"|AR Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/24071849 The Cancer Genome Atlas Pan-Cancer analysis project.]
|-
|style="padding:.4em;"|2014-4
|style="padding:.4em;"|AR Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/23539594 Cancer genome landscapes.]
|-
|style="padding:.4em;"|2014-3
|style="padding:.4em;"|AR Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=21900272 Distinguishing driver and passenger mutations in an evolutionary history categorized by interference.]
|-
|style="padding:.4em;" rowspan=2|2014/07/15
|style="padding:.4em;"|2014-2
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=24670764 A promoter-level mammalian expression atlas.]
|-
|style="padding:.4em;"|2014-1
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=24670763 An atlas of active enhancers across human cell types and tissues.]
|}
{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2013
|-
!scope="col" style="padding:.4em" | Date
!scope="col" style="padding:.4em" | Paper index
!scope="col" style="padding:.4em" | Presenter
!scope="col" style="padding:.4em" | Paper title
|-
|style="padding:.4em;" rowspan=2|2013/06/26
|style="padding:.4em;"|2013-31
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|[http://nar.oxfordjournals.org/content/40/16/7690.long Integration of Hi-C and ChIP-seq data reveals distinct types of chromatin linkages]
|-
|style="padding:.4em;"|2013-30
|style="padding:.4em;"|YH Ko
|style="padding:.4em;text-align:left"|[http://www.nature.com/nbt/journal/v31/n4/full/nbt.2519.html Deciphering molecular circuits from genetic variation underlying transcriptional responsiveness to stimuli]
|-
|style="padding:.4em;" rowspan=2|2013/06/04
|style="padding:.4em;"|2013-29
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|[https://www.cell.com/abstract/S0092-8674(13)00439-X Mapping the Human miRNA Interactome by CLASH Reveals Frequent Noncanonical Binding]
|-
|style="padding:.4em;"|2013-28
|style="padding:.4em;"|ER Kim
|style="padding:.4em;text-align:left"|[http://www.nature.com/nbt/journal/v31/n3/full/nbt.2514.html Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples]
|-
|style="padding:.4em;" rowspan=2|2013/05/28
|style="padding:.4em;"|2013-27
|style="padding:.4em;"|YH Ko
|style="padding:.4em;text-align:left"|[http://www.pnas.org/content/102/38/13544.long Discovering statistically significant pathways in expression profiling studies]
|-
|style="padding:.4em;"|2013-26
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|[http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0058977 Prediction and Validation of Gene-Disease Associations Using Methods Inspired by Social Network Analyses]
|-
|style="padding:.4em;" rowspan=2|2013/05/21
|style="padding:.4em;"|2013-25
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|[http://www.nature.com/nature/journal/v496/n7446/full/nature11981.html Dynamic regulatory network controlling TH17 cell differentiation]
|-
|style="padding:.4em;"|2013-24
|style="padding:.4em;"|T Lee
|style="padding:.4em;text-align:left"|[http://www.sciencedirect.com/science/article/pii/S0092867412013529 Deciphering and Prediction of Transcriptome Dynamics under Fluctuating Field Conditions]

|-
|style="padding:.4em;" rowspan=2|2013/05/14
|style="padding:.4em;"|2013-23
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|[http://www.sciencedirect.com/science/article/pii/S0092867412015565 Integrative eQTL-Based Analyses Reveal the Biology of Breast Cancer Risk Loci]

|-
|style="padding:.4em;"|2013-22
|style="padding:.4em;"|HJ Kim
|style="padding:.4em;text-align:left"|[http://www.nature.com/ng/journal/v45/n2/full/ng.2504.html Chromatin marks identify critical cell types for fine mapping complex trait variants]
|-
|style="padding:.4em;" rowspan=2|'''2013/05/07'''
|style="padding:.4em;"|2013-21
|style="padding:.4em;"|ER Kim
|style="padding:.4em;text-align:left"|[http://www.cell.com/abstract/S0092-8674(12)01555-3 Genome-wide Chromatin State Transitions Associated with Developmental and Environmental Cues]
|-
|style="padding:.4em;"|2013-20
|style="padding:.4em;"|HS Shim
|style="padding:.4em;text-align:left"|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1003201 Human Disease-Associated Genetic Variation Impacts Large Intergenic Non-Coding RNA Expression]
|-
|style="padding:.4em;"|2013/04/09
|style="padding:.4em;"|2013-19
|style="padding:.4em;"|HJ Kim
|style="padding:.4em;text-align:left"|[http://genome.cshlp.org/content/22/9/1790.long Annotation of functional variation in personal genomes using RegulomeDB]
|-
|style="padding:.4em;"|2013/04/02
|style="padding:.4em;"|2013-18
|style="padding:.4em;"|HJ Kim
|style="padding:.4em;text-align:left"|[http://genome.cshlp.org/content/22/9/1775.long The GENCODE v7 catalog of human long noncoding RNAs: Analysis of their gene structure, evolution, and expression]
|-
|style="padding:.4em;" |2013/03/12
|style="padding:.4em;"|2013-17
|style="padding:.4em;"| ER Kim
|style="padding:.4em;text-align:left"|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002311 Global Mapping of Cell Type–Specific Open Chromatin by FAIRE-seq Reveals the Regulatory Role of the NFI Family in Adipocyte Differentiation]
|-
|style="padding:.4em;" rowspan=1|2013/03/05
|style="padding:.4em;"|2013-16
|style="padding:.4em;"| ER Kim
|style="padding:.4em;text-align:left"|[http://www.nature.com/nrg/journal/v10/n10/full/nrg2641.html ChIP–seq: advantages and challenges of a maturing technology]
|-
|style="padding:.4em;" rowspan=3|2013/02/21
|style="padding:.4em;"|2013-15
|style="padding:.4em;"| KS Kim
|style="padding:.4em;text-align:left"|[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002830 Novel Modeling of Combinatorial miRNA Targeting Identifies SNP with Potential Role in Bone Density]
|-
|style="padding:.4em;"|2013-14
|style="padding:.4em;"| KS Kim
|style="padding:.4em;text-align:left"|[http://www.cell.com/abstract/S0092-8674(12)01424-9 A Molecular Roadmap of Reprogramming Somatic Cells into iPS Cells]
|-
|style="padding:.4em;"|2013-13
|style="padding:.4em;"| HJ Han
|style="padding:.4em;text-align:left"|[http://genome.cshlp.org/content/22/6/1015.long Differential DNase I hypersensitivity reveals factor-dependent chromatin dynamics.]
|-
|style="padding:.4em;" rowspan=3|2013/02/15
|style="padding:.4em;"|2013-12
|style="padding:.4em;"| HJ Han
|style="padding:.4em;text-align:left"|[http://www.nature.com/ng/journal/v43/n3/full/ng.759.html Chromatin accessibility pre-determines glucocorticoid receptor binding patterns]
|-
|style="padding:.4em;"|2013-11
|style="padding:.4em;"| JE Shim, CY Kim
|style="padding:.4em;text-align:left"|[http://www.nature.com/nbt/journal/v30/n11/full/nbt.2422.html Interpreting noncoding genetic variation in complex traits and human disease]
|-
|style="padding:.4em;"|2013-10
|style="padding:.4em;"|HJ Han, JH Kim
|style="padding:.4em;text-align:left"|[http://genome.cshlp.org/content/21/3/447.full.pdf+html Accurate inference of transcription factor binding from DNA sequence and chromatin accessibility data]
|-
|style="padding:.4em;" rowspan=3|2013/02/08
|style="padding:.4em;"|2013-09
|style="padding:.4em;"|HJ Kim
|style="padding:.4em;text-align:left"|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002599 Widespread Site-Dependent Buffering of Human Regulatory Polymorphism]
|-
|style="padding:.4em;"|2013-08
|style="padding:.4em;"|JE Shim, CY Kim
|style="padding:.4em;text-align:left;"|[http://genome.cshlp.org/content/22/9/1748.long Linking disease associations with regulatory information in the human genome]
|-
|style="padding:.4em;"|2013-07
|style="padding:.4em;"|HJ Han, JH Kim
|style="padding:.4em;text-align:left"|[http://genome.cshlp.org/content/22/9/1658.long Understanding transcriptional regulation by integrative analysis of transcription factor binding data]
|-
|style="padding:.4em;" rowspan=3|2013/01/25
|style="padding:.4em;"|2013-06
|style="padding:.4em;"|HJ Han, '''JH Kim'''
|style="padding:.4em;text-align:left;"|[http://www.nature.com/nature/journal/v489/n7414/full/nature11279.html The long-range interaction landscape of gene promoters]
|-
|style="padding:.4em;"|2013-05
|style="padding:.4em;"|'''ER Kim''', HS Shim
|style="padding:.4em;text-align:left;"|[http://www.nature.com/nature/journal/v489/n7414/full/nature11233.html Landscape of transcription in human cells]
|-
|style="padding:.4em;"|2013-04
|style="padding:.4em;"|'''HJ Han''', JH Kim
|style="padding:.4em;text-align:left;"|[http://www.nature.com/nature/journal/v489/n7414/full/nature11245.html Architecture of the human regulatory network derived from ENCODE data]

|-
|style="padding:.4em;" rowspan=2|2013/01/18
|style="padding:.4em;"|2013-03
|style="padding:.4em;"|KS Kim, '''TH Kim'''
|style="padding:.4em;text-align:left;"|[http://www.nature.com/nature/journal/v489/n7414/full/nature11212.html An expansive human regulatory lexicon encoded in transcription factor footprints]
|-
|style="padding:.4em;"|2013-02
|style="padding:.4em;"|'''HJ Han''', JH Kim
|style="padding:.4em;text-align:left;"|[http://www.nature.com/nature/journal/v489/n7414/full/nature11232.html The accessible chromatin landscape of the human genome]
|-
|style="padding:.4em;" rowspan=1|2013/01/11
|style="padding:.4em;"|2013-01
|style="padding:.4em;"| '''JE Shim''', CY Kim
|style="padding:.4em;text-align:left"|[http://www.nature.com/nature/journal/v489/n7414/full/nature11247.html An integrated encyclopedia of DNA elements in the human genome]
|}

2012
{|border ="1"
|style="color:black; background-color:#dcdcdc; padding:5px;" align="center" |Date
|style="color:black; background-color:#dcdcdc;" align="center"|Paper index
|style="color:black; background-color:#dcdcdc;" align="center"|Paper title
|-
|rowspan = "1"|2013/01/11
|align ="center"|2012-81
|[http://genome.cshlp.org/content/22/8/1589.full (TH Kim) MuSiC: identifying mutational significance in cancer genomes.]
|-
|rowspan = "1"|2012/12/04
|align ="center"|2012-80
|[http://genome.cshlp.org/content/22/8/1383 (CY KIM) Human genomic disease variants: A neutral evolutionary explanation]
|-
|rowspan = "2"|2012/11/20
| align="center"|2012-79
|[http://www.cell.com/abstract/S0092-8674(12)00639-3 (HS Shim) Circuitry and Dynamics of Human Transcription Factor Regulatory Networks]
|-
| align="center"|2012-78
|[http://www.nature.com/nature/journal/v487/n7408/full/nature11288.html (HJ Kim) Interpreting cancer genomes using systematic host network perturbations by tumour virus proteins]
|-
|rowspan = "2"|2012/11/06
| align="center"|2012-77
|[http://www.sciencemag.org/content/337/6099/1190.short (HJ Han) Systematic Localization of Common Disease-Associated Variation in Regulatory DNA]
|-
| align="center"|2012-76
|[http://www.pnas.org/cgi/doi/10.1073/pnas.1201904109 (KS Kim) A public resource facilitating clinical use of genomes]
|-
|rowspan = "6" |2012/07/19
| align="center"|2012-75
|[http://genome.cshlp.org/content/22/7/1334 (HJ Han & YH Ko) Predictive regulatory models in Drosophila melanogaster by integrative inference of transcriptional networks]
|-
| align="center"|2012-74
|[http://www.sciencemag.org/content/337/6090/100.full (JE Shim) An Abundance of Rare Functional Variants in 202 Drug Target Genes Sequenced in 14,002 People]
|-
| align="center"|2012-73
|[http://www.sciencemag.org/content/337/6090/64.abstract (JE Shim) Evolution and Functional Impact of Rare Coding Variation from Deep Sequencing of Human Exomes]
|-
| align="center"|2012-72
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2063581/ (SH Hwang) Network-based classification of breast cancer metastasis]
|-
| align="center"|2012-71
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002707 (T Lee&CY Kim)Brain Expression Genome-Wide Association Study (eGWAS) Identifies Human Disease-Associated Variants]
|-
| align="center"|2012-70
|[http://genome.cshlp.org/content/20/9/1297 (ER Kim&TH Kim)The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data]
|-
|rowspan = "8" |2012/07/16
| align="center"|2012-69
|[http://www.nature.com/ng/journal/v43/n5/full/ng.806.html (ER Kim&TH Kim)A framework for variation discovery and genotyping using next-generation DNA sequencing data]
|-
| align="center"|2012-66
|[http://bioinformatics.oxfordjournals.org/content/25/16/2078.long (ER Kim&TH Kim)The Sequence Alignment/Map format and SAMtools]
|-
| align="center"|2012-65
|[http://bioinformatics.oxfordjournals.org/content/early/2011/06/07/bioinformatics.btr330 (ER Kim&TH Kim)The Variant Call Format and VCFtools]
|-
| align="center"|2012-64
|[http://www.cell.com/abstract/S0092-8674(12)00104-3 (YH Go&HJ Han)The Impact of the Gut Microbiota on Human Health: An Integrative View]
|-
|align="center"|2012-63
|[http://www.sciencemag.org/content/336/6086/1262.abstract (T Lee&CY Kim)Host-Gut Microbiota Metabolic Interactions]
|-
|align="center"|2012-62
|[http://www.sciencemag.org/content/336/6086/1268.full (AR Cho,JH Ju)Interactions Between the Microbiota and the Immune System]
|-
|align="center"|2012-61
|[http://www.sciencemag.org/content/336/6086/1255.abstract (SH Hwang&HJ Cho)The Application of Ecological Theory Toward an Understanding of the Human Microbiome]
|-
|align="center"|2012-60
|[http://stm.sciencemag.org/content/4/137/137rv5 (SH Hwang&HJ Cho)Microbiota-Targeted Therapies: An Ecological Perspective]
|-
|rowspan = "3" |2012/07/13
|align="center"|2012-59
|[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002358 (JH Shin&HJ Kim)Metabolic Reconstruction for Metagenomic Data and Its Application to the Human Microbiome]
|-
| align="center"|2012-58
|[http://www.nature.com/nature/journal/v486/n7402/full/nature11209.html (JH Shin&HJ Kim)A framework for human microbiome research]
|-
|align="center"|2012-57
|[http://www.nature.com/nature/journal/v486/n7402/full/nature11234.html (JH Shin&HJ Kim)Structure, function and diversity of the healthy human microbiome]
|-
|rowspan = "4" |2012/07/12
|align="center"|2012-56
|[http://nar.oxfordjournals.org/content/40/D1/D957.long (AR Cho&JH Ju)COLT-Cancer: functional genetic screening resource for essential genes in human cancer cell lines]
|-
|align="center"|2012-55
|[http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002511 (YH Go&HJ Han)Google Goes Cancer: Improving Outcome Prediction for Cancer Patients by Network-Based Ranking of Marker Genes]
|-
| align="center"|2012-54
|[http://www.nature.com/msb/journal/v7/n1/full/msb201147.html (YH Go&HJ Han)A pharmacogenomic method for individualized prediction of drug sensitivity]
|-
|align="center"|2012-53
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature10983.html (JH Soh)The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups]
|-
|rowspan = "6" |2012/07/09
|align="center"|2012-52
|[http://www.nature.com/nature/journal/v483/n7391/full/nature11003.html (ER Kim&TH Kim)The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity]
|-
|align="center"|2012-51
|[http://www.nature.com/nature/journal/v483/n7391/full/nature11005.html (ER Kim&TH Kim)Systematic identification of genomic markers of drug sensitivity in cancer cells]
|-
| align="center"|2012-50
|[http://www.pnas.org/content/early/2011/10/13/1018854108.abstract (ER Kim&TH Kim)Subtype and pathway specific responses to anticancer compounds in breast cancer]
|-
|align="center"|2012-49
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature10945.html (JE Shim&KS Kim)De novo mutations revealed by whole-exome sequencing are strongly associated with autism]
|-
|align="center"|2012-48
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature11011.html (JE Shim&KS Kim)Patterns and rates of exonic de novo mutations in autism spectrum disorders]
|-
|align="center"|2012-47
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature10989.html (JE Shim&KS Kim)Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations]
|-
|rowspan = "6" |2012/07/06
| align="center"|2012-46
|[http://www.g3journal.org/content/1/3/233.full (T Lee&CY Kim)Integrating Rare-Variant Testing, Function Prediction, and Gene Network in Composite Resequencing-Based Genome-Wide Association Studies (CR-GWAS)]
|-
|align="center"|2012-45
|[http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002621 (T Lee&CY Kim)Type 2 Diabetes Risk Alleles Demonstrate Extreme Directional Differentiation among Human Populations, Compared to Other Diseases]
|-
|align="center"|2012-44
|[http://www.nature.com/nature/journal/v480/n7376/full/nature10665.html (AR Cho&JH Ju)Predicting mutation outcome from early stochastic variation in genetic interaction partners]
|-
|align="center"|2012-43
|[http://www.sciencemag.org/content/335/6064/82 (AR Cho&JH Ju)Fitness Trade-Offs and Environmentally Induced Mutation Buffering in Isogenic C. elegans]
|-
| align="center"|2012-42
|[http://genome.cshlp.org/content/22/6/1163 (JE Shim&KS Kim)Identification of microRNA-regulated gene networks by expression analysis of target genes]
|-
|align="center"|2012-41
|[http://www.nature.com/ng/journal/v44/n6/full/ng.2303.html (JE Shim&KS Kim)Exome sequencing and the genetic basis of complex traits]
|-
|rowspan = "6" |2012/07/02
|align="center"|2012-40
|[http://www.cell.com/abstract/S0092-8674(12)00573-9 (JH Soh)Functional Repurposing Revealed by Comparing S. pombe and S. cerevisiae Genetic Interactions]
|-
|align="center"|2012-39
|[http://genome.cshlp.org/content/22/2/375.long (ER Kim&TH Kim)De novo discovery of mutated driver pathways in cancer]
|-
| align="center"|2012-38
|[http://www.nature.com/msb/journal/v4/n1/full/msb20082.html (YH Go&HJ Han)A systems biology approach to prediction of oncogenes and molecular perturbation targets in B-cell lymphomas]
|-
|align="center"|2012-37
|[http://www.nature.com/msb/journal/v7/n1/full/msb201126.html (SH Hwang&HJ Cho)PREDICT: a method for inferring novel drug indications with application to personalized medicine]
|-
|align="center"|2012-36
|[http://www.nature.com/nature/journal/v453/n7198/full/nature06973.html (SH Hwang&HJ Cho)Synergistic response to oncogenic mutations defines gene class critical to cancer phenotype]
|-
|align="center"|2012-35
|[http://www.sciencemag.org/content/334/6062/1518.full (JH Shin&HJ Kim)Detecting Novel Associations in Large Data Sets]
|-
|rowspan = "3" |2012/03/05
| align="center"|2012-34
|[http://www.ncbi.nlm.nih.gov/pubmed/18516045 (8,HH Kim)Mapping and quantifying mammalian transcriptomes by RNA-Seq.]
|-
|align="center"|2012-33
|[http://genomebiology.com/2010/11/10/R106 (11,Go&Ju)Differential expression analysis for sequence count data]
|-
|align="center"|2012-32
|[http://bioinformatics.oxfordjournals.org/content/26/1/139.short (12,Go&Ju)edgeR: a Bioconductor package for differential expression analysis of digital gene expression data ]
|-
|rowspan = "7" |2012/02/27 2012/02/28
| align="center"|2012-31
|[http://www.nature.com/nrg/journal/v10/n1/full/nrg2484.html (1,JW Song)RNA-Seq: a revolutionary tool for transcriptomics]
|-
|align="center"|2012-30
|[http://www.nature.com/nmeth/journal/v8/n6/full/nmeth.1613.html (2,JW Song)Computational methods for transcriptome annotation and quantification using RNA-seq]
|-
|align="center"|2012-29
|[http://genomebiology.com/2010/11/12/220 (3,HJ Han)From RNA-seq reads to differential expression results]
|-
|align="center"|2012-28
|[http://www.nature.com/nmeth/journal/v7/n9/full/nmeth.1491.html (4,AR Cho)Comprehensive comparative analysis of strand-specific RNA sequencing methods]
|-
|align="center"|2012-27
|[http://www.plosone.org/article/info:doi%2F10.1371%2Fjournal.pone.0026426 (5,T Lee)A Low-Cost Library Construction Protocol and Data Analysis Pipeline for Illumina-Based Strand-Specific Multiplex RNA-Seq]
|-
|align="center"|2012-26
|[http://www.nature.com/nbt/journal/v28/n5/abs/nbt.1621.html (6,So&Shin)Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation]
|-
|align="center"|2012-25
|[http://bioinformatics.oxfordjournals.org/content/25/9/1105.abstract (7,So&Shin)TopHat: discovering splice junctions with RNA-Seq]
|-
|rowspan = "5" |2012/02/06
| align="center"|2012-24
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3125733/ mirConnX: condition-specific mRNA-microRNA network integrator]
|-
|align="center"|2012-23
|[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002190 Construction and Analysis of an Integrated Regulatory Network Derived from High-Throughput Sequencing Data]
|-
|align="center"|2012-22
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002415 A Densely Interconnected Genome-Wide Network of MicroRNAs and Oncogenic Pathways Revealed Using Gene Expression Signatures]
|-
|align="center"|2012-21
|[http://genome.cshlp.org/content/20/5/589.short Reprogramming of miRNA networks in cancer and leukemia]
|-
|align="center"|2012-20
|[http://www.cell.com/abstract/S0092-8674(11)01152-4 An Extensive MicroRNA-Mediated Network of RNA-RNA Interactions Regulates Established Oncogenic Pathways in Glioblastoma]
|-
|rowspan = "5" |2012/01/30
|align="center"|2012-19
|[http://www.cell.com/abstract/S0092-8674(11)00237-6 Principles and Strategies for Developing Network Models in Cancer]
|-
|align="center"|2012-18
|[http://www.nature.com/ng/journal/v37/n4/full/ng1532.html Reverse engineering of regulatory networks in human B cells]
|-
|align="center"|2012-17
|[http://www.nature.com/nature/journal/v452/n7186/abs/nature06757.html Variations in DNA elucidate molecular networks that cause disease]
|-
|align="center"|2012-16
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2779083/ Harnessing gene expression to identify the genetic basis of drug resistance]
|-
|align="center"|2012-15
|[http://www.sciencedirect.com/science/article/pii/S0092867410012936 An Integrated Approach to Uncover Drivers of Cancer]
|-
|rowspan = "3" |2012/01/09
|align="center"|2012-14
|[http://www.ncbi.nlm.nih.gov/pubmed/18704161 Genetic variation in an individual human exome.]
|-
|align="center"|2012-13
|[http://www.ncbi.nlm.nih.gov/pubmed/22081227 Predicting phenotypic variation in yeast from individual genome sequences.]
|-
|align="center"|2012-12
|[http://www.ncbi.nlm.nih.gov/pubmed/20435227 Clinical assessment incorporating a personal genome.]
|-
|rowspan = "6" |2012/01/09 2012/01/16
| align="center"|2012-11
|[http://www.ncbi.nlm.nih.gov/pubmed/20399638 Human allelic variation: perspective from protein function, structure, and evolution.]
|-
|align="center"|2012-10
|[http://www.ncbi.nlm.nih.gov/pubmed/19561590 Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm.]
|-
|align="center"|2012-09
|[http://www.ncbi.nlm.nih.gov/pubmed/11230178 Prediction of deleterious human alleles.]
|-
|align="center"|2012-08
|[http://www.ncbi.nlm.nih.gov/pubmed/12202775 Human non-synonymous SNPs: server and survey.]
|-
|align="center"|2012-07
|[http://www.ncbi.nlm.nih.gov/pubmed/20354512 A method and server for predicting damaging missense mutations.]
|-
|align="center"|2012-06
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1920242/ SNAP: predict effect of non-synonymous polymorphisms on function]
|-
|rowspan = "3" |2012/01/09 2012/01/16
|align="center"|2012-05
|[http://www.ncbi.nlm.nih.gov/pubmed/21920052 Computational and statistical approaches to analyzing variants identified by exome sequencing.]
|-
|align="center"|2012-04
|[http://www.ncbi.nlm.nih.gov/pubmed/18179889 Shifting paradigm of association studies: value of rare single-nucleotide polymorphisms.]
|-
|align="center"|2012-03
|[http://www.ncbi.nlm.nih.gov/pubmed/19684571 Targeted capture and massively parallel sequencing of 12 human exomes.]
|-
|rowspan = "2" |2012/01/09 2012/01/16
|align="center"|2012-02
|[http://www.ncbi.nlm.nih.gov/pubmed/17637733 The distribution of fitness effects of new mutations.]
|-
|align="center"|2012-01
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1852724/ Most Rare Missense Alleles Are Deleterious in Humans: Implications for Complex Disease and Association Studies]
|}
2011
{|border ="1"
|style="color:black; background-color:#dcdcdc;" align="center" |Date
|style="color:black; background-color:#dcdcdc;" align="center"|Paper_index
|style="color:black; background-color:#dcdcdc;" align="center"|Paper_title
|-
|rowspan = "6" |2011/11/28
|align="center"|2011-49
|[http://bmir.stanford.edu/file_asset/index.php/1407/BMIR-2009-1355.pdf (Shin)Data-Driven Methods to Discover Molecular Determinants of Serious Adverse Drug Events]
|-
|align="center"|2011-48
|[http://www.nature.com/nchembio/journal/v4/n11/abs/nchembio.118.html (Shin)Network pharmacology: the next paradigm in drug discovery]
|-
|align="center"|2011-47
|[http://www.nature.com/msb/journal/v7/n1/full/msb201171.html (Oh)Systematic exploration of synergistic drug pairs]
|-
|align="center"|2011-46
|[http://www.nature.com/msb/journal/v5/n1/full/msb200995.html (Shim)Chemogenomic profiling predicts antifungal synergies]
|-
|align="center"|2011-45
|[http://www.pnas.org/content/104/32/13086 (Beck)A strategy for predicting the chemosensitivity of human cancers and its application to drug discovery]
|-
|align="center"|2011-44
|[http://www.nature.com/nchembio/journal/v1/n7/full/nchembio747.html (Hwang)Analysis of drug-induced effect patterns to link structure and side effects of medicines]
|-
|rowspan = "3" |2011/11/14
|align="center"|2011-43
|[http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000662 Network-Based Elucidation of Human Disease Similarities Reveals Common Functional Modules Enriched for Pluripotent Drug Targets]
|-
|align="center"|2011-42
|[http://www.nature.com/msb/journal/v7/n1/full/msb201131.html Cross-species discovery of syncretic drug combinations that potentiate the antifungal fluconazole]
|-
|align="center"|2011-41
|[http://www.nature.com/msb/journal/v7/n1/full/msb20115.html Analysis of multiple compound–protein interactions reveals novel bioactive molecules]
|-
|rowspan = "4" |2011/11/07
|align="center"|2011-40
|[http://www.nature.com/nbt/journal/v25/n10/abs/nbt1338.html Drug—target network]
|-
|align="center"|2011-39
|[http://www.nature.com/msb/journal/v6/n1/full/msb200998.html A side effect resource to capture phenotypic effects of drugs]
|-
|align="center"|2011-38
|[http://genome.cshlp.org/content/18/2/206.long Quantitative systems-level determinants of human genes targeted by successful drugs]
|-
|align="center"|2011-37
|[http://www.sciencemag.org/content/321/5886/263.short Drug Target Identification Using Side-Effect Similarity]
|-
|rowspan = "3" |2011/11/07
|align="center"|2011-36
|[http://stm.sciencemag.org/content/3/96/96ra77.short Discovery and Preclinical Validation of Drug Indications Using Compendia of Public Gene Expression Data]
|-
|align="center"|2011-35
|[http://bib.oxfordjournals.org/content/12/4/303.short Exploiting drug–disease relationships for computational drug repositioning]
|-
|align="center"|2011-34
|[http://www.springerlink.com/content/4489r051nu2t0ul1/ Drug Discovery in a Multidimensional World: Systems, Patterns, and Networks]
|-
|rowspan = "3" |2011/10/05
|align="center"|2011-33
|[http://genome.cshlp.org/content/20/7/960.full Analysis of membrane proteins in metagenomics: Networks of correlated environmental features and protein families]
|-
|align="center"|2011-32
|[http://www.pnas.org/content/106/5/1374.full Quantifying environmental adaptation of metabolic pathways in metagenomics]
|-
|align="center"|2011-31
|[http://www.pnas.org/content/104/35/13913.abstract Quantitative assessment of protein function prediction from metagenomics shotgun sequences]
|-
|rowspan = "4" |2011/10/04
|align="center"|2011-30
|[http://www.nature.com/nature/journal/v464/n7285/full/nature08821.html A human gut microbial gene catalogue established by metagenomic sequencing]
|-
|align="center"|2011-29
|[http://www.nature.com/nrmicro/journal/v6/n9/abs/nrmicro1935.html Molecular eco-systems biology: towards an understanding of community function]
|-
|align="center"|2011-28
|[http://genome.cshlp.org/content/early/2009/04/20/gr.085464.108 Microbial community profiling for human microbiome projects: Tools, techniques, and challenges]
|-
|align="center"|2011-27
|[http://www.nature.com/nrmicro/journal/v9/n4/full/nrmicro2540.html Unravelling the effects of the environment and host genotype on the gut microbiome]
|-
|rowspan = "2" |2011/09/19
|align="center"|2011-26
|[http://www.sciencemag.org/content/333/6042/596.full independently evolved virulence effectors converge onto hubs in a plant immune system Network]
|-
|align="center"|2011-25
|[http://www.sciencemag.org/content/333/6042/601.full Evidence for network evolution in an arabidopsis interactome Map]
|-
|rowspan = "2" |2011/09/05
|align="center"|2011-24
|[http://www.sciencedirect.com/science/article/pii/S0092867411005861 Exome Sequencing of Ion Channel Genes Reveals Complex Profiles Confounding Personal Risk Assessment in Epilepsy]
|-
|align="center"|2011-23
|[http://www.cell.com/abstract/S0092-8674(11)00543-5 Pluripotency factors in Embryonic stem cells Regulate Differentiation into Germ Layers]
|-
|rowspan = "2" |2011/09/22
|align="center"|2011-22
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002077 Integrated Genome-scale predition of Detrimental Mutations in Transcription Networks]
|-
|align="center"|2011-21
|[http://www.nature.com/nrg/journal/v12/n4/abs/nrg2969.html From expression QTLs to personalized transcriptomics]
|-
|2011/06/20
|align="center"|2011-20
|[http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001046 a user's guide to the encyclopedia of DNA elements(ENCODE)]
|-
|rowspan = "2" |2011/03/30
|align="center"|2011-19
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature09944.html enterotypes of the human gut microbiome]
|-
|align="center"|2011-18
|[http://www.nature.com/msb/journal/v7/n1/full/msb20116.html toward molecular trait-based ecology, through intergration of biogeochemical, geographical and metagenomic data]
|-
|rowspan = "2" |2011/03/16
|align="center"|2011-17
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002047 variable pathogenicity determines individual lifespan in ''caenorhabditis elegans'']
|-
|align="center"|2011-16
|[http://www.cell.com/abstract/S0092-8674(11)00371-0 a high-resolution ''c.elegans'' essential gene network based on phenotypic profiling of a complex tissue]
|-
|rowspan = "3" |2011/04/25
|align="center"|2011-15
|[http://www.ncbi.nlm.nih.gov/pubmed/21376230 Hallmarks of Cancer : The next generation]
|-
|align="center"|2011-14
|[http://www.cell.com/abstract/S0092-8674(11)00296-0 Mapping Cancer Origins]
|-
|align="center"|2011-13
|[http://www.cell.com/abstract/S0092-8674(11)00297-2 Genetic Interactions in Cancer Progression and Treatment]
|-
|rowspan = "2" |2011/04/11
|align="center"|2011-12
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1001273 Proteins encoded in genomic regions associated with immune-mediated disease physically interact and suggest underlying biology]
|-
|align="center"|2011-11
|***Changed!***
[http://www.ncbi.nlm.nih.gov/pubmed/19557189 Identifying relationships among genomic disease regions: predicting genes at pathogenic SNP associations and rare deletions]
|-
|rowspan = "2"|2011/03/28
|align="center"|2011-10
|[http://www.pnas.org/content/106/44/18843.long profiling translatomes of discrete cell populations resolves altered cellular priorities during hypoxia in Arabidopsis]
|-
|align="center"|2011-09
|[http://www.nature.com/msb/journal/v6/n1/full/msb201076.html cell-type specific analysis of translating RNAs in developing flowers reveals new levels of control]
|-
|rowspan = "2"|2011/03/14
|align="center"|2011-08
|[http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6WSN-51SFHJD-1&_user=44062&_coverDate=01%2F07%2F2011&_rdoc=1&_fmt=high&_orig=search&_origin=search&_sort=d&_docanchor=&view=c&_acct=C000004738&_version=1&_urlVersion=0&_userid=44062&md5=2f6017aab4c794ed7e78fbbd8447077a&searchtype=a phenotypic landscape of a bacterial cell]
|-
|align="center"|2011-07
|[http://www.nature.com/msb/journal/v6/n1/full/msb2010107.html cross-species chemogenomic profiling reveals evolutionarily conserved drug mode of action]
|-
|rowspan = "2"|2011/02/28
|align="center"|2011-06
|[http://www.pnas.org/content/early/2010/09/23/1004666107.abstract genomic patterns of pleiotropy and the evolution of complexity]
|-
|align="center"|2011-05
|[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1001009 simultaneous genome-wide inference of physical, genetic, regulatory, and functional pathway]
|-
|2011/02/21
|align="center"|2011-04
|[http://www.nature.com/msb/journal/v6/n1/full/msb201071.html dynamic interaction networks in a hierarchically organized tissue]
|-
|2011/02/14
|align="center"|2011-03
|[http://www.sciencemag.org/content/330/6009/1385.abstract rewiring of genetic networks in response to DNA damage]
|-
|rowspan = "2"|2011/01/31
|align="center"|2011-02
|[http://www.nature.com/nrg/journal/v10/n9/abs/nrg2633.html Applying mass spectrometry-based proteomics to genetics, genomics and network biology]
|-
|align="center"|2011-01
|[http://physiolgenomics.physiology.org/content/33/1/18.long Data analysis and bioinformatics tools for tandem mass spectrometry in proteomics]
|}
2010
{|border ="1"
|style="color:black; background-color:#dcdcdc;" align="center" |Date
|style="color:black; background-color:#dcdcdc;" align="center"|Paper_index
|style="color:black; background-color:#dcdcdc;" align="center"|Paper_title
|-
|rowspan = "2"|2010/12/27
|align="center"|2010-29
|[http://www.nature.com/nature/journal/v467/n7312/full/nature09326.html Functional_roles_fornoise_in_genetic_circuits]
|-
|align="center"|2010-28
|[http://www.nature.com/ng/journal/v42/n7/full/ng.610.html Estimation_of_effect_size_distribution_from_genome-wide_association_studies_and_implications_for_future_discoveries]
|-
|rowspan = "2"|2010/11/15
|align="center"|2010-27
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1000932 Liver_and_Adipose_Expression_associated_SNPs_are_enriched_for_association_to_type_2_diabetes]
|-
|align="center"|2010-26
|[http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B8JDC-50J4MRJ-2&_user=44062&_coverDate=10%2F10%2F2010&_rdoc=1&_fmt=high&_orig=search&_origin=search&_sort=d&_docanchor=&view=c&_acct=C000004738&_version=1&_urlVersion=0&_userid=44062&md5=7667a67cf8ba3f68117b8d0ff85a8887&searchtype=a It's_the_machine_that_matters:_predicting_gene_function_and_phenotype_from_protein_networks]
|-
|rowspan = "2"|2010/11/01
|align="center"|2010-25
|[http://genome.cshlp.org/content/early/2010/06/15/gr.104216.109 A_genome-wide_map_of_human_genetic_interactions_inferred_from_radiation_hybrid_genotypes]
|-
|align="center"|2010-24
|[http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0012139 A_Genome-Wide_Gene_Function_Prediction_Resource_for_Drosophila_melanogaster]
|-
|2010/10/11
|align="center"|2010-23
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2913399/?tool=pubmed Dissecting_spatio-temporal_protein_networks_driving_human_heart_development_and_related_disorders]
|-
|rowspan = "2"|2010/09/13
|align="center"|2010-22
|[http://www.nature.com/ng/journal/v42/n7/full/ng.600.html Transposable_elements_have_rewired_the_core_regulatory_network_of_human_embryonic_stem_cells]
|-
|align="center"|2010-21
|[http://www.nature.com/ng/journal/v42/n7/full/ng0710-557.html Limits_of_sequence_and_functional_conservation]
|-
|2010/05/26
|align="center"|2010-20
|[[media:100428_network_based_elucidation_of_human_disease_similarities_reveals_common_functional_modules_enriched_for_pluripotent_drug_targets.pdf|network_based_elucidation_of_human_disease_similarities_reveals_common_functional_modules_enriched_for_pluripotent_drug_targets.pdf]]
|-
|2010/05/19
|align="center"|2010-19
|[[media:100421_interpreting_metabolomic_profiles_using_unbiased_pathway_models.pdf|interpreting_metabolomic_profiles_using_unbiased_pathway_models.pdf]]
|-
|2010/04/21
|align="center"|2010-18
|[[media:100414_identification_of_networks_of_co_occurring_tumor_related_dna_copy_number_changes_using_a_genome_wide_scoringapproach.pdf|identification_of_networks_of_co_occurring_tumor_related_dna_copy_number_changes_using_a_genome_wide_scoringapproach.pdf]]
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|2010/04/14
|align="center"|2010-17
|[http://www.cell.com/retrieve/pii/S0092867410000796 an_atlas_of_combinatorial_transcriptional_regulation_in_mouse_and_man]
|-
|rowspan = "2" |2010/04/07
|align="center"|2010-16
|[http://www.pnas.org/content/early/2010/03/11/0910200107.long systematic_discovery_of_nonobvious_human_disease_models_through_orthologous_phenotypes]
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|align="center"|2010-15
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature08800.html genome_side_association_study_of_107_phenotypes_in_Arabidopsis_thaliana_inbred_lines]
|-
|rowspan="2"|2010/03/31
|align="center"|2010-14
|[[media:100331_toward_stem_cell_systems_biology_from_molecules_to_networks_and_landscapes.pdf|toward_stem_cell_systems_biology_from_molecules_to_networks_and_landscapes.pdf]]
|-
|align="center"|2010-13
|[http://www.nature.com/nrm/journal/v10/n10/abs/nrm2766.html systems_biology_of_stem_cell_fate_and_cellular_reprogramming]
|-
|2010/03/24
|align="center"|2010-12
|[http://www.nature.com/nbt/journal/v27/n2/abs/nbt.1522.html dynamic_modularity_in_protein_interaction_networks_predicts_breast_cancer_outcome]
|-
|2010/01/18
|align="center"|2010-11
|JournalClub_100118_a_tutorial_on_statistical_methods_for_population_association_studies
|-
|2010/01/16
|align="center"|2010-10
|systems-level_dinamic_analyses_of_fate_change_in_murine_embryonic_stem_cells
|-
|2010/01/15
|align="center"|2010-09
|distinguishing_direct_versus_indirect_transcription_factor-DNA-interactions
|-
|2010/01/14
|align="center"|2010-08
|chemogenomic_profiling_predicts_antifungal_synergies
|-
|2010/01/13
|align="center"|2010-07
|edgetic_perturbation_models_of_human_inherited_disorders
|-
|2010/01/12
|align="center"|2010-06
|analysis_of_cell_fate_from_single-cell_gene_expression_profiles_in_C.elegans
|-
|2010/01/11
|align="center"|2010-05
|predicting_new_molecular_targets_for_known_drugs.pdf
Reference:SEA(Similarity Ensemble Approach)
|-
|2010/01/09
|align="center"|2010-04
|harnessing_gene_expression_to_identify_the_genetic_basis_of_drug_resistance
|-
|2010/01/08
|align="center"|2010-03
|an_integrative_approach_to_reveal_driver_gene_fusions_from_paired_end_sequencing_data_in_cancer
|-
|2010/01/07
|align="center"|2010-02
|a_phenotypic_profile_of_the_candida_albicans_regulatory_network
|-
|2010/01/06
|align="center"|2010-01
|a_global_view_of_protein_expression_in_human_cells_tissues_and_organs
|}

Journal Club

2023-08-24T00:36:27Z

Bsb0613:

{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2023-2 scOmics
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!scope="col" style="padding:.4em" | Date
!scope="col" style="padding:.4em" | Team
!scope="col" style="padding:.4em" | Paper index
!scope="col" style="padding:.4em" | Presenter
!scope="col" style="padding:.4em" | Paper title
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|style="padding:.4em;" rowspan=1|2023/11/07
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-33
|style="padding:.4em;"|EJ Sung
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41588-022-01273-y MHC II immunogenicity shapes the neoepitope landscape in human tumors]
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|style="padding:.4em;" rowspan=1|2023/10/31
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-32
|style="padding:.4em;"|IS Choi
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41586-023-06130-4 Hallmarks of transcriptional intratumour heterogeneity across a thousand tumours]
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|style="padding:.4em;" rowspan=1|2023/10/24
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-31
|style="padding:.4em;"|JW Yu
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41467-023-37353-8 Pan-cancer classification of single cells in the tumour microenvironment]
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|style="padding:.4em;" rowspan=1|2023/10/17
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-30
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41587-023-01686-y Single-cell mapping of combinatorial target antigens for CAR switches using logic gates]
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|style="padding:.4em;" rowspan=1|2023/10/10
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-29
|style="padding:.4em;"|SB Baek
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41587-023-01782-z Comparative analysis of cell–cell communication at single-cell resolution]
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|style="padding:.4em;" rowspan=1|2023/09/26
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-28
|style="padding:.4em;"|EJ Sung
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s43018-023-00566-3 Phenotypic diversity of T cells in human primary and metastatic brain tumors revealed by multiomic interrogation]
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|style="padding:.4em;" rowspan=1|2023/09/19
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-27
|style="padding:.4em;"|IS Choi
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41591-023-02324-5 An integrated tumor, immune and microbiome atlas of colon cancer]
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|style="padding:.4em;" rowspan=1|2023/09/12
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-26
|style="padding:.4em;"|SB Baek
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41587-022-01476-y Multi-omic single-cell velocity models epigenome–transcriptome interactions and improves cell fate prediction]
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|style="padding:.4em;" rowspan=1|2023/09/05
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-25
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.immuni.2023.04.010 Recruitment of epitope-specific T cell clones with a low-avidity threshold supports efficacy against mutational escape upon re-infection]
|}

{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2023-2 Microbiome
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!scope="col" style="padding:.4em" | Date
!scope="col" style="padding:.4em" | Team
!scope="col" style="padding:.4em" | Paper index
!scope="col" style="padding:.4em" | Presenter
!scope="col" style="padding:.4em" | Paper title
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|style="padding:.4em;" rowspan=1|2023/11/22
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-48
|style="padding:.4em;"|SH Ahn
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41467-023-39264-0 A data-driven approach for predicting the impact of drugs on the human microbiome]
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|style="padding:.4em;" rowspan=1|2023/11/08
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-47
|style="padding:.4em;"|HJ Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2023.04.06.535777 Activation of programmed cell death and counter-defense functions of phage accessory genes]
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|style="padding:.4em;" rowspan=1|2023/11/08
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-46
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41467-023-39459-5 Top-down identification of keystone taxa in the microbiome]
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|style="padding:.4em;" rowspan=1|2023/11/01
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-45
|style="padding:.4em;"|JY Ma
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.cels.2022.12.007 Pitfalls of genotyping microbial communities with rapidly growing genome collections]
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|style="padding:.4em;" rowspan=1|2023/11/01
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-44
|style="padding:.4em;"|NY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1186/s13059-023-03028-2 Reconstruction of the last bacterial common ancestor from 183 pangenomes reveals a versatile ancient core genome]
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|style="padding:.4em;" rowspan=1|2023/10/25
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-43
|style="padding:.4em;"|SH Lee
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41587-023-01868-8 Generation of accurate, expandable phylogenomic trees with uDance]
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|style="padding:.4em;" rowspan=1|2023/10/25
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-42
|style="padding:.4em;"|WJ Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.immuni.2023.04.003 Phage display sequencing reveals that genetic, environmental, and intrinsic factors influence variation of human antibody epitope repertoire]
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|style="padding:.4em;" rowspan=1|2023/10/18
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-41
|style="padding:.4em;"|JY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.immuni.2023.04.017 Phage-display immunoprecipitation sequencing of the antibody epitope repertoire in inflammatory bowel disease reveals distinct antibody signatures]
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|style="padding:.4em;" rowspan=1|2023/10/18
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-40
|style="padding:.4em;"|G Koh
|style="padding:.4em;text-align:left"|
[https://doi.org/10.15252/msb.202311525 Consistency across multi-omics layers in a drug-perturbed gut microbial community]
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|style="padding:.4em;" rowspan=1|2023/10/11
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-39
|style="padding:.4em;"|SH Ahn
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41591-023-02407-3 Microbiome-derived cobalamin and succinyl-CoA as biomarkers for improved screening of anal cancer]
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|style="padding:.4em;" rowspan=1|2023/10/11
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-38
|style="padding:.4em;"|HJ Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2023.03.05.531206 You can move, but you can’t hide: identification of mobile genetic elements with geNomad]
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|style="padding:.4em;" rowspan=1|2023/10/04
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-37
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41591-023-02424-2 The airway microbiome mediates the interaction between environmental exposure and respiratory health in humans]
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|style="padding:.4em;" rowspan=1|2023/10/04
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-36
|style="padding:.4em;"|JY Ma
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1080/19490976.2023.2224474 Ordering taxa in image convolution networks improves microbiome-based machine learning accuracy]
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|style="padding:.4em;" rowspan=1|2023/09/27
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-35
|style="padding:.4em;"|NY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2023.08.12.553040 The defensome of complex bacterial communities]
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|style="padding:.4em;" rowspan=1|2023/09/27
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-34
|style="padding:.4em;"|SH Lee
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.cell.2023.03.011 Dietary tryptophan metabolite released by intratumoral Lactobacillus reuteri facilitates immune checkpoint inhibitor treatment]
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|style="padding:.4em;" rowspan=1|2023/09/20
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-33
|style="padding:.4em;"|WJ Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s43587-022-00306-9 Toward an improved definition of a healthy microbiome for healthy aging]
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|style="padding:.4em;" rowspan=1|2023/09/20
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-32
|style="padding:.4em;"|JY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s43587-022-00287-9 Associations of the skin, oral and gut microbiome with aging, frailty and infection risk reservoirs in older adults]
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|style="padding:.4em;" rowspan=1|2023/09/13
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-31
|style="padding:.4em;"|G Koh
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1186/s40168-023-01614-x Statistical modeling of gut microbiota for personalized health status monitoring]
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|style="padding:.4em;" rowspan=1|2023/09/13
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-30
|style="padding:.4em;"|SH Ahn
|style="padding:.4em;text-align:left"|
[https://doi.org/10.7554/eLife.50240 Adjusting for age improves identification of gut microbiome alterations in multiple diseases]
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|style="padding:.4em;" rowspan=1|2023/09/06
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-29
|style="padding:.4em;"|HJ Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41564-023-01370-6 Centenarians have a diverse gut virome with the potential to modulate metabolism and promote healthy lifespan]
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|style="padding:.4em;" rowspan=1|2023/09/06
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-28
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.chom.2023.01.003 Longitudinal comparison of the developing gut virome in infants and their mothers]
|}

{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2023-1 ADVANCED MICROBIOME DATA ANALYSIS
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!scope="col" style="padding:.4em" | Date
!scope="col" style="padding:.4em" | Team
!scope="col" style="padding:.4em" | Paper index
!scope="col" style="padding:.4em" | Presenter
!scope="col" style="padding:.4em" | Paper title
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|style="padding:.4em;" rowspan=1|2023/06/13
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-24
|style="padding:.4em;"|JY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.chom.2020.03.005 Structure of the Mucosal and Stool Microbiome in Lynch Syndrome]
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|style="padding:.4em;" rowspan=1|2023/06/13
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-23
|style="padding:.4em;"|WJ Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41586-019-1237-9 Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases]
|-
|style="padding:.4em;" rowspan=1|2023/06/13
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-22
|style="padding:.4em;"|SH Ahn
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.jare.2022.03.007 Roles of oral microbiota and oral-gut microbial transmission in hypertension]
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|style="padding:.4em;" rowspan=1|2023/05/30
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-21
|style="padding:.4em;"|SY Lim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.chom.2022.08.009 Human gut microbiota stimulate defined innate immune responses that vary from phylum to strain]
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|style="padding:.4em;" rowspan=1|2023/05/30
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-20
|style="padding:.4em;"|BS Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41591-023-02217-7 Gut microbial metabolism of 5-ASA diminishes its clinical efficacy in inflammatory bowel disease]
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|style="padding:.4em;" rowspan=1|2023/05/30
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-19
|style="padding:.4em;"|JY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.chom.2023.01.004 Deficient butyrate-producing capacity in the gut microbiome is associated with bacterial network disturbances and fatigue symptoms in ME/CFS]
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|style="padding:.4em;" rowspan=1|2023/05/23
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-18
|style="padding:.4em;"|EJ Sung
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.ccell.2022.11.013 Gut microbiota-mediated nucleotide synthesis attenuates the response to neoadjuvant chemoradiotherapy in rectal cancer]
|-
|style="padding:.4em;" rowspan=1|2023/05/23
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-17
|style="padding:.4em;"|G Koh
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.chom.2021.06.019 Multi-omics reveal microbial determinants impacting responses to biologic therapies in inflammatory bowel disease]
|-
|style="padding:.4em;" rowspan=1|2023/05/23
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-16
|style="padding:.4em;"|SH Lee
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41586-022-05181-3 Identification of trypsin-degrading commensals in the large intestine]
|-
|style="padding:.4em;" rowspan=1|2023/05/16
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-15
|style="padding:.4em;"|JP Hong
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41586-022-05546-8 Questioning the fetal microbiome illustrates pitfalls of low-biomass microbial studies]
|-
|style="padding:.4em;" rowspan=1|2023/05/16
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-14
|style="padding:.4em;"|MR Jang
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.chom.2023.01.013 Tissue-resident Lachnospiraceae family bacteria protect against colorectal carcinogenesis by promoting tumor immune surveillance]
|-
|style="padding:.4em;" rowspan=1|2023/05/16
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-13
|style="padding:.4em;"|JW Yu
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.cell.2022.09.005 Pan-cancer analyses reveal cancer-type-specific fungal ecologies and bacteriome interactions]
|-
|style="padding:.4em;" rowspan=1|2023/05/09
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-12
|style="padding:.4em;"|NY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.cell.2022.09.015 A pan-cancer mycobiome analysis reveals fungal involvement in gastrointestinal and lung tumors]
|-
|style="padding:.4em;" rowspan=1|2023/05/09
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-11
|style="padding:.4em;"|HR Shin
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41564-021-01030-7 Multi-kingdom microbiota analyses identify bacterial–fungal interactions and biomarkers of colorectal cancer across cohorts]
|-
|style="padding:.4em;" rowspan=1|2023/05/09
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-10
|style="padding:.4em;"|SG Oh
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s42255-022-00716-4 The antitumour effects of caloric restriction are mediated by the gut microbiome]
|-
|style="padding:.4em;" rowspan=1|2023/05/02
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-9
|style="padding:.4em;"|WJ Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41591-022-01964-3 Variability of strain engraftment and predictability of microbiome composition after fecal microbiota transplantation across different diseases]
|-
|style="padding:.4em;" rowspan=1|2023/05/02
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-8
|style="padding:.4em;"|SM Han
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41591-022-01913-0 Drivers and determinants of strain dynamics following fecal microbiota transplantation]
|-
|style="padding:.4em;" rowspan=1|2023/05/02
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-7
|style="padding:.4em;"|YY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.cell.2022.11.023 Mobile genetic elements from the maternal microbiome shape infant gut microbial assembly and metabolism]
|-
|style="padding:.4em;" rowspan=1|2023/04/18
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-6
|style="padding:.4em;"|SH Heo
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.chom.2023.01.018 Mother-to-infant microbiota transmission and infant microbiota development across multiple body sites]
|-
|style="padding:.4em;" rowspan=1|2023/04/18
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-5
|style="padding:.4em;"|SY Yang
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41467-023-36633-7 Population-level impacts of antibiotic usage on the human gut microbiome]
|-
|style="padding:.4em;" rowspan=1|2023/04/18
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-4
|style="padding:.4em;"|YH Yoon
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41586-022-05438-x Enterococci enhance Clostridioides difficile pathogenesis]
|-
|style="padding:.4em;" rowspan=1|2023/04/11
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-3
|style="padding:.4em;"|DH Lee
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1002/imt2.61 Targeting keystone species helps restore the dysbiosis of butyrate‐producing bacteria in nonalcoholic fatty liver disease]
|-
|style="padding:.4em;" rowspan=1|2023/04/11
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-2
|style="padding:.4em;"|YJ Roh
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1002/advs.202203115 Differential Oral Microbial Input Determines Two Microbiota Pneumo-Types Associated with Health Status]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|SH Ahn
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[https://doi.org/10.1016/j.cell.2022.08.021 Gut microbiome of multiple sclerosis patients and paired household healthy controls reveal associations with disease risk and course]
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|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|JW Yu
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[https://doi.org/10.1101/2023.01.17.524482 Decoupling the correlation between cytotoxic and exhausted T lymphocyte transcriptomic signatures enhances melanoma immunotherapy response prediction from tumor expression]
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|style="padding:.4em;" rowspan=1|2023/08/09
|style="padding:.4em;" rowspan=1|Single-cell
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[https://doi.org/10.1101/2023.07.28.550993 Major data analysis errors invalidate cancer microbiome findings]
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|style="padding:.4em;" rowspan=1|Single-cell
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[https://doi.org/10.1038/s43018-022-00475-x A single-cell atlas of glioblastoma evolution under therapy reveals cell-intrinsic and cell-extrinsic therapeutic targets]
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|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|G Koh
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[https://doi.org/10.1038/s43018-022-00433-7 Single-cell meta-analyses reveal responses of tumor-reactive CXCL13+ T cells to immune-checkpoint blockade]
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|style="padding:.4em;"|JW Yu
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[https://doi.org/10.1038/s41587-022-01342-x Estimation of tumor cell total mRNA expression in 15 cancer types predicts disease progression]
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|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-19
|style="padding:.4em;"|JH Cha
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[https://doi.org/10.1038/s41587-022-01288-0 DIALOGUE maps multicellular programs in tissue from single-cell or spatial transcriptomics data]
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|style="padding:.4em;" rowspan=1|2023/07/05
|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|SB Baek
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[https://doi.org/10.1038/s41591-023-02371-y Pan-cancer T cell atlas links a cellular stress response state to immunotherapy resistance]
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|style="padding:.4em;" rowspan=1|2023/06/28
|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|EJ Sung
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[https://doi.org/10.1016/j.patter.2022.100651 Self-supervised graph representation learning integrates multiple molecular networks and decodes gene-disease relationships]
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|style="padding:.4em;" rowspan=1|2023/06/21
|style="padding:.4em;" rowspan=1|Single-cell
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[https://doi.org/10.1016/j.ccell.2022.10.008 High-resolution single-cell atlas reveals diversity and plasticity of tissue-resident neutrophils in non-small cell lung cancer]
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|style="padding:.4em;" rowspan=1|2023/06/14
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-15
|style="padding:.4em;"|G Koh
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[https://doi.org/10.1186/s13059-022-02828-2 Parallel single-cell and bulk transcriptome analyses reveal key features of the gastric tumor microenvironment]
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|style="padding:.4em;" rowspan=1|2023/05/31
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-14
|style="padding:.4em;"|JW Yu
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[https://doi.org/10.1038/s41467-022-32838-4 Mutated processes predict immune checkpoint inhibitor therapy benefit in metastatic melanoma]
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|style="padding:.4em;"|JH Cha
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[https://doi.org/10.1038/s43018-021-00292-8 Temporal single-cell tracing reveals clonal revival and expansion of precursor exhausted T cells during anti-PD-1 therapy in lung cancer]
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|style="padding:.4em;" rowspan=1|2023/05/17
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|style="padding:.4em;"|SB Baek
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[https://doi.org/10.1016/j.cell.2022.11.028 Integrative single-cell analysis of cardiogenesis indentifies developmental trajectories and non-conding mutations in congenital heart disease]
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|style="padding:.4em;"|EJ Sung
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[https://doi.org/10.1101/2022.12.20.521311 Supervised discovery of interpretable gene programs from single-cell data]
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[https://doi.org/10.1038/s41586-022-05435-0 Effect of the intratumoral microbiota on spatial and cellular heterogeneity in cancer]
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|style="padding:.4em;"|G Koh
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[https://www.nature.com/articles/s41588-022-01141-9 Cancer cell states recur across tumor types and form specific interactions with the tumor microenvironment]
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[https://www.biorxiv.org/content/10.1101/2022.08.05.502989v1 MetaTiME: Meta-components of the Tumor Immune Microenvironment]
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[https://www.nature.com/articles/s41590-022-01262-7 Pre-encoded responsiveness to type I interferon in the peripheral immune system defines outcome of PD1 blockade therapy]
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|style="padding:.4em;"|SB Baek
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[https://www.biorxiv.org/content/10.1101/2022.03.16.484513v1 Integrated single-cell profiling dissects cell-state-specific enhancer landscapes of human tumor-infiltrating T cells]
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|style="padding:.4em;"|EJ Sung
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[https://www.nature.com/articles/s41591-022-01799-y A T cell resilience model associated with response to immunotherapy in multiple tumor types]
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|style="padding:.4em;"|IS Choi
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[https://www.nature.com/articles/s41588-022-01134-8 Single-nucleus and spatial transcriptome profiling of pancreatic cancer identifies multicellular dynamics associated with neoadjuvant treatment]
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[https://pubmed.ncbi.nlm.nih.gov/35649411/ Cross-tissue, single-cell stromal atlas identifies shared pathological fibroblast phenotypes in four chronic inflammatory diseases]
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[https://www.sciencedirect.com/science/article/pii/S1535610822003178 Pan-cancer integrative histology-genomic analysis via multimodal deep learning]
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|style="padding:.4em;"|JY Ma
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[https://doi.org/10.1016/j.chom.2023.05.024 Enterosignatures define common bacterial guilds in the human gut microbiome]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|NY Kim
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-25
|style="padding:.4em;"|SH Lee
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[https://doi.org/10.1016/j.chom.2023.05.027 The TaxUMAP atlas: Efficient display of large clinical microbiome data reveals ecological competition in protection against bacteremia]
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|style="padding:.4em;"|WJ Kim
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[http://dx.doi.org/10.1038/nbt.3704 Measurement of bacterial replication rates in microbial communities]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|JY Kim
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[https://doi.org/10.1186/s40168-023-01564-4 Skin microbiome diferentiates into distinct cutotypes with unique metabolic functions upon exposure to polycyclic aromatic hydrocarbons]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-22
|style="padding:.4em;"|SH Ahn
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[https://doi.org/10.1016/j.xcrm.2023.100920 Enrichment of oral-derived bacteria in inflamed colorectal tumors and distinct associations of Fusobacterium in the mesenchymal subtype]
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|style="padding:.4em;" rowspan=1|2023/08/04
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[https://doi.org/10.1038/s41586-023-05989-7 Profiling the human intestinal environment under physiological conditions]
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[https://doi.org/10.1186/s40168-023-01494-1 Genome-centric metagenomics reveals the host-driven dynamics and ecological role of CPR bacteria in an activated sludge system]
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[https://doi.org/10.1016/j.patter.2022.100658 Enhanced metagenomic deep learning for disease prediction and consistent signature recognition by restructured microbiome 2D representations]
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[https://doi.org/10.1186/s13059-022-02809-5 Gene fow and introgression are pervasive forces shaping the evolution of bacterial species]
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[https://doi.org/10.1186/s40168-022-01435-4 Alterations of oral microbiota and impact on the gut microbiome in type 1 diabetes mellitus revealed by integrated multi‑omic analyses]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|JY Ma
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[https://doi.org/10.1016/j.immuni.2022.08.016 The CD4+ T cell response to a commensal-derived epitope transitions from a tolerant to an inflammatory state in Crohn’s disease]
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|style="padding:.4em;"|NY Kim
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|SH Lee
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[https://doi.org/10.1101/2022.10.11.511790 Single Cell Transcriptomics Reveals the Hidden Microbiomes of Human Tissues]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|JY Kim
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|WJ Kim
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[https://doi.org/10.1038/s41564-017-0084-4 Metatranscriptome of human faecal microbial communities in a cohort of adult men]
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|style="padding:.4em;" rowspan=1|2023/03/24
|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|SH Ahn
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[https://doi.org/10.1016/j.ccell.2022.09.009 Tumor microbiome links cellular programs and immunity in pancreatic cancer]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|HJ Kim
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|style="padding:.4em;" rowspan=1|2023/03/10
|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|JH Cha
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[https://doi.org/10.1038/s41586-022-05620-1 The person-to-person transmission landscape of the gut and oral microbiomes]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|JY Ma
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[https://doi.org/10.1101/2023.01.30.526328 BIRDMAn: A Bayesian differential abundance framework that enables robust inference of host-microbe associations]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|NY Kim
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[https://www.nature.com/articles/s41564-022-01157-1 Phage–host coevolution in natural populations]
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|style="padding:.4em;"|SH Lee
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[https://www.nature.com/articles/s41467-022-29968-0 A randomized controlled trial for response of microbiome network to exercise and diet intervention in patients with nonalcoholic fatty liver disease]
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|style="padding:.4em;"|SH Ahn
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[https://www.biorxiv.org/content/10.1101/2022.05.19.492684v1 Scalable power analysis and effect size exploration of microbiome community differences with Evident]
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[https://www.biorxiv.org/content/10.1101/2022.08.19.504505v1 SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks]
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|style="padding:.4em;"|JH Cha, SB Baek, IS Choi
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[https://www.pnas.org/doi/10.1073/pnas.2105859118 Representation learning of RNA velocity reveals robust cell transitions]
[https://www.nature.com/articles/s41467-022-34188-7 UniTVelo: temporally unified RNA velocity reinforces single-cell trajectory inference]
[https://www.sciencedirect.com/science/article/pii/S0092867421015774 Mapping transcriptomic vector fields of single cells]
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[https://www.nature.com/articles/s41467-022-31535-6 Network-based machine learning approach to predict immunotherapy response in cancer patients]
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[https://www.biorxiv.org/content/10.1101/2022.05.04.490536v1 Modeling fragment counts improves single-cell ATAC-seq analysis]
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[https://www.biorxiv.org/content/10.1101/2022.02.21.480893v1 Integrating phylogenetic and functional data in microbiome studies]
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[https://pubmed.ncbi.nlm.nih.gov/34618582/ Commensal bacteria promote endocrine resistance in prostate cancer through androgen biosynthesis]
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[https://www.biorxiv.org/content/10.1101/2022.06.16.496510v2 MIDAS2: Metagenomic Intra-species Diversity Analysis System]
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[https://www.biorxiv.org/content/10.1101/2022.02.01.478746v2 Scalable microbial strain inference in metagenomic data using StrainFacts]
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[https://www.biorxiv.org/content/10.1101/2022.02.15.480535v1 StrainPanDA: linked reconstruction of strain composition and gene content profiles via pangenome-based decomposition of metagenomic data]
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[https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-022-01251-w Metagenomic strain detection with SameStr: identification of a persisting core gut microbiota transferable by fecal transplantation]
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[https://www.nature.com/articles/s41587-021-01102-3 Fast and accurate metagenotyping of the human gut microbiome with GT-Pro]
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[https://www.nature.com/articles/s41587-020-00797-0 inStrain profiles population microdiversity from metagenomic data and sensitively detects shared microbial strains]
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[https://genome.cshlp.org/content/early/2021/07/22/gr.265058.120 Longitudinal linked-read sequencing reveals ecological and evolutionary responses of a human gut microbiome during antibiotic treatment]
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[https://www.sciencedirect.com/science/article/pii/S1931312821002365 Dispersal strategies shape persistence and evolution of human gut bacteria]
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[https://www.sciencedirect.com/science/article/pii/S0092867421003524 The long-term genetic stability and individual specificity of the human gut microbiome]
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[https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02042-y Analysis of 1321 Eubacterium rectale genomes from metagenomes uncovers complex phylogeographic population structure and subspecies functional adaptations]
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[https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3000102 Evolutionary dynamics of bacteria in the gut microbiome within and across hosts]
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[https://elifesciences.org/articles/42693 Extensive transmission of microbes along the gastrointestinal tract]
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[https://www.cell.com/cell-host-microbe/fulltext/S1931-3128(19)30041-1?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS1931312819300411%3Fshowall%3Dtrue Distinct Genetic and Functional Traits of Human Intestinal Prevotella copri Strains Are Associated with Different Habitual Diets]
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[https://www.nature.com/articles/nmeth.3802 Strain-level microbial epidemiology and population genomics from shotgun metagenomics]
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[https://www.cell.com/cell/fulltext/S0092-8674(21)00942-9#secsectitle0025 Chromatin and gene-regulatory dynamics of the developing human cerebral cortex at single-cell resolution]
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[https://www.biorxiv.org/content/10.1101/2021.02.09.430114v2 Single-cell ATAC and RNA sequencing reveal pre-existing and persistent subpopulations of cells associated with relapse of prostate cancer]
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[https://www.biorxiv.org/content/10.1101/2021.03.16.435578v1 Integrated single-cell transcriptomics and epigenomics reveals strong germinal center-associated etiology of autoimmune risk loci]
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[https://www.biorxiv.org/content/10.1101/2021.07.28.453784v1 Functional Inference of Gene Regulation using Single-Cell Multi-Omics]
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[https://www.biorxiv.org/content/10.1101/2021.03.24.436532v1 Single-cell analyses reveal a continuum of cell state and composition changes in the malignant transformation of polyps to colorectal cancer]
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[https://www.cell.com/cancer-cell/fulltext/S1535-6108(21)00165-3 Single-cell sequencing links multiregional immune landscapes and tissue-resident T cells in ccRCC to tumor topology and therapy efficacy]
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[https://www.sciencedirect.com/science/article/pii/S1535610821001173 Tumor and immune reprogramming during immunotherapy in advanced renal cell carcinoma]
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[https://www.sciencedirect.com/science/article/pii/S1074761321001199 Single-cell chromatin accessibility landscape identifies tissue repair program in human regulatory T cells]
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[https://www.nature.com/articles/s41591-021-01232-w Identification of resistance pathways and therapeutic targets in relapsed multiple myeloma patients through single-cell sequencing]
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[https://www.nature.com/articles/s41591-021-01323-8 A single-cell map of intratumoral changes during anti-PD1 treatment of patients with breast cancer]
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[https://www.sciencedirect.com/science/article/abs/pii/S0092867420316135 Single-cell landscape of the ecosystem in early-relapse hepatocellular carcinoma]
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[https://www.nature.com/articles/s41586-021-03552-w Interpreting type 1 diabetes risk with genetics and single-cell epigenomics]
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[https://www.sciencedirect.com/science/article/pii/S0092867421000726 Massive expansion of human gut bacteriophage diversity]
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[https://www.sciencedirect.com/science/article/pii/S1931312821001451 The infant gut resistome associates with E. coli, environmental exposures, gut microbiome maturity, and asthma-associated bacterial composition]
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[https://www.sciencedirect.com/science/article/pii/S1931312820306703?dgcid=rss_sd_all Methotrexate impacts conserved pathways in diverse human gut bacteria leading to decreased host immune activation]
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[https://science.sciencemag.org/content/366/6471/eaax9176 A metagenomic strategy for harnessing the chemical repertoire of the human microbiome]
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[https://www.nature.com/articles/s41467-019-10927-1 Predictive metabolomic profiling of microbial communities using amplicon or metagenomic sequences]
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[https://www.nature.com/articles/s41564-018-0306-4 Gut microbiome structure and metabolic activity in inflammatory bowel disease]
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[https://www.nature.com/articles/s41591-020-01183-8 Microbiome connections with host metabolism and habitual diet from 1,098 deeply phenotyped individuals]
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[https://www.nature.com/articles/s41467-020-18476-8 A predictive index for health status using species-level gut microbiome profiling]
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[https://www.nature.com/articles/s41586-019-1237-9 Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases]
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[https://www.nature.com/articles/s41467-021-21475-y Gastrointestinal microbiota composition predicts peripheral inflammatory state during treatment of human tuberculosis]
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[https://www.sciencedirect.com/science/article/pii/S1931312820301694 Structure of the Mucosal and Stool Microbiome in Lynch Syndrome]
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[https://science.sciencemag.org/content/369/6506/936 Cross-reactivity between tumor MHC class 1-restricted antigens and an enterococcal bacteriophage]
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[https://www.nature.com/articles/s41564-020-00831-6 Bifidobacterium bifidum strains synergize with immune checkpoint inhibitors to reduce tumour burden in mice]
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[https://www.nature.com/articles/s41591-020-01223-3 The gut microbiome modulates the protective association between a Mediterranean diet and cardiometabolic disease risk]
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[https://www.nature.com/articles/s41467-019-14177-z Impact of commonly used drugs on the composition and metabolic function of the gut microbiota]
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[https://www.sciencedirect.com/science/article/pii/S0092867420305638 Personalized Mapping of Drug Metabolism by the Human Gut Microbiome]
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[https://www.cell.com/fulltext/S0092-8674(17)30107-1 Mining the Human Gut Microbiota for Immunomodulatory Organisms]
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[https://www.nature.com/articles/s41586-020-2095-1 Microbiome analyses of blood and tissues suggest cancer diagnostic approach]
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[https://science.sciencemag.org/content/368/6494/973 The human tumor microbiome is composed of tumor type-specific intracellular bacteria]
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[https://www.nature.com/articles/s41590-020-0784-4 Functional CRISPR dissection of gene networks controlling human regulatory T cell identity]
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[https://www.sciencedirect.com/science/article/pii/S0092867420306887 Molecular Pathways of Colon Inflammation Induced by Cancer Immunotherapy]
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[https://www.nature.com/articles/s41588-020-00721-x Single-cell epigenomic analyses implicate candidate causal variants at inherited risk loci for Alzheimer’s and Parkinson’s diseases]
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[https://www.nature.com/articles/s41467-020-14766-3 Trajectory-based differential expression analysis for single-cell sequencing data]
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[https://www.nature.com/articles/s41588-018-0156-2 Genetic determinants of co-accessible chromatin regions in activated T cells across humans]
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[https://www.sciencedirect.com/science/article/pii/S009286742030341X Single-Cell Analyses Inform Mechanisms of Myeloid-Targeted Therapies in Colon Cancer]
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[https://www.sciencedirect.com/science/article/pii/S0092867419308967?via%3Dihub Single-Cell Analysis of Crohn's Disease Lesions Identifies a Pathogenic Cellular Module Associated with Resistance to Anti-TNF Therapy]
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|style="padding:.4em;"|SY Park
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[https://pubmed.ncbi.nlm.nih.gov/29434354/ Single-cell Gene Expression Reveals a Landscape of Regulatory T Cell Phenotypes Shaped by the TCR]
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|style="padding:.4em;"|20-3
|style="padding:.4em;"|NY Kim
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[https://pubmed.ncbi.nlm.nih.gov/30078704/ A Single-Cell Atlas of In Vivo Mammalian Chromatin Accessibility]
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|style="padding:.4em;" rowspan=2|2020/04/28
|style="padding:.4em;" rowspan=2|Single-cell
|style="padding:.4em;"|20-2
|style="padding:.4em;"|SB Baek
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[https://pubmed.ncbi.nlm.nih.gov/32014031/ scAI: An Unsupervised Approach for the Integrative Analysis of Parallel Single-Cell Transcriptomic and Epigenomic Profiles]
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|style="padding:.4em;"|20-1
|style="padding:.4em;"|JH Cha
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[https://pubmed.ncbi.nlm.nih.gov/31792411/ Single-cell Multiomic Analysis Identifies Regulatory Programs in Mixed-Phenotype Acute Leukemia]
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{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2019-2nd semester
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|style="padding:.4em;" rowspan=2|2019/10/15
|style="padding:.4em;" rowspan=2|Microbiome
|style="padding:.4em;"|19-51
|style="padding:.4em;"|CY Kim
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[https://www.sciencedirect.com/science/article/pii/S1931312819303488 Obese Individuals with and without Type 2 Diabetes Show Different Gut Microbial Functional Capacity and Composition]
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|style="padding:.4em;"|19-50
|style="padding:.4em;"|SH Lee
|style="padding:.4em;text-align:left"|
[https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-019-0722-6 Clustering co-abundant genes identifies components of the gut microbiome that are reproducibly associated with colorectal cancer and inflammatory bowel disease]
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|style="padding:.4em;" rowspan=2|2019/10/08
|style="padding:.4em;" rowspan=2|Single-cell
|style="padding:.4em;"|19-49
|style="padding:.4em;"|SB Baek
|style="padding:.4em;text-align:left"|
[https://www.nature.com/articles/s41587-019-0206-z Massively parallel single-cell chromatin landscapes of human immune cell development and intratumoral T cell exhaustion]
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|style="padding:.4em;"|19-48
|style="padding:.4em;"|SB Baek
|style="padding:.4em;text-align:left"|
[https://www.biorxiv.org/content/10.1101/739011v1 Assessment of computational methods for the analysis of single-cell ATAC-seq data]
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|style="padding:.4em;" rowspan=2|2019/10/01
|style="padding:.4em;" rowspan=2|Microbiome
|style="padding:.4em;"|19-47
|style="padding:.4em;"|MY Lee
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[https://www.sciencedirect.com/science/article/pii/S1931312819303026 Meta-Analysis Reveals Reproducible Gut Microbiome Alterations in Response to a High-Fat Diet]
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|style="padding:.4em;"|19-46
|style="padding:.4em;"|NY Kim
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S0092867419307731 Tumor Microbiome Diversity and Composition Influence Pancreatic Cancer Outcomes]
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|style="padding:.4em;" rowspan=2|2019/09/24
|style="padding:.4em;" rowspan=2|Single-cell
|style="padding:.4em;"|19-45
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://genome.cshlp.org/content/early/2019/04/01/gr.243725.118 The accessible chromatin landscape of the murine hippocampus at single-cell resolution]
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|style="padding:.4em;"|19-44
|style="padding:.4em;"|SB Baek
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S009286741830446X Integrated Single-Cell Analysis Maps the Continuous Regulatory Landscape of Human Hematopoietic Differentiation]
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|style="padding:.4em;" rowspan=2|2019/09/17
|style="padding:.4em;" rowspan=2|Microbiome
|style="padding:.4em;"|19-43
|style="padding:.4em;"|SH Lee
|style="padding:.4em;text-align:left"|
[https://science.sciencemag.org/content/365/6449/eaau4735 A sparse covarying unit that describes healthy and impaired human gut microbiota development]
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|style="padding:.4em;"|19-42
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S0092867419307810 Large-Scale Analyses of Human Microbiomes Reveal Thousands of Small, Novel Genes]
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|style="padding:.4em;" rowspan=2|2019/09/10
|style="padding:.4em;" rowspan=2|Single-cell
|style="padding:.4em;"|19-41
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S0092867419307329?via%3Dihub Intra- and Inter-cellular Rewiring of the Human Colon during Ulcerative Colitis]
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|style="padding:.4em;"|19-40
|style="padding:.4em;"|IS Choi
|style="padding:.4em;text-align:left"|
[https://www.nature.com/articles/nbt.4042 Multiplexed droplet single-cell RNA-sequencing using natural genetic variation]
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|style="padding:.4em;" rowspan=2|2019/09/03
|style="padding:.4em;" rowspan=2|Microbiome
|style="padding:.4em;"|19-39
|style="padding:.4em;"|NY Kim
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S193131281930352X?via%3Dihub The Landscape of Genetic Content in the Gut and Oral Human Microbiome]
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|style="padding:.4em;"|19-38
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S0092867419307755?via%3Dihub Benchmarking Metagenomics Tools for Taxonomic Classification]
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{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2019
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|style="padding:.4em;" rowspan=2|2019/08/20
|style="padding:.4em;"|19-37
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://www.biorxiv.org/content/10.1101/719088v1 Coexpression uncovers a unified single-cell transcriptomic landscape]
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|style="padding:.4em;"|19-36
|style="padding:.4em;"|MY Lee
|style="padding:.4em;text-align:left"|
[https://www.biorxiv.org/content/10.1101/586859v1 Single-cell interactomes of the human brain reveal cell-type specific convergence of brain disorders]
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|style="padding:.4em;" rowspan=3|2019/08/14
|style="padding:.4em;"|19-35
|style="padding:.4em;"|SH Lee
|style="padding:.4em;text-align:left"|
[https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004075 Proportionality: a valid alternative to correlation for relative data]
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|style="padding:.4em;"|19-34
|style="padding:.4em;"|SH Lee
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[https://www.nature.com/articles/s41598-017-16520-0 propr: An R-package for Identifying Proportionally Abundant Features Using Compositional Data Analysis]
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|style="padding:.4em;"|19-33
|style="padding:.4em;"|HJ Han
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[https://www.nature.com/articles/s41591-018-0157-9 Robust prediction of response to immune checkpoint blockade therapy in metastatic melanoma]
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|style="padding:.4em;" rowspan=2|2019/08/13
|style="padding:.4em;"|19-32
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[https://www.nature.com/articles/s41592-018-0254-1 A test metric for assessing single-cell RNA-seq batch correction]
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|style="padding:.4em;"|19-31
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S0092867419305598 Comprehensive Integration of Single-Cell Data]
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|style="padding:.4em;" rowspan=2|2019/08/08
|style="padding:.4em;"|19-30
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S0092867418313941 Defining T Cell States Associated with Response to Checkpoint Immunotherapy in Melanoma]
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|style="padding:.4em;"|19-29
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S009286741831242X High-Dimensional Analysis Delineates Myeloid and Lymphoid Compartment Remodeling during Successful Immune-Checkpoint Cancer Therapy]
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|style="padding:.4em;" rowspan=2|2019/08/07
|style="padding:.4em;"|19-28
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[https://www.biorxiv.org/content/10.1101/555557v1 A single-cell reference map for human blood and tissue T cell activation reveals functional states in health and disease]
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|style="padding:.4em;"|19-27
|style="padding:.4em;"|IS Choi
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S009286741831568X Dysfunctional CD8 T Cells Form a Proliferative, Dynamically Regulated Compartment within Human Melanoma]
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|style="padding:.4em;" rowspan=2|2019/07/30
|style="padding:.4em;"|19-26
|style="padding:.4em;"|IS Choi
|style="padding:.4em;text-align:left"|
[https://www.nature.com/articles/nm.4466 High-dimensional single-cell analysis predicts response to anti-PD-1 immunotherapy]
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|style="padding:.4em;"|19-25
|style="padding:.4em;"|JW Cho
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S0092867418311784 A Cancer Cell Program Promotes T Cell Exclusion and Resistance to Checkpoint Blockade]
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|style="padding:.4em;" rowspan=2|2019/07/23
|style="padding:.4em;"|19-24
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/26006008 COMPASS identifies T-cell subsets correlated with clinical outcomes.]
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|style="padding:.4em;"|19-23
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[https://www.nature.com/articles/ncomms14825 Sensitive detection of rare disease-associated cell subsets via representation learning]
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|style="padding:.4em;" rowspan=3|2019/05/30
|style="padding:.4em;"|19-22
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[https://www.ncbi.nlm.nih.gov/pubmed/30936547 Meta-analysis of fecal metagenomes reveals global microbial signatures that are specific for colorectal cancer]
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|style="padding:.4em;"|19-21
|style="padding:.4em;"|
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[https://www.ncbi.nlm.nih.gov/pubmed/30936548 Metagenomic analysis of colorectal cancer datasets identifies cross-cohort microbial diagnostic signatures and a link with choline degradation]
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|style="padding:.4em;"|19-20
|style="padding:.4em;"|
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[https://www.ncbi.nlm.nih.gov/pubmed/30664783 Microbial network disturbances in relapsing refractory Crohn's disease.]
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|style="padding:.4em;" rowspan=3|2019/05/23
|style="padding:.4em;"|19-19
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[https://www.ncbi.nlm.nih.gov/pubmed/30867587 New insights from uncultivated genomes of the global human gut microbiome]
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|style="padding:.4em;"|19-18
|style="padding:.4em;"|
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[https://www.ncbi.nlm.nih.gov/pubmed/30745586 A new genomic blueprint of the human gut microbiota]
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|style="padding:.4em;"|19-17
|style="padding:.4em;"|
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[https://www.ncbi.nlm.nih.gov/pubmed/30661755 Extensive Unexplored Human Microbiome Diversity Revealed by Over 150,000 Genomes from Metagenomes Spanning Age, Geography, and Lifestyle.]
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|style="padding:.4em;" rowspan=2|2019/05/16
|style="padding:.4em;"|19-16
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[https://www.ncbi.nlm.nih.gov/pubmed/29311644 Dynamics of metatranscription in the inflammatory bowel disease gut microbiome.]
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|style="padding:.4em;"|19-15
|style="padding:.4em;"|
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[https://www.ncbi.nlm.nih.gov/pubmed/29335555 Metatranscriptome of human faecal microbial communities in a cohort of adult men.]
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|style="padding:.4em;" rowspan=2|2019/05/09
|style="padding:.4em;"|19-14
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[https://www.ncbi.nlm.nih.gov/pubmed/30193113 Post-Antibiotic Gut Mucosal Microbiome Reconstitution Is Impaired by Probiotics and Improved by Autologous FMT.]
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|style="padding:.4em;"|19-13
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[https://www.ncbi.nlm.nih.gov/pubmed/30193112 Personalized Gut Mucosal Colonization Resistance to Empiric Probiotics Is Associated with Unique Host and Microbiome Features.]
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|style="padding:.4em;" rowspan=2|2019/05/02
|style="padding:.4em;"|19-12
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[https://www.ncbi.nlm.nih.gov/pubmed/30753825 Distinct Immune Cell Populations Define Response to Anti-PD-1 Monotherapy and Anti-PD-1/Anti-CTLA-4 Combined Therapy.]
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[https://www.ncbi.nlm.nih.gov/pubmed/30778252 Subsets of exhausted CD8+ T cells differentially mediate tumor control and respond to checkpoint blockade.]
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|style="padding:.4em;" rowspan=2|2019/4/11
|style="padding:.4em;"|19-10
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[https://www.ncbi.nlm.nih.gov/pubmed/30479382 Lineage tracking reveals dynamic relationships of T cells in colorectal cancer.]
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[https://www.ncbi.nlm.nih.gov/pubmed/30523328 Single cell dissection of plasma cell heterogeneity in symptomatic and asymptomatic myeloma.]
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|style="padding:.4em;"|19-8
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[https://www.ncbi.nlm.nih.gov/pubmed/29942092 Single-cell profiling of breast cancer T cells reveals a tissue-resident memory subset associated with improved prognosis.]
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|style="padding:.4em;"|19-7
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[https://www.ncbi.nlm.nih.gov/pubmed/29942094 Global characterization of T cells in non-small-cell lung cancer by single-cell sequencing]
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|style="padding:.4em;" rowspan=2|2019/3/28
|style="padding:.4em;"|19-6
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[https://www.ncbi.nlm.nih.gov/pubmed/28319088 Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors.]
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|style="padding:.4em;"|19-5
|style="padding:.4em;"|
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[https://www.ncbi.nlm.nih.gov/pubmed/28622514 Landscape of Infiltrating T Cells in Liver Cancer Revealed by Single-Cell Sequencing.]
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|style="padding:.4em;" rowspan=3|2019/3/21
|style="padding:.4em;"|19-4
|style="padding:.4em;"|
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[https://www.ncbi.nlm.nih.gov/pubmed/29988129 Phenotype molding of stromal cells in the lung tumor microenvironment.]
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|style="padding:.4em;"|19-3-1
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[https://www.ncbi.nlm.nih.gov/pubmed/30787436 A single-cell molecular map of mouse gastrulation and early organogenesis]
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|style="padding:.4em;"|19-3
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[https://www.ncbi.nlm.nih.gov/pubmed/30787437 The single-cell transcriptional landscape of mammalian organogenesis]
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|style="padding:.4em;" rowspan=2|2019/3/14
|style="padding:.4em;"|19-2
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[https://www.ncbi.nlm.nih.gov/pubmed/29961579 Single-Cell Map of Diverse Immune Phenotypes in the Breast Tumor Microenvironment]
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[https://www.ncbi.nlm.nih.gov/pubmed/29198524 Single-Cell Transcriptomic Analysis of Primary and Metastatic Tumor Ecosystems in Head and Neck Cancer]
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{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2018
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|style="padding:.4em;" rowspan=2|2018/06/14
|style="padding:.4em;"|18-12
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=A+pan-cancer+analysis+of+enhancer+expression+in+nearly+9000+patient+samples A Pan-Cancer Analysis of Enhancer Expression in Nearly 9000 Patient Samples.]
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=Machine+learning+identifies+stemness+features+associated+with+oncogenic+dedifferentiation Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation.]
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|style="padding:.4em;" rowspan=2|2018/06/07
|style="padding:.4em;"|18-10
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=Developmental+and+oncogenic+programs+in+H3K27M+gliomas+dissected+by+single-cell+RNA-seq Developmental and oncogenic programs in H3K27M gliomas dissected by single-cell RNA-seq.]
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=Chemoresistance+evolution+in+triple-negative+breast+cancer+delineated+by+single-cell+sequencing Chemoresistance Evolution in Triple-Negative Breast Cancer Delineated by Single-Cell Sequencing.]
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|style="padding:.4em;" rowspan=2|2018/05/31
|style="padding:.4em;"|18-8
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=Mapping+human+pluripotent+stem+cell+differentiation+pathways+using+high+throughput+single-cell+RNA-sequencing Mapping human pluripotent stem cell differentiation pathways using high throughput single-cell RNA-sequencing.]
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=A+single-cell+RNA-seq+survey+of+the+developmental+landscape+of+the+human+prefrontal+cortex A single-cell RNA-seq survey of the developmental landscape of the human prefrontal cortex.]
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|style="padding:.4em;" rowspan=2|2018/05/24
|style="padding:.4em;"|18-6
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=Single-cell+RNA+sequencing+identifies+celltype-specific+cis-eQTLs+and+co-expression+QTLs Single-cell RNA sequencing identifies celltype-specific cis-eQTLs and co-expression QTLs.]
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|style="padding:.4em;"|18-5
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=FOCS%3A+a+novel+method+for+analyzing+enhancer+and+gene+activity+patterns+infers+an+extensive+enhancer-promoter+map FOCS: a novel method for analyzing enhancer and gene activity patterns infers an extensive enhancer-promoter map.]
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|style="padding:.4em;" rowspan=2|2018/05/17
|style="padding:.4em;"|18-4
|style="padding:.4em;"|KS Kim
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[https://www.ncbi.nlm.nih.gov/pubmed/29149608 A Distinct Gene Module for Dysfunction Uncoupled from Activation in Tumor-Infiltrating T Cells.]
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|style="padding:.4em;"|18-3
|style="padding:.4em;"|SH Lee
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[https://www.ncbi.nlm.nih.gov/pubmed/29610481 A global transcriptional network connecting noncoding mutations to changes in tumor gene expression.]
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|style="padding:.4em;" rowspan=2|2018/05/10
|style="padding:.4em;"|18-2
|style="padding:.4em;"|JW Cho
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=Inferring+regulatory+element+landscapes+and+transcription+factor+networks+from+cancer+methylomes Inferring regulatory element landscapes and transcription factor networks from cancer methylomes.]
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|style="padding:.4em;"|DS Bae
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[https://www.ncbi.nlm.nih.gov/pubmed/28129544 Genomic and Transcriptomic Features of Response to Anti-PD-1 Therapy in Metastatic Melanoma.]
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{|class=wikitable style="text-align:center;"
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[https://www.ncbi.nlm.nih.gov/pubmed/28104840 Tumor aneuploidy correlates with markers of immune evasion and with reduced response to immunotherapy.]
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|style="padding:.4em;"|17-35
|style="padding:.4em;"|JW Cho
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[https://www.ncbi.nlm.nih.gov/pubmed/27240091 Landscape of tumor-infiltrating T cell repertoire of human cancers.]
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|style="padding:.4em;"|17-34
|style="padding:.4em;"|JW Cho
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[http://biorxiv.org/content/early/2015/09/01/025908 The landscape of T cell infiltration in human cancer and its association with antigen presenting gene expression.]
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|style="padding:.4em;"|17-33
|style="padding:.4em;"|MY Lee
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.08.052 A Distinct Gene Module for Dysfunction Uncoupled from Activation in Tumor-Infiltrating T Cells.]
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[http://www.tandfonline.com/doi/full/10.1080/2162402X.2016.1253654 Identification of genetic determinants of breast cancer immune phenotypes by integrative genome-scale analysis.]
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|style="padding:.4em;"|17-31
|style="padding:.4em;"|MY Lee
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.celrep.2016.03.075 Genomic Correlates of Immune-Cell Infiltrates in Colorectal Carcinoma.]
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|style="padding:.4em;" rowspan=2|2017/05/31
|style="padding:.4em;"|17-30
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.02.065 Genomic and Transcriptomic Features of Response to Anti-PD-1 Therapy in Metastatic Melanoma.]
|-
|style="padding:.4em;"|17-29
|style="padding:.4em;"|JW Cho
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.celrep.2016.12.019 Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade.]
|-
|style="padding:.4em;" rowspan=2|2017/05/24
|style="padding:.4em;"|17-28
|style="padding:.4em;"|EB Kim
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.12.022 Systemic Immunity Is Required for Effective Cencer Immunotherapy.]
|-
|style="padding:.4em;"|17-27
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.10.020 Linking the Human Gut Microbiome to Inflammatory Cytokine Production Capacity.]
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|style="padding:.4em;" rowspan=2|2017/05/17
|style="padding:.4em;"|17-26
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.10.018 Host and Environmental Factors Influencing Individual Human Cytokine Responses.]
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|style="padding:.4em;"|17-25
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.10.017 A Functional Genomics Approach to Understand Variation in Cytokine Production in Humans.]
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|style="padding:.4em;" rowspan=2|2017/04/26
|style="padding:.4em;"|17-24
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1038%2FNMETH.4177 Pooled CRISPR screening with single-cell transcriptome readout.]
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|style="padding:.4em;"|17-23
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.11.039 Dissecting Immune Circuits by Linking CRISPR-Pooled Screens with Single-Cell RNA-Seq.]
|-
|style="padding:.4em;" rowspan=2|2017/04/12
|style="padding:.4em;"|17-22
|style="padding:.4em;"|SH Lee
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.11.038 Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens.]
|-
|style="padding:.4em;"|17-21
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1038%2Fnbt.3569 Wishbone identifies bifurcating developmental trajectories from single-cell data.]
|-
|style="padding:.4em;" rowspan=2|2017/04/05
|style="padding:.4em;"|17-20
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[http://biorxiv.org/content/early/2017/02/21/110668 Reversed graph embedding resolves complex single-cell developmental trajectories.]
|-
|style="padding:.4em;"|17-19
|style="padding:.4em;"|SH Lee
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1038%2Fnmeth.4150 Single-cell mRNA quantification and differential analysis with Census.]
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|style="padding:.4em;" rowspan=2|2017/03/29
|style="padding:.4em;"|17-18
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.celrep.2016.12.060 Single-Cell Transcriptomic Analysis Defines Heterogeneity and Transcriptional Dynamics in the Adult Neural Stem Cell Lineage.]
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|style="padding:.4em;"|17-17
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/26051941 Single-Cell Network Analysis Identifies DDIT3 as a Nodal Lineage Regulator in Hematopoiesis.]
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|style="padding:.4em;" rowspan=2|2017/03/22
|style="padding:.4em;"|17-16
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/27580035 Single-cell analysis of mixed-lineage states leading to a binary cell fate choice.]
|-
|style="padding:.4em;"|17-15
|style="padding:.4em;"|EB Kim
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/27281220 Dissecting direct reprogramming from fibroblast to neuron using single-cell RNA-seq.]
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|style="padding:.4em;" rowspan=2|2017/03/15
|style="padding:.4em;"|17-14
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1126%2Fscience.aad0501 Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq.]
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|style="padding:.4em;"|17-13
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/26084335 Single-cell mRNA sequencing identifies subclonal heterogeneity in anti-cancer drug responses of lung adenocarcinoma cells.]
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|style="padding:.4em;" rowspan=2|2017/02/28
|style="padding:.4em;"|17-12
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/27824113 A Comprehensive Characterization of the Function of LincRNAs in Transcriptional Regulation Through Long-Range Chromatin Interactions.]
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|style="padding:.4em;"|17-11
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[//pubmed.gov/27851969 Epigenomic Deconvolution of Breast Tumors Reveals Metabolic Coupling between Constituent Cell Types.]
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|style="padding:.4em;" rowspan=2|2017/02/21
|style="padding:.4em;"|17-10
|style="padding:.4em;"|EB Kim
|style="padding:.4em;text-align:left"|
[http://biorxiv.org/content/early/2016/12/30/097451 Convergence of dispersed regulatory mutations predicts driver genes in prostate cancer.]
|-
|style="padding:.4em;"|17-09
|style="padding:.4em;"|EB Kim
|style="padding:.4em;text-align:left"|
[//pubmed.gov/27723759 Chromatin structure-based prediction of recurrent noncoding mutations in cancer.]
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|style="padding:.4em;" rowspan=2|2017/02/07
|style="padding:.4em;"|17-08
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[http://biorxiv.org/content/early/2016/11/17/088286 Somatic Mutations and Neoepitope Homology in Melanomas Treated with CTLA-4 Blockade.]
|-
|style="padding:.4em;"|17-07
|style="padding:.4em;"|MY Lee
|style="padding:.4em;text-align:left"|
[http://biorxiv.org/content/early/2016/11/28/090134 Chemotherapy weakly contributes to predicted neoantigen expression in ovarian cancer.]
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|style="padding:.4em;" rowspan=1|2017/01/31
|style="padding:.4em;"|17-06
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[//www.ncbi.nlm.nih.gov/pubmed/27912059 Microbiota Diurnal Rhythmicity Programs Host Transcriptome Oscillations.]
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|style="padding:.4em;" rowspan=1|2017/01/24
|style="padding:.4em;"|17-05
|style="padding:.4em;"|JW Cho
|style="padding:.4em;text-align:left"|
[//www.ncbi.nlm.nih.gov/pubmed/27851914 Transcriptional Landscape of Human Tissue Lymphocytes Unveils Uniqueness of Tumor-Infiltrating T Regulatory Cells.]
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|style="padding:.4em;" rowspan=2|2017/01/17
|style="padding:.4em;"|17-04
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[//www.ncbi.nlm.nih.gov/pubmed/25938943 Mapping long-range promoter contacts in human cells with high-resolution capture Hi-C]
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|style="padding:.4em;"|17-03
|style="padding:.4em;"|EB Kim
|style="padding:.4em;text-align:left"|
[//www.ncbi.nlm.nih.gov/pubmed/27306882 CHiCAGO: robust detection of DNA looping interactions in Capture Hi-C data.]
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|style="padding:.4em;" rowspan=1|2017/01/10
|style="padding:.4em;"|17-02
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[//www.ncbi.nlm.nih.gov/pubmed/26527291 ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis]
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|style="padding:.4em;" rowspan=1|2017/01/03
|style="padding:.4em;"|17-01
|style="padding:.4em;"|SH Lee
|style="padding:.4em;text-align:left"|
[//www.ncbi.nlm.nih.gov/pubmed/26287467 Single-cell messenger RNA sequencing reveals rare intestinal cell types]
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{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2016
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!scope="col" style="padding:.4em" |Date
!scope="col" style="padding:.4em" | Paper index
!scope="col" style="padding:.4em" | Presenter
!scope="col" style="padding:.4em" | Paper title
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|style="padding:.4em;" rowspan=1|2016/12/27
|style="padding:.4em;"|2016-31
|style="padding:.4em;"|EB Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/25664528 Decoding the regulatory network of early blood development from single-cell gene expression measurements.]
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|style="padding:.4em;" rowspan=1|2016/12/6
|style="padding:.4em;"|2016-30
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26299571 Single-Cell RNA-Seq with Waterfall Reveals Molecular Cascades underlying Adult Neurogenesis.]
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|style="padding:.4em;" rowspan=1|2016/11/29
|style="padding:.4em;"|2016-29
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/24658644 The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells.]
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|style="padding:.4em;" rowspan=1|2016/11/22
|style="padding:.4em;"|2016-28
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26887813 Classification of low quality cells from single-cell RNA-seq data.]
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|style="padding:.4em;" rowspan=1|2016/11/15
|style="padding:.4em;"|2016-27
|style="padding:.4em;"|MY Lee
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26000487 Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells.]
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|style="padding:.4em;" rowspan=1|2016/11/8
|style="padding:.4em;"|2016-26
|style="padding:.4em;"|JW Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26000488 Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets.]
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|style="padding:.4em;" rowspan=1|2016/11/1
|style="padding:.4em;"|2016-25
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27426982 Causal Mechanistic Regulatory Network for Glioblastoma Deciphered Using Systems Genetics Network Analysis.]
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|style="padding:.4em;" rowspan=1|2016/10/25
|style="padding:.4em;"|2016-24
|style="padding:.4em;"|MY Lee
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27549193 Comprehensive analyses of tumor immunity: implications for cancer immunotherapy.]
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|style="padding:.4em;" rowspan=1|2016/10/11
|style="padding:.4em;"|2016-23
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=sidespread+parainflammation Widespread parainflammation in human cancer]
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|style="padding:.4em;" rowspan=1|2016/9/27
|style="padding:.4em;"|2016-22
|style="padding:.4em;"|ER Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27535533 Analysis of protein-coding genetic variation in 60,706 humans]
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|style="padding:.4em;" rowspan=1|2016/9/20
|style="padding:.4em;"|2016-21
|style="padding:.4em;"|SM Yang
|style="padding:.4em;text-align:left"|
Functional characterization of somatic mutations in cancer using network-based inference of protein activity
[http://www.ncbi.nlm.nih.gov/pubmed/27322546 pubmed]
[http://www.nature.com/ng/journal/v48/n8/full/ng.3593.html fulltext]
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|style="padding:.4em;" rowspan=1|2016/9/13
|style="padding:.4em;"|2016-20
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=exploiting+single-cell+expression+to+characterize Exploiting single-cell expression to characterize co-expression replicability.]
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|style="padding:.4em;" rowspan=1|2016/9/6
|style="padding:.4em;"|2016-19
|style="padding:.4em;"|T Lee
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26940869 Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade]
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|style="padding:.4em;" rowspan=1|2016/8/31
|style="padding:.4em;"|2016-18
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27264179 A Computational Drug Repositioning Approach for Targeting Oncogenic Transcription Factors]
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|style="padding:.4em;" rowspan=1|2016/8/16
|style="padding:.4em;"|2016-17
|style="padding:.4em;"|JW Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27309802 The landscape of accessible chromatin in mammalian preimplantation embryos]
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|style="padding:.4em;" rowspan=1|2016/8/8
|style="padding:.4em;"|2016-16
|style="padding:.4em;"|EB Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27064255 Enhancer-promoter interactions are encoded by complex genomic signatures on looping chromatin]
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|style="padding:.4em;" rowspan=1|2016/8/1
|style="padding:.4em;"|2016-15
|style="padding:.4em;"|MY Lee
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27040498 Personalized Immunomonitoring Uncovers Molecular Networks that Stratify Lupus Patients]
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|style="padding:.4em;" rowspan=1|2016/7/25
|style="padding:.4em;"|2016-14
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=integration+of+summary+data+from+gwas+and+eqtl+studies Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets]
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|style="padding:.4em;" rowspan=1|2016/7/18
|style="padding:.4em;"|2016-13
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/23624555 Identification of transcriptional regulators in the mouse immune system]
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|style="padding:.4em;" rowspan=2|2016/6/8
|style="padding:.4em;"|2016-12
|style="padding:.4em;"|DS Bae, CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26619012 Mapping the effects of drugs on the immune system]
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|style="padding:.4em;"| 2016-11
|style="padding:.4em;"|DS Bae, CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26186195 Elucidating compound mechanism of action by network perturbation analysis]
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|style="padding:.4em;" rowspan=2|2016/6/1
|style="padding:.4em;"|2016-10
|style="padding:.4em;"|MY Lee,SM Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26854917 Integrative approaches for large-scale transcriptome-wide association studies]
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|style="padding:.4em;"| 2016-9
|style="padding:.4em;"|MY Lee,SM Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26950747 Tissue-specific regulatory circuits reveal variable modular perturbations across complex diseases]
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|style="padding:.4em;" rowspan=2|2016/5/18
|style="padding:.4em;"|2016-8
|style="padding:.4em;"|CY Kim,SJ Kwon
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27013732 Survey of variation in human transcription factors reveals prevalent DNA binding changes]
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|style="padding:.4em;"| 2016-7
|style="padding:.4em;"| CY Kim,SJ Kwon
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26502339 Large-scale identification of sequence variants influencing human transcription factor occupancy in vivo]
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|style="padding:.4em;" rowspan=2|2016/5/11
|style="padding:.4em;"|2016-6
|style="padding:.4em;"|DS Bae, CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/25171417 Predicting Cancer-specific vulnerability via data-driven detection of synthetic lethality]
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|style="padding:.4em;"| 2016-5
|style="padding:.4em;"|DS Bae, CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/23467089 Dynamic regulatory network controlling Th17 cell differentiation]
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|style="padding:.4em;" rowspan=2|2016/5/4
|style="padding:.4em;"|2016-4
|style="padding:.4em;"|MY Lee,SM Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/25853550 Characterization of the immunophenotypes and antigenomes of colorectal cancers reveals distinct tumor escape mechanisms and novel targets for immunotherapy]
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|style="padding:.4em;"| 2016-3
|style="padding:.4em;"|MY Lee,SM Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26618344 Regulators of genetic risk of breast cancer identified by integrative network analysis]
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|style="padding:.4em;" rowspan=2|2016/4/27
|style="padding:.4em;"|2016-2
|style="padding:.4em;"|CY Kim,SJ Kwon
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26780608 A predictive computational framework for direct reprogramming between human cell types]
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|style="padding:.4em;"| 2016-1
|style="padding:.4em;"| CY Kim,SJ Kwon
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/25126793 CellNet: Network biology applied to stem cell engineering]
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{|class=wikitable style="text-align:center;"
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|style="padding:.4em;" rowspan=2|2015/06/11
|style="padding:.4em;"|2015-55
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[http://genome.cshlp.org/content/24/2/340.long Improved exome prioritization of disease genes through cross-species phenotype comparison.]
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|style="padding:.4em;"|2015-54
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[http://www.sciencedirect.com/science/article/pii/S0002929714001128 Phevor combines multiple biomedical ontologies for accurate identification of disease-causing alleles in single individuals and small nuclear families.]
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|style="padding:.4em;" rowspan=2|2015/06/04
|style="padding:.4em;"|2015-53
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[http://www.nature.com/nmeth/journal/v10/n11/full/nmeth.2656.html eXtasy: variant prioritization by genomic data fusion.]
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|style="padding:.4em;"|2015-52
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[http://genome.cshlp.org/content/21/9/1529.long A probabilistic disease-gene finder for personal genomes.]
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|style="padding:.4em;" rowspan=2|2015/05/28
|style="padding:.4em;"|2015-51
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[http://www.nature.com/ng/journal/v46/n12/full/ng.3141.html Systematic analysis of noncoding somatic mutations and gene expression alterations across 14 tumor types.]
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|style="padding:.4em;"|2015-50
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[http://www.nature.com/nbt/journal/v28/n5/full/nbt.1630.html GREAT improves functional interpretation of cis-regulatory regions.]
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|style="padding:.4em;" rowspan=2|2015/05/21
|style="padding:.4em;"|2015-49
|style="padding:.4em;"|
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[http://www.nature.com/nmeth/journal/v11/n3/full/nmeth.2832.html Functional annotation of noncoding sequence variants.]
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|style="padding:.4em;"|2015-48
|style="padding:.4em;"|
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[http://genomebiology.com/content/15/10/480 FunSeq2: A framework for prioritizing noncoding regulatory variants in cancer.]
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|style="padding:.4em;" rowspan=2|2015/05/14
|style="padding:.4em;"|2015-47
|style="padding:.4em;"|
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[http://www.nature.com/nmeth/journal/v12/n2/full/nmeth.3215.html Selecting causal genes from genome-wide association studies via functionally coherent subnetworks.]
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|style="padding:.4em;"|2015-46
|style="padding:.4em;"|
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[http://www.nature.com/ncomms/2015/150119/ncomms6890/full/ncomms6890.html Biological interpretation of genome-wide association studies using predicted gene functions.]
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|style="padding:.4em;" rowspan=3|2015/05/07
|style="padding:.4em;"|2015-45
|style="padding:.4em;"|
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[http://www.nature.com/ncomms/2014/140626/ncomms5212/full/ncomms5212.html Human symptoms-disease network.]
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|style="padding:.4em;"|2015-44
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867413010246 A nondegenerate code of deleterious variants in Mendelian loci contributes to complex disease risk.]
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|style="padding:.4em;"|2015-43
|style="padding:.4em;"|
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[http://www.sciencemag.org/content/347/6224/1257601.long Uncovering disease-disease relationships through the incomplete interactome.]

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|style="padding:.4em;" rowspan=2|2015/04/30
|style="padding:.4em;"|2015-42
|style="padding:.4em;"|
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[http://genome.cshlp.org/content/25/1/142.long The discovery of integrated gene networks for autism and related disorders.]
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|style="padding:.4em;"|2015-41
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[http://msb.embopress.org/content/10/12/774.long Integrated systems analysis reveals a molecular network underlying autism spectrum disorders.]
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|style="padding:.4em;" rowspan=3|2015/04/23
|style="padding:.4em;"|2015-40
|style="padding:.4em;"|
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[http://www.nature.com/nature/journal/v518/n7539/full/nature13990.html Dissecting neural differentiation regulatory networks through epigenetic footprinting.]
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|style="padding:.4em;"|2015-39
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[http://www.nature.com/nature/journal/v518/n7539/full/nature14221.html Cell-of-origin chromatin organization shapes the mutational landscape of cancer.]
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|style="padding:.4em;"|2015-38
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[http://www.nature.com/nature/journal/v518/n7539/full/nature14248.html Integrative analysis of 111 reference human epigenomes.]
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|style="padding:.4em;" rowspan=2|2015/04/09
|style="padding:.4em;"|2015-37
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[http://genome.cshlp.org/content/25/2/246.long Genome-wide analysis of local chromatin packing in Arabidopsis thaliana.]
|-
|style="padding:.4em;"|2015-36
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414014974 A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping.]
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|style="padding:.4em;" rowspan=2|2015/04/02
|style="padding:.4em;"|2015-35
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nrg/journal/v14/n6/full/nrg3454.html Exploring the three-dimensional organization of genomes: interpreting chromatin interaction data.]
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|style="padding:.4em;"|2015-34
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.sciencemag.org/content/347/6225/1010.long Transcribed enhancers lead waves of coordinated transcription in transitioning mammalian cells.]
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|style="padding:.4em;" rowspan=2|2015/03/26
|style="padding:.4em;"|2015-33
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://genome.cshlp.org/content/25/2/257.long Cupid: simultaneous reconstruction of microRNA-target and ceRNA networks.]
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|style="padding:.4em;"|2015-32
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://genome.cshlp.org/content/25/1/41.long Characterization of the neural stem cell gene regulatory network identifies OLIG2 as a multifunctional regulator of self-renewal.]
|-
|style="padding:.4em;" rowspan=2|2015/03/19
|style="padding:.4em;"|2015-31
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nbt/journal/vaop/ncurrent/full/nbt.3154.html Decoding the regulatory network of early blood development from single-cell gene expression measurements.]
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|style="padding:.4em;"|2015-30
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nrg/journal/v16/n3/full/nrg3833.html Computational and analytical challenges in single-cell transcriptomics.]
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|style="padding:.4em;" rowspan=3|2015/03/12
|style="padding:.4em;"|2015-29
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867415000136 Extensive Strain-Level Copy-Number Variation across Human Gut Microbiome Species.]
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|style="padding:.4em;"|2015-28
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v500/n7464/full/nature12506.html Richness of human gut microbiome correlates with metabolic markers.]
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|style="padding:.4em;"|2015-27
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v490/n7418/full/nature11450.html A metagenome-wide association study of gut microbiota in type 2 diabetes.]
|-
|style="padding:.4em;" rowspan=3|2015/03/09
|style="padding:.4em;"|2015-26
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003326 Practical guidelines for the comprehensive analysis of ChIP-seq data.]
|-
|style="padding:.4em;"|2015-25
|style="padding:.4em;"|T Lee
|style="padding:.4em;text-align:left"|
[http://genome.cshlp.org/content/23/5/777.long Patterns of regulatory activity across diverse human cell types predict tissue identity, transcription factor binding, and long-range interactions.]
|-
|style="padding:.4em;"|2015-24
|style="padding:.4em;"|ER Kim
|style="padding:.4em;text-align:left"|
[http://msb.embopress.org/content/10/11/760.long Rapid neurogenesis through transcriptional activation in human stem cells.]
|-
|style="padding:.4em;" rowspan=3|2015/03/02
|style="padding:.4em;"|2015-23
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414011787 Genome-Scale CRISPR-Mediated Control of Gene Repression and Activation.]
|-
|style="padding:.4em;"|2015-22
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1004237 Integrating multiple genomic data to predict disease-causing nonsynonymous single nucleotide variants in exome sequencing studies.]
|-
|style="padding:.4em;"|2015-21
|style="padding:.4em;"|ER Kim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/ng/journal/v45/n6/full/ng.2653.html The Genotype-Tissue Expression (GTEx) project.]
|-
|style="padding:.4em;" rowspan=3|2015/02/24
|style="padding:.4em;"|2015-20
|style="padding:.4em;"|ER Kim
|style="padding:.4em;text-align:left"|
[http://www.sciencemag.org/content/346/6212/1007.long Mouse regulatory DNA landscapes reveal global principles of cis-regulatory evolution.]
|-
|style="padding:.4em;"|2015-19
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v515/n7527/full/nature13985.html Principles of regulatory information conservation between mouse and human.]
|-
|style="padding:.4em;"|2015-18
|style="padding:.4em;"|BH Kang
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v515/n7527/full/nature13972.html Conservation of trans-acting circuitry during mammalian regulatory evolution.]
|-
|style="padding:.4em;" rowspan=3|2015/02/16
|style="padding:.4em;"|2015-17
|style="padding:.4em;"|T Lee
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature13835.html Genetic and epigenetic fine mapping of causal autoimmune disease variants.]
|-
|style="padding:.4em;"|2015-16
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/ng/journal/v46/n3/full/ng.2892.html A general framework for estimating the relative pathogenicity of human genetic variants.]
|-
|style="padding:.4em;"|2015-15
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003825 A probabilistic model to predict clinical phenotypic traits from genome sequencing.]
|-
|style="padding:.4em;" rowspan=4|2015/02/02
|style="padding:.4em;"|2015-14
|style="padding:.4em;"|BH Kang
|style="padding:.4em;text-align:left"|
[http://www.pnas.org/content/111/22/E2329.long Relating the metatranscriptome and metagenome of the human gut.]
|-
|style="padding:.4em;"|2015-13
|style="padding:.4em;"|BH Kang
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v513/n7516/full/nature13568.html Alterations of the human gut microbiome in liver cirrhosis.]
|-
|style="padding:.4em;"|2015-12
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nbt/journal/v32/n8/full/nbt.2942.html An integrated catalog of reference genes in the human gut microbiome.]
|-
|style="padding:.4em;"|2015-11
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nbt/journal/v32/n8/full/nbt.2939.html Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes.]
|-
|style="padding:.4em;" rowspan=5|2015/01/26
|style="padding:.4em;"|2015-10
|style="padding:.4em;"|HH Kim
|style="padding:.4em;text-align:left"|
[http://msb.embopress.org/content/9/1/666.long Computational meta'omics for microbial community studies.]
|-
|style="padding:.4em;"|2015-09
|style="padding:.4em;"|HH Kim
|style="padding:.4em;text-align:left"|
[http://genomemedicine.com/content/5/7/65 Functional profiling of the gut microbiome in disease-associated inflammation.]
|-
|style="padding:.4em;"|2015-08
|style="padding:.4em;"|BH Kang
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S016895251200145X Biodiversity and functional genomics in the human microbiome.]
|-
|style="padding:.4em;"|2015-07
|style="padding:.4em;"|BH Kang
|style="padding:.4em;text-align:left"|
[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002808 Chapter 12: Human Microbiome Analysis.]
|-
|style="padding:.4em;"|2015-06
|style="padding:.4em;"|BH Kang
|style="padding:.4em;text-align:left"|
[http://www.cell.com/cell/abstract/S0092-8674%2814%2900864-2 Conducting a Microbiome Study.]
|-
|style="padding:.4em;" rowspan=3|2015/01/12
|style="padding:.4em;"|2015-05
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/ncomms/2014/141210/ncomms6522/full/ncomms6522.html Small RNA changes en route to distinct cellular states of induced pluripotency.]
|-
|style="padding:.4em;"|2015-04
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v516/n7530/full/nature14046.html Genome-wide characterization of the routes to pluripotency.]
|-
|style="padding:.4em;"|2015-03
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v516/n7530/full/nature14047.html Divergent reprogramming routes lead to alternative stem-cell states.]
|-
|style="padding:.4em;" rowspan=2|2015/01/05
|style="padding:.4em;"|2015-02
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.pnas.org/content/111/21/E2191.long Global view of enhancer-promoter interactome in human cells.]
|-
|style="padding:.4em;"|2015-01
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://nar.oxfordjournals.org/content/41/22/10391.long Combining Hi-C data with phylogenetic correlation to predict the target genes of distal regulatory elements in human genome.]
|}
{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2014
|-
!scope="col" style="padding:.4em" | Date
!scope="col" style="padding:.4em" | Paper index
!scope="col" style="padding:.4em" | Presenter
!scope="col" style="padding:.4em" | Paper title
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|style="padding:.4em;" rowspan=2|2014/12/23
|style="padding:.4em;"|2014-41
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867413012270 Super-enhancers in the control of cell identity and disease.]
|-
|style="padding:.4em;"|2014-40
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867413003929 Master transcription factors and mediator establish super-enhancers at key cell identity genes.]
|-
|style="padding:.4em;" rowspan=4|2014/12/09
|style="padding:.4em;"|2014-39
|style="padding:.4em;"|HH Kim
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414013713 Unraveling the biology of a fungal meningitis pathogen using chemical genetics.]
|-
|style="padding:.4em;"|2014-38
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414014226 A proteome-scale map of the human interactome network.]
|-
|style="padding:.4em;"|2014-37
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[http://msb.embopress.org/content/10/9/752.long The role of the interactome in the maintenance of deleterious variability in human populations.]
|-
|style="padding:.4em;"|2014-36
|style="padding:.4em;"|HS Shim
|style="padding:.4em;text-align:left"|
[http://www.sciencemag.org/content/342/6154/1235587.long Integrative annotation of variants from 1092 humans: application to cancer genomics.]
|-
|style="padding:.4em;" rowspan=2|2014/12/02
|style="padding:.4em;"|2014-35
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://genome.cshlp.org/content/23/8/1319.long Mapping functional transcription factor networks from gene expression data.]
|-
|style="padding:.4em;"|2014-34
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nrg/journal/v15/n7/full/nrg3684.html In pursuit of design principles of regulatory sequences.]
|-
|style="padding:.4em;" rowspan=2|2014/11/25
|style="padding:.4em;"|2014-33
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[http://genomemedicine.com/content/3/6/36 Epigenomics of human embryonic stem cells and induced pluripotent stem cells: insights into pluripotency and implications for disease.]
|-
|style="padding:.4em;"|2014-32
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S009286741300891X Developmental fate and cellular maturity encoded in human regulatory DNA landscapes.]
|-
|style="padding:.4em;" rowspan=2|2014/11/18
|style="padding:.4em;"|2014-31
|style="padding:.4em;"|SM Yang
|style="padding:.4em;text-align:left"|
[http://genomemedicine.com/content/6/8/64 The 'dnet' approach promotes emerging research on cancer patient survival.]
|-
|style="padding:.4em;"|2014-30
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414010368 Determination and inference of eukaryotic transcription factor sequence specificity.]
|-
|style="padding:.4em;" rowspan=3|2014/11/11
|style="padding:.4em;"|2014-29
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[http://www.pnas.org/content/110/16/6412.long Transcription factors interfering with dedifferentiation induce cell type-specific transcriptional profiles.]
|-
|style="padding:.4em;"|2014-28
|style="padding:.4em;"|HH Kim
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414009350 Dissecting engineered cell types and enhancing cell fate conversion via CellNet.]
|-
|style="padding:.4em;"|2014-27
|style="padding:.4em;"|HH Kim
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414009349 CellNet: network biology applied to stem cell engineering.]
|-
|style="padding:.4em;" rowspan=2|2014/11/04
|style="padding:.4em;"|2014-26
|style="padding:.4em;"|HS Shim
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414009775 Predicting Cancer-Specific Vulnerability via Data-Driven Detection of Synthetic Lethality.]
|-
|style="padding:.4em;"|2014-25
|style="padding:.4em;"|JH Shin
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nmeth/journal/v11/n9/full/nmeth.3046.html Phen-Gen: combining phenotype and genotype to analyze rare disorders.]
|-
|style="padding:.4em;" rowspan=2|2014/10/28
|style="padding:.4em;"|2014-24
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nbt/journal/vaop/ncurrent/full/nbt.2877.html A community effort to assess and improve drug sensitivity prediction algorithms.]
|-
|style="padding:.4em;"|2014-23
|style="padding:.4em;"|HS Shim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/ng/journal/v46/n9/full/ng.3051.html Multi-tiered genomic analysis of head and neck cancer ties TP53 mutation to 3p loss.]
|-
|style="padding:.4em;" rowspan=2|2014/09/30
|style="padding:.4em;"|2014-22
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nmeth/journal/v10/n11/full/nmeth.2651.html Network-based stratification of tumor mutations.]
|-
|style="padding:.4em;"|2014-21
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414001457 Synonymous mutations frequently act as driver mutations in human cancers.]
|-
|style="padding:.4em;" rowspan=2|2014/09/23
|style="padding:.4em;"|2014-20
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003460 VarWalker: Personalized Mutation Network Analysis of Putative Cancer Genes from Next-Generation Sequencing Data.]
|-
|style="padding:.4em;"|2014-19
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://genomebiology.com/content/13/12/R124 DriverNet: uncovering the impact of somatic driver mutations on transcriptional networks in cancer.]
|-
|style="padding:.4em;" rowspan=2|2014/09/16
|style="padding:.4em;"|2014-18
|style="padding:.4em;"|JH Shin
|style="padding:.4em;text-align:left"|
[http://genomebiology.com/content/14/5/R52 Integrated analysis of recurrent properties of cancer genes to identify novel drivers.]
|-
|style="padding:.4em;"|2014-17
|style="padding:.4em;"|AR Cho
|style="padding:.4em;text-align:left"|
[http://genomemedicine.com/content/4/11/89 Improving the prediction of the functional impact of cancer mutations by baseline tolerance transformation.]
|-
|style="padding:.4em;" rowspan=2|2014/09/02
|style="padding:.4em;"|2014-16
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=23228031 A network module-based method for identifying cancer prognostic signatures.]
|-
|style="padding:.4em;"|2014-15
|style="padding:.4em;"|AR Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=24429628 Realizing the promise of cancer predisposition genes.]
|-
|style="padding:.4em;" rowspan=2|2014/08/19
|style="padding:.4em;"|2014-14
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=24952901 Assessing the clinical utility of cancer genomic and proteomic data across tumor types.]
|-
|style="padding:.4em;"|2014-13
|style="padding:.4em;"|HS Shim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=24132290 Mutational landscape and significance across 12 major cancer types.]
|-
|style="padding:.4em;" rowspan=2|2014/08/12
|style="padding:.4em;"|2014-12
|style="padding:.4em;"|HS Shim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/23945592 Signatures of mutational processes in human cancer.]
|-
|style="padding:.4em;"|2014-11
|style="padding:.4em;"|HJ Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/24390350 Discovery and saturation analysis of cancer genes across 21 tumour types.]
|-
|style="padding:.4em;" rowspan=2|2014/08/05
|style="padding:.4em;"|2014-10
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=24084849 Comprehensive identification of mutational cancer driver genes across 12 tumor types.]
|-
|style="padding:.4em;"|2014-9
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=24037244 IntOGen-mutations identifies cancer drivers across tumor types.]
|-
|style="padding:.4em;" rowspan=3|2014/07/29
|style="padding:.4em;"|2014-8
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/23900255 Computational approaches to identify functional genetic variants in cancer genomes.]
|-
|style="padding:.4em;"|2014-7
|style="padding:.4em;"|AR Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=23770567 Mutational heterogeneity in cancer and the search for new cancer-associated genes.]
|-
|style="padding:.4em;"|2014-6
|style="padding:.4em;"|AR Cho
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nmeth/journal/v11/n4/abs/nmeth.2891.html Cancer genomes: discerning drivers from passengers]
|-
|style="padding:.4em;" rowspan=3|2014/07/22
|style="padding:.4em;"|2014-5
|style="padding:.4em;"|AR Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/24071849 The Cancer Genome Atlas Pan-Cancer analysis project.]
|-
|style="padding:.4em;"|2014-4
|style="padding:.4em;"|AR Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/23539594 Cancer genome landscapes.]
|-
|style="padding:.4em;"|2014-3
|style="padding:.4em;"|AR Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=21900272 Distinguishing driver and passenger mutations in an evolutionary history categorized by interference.]
|-
|style="padding:.4em;" rowspan=2|2014/07/15
|style="padding:.4em;"|2014-2
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=24670764 A promoter-level mammalian expression atlas.]
|-
|style="padding:.4em;"|2014-1
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=24670763 An atlas of active enhancers across human cell types and tissues.]
|}
{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2013
|-
!scope="col" style="padding:.4em" | Date
!scope="col" style="padding:.4em" | Paper index
!scope="col" style="padding:.4em" | Presenter
!scope="col" style="padding:.4em" | Paper title
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|style="padding:.4em;" rowspan=2|2013/06/26
|style="padding:.4em;"|2013-31
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|[http://nar.oxfordjournals.org/content/40/16/7690.long Integration of Hi-C and ChIP-seq data reveals distinct types of chromatin linkages]
|-
|style="padding:.4em;"|2013-30
|style="padding:.4em;"|YH Ko
|style="padding:.4em;text-align:left"|[http://www.nature.com/nbt/journal/v31/n4/full/nbt.2519.html Deciphering molecular circuits from genetic variation underlying transcriptional responsiveness to stimuli]
|-
|style="padding:.4em;" rowspan=2|2013/06/04
|style="padding:.4em;"|2013-29
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|[https://www.cell.com/abstract/S0092-8674(13)00439-X Mapping the Human miRNA Interactome by CLASH Reveals Frequent Noncanonical Binding]
|-
|style="padding:.4em;"|2013-28
|style="padding:.4em;"|ER Kim
|style="padding:.4em;text-align:left"|[http://www.nature.com/nbt/journal/v31/n3/full/nbt.2514.html Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples]
|-
|style="padding:.4em;" rowspan=2|2013/05/28
|style="padding:.4em;"|2013-27
|style="padding:.4em;"|YH Ko
|style="padding:.4em;text-align:left"|[http://www.pnas.org/content/102/38/13544.long Discovering statistically significant pathways in expression profiling studies]
|-
|style="padding:.4em;"|2013-26
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|[http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0058977 Prediction and Validation of Gene-Disease Associations Using Methods Inspired by Social Network Analyses]
|-
|style="padding:.4em;" rowspan=2|2013/05/21
|style="padding:.4em;"|2013-25
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|[http://www.nature.com/nature/journal/v496/n7446/full/nature11981.html Dynamic regulatory network controlling TH17 cell differentiation]
|-
|style="padding:.4em;"|2013-24
|style="padding:.4em;"|T Lee
|style="padding:.4em;text-align:left"|[http://www.sciencedirect.com/science/article/pii/S0092867412013529 Deciphering and Prediction of Transcriptome Dynamics under Fluctuating Field Conditions]

|-
|style="padding:.4em;" rowspan=2|2013/05/14
|style="padding:.4em;"|2013-23
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|[http://www.sciencedirect.com/science/article/pii/S0092867412015565 Integrative eQTL-Based Analyses Reveal the Biology of Breast Cancer Risk Loci]

|-
|style="padding:.4em;"|2013-22
|style="padding:.4em;"|HJ Kim
|style="padding:.4em;text-align:left"|[http://www.nature.com/ng/journal/v45/n2/full/ng.2504.html Chromatin marks identify critical cell types for fine mapping complex trait variants]
|-
|style="padding:.4em;" rowspan=2|'''2013/05/07'''
|style="padding:.4em;"|2013-21
|style="padding:.4em;"|ER Kim
|style="padding:.4em;text-align:left"|[http://www.cell.com/abstract/S0092-8674(12)01555-3 Genome-wide Chromatin State Transitions Associated with Developmental and Environmental Cues]
|-
|style="padding:.4em;"|2013-20
|style="padding:.4em;"|HS Shim
|style="padding:.4em;text-align:left"|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1003201 Human Disease-Associated Genetic Variation Impacts Large Intergenic Non-Coding RNA Expression]
|-
|style="padding:.4em;"|2013/04/09
|style="padding:.4em;"|2013-19
|style="padding:.4em;"|HJ Kim
|style="padding:.4em;text-align:left"|[http://genome.cshlp.org/content/22/9/1790.long Annotation of functional variation in personal genomes using RegulomeDB]
|-
|style="padding:.4em;"|2013/04/02
|style="padding:.4em;"|2013-18
|style="padding:.4em;"|HJ Kim
|style="padding:.4em;text-align:left"|[http://genome.cshlp.org/content/22/9/1775.long The GENCODE v7 catalog of human long noncoding RNAs: Analysis of their gene structure, evolution, and expression]
|-
|style="padding:.4em;" |2013/03/12
|style="padding:.4em;"|2013-17
|style="padding:.4em;"| ER Kim
|style="padding:.4em;text-align:left"|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002311 Global Mapping of Cell Type–Specific Open Chromatin by FAIRE-seq Reveals the Regulatory Role of the NFI Family in Adipocyte Differentiation]
|-
|style="padding:.4em;" rowspan=1|2013/03/05
|style="padding:.4em;"|2013-16
|style="padding:.4em;"| ER Kim
|style="padding:.4em;text-align:left"|[http://www.nature.com/nrg/journal/v10/n10/full/nrg2641.html ChIP–seq: advantages and challenges of a maturing technology]
|-
|style="padding:.4em;" rowspan=3|2013/02/21
|style="padding:.4em;"|2013-15
|style="padding:.4em;"| KS Kim
|style="padding:.4em;text-align:left"|[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002830 Novel Modeling of Combinatorial miRNA Targeting Identifies SNP with Potential Role in Bone Density]
|-
|style="padding:.4em;"|2013-14
|style="padding:.4em;"| KS Kim
|style="padding:.4em;text-align:left"|[http://www.cell.com/abstract/S0092-8674(12)01424-9 A Molecular Roadmap of Reprogramming Somatic Cells into iPS Cells]
|-
|style="padding:.4em;"|2013-13
|style="padding:.4em;"| HJ Han
|style="padding:.4em;text-align:left"|[http://genome.cshlp.org/content/22/6/1015.long Differential DNase I hypersensitivity reveals factor-dependent chromatin dynamics.]
|-
|style="padding:.4em;" rowspan=3|2013/02/15
|style="padding:.4em;"|2013-12
|style="padding:.4em;"| HJ Han
|style="padding:.4em;text-align:left"|[http://www.nature.com/ng/journal/v43/n3/full/ng.759.html Chromatin accessibility pre-determines glucocorticoid receptor binding patterns]
|-
|style="padding:.4em;"|2013-11
|style="padding:.4em;"| JE Shim, CY Kim
|style="padding:.4em;text-align:left"|[http://www.nature.com/nbt/journal/v30/n11/full/nbt.2422.html Interpreting noncoding genetic variation in complex traits and human disease]
|-
|style="padding:.4em;"|2013-10
|style="padding:.4em;"|HJ Han, JH Kim
|style="padding:.4em;text-align:left"|[http://genome.cshlp.org/content/21/3/447.full.pdf+html Accurate inference of transcription factor binding from DNA sequence and chromatin accessibility data]
|-
|style="padding:.4em;" rowspan=3|2013/02/08
|style="padding:.4em;"|2013-09
|style="padding:.4em;"|HJ Kim
|style="padding:.4em;text-align:left"|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002599 Widespread Site-Dependent Buffering of Human Regulatory Polymorphism]
|-
|style="padding:.4em;"|2013-08
|style="padding:.4em;"|JE Shim, CY Kim
|style="padding:.4em;text-align:left;"|[http://genome.cshlp.org/content/22/9/1748.long Linking disease associations with regulatory information in the human genome]
|-
|style="padding:.4em;"|2013-07
|style="padding:.4em;"|HJ Han, JH Kim
|style="padding:.4em;text-align:left"|[http://genome.cshlp.org/content/22/9/1658.long Understanding transcriptional regulation by integrative analysis of transcription factor binding data]
|-
|style="padding:.4em;" rowspan=3|2013/01/25
|style="padding:.4em;"|2013-06
|style="padding:.4em;"|HJ Han, '''JH Kim'''
|style="padding:.4em;text-align:left;"|[http://www.nature.com/nature/journal/v489/n7414/full/nature11279.html The long-range interaction landscape of gene promoters]
|-
|style="padding:.4em;"|2013-05
|style="padding:.4em;"|'''ER Kim''', HS Shim
|style="padding:.4em;text-align:left;"|[http://www.nature.com/nature/journal/v489/n7414/full/nature11233.html Landscape of transcription in human cells]
|-
|style="padding:.4em;"|2013-04
|style="padding:.4em;"|'''HJ Han''', JH Kim
|style="padding:.4em;text-align:left;"|[http://www.nature.com/nature/journal/v489/n7414/full/nature11245.html Architecture of the human regulatory network derived from ENCODE data]

|-
|style="padding:.4em;" rowspan=2|2013/01/18
|style="padding:.4em;"|2013-03
|style="padding:.4em;"|KS Kim, '''TH Kim'''
|style="padding:.4em;text-align:left;"|[http://www.nature.com/nature/journal/v489/n7414/full/nature11212.html An expansive human regulatory lexicon encoded in transcription factor footprints]
|-
|style="padding:.4em;"|2013-02
|style="padding:.4em;"|'''HJ Han''', JH Kim
|style="padding:.4em;text-align:left;"|[http://www.nature.com/nature/journal/v489/n7414/full/nature11232.html The accessible chromatin landscape of the human genome]
|-
|style="padding:.4em;" rowspan=1|2013/01/11
|style="padding:.4em;"|2013-01
|style="padding:.4em;"| '''JE Shim''', CY Kim
|style="padding:.4em;text-align:left"|[http://www.nature.com/nature/journal/v489/n7414/full/nature11247.html An integrated encyclopedia of DNA elements in the human genome]
|}

2012
{|border ="1"
|style="color:black; background-color:#dcdcdc; padding:5px;" align="center" |Date
|style="color:black; background-color:#dcdcdc;" align="center"|Paper index
|style="color:black; background-color:#dcdcdc;" align="center"|Paper title
|-
|rowspan = "1"|2013/01/11
|align ="center"|2012-81
|[http://genome.cshlp.org/content/22/8/1589.full (TH Kim) MuSiC: identifying mutational significance in cancer genomes.]
|-
|rowspan = "1"|2012/12/04
|align ="center"|2012-80
|[http://genome.cshlp.org/content/22/8/1383 (CY KIM) Human genomic disease variants: A neutral evolutionary explanation]
|-
|rowspan = "2"|2012/11/20
| align="center"|2012-79
|[http://www.cell.com/abstract/S0092-8674(12)00639-3 (HS Shim) Circuitry and Dynamics of Human Transcription Factor Regulatory Networks]
|-
| align="center"|2012-78
|[http://www.nature.com/nature/journal/v487/n7408/full/nature11288.html (HJ Kim) Interpreting cancer genomes using systematic host network perturbations by tumour virus proteins]
|-
|rowspan = "2"|2012/11/06
| align="center"|2012-77
|[http://www.sciencemag.org/content/337/6099/1190.short (HJ Han) Systematic Localization of Common Disease-Associated Variation in Regulatory DNA]
|-
| align="center"|2012-76
|[http://www.pnas.org/cgi/doi/10.1073/pnas.1201904109 (KS Kim) A public resource facilitating clinical use of genomes]
|-
|rowspan = "6" |2012/07/19
| align="center"|2012-75
|[http://genome.cshlp.org/content/22/7/1334 (HJ Han & YH Ko) Predictive regulatory models in Drosophila melanogaster by integrative inference of transcriptional networks]
|-
| align="center"|2012-74
|[http://www.sciencemag.org/content/337/6090/100.full (JE Shim) An Abundance of Rare Functional Variants in 202 Drug Target Genes Sequenced in 14,002 People]
|-
| align="center"|2012-73
|[http://www.sciencemag.org/content/337/6090/64.abstract (JE Shim) Evolution and Functional Impact of Rare Coding Variation from Deep Sequencing of Human Exomes]
|-
| align="center"|2012-72
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2063581/ (SH Hwang) Network-based classification of breast cancer metastasis]
|-
| align="center"|2012-71
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002707 (T Lee&CY Kim)Brain Expression Genome-Wide Association Study (eGWAS) Identifies Human Disease-Associated Variants]
|-
| align="center"|2012-70
|[http://genome.cshlp.org/content/20/9/1297 (ER Kim&TH Kim)The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data]
|-
|rowspan = "8" |2012/07/16
| align="center"|2012-69
|[http://www.nature.com/ng/journal/v43/n5/full/ng.806.html (ER Kim&TH Kim)A framework for variation discovery and genotyping using next-generation DNA sequencing data]
|-
| align="center"|2012-66
|[http://bioinformatics.oxfordjournals.org/content/25/16/2078.long (ER Kim&TH Kim)The Sequence Alignment/Map format and SAMtools]
|-
| align="center"|2012-65
|[http://bioinformatics.oxfordjournals.org/content/early/2011/06/07/bioinformatics.btr330 (ER Kim&TH Kim)The Variant Call Format and VCFtools]
|-
| align="center"|2012-64
|[http://www.cell.com/abstract/S0092-8674(12)00104-3 (YH Go&HJ Han)The Impact of the Gut Microbiota on Human Health: An Integrative View]
|-
|align="center"|2012-63
|[http://www.sciencemag.org/content/336/6086/1262.abstract (T Lee&CY Kim)Host-Gut Microbiota Metabolic Interactions]
|-
|align="center"|2012-62
|[http://www.sciencemag.org/content/336/6086/1268.full (AR Cho,JH Ju)Interactions Between the Microbiota and the Immune System]
|-
|align="center"|2012-61
|[http://www.sciencemag.org/content/336/6086/1255.abstract (SH Hwang&HJ Cho)The Application of Ecological Theory Toward an Understanding of the Human Microbiome]
|-
|align="center"|2012-60
|[http://stm.sciencemag.org/content/4/137/137rv5 (SH Hwang&HJ Cho)Microbiota-Targeted Therapies: An Ecological Perspective]
|-
|rowspan = "3" |2012/07/13
|align="center"|2012-59
|[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002358 (JH Shin&HJ Kim)Metabolic Reconstruction for Metagenomic Data and Its Application to the Human Microbiome]
|-
| align="center"|2012-58
|[http://www.nature.com/nature/journal/v486/n7402/full/nature11209.html (JH Shin&HJ Kim)A framework for human microbiome research]
|-
|align="center"|2012-57
|[http://www.nature.com/nature/journal/v486/n7402/full/nature11234.html (JH Shin&HJ Kim)Structure, function and diversity of the healthy human microbiome]
|-
|rowspan = "4" |2012/07/12
|align="center"|2012-56
|[http://nar.oxfordjournals.org/content/40/D1/D957.long (AR Cho&JH Ju)COLT-Cancer: functional genetic screening resource for essential genes in human cancer cell lines]
|-
|align="center"|2012-55
|[http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002511 (YH Go&HJ Han)Google Goes Cancer: Improving Outcome Prediction for Cancer Patients by Network-Based Ranking of Marker Genes]
|-
| align="center"|2012-54
|[http://www.nature.com/msb/journal/v7/n1/full/msb201147.html (YH Go&HJ Han)A pharmacogenomic method for individualized prediction of drug sensitivity]
|-
|align="center"|2012-53
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature10983.html (JH Soh)The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups]
|-
|rowspan = "6" |2012/07/09
|align="center"|2012-52
|[http://www.nature.com/nature/journal/v483/n7391/full/nature11003.html (ER Kim&TH Kim)The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity]
|-
|align="center"|2012-51
|[http://www.nature.com/nature/journal/v483/n7391/full/nature11005.html (ER Kim&TH Kim)Systematic identification of genomic markers of drug sensitivity in cancer cells]
|-
| align="center"|2012-50
|[http://www.pnas.org/content/early/2011/10/13/1018854108.abstract (ER Kim&TH Kim)Subtype and pathway specific responses to anticancer compounds in breast cancer]
|-
|align="center"|2012-49
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature10945.html (JE Shim&KS Kim)De novo mutations revealed by whole-exome sequencing are strongly associated with autism]
|-
|align="center"|2012-48
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature11011.html (JE Shim&KS Kim)Patterns and rates of exonic de novo mutations in autism spectrum disorders]
|-
|align="center"|2012-47
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature10989.html (JE Shim&KS Kim)Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations]
|-
|rowspan = "6" |2012/07/06
| align="center"|2012-46
|[http://www.g3journal.org/content/1/3/233.full (T Lee&CY Kim)Integrating Rare-Variant Testing, Function Prediction, and Gene Network in Composite Resequencing-Based Genome-Wide Association Studies (CR-GWAS)]
|-
|align="center"|2012-45
|[http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002621 (T Lee&CY Kim)Type 2 Diabetes Risk Alleles Demonstrate Extreme Directional Differentiation among Human Populations, Compared to Other Diseases]
|-
|align="center"|2012-44
|[http://www.nature.com/nature/journal/v480/n7376/full/nature10665.html (AR Cho&JH Ju)Predicting mutation outcome from early stochastic variation in genetic interaction partners]
|-
|align="center"|2012-43
|[http://www.sciencemag.org/content/335/6064/82 (AR Cho&JH Ju)Fitness Trade-Offs and Environmentally Induced Mutation Buffering in Isogenic C. elegans]
|-
| align="center"|2012-42
|[http://genome.cshlp.org/content/22/6/1163 (JE Shim&KS Kim)Identification of microRNA-regulated gene networks by expression analysis of target genes]
|-
|align="center"|2012-41
|[http://www.nature.com/ng/journal/v44/n6/full/ng.2303.html (JE Shim&KS Kim)Exome sequencing and the genetic basis of complex traits]
|-
|rowspan = "6" |2012/07/02
|align="center"|2012-40
|[http://www.cell.com/abstract/S0092-8674(12)00573-9 (JH Soh)Functional Repurposing Revealed by Comparing S. pombe and S. cerevisiae Genetic Interactions]
|-
|align="center"|2012-39
|[http://genome.cshlp.org/content/22/2/375.long (ER Kim&TH Kim)De novo discovery of mutated driver pathways in cancer]
|-
| align="center"|2012-38
|[http://www.nature.com/msb/journal/v4/n1/full/msb20082.html (YH Go&HJ Han)A systems biology approach to prediction of oncogenes and molecular perturbation targets in B-cell lymphomas]
|-
|align="center"|2012-37
|[http://www.nature.com/msb/journal/v7/n1/full/msb201126.html (SH Hwang&HJ Cho)PREDICT: a method for inferring novel drug indications with application to personalized medicine]
|-
|align="center"|2012-36
|[http://www.nature.com/nature/journal/v453/n7198/full/nature06973.html (SH Hwang&HJ Cho)Synergistic response to oncogenic mutations defines gene class critical to cancer phenotype]
|-
|align="center"|2012-35
|[http://www.sciencemag.org/content/334/6062/1518.full (JH Shin&HJ Kim)Detecting Novel Associations in Large Data Sets]
|-
|rowspan = "3" |2012/03/05
| align="center"|2012-34
|[http://www.ncbi.nlm.nih.gov/pubmed/18516045 (8,HH Kim)Mapping and quantifying mammalian transcriptomes by RNA-Seq.]
|-
|align="center"|2012-33
|[http://genomebiology.com/2010/11/10/R106 (11,Go&Ju)Differential expression analysis for sequence count data]
|-
|align="center"|2012-32
|[http://bioinformatics.oxfordjournals.org/content/26/1/139.short (12,Go&Ju)edgeR: a Bioconductor package for differential expression analysis of digital gene expression data ]
|-
|rowspan = "7" |2012/02/27 2012/02/28
| align="center"|2012-31
|[http://www.nature.com/nrg/journal/v10/n1/full/nrg2484.html (1,JW Song)RNA-Seq: a revolutionary tool for transcriptomics]
|-
|align="center"|2012-30
|[http://www.nature.com/nmeth/journal/v8/n6/full/nmeth.1613.html (2,JW Song)Computational methods for transcriptome annotation and quantification using RNA-seq]
|-
|align="center"|2012-29
|[http://genomebiology.com/2010/11/12/220 (3,HJ Han)From RNA-seq reads to differential expression results]
|-
|align="center"|2012-28
|[http://www.nature.com/nmeth/journal/v7/n9/full/nmeth.1491.html (4,AR Cho)Comprehensive comparative analysis of strand-specific RNA sequencing methods]
|-
|align="center"|2012-27
|[http://www.plosone.org/article/info:doi%2F10.1371%2Fjournal.pone.0026426 (5,T Lee)A Low-Cost Library Construction Protocol and Data Analysis Pipeline for Illumina-Based Strand-Specific Multiplex RNA-Seq]
|-
|align="center"|2012-26
|[http://www.nature.com/nbt/journal/v28/n5/abs/nbt.1621.html (6,So&Shin)Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation]
|-
|align="center"|2012-25
|[http://bioinformatics.oxfordjournals.org/content/25/9/1105.abstract (7,So&Shin)TopHat: discovering splice junctions with RNA-Seq]
|-
|rowspan = "5" |2012/02/06
| align="center"|2012-24
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3125733/ mirConnX: condition-specific mRNA-microRNA network integrator]
|-
|align="center"|2012-23
|[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002190 Construction and Analysis of an Integrated Regulatory Network Derived from High-Throughput Sequencing Data]
|-
|align="center"|2012-22
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002415 A Densely Interconnected Genome-Wide Network of MicroRNAs and Oncogenic Pathways Revealed Using Gene Expression Signatures]
|-
|align="center"|2012-21
|[http://genome.cshlp.org/content/20/5/589.short Reprogramming of miRNA networks in cancer and leukemia]
|-
|align="center"|2012-20
|[http://www.cell.com/abstract/S0092-8674(11)01152-4 An Extensive MicroRNA-Mediated Network of RNA-RNA Interactions Regulates Established Oncogenic Pathways in Glioblastoma]
|-
|rowspan = "5" |2012/01/30
|align="center"|2012-19
|[http://www.cell.com/abstract/S0092-8674(11)00237-6 Principles and Strategies for Developing Network Models in Cancer]
|-
|align="center"|2012-18
|[http://www.nature.com/ng/journal/v37/n4/full/ng1532.html Reverse engineering of regulatory networks in human B cells]
|-
|align="center"|2012-17
|[http://www.nature.com/nature/journal/v452/n7186/abs/nature06757.html Variations in DNA elucidate molecular networks that cause disease]
|-
|align="center"|2012-16
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2779083/ Harnessing gene expression to identify the genetic basis of drug resistance]
|-
|align="center"|2012-15
|[http://www.sciencedirect.com/science/article/pii/S0092867410012936 An Integrated Approach to Uncover Drivers of Cancer]
|-
|rowspan = "3" |2012/01/09
|align="center"|2012-14
|[http://www.ncbi.nlm.nih.gov/pubmed/18704161 Genetic variation in an individual human exome.]
|-
|align="center"|2012-13
|[http://www.ncbi.nlm.nih.gov/pubmed/22081227 Predicting phenotypic variation in yeast from individual genome sequences.]
|-
|align="center"|2012-12
|[http://www.ncbi.nlm.nih.gov/pubmed/20435227 Clinical assessment incorporating a personal genome.]
|-
|rowspan = "6" |2012/01/09 2012/01/16
| align="center"|2012-11
|[http://www.ncbi.nlm.nih.gov/pubmed/20399638 Human allelic variation: perspective from protein function, structure, and evolution.]
|-
|align="center"|2012-10
|[http://www.ncbi.nlm.nih.gov/pubmed/19561590 Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm.]
|-
|align="center"|2012-09
|[http://www.ncbi.nlm.nih.gov/pubmed/11230178 Prediction of deleterious human alleles.]
|-
|align="center"|2012-08
|[http://www.ncbi.nlm.nih.gov/pubmed/12202775 Human non-synonymous SNPs: server and survey.]
|-
|align="center"|2012-07
|[http://www.ncbi.nlm.nih.gov/pubmed/20354512 A method and server for predicting damaging missense mutations.]
|-
|align="center"|2012-06
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1920242/ SNAP: predict effect of non-synonymous polymorphisms on function]
|-
|rowspan = "3" |2012/01/09 2012/01/16
|align="center"|2012-05
|[http://www.ncbi.nlm.nih.gov/pubmed/21920052 Computational and statistical approaches to analyzing variants identified by exome sequencing.]
|-
|align="center"|2012-04
|[http://www.ncbi.nlm.nih.gov/pubmed/18179889 Shifting paradigm of association studies: value of rare single-nucleotide polymorphisms.]
|-
|align="center"|2012-03
|[http://www.ncbi.nlm.nih.gov/pubmed/19684571 Targeted capture and massively parallel sequencing of 12 human exomes.]
|-
|rowspan = "2" |2012/01/09 2012/01/16
|align="center"|2012-02
|[http://www.ncbi.nlm.nih.gov/pubmed/17637733 The distribution of fitness effects of new mutations.]
|-
|align="center"|2012-01
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1852724/ Most Rare Missense Alleles Are Deleterious in Humans: Implications for Complex Disease and Association Studies]
|}
2011
{|border ="1"
|style="color:black; background-color:#dcdcdc;" align="center" |Date
|style="color:black; background-color:#dcdcdc;" align="center"|Paper_index
|style="color:black; background-color:#dcdcdc;" align="center"|Paper_title
|-
|rowspan = "6" |2011/11/28
|align="center"|2011-49
|[http://bmir.stanford.edu/file_asset/index.php/1407/BMIR-2009-1355.pdf (Shin)Data-Driven Methods to Discover Molecular Determinants of Serious Adverse Drug Events]
|-
|align="center"|2011-48
|[http://www.nature.com/nchembio/journal/v4/n11/abs/nchembio.118.html (Shin)Network pharmacology: the next paradigm in drug discovery]
|-
|align="center"|2011-47
|[http://www.nature.com/msb/journal/v7/n1/full/msb201171.html (Oh)Systematic exploration of synergistic drug pairs]
|-
|align="center"|2011-46
|[http://www.nature.com/msb/journal/v5/n1/full/msb200995.html (Shim)Chemogenomic profiling predicts antifungal synergies]
|-
|align="center"|2011-45
|[http://www.pnas.org/content/104/32/13086 (Beck)A strategy for predicting the chemosensitivity of human cancers and its application to drug discovery]
|-
|align="center"|2011-44
|[http://www.nature.com/nchembio/journal/v1/n7/full/nchembio747.html (Hwang)Analysis of drug-induced effect patterns to link structure and side effects of medicines]
|-
|rowspan = "3" |2011/11/14
|align="center"|2011-43
|[http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000662 Network-Based Elucidation of Human Disease Similarities Reveals Common Functional Modules Enriched for Pluripotent Drug Targets]
|-
|align="center"|2011-42
|[http://www.nature.com/msb/journal/v7/n1/full/msb201131.html Cross-species discovery of syncretic drug combinations that potentiate the antifungal fluconazole]
|-
|align="center"|2011-41
|[http://www.nature.com/msb/journal/v7/n1/full/msb20115.html Analysis of multiple compound–protein interactions reveals novel bioactive molecules]
|-
|rowspan = "4" |2011/11/07
|align="center"|2011-40
|[http://www.nature.com/nbt/journal/v25/n10/abs/nbt1338.html Drug—target network]
|-
|align="center"|2011-39
|[http://www.nature.com/msb/journal/v6/n1/full/msb200998.html A side effect resource to capture phenotypic effects of drugs]
|-
|align="center"|2011-38
|[http://genome.cshlp.org/content/18/2/206.long Quantitative systems-level determinants of human genes targeted by successful drugs]
|-
|align="center"|2011-37
|[http://www.sciencemag.org/content/321/5886/263.short Drug Target Identification Using Side-Effect Similarity]
|-
|rowspan = "3" |2011/11/07
|align="center"|2011-36
|[http://stm.sciencemag.org/content/3/96/96ra77.short Discovery and Preclinical Validation of Drug Indications Using Compendia of Public Gene Expression Data]
|-
|align="center"|2011-35
|[http://bib.oxfordjournals.org/content/12/4/303.short Exploiting drug–disease relationships for computational drug repositioning]
|-
|align="center"|2011-34
|[http://www.springerlink.com/content/4489r051nu2t0ul1/ Drug Discovery in a Multidimensional World: Systems, Patterns, and Networks]
|-
|rowspan = "3" |2011/10/05
|align="center"|2011-33
|[http://genome.cshlp.org/content/20/7/960.full Analysis of membrane proteins in metagenomics: Networks of correlated environmental features and protein families]
|-
|align="center"|2011-32
|[http://www.pnas.org/content/106/5/1374.full Quantifying environmental adaptation of metabolic pathways in metagenomics]
|-
|align="center"|2011-31
|[http://www.pnas.org/content/104/35/13913.abstract Quantitative assessment of protein function prediction from metagenomics shotgun sequences]
|-
|rowspan = "4" |2011/10/04
|align="center"|2011-30
|[http://www.nature.com/nature/journal/v464/n7285/full/nature08821.html A human gut microbial gene catalogue established by metagenomic sequencing]
|-
|align="center"|2011-29
|[http://www.nature.com/nrmicro/journal/v6/n9/abs/nrmicro1935.html Molecular eco-systems biology: towards an understanding of community function]
|-
|align="center"|2011-28
|[http://genome.cshlp.org/content/early/2009/04/20/gr.085464.108 Microbial community profiling for human microbiome projects: Tools, techniques, and challenges]
|-
|align="center"|2011-27
|[http://www.nature.com/nrmicro/journal/v9/n4/full/nrmicro2540.html Unravelling the effects of the environment and host genotype on the gut microbiome]
|-
|rowspan = "2" |2011/09/19
|align="center"|2011-26
|[http://www.sciencemag.org/content/333/6042/596.full independently evolved virulence effectors converge onto hubs in a plant immune system Network]
|-
|align="center"|2011-25
|[http://www.sciencemag.org/content/333/6042/601.full Evidence for network evolution in an arabidopsis interactome Map]
|-
|rowspan = "2" |2011/09/05
|align="center"|2011-24
|[http://www.sciencedirect.com/science/article/pii/S0092867411005861 Exome Sequencing of Ion Channel Genes Reveals Complex Profiles Confounding Personal Risk Assessment in Epilepsy]
|-
|align="center"|2011-23
|[http://www.cell.com/abstract/S0092-8674(11)00543-5 Pluripotency factors in Embryonic stem cells Regulate Differentiation into Germ Layers]
|-
|rowspan = "2" |2011/09/22
|align="center"|2011-22
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002077 Integrated Genome-scale predition of Detrimental Mutations in Transcription Networks]
|-
|align="center"|2011-21
|[http://www.nature.com/nrg/journal/v12/n4/abs/nrg2969.html From expression QTLs to personalized transcriptomics]
|-
|2011/06/20
|align="center"|2011-20
|[http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001046 a user's guide to the encyclopedia of DNA elements(ENCODE)]
|-
|rowspan = "2" |2011/03/30
|align="center"|2011-19
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature09944.html enterotypes of the human gut microbiome]
|-
|align="center"|2011-18
|[http://www.nature.com/msb/journal/v7/n1/full/msb20116.html toward molecular trait-based ecology, through intergration of biogeochemical, geographical and metagenomic data]
|-
|rowspan = "2" |2011/03/16
|align="center"|2011-17
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002047 variable pathogenicity determines individual lifespan in ''caenorhabditis elegans'']
|-
|align="center"|2011-16
|[http://www.cell.com/abstract/S0092-8674(11)00371-0 a high-resolution ''c.elegans'' essential gene network based on phenotypic profiling of a complex tissue]
|-
|rowspan = "3" |2011/04/25
|align="center"|2011-15
|[http://www.ncbi.nlm.nih.gov/pubmed/21376230 Hallmarks of Cancer : The next generation]
|-
|align="center"|2011-14
|[http://www.cell.com/abstract/S0092-8674(11)00296-0 Mapping Cancer Origins]
|-
|align="center"|2011-13
|[http://www.cell.com/abstract/S0092-8674(11)00297-2 Genetic Interactions in Cancer Progression and Treatment]
|-
|rowspan = "2" |2011/04/11
|align="center"|2011-12
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1001273 Proteins encoded in genomic regions associated with immune-mediated disease physically interact and suggest underlying biology]
|-
|align="center"|2011-11
|***Changed!***
[http://www.ncbi.nlm.nih.gov/pubmed/19557189 Identifying relationships among genomic disease regions: predicting genes at pathogenic SNP associations and rare deletions]
|-
|rowspan = "2"|2011/03/28
|align="center"|2011-10
|[http://www.pnas.org/content/106/44/18843.long profiling translatomes of discrete cell populations resolves altered cellular priorities during hypoxia in Arabidopsis]
|-
|align="center"|2011-09
|[http://www.nature.com/msb/journal/v6/n1/full/msb201076.html cell-type specific analysis of translating RNAs in developing flowers reveals new levels of control]
|-
|rowspan = "2"|2011/03/14
|align="center"|2011-08
|[http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6WSN-51SFHJD-1&_user=44062&_coverDate=01%2F07%2F2011&_rdoc=1&_fmt=high&_orig=search&_origin=search&_sort=d&_docanchor=&view=c&_acct=C000004738&_version=1&_urlVersion=0&_userid=44062&md5=2f6017aab4c794ed7e78fbbd8447077a&searchtype=a phenotypic landscape of a bacterial cell]
|-
|align="center"|2011-07
|[http://www.nature.com/msb/journal/v6/n1/full/msb2010107.html cross-species chemogenomic profiling reveals evolutionarily conserved drug mode of action]
|-
|rowspan = "2"|2011/02/28
|align="center"|2011-06
|[http://www.pnas.org/content/early/2010/09/23/1004666107.abstract genomic patterns of pleiotropy and the evolution of complexity]
|-
|align="center"|2011-05
|[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1001009 simultaneous genome-wide inference of physical, genetic, regulatory, and functional pathway]
|-
|2011/02/21
|align="center"|2011-04
|[http://www.nature.com/msb/journal/v6/n1/full/msb201071.html dynamic interaction networks in a hierarchically organized tissue]
|-
|2011/02/14
|align="center"|2011-03
|[http://www.sciencemag.org/content/330/6009/1385.abstract rewiring of genetic networks in response to DNA damage]
|-
|rowspan = "2"|2011/01/31
|align="center"|2011-02
|[http://www.nature.com/nrg/journal/v10/n9/abs/nrg2633.html Applying mass spectrometry-based proteomics to genetics, genomics and network biology]
|-
|align="center"|2011-01
|[http://physiolgenomics.physiology.org/content/33/1/18.long Data analysis and bioinformatics tools for tandem mass spectrometry in proteomics]
|}
2010
{|border ="1"
|style="color:black; background-color:#dcdcdc;" align="center" |Date
|style="color:black; background-color:#dcdcdc;" align="center"|Paper_index
|style="color:black; background-color:#dcdcdc;" align="center"|Paper_title
|-
|rowspan = "2"|2010/12/27
|align="center"|2010-29
|[http://www.nature.com/nature/journal/v467/n7312/full/nature09326.html Functional_roles_fornoise_in_genetic_circuits]
|-
|align="center"|2010-28
|[http://www.nature.com/ng/journal/v42/n7/full/ng.610.html Estimation_of_effect_size_distribution_from_genome-wide_association_studies_and_implications_for_future_discoveries]
|-
|rowspan = "2"|2010/11/15
|align="center"|2010-27
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1000932 Liver_and_Adipose_Expression_associated_SNPs_are_enriched_for_association_to_type_2_diabetes]
|-
|align="center"|2010-26
|[http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B8JDC-50J4MRJ-2&_user=44062&_coverDate=10%2F10%2F2010&_rdoc=1&_fmt=high&_orig=search&_origin=search&_sort=d&_docanchor=&view=c&_acct=C000004738&_version=1&_urlVersion=0&_userid=44062&md5=7667a67cf8ba3f68117b8d0ff85a8887&searchtype=a It's_the_machine_that_matters:_predicting_gene_function_and_phenotype_from_protein_networks]
|-
|rowspan = "2"|2010/11/01
|align="center"|2010-25
|[http://genome.cshlp.org/content/early/2010/06/15/gr.104216.109 A_genome-wide_map_of_human_genetic_interactions_inferred_from_radiation_hybrid_genotypes]
|-
|align="center"|2010-24
|[http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0012139 A_Genome-Wide_Gene_Function_Prediction_Resource_for_Drosophila_melanogaster]
|-
|2010/10/11
|align="center"|2010-23
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2913399/?tool=pubmed Dissecting_spatio-temporal_protein_networks_driving_human_heart_development_and_related_disorders]
|-
|rowspan = "2"|2010/09/13
|align="center"|2010-22
|[http://www.nature.com/ng/journal/v42/n7/full/ng.600.html Transposable_elements_have_rewired_the_core_regulatory_network_of_human_embryonic_stem_cells]
|-
|align="center"|2010-21
|[http://www.nature.com/ng/journal/v42/n7/full/ng0710-557.html Limits_of_sequence_and_functional_conservation]
|-
|2010/05/26
|align="center"|2010-20
|[[media:100428_network_based_elucidation_of_human_disease_similarities_reveals_common_functional_modules_enriched_for_pluripotent_drug_targets.pdf|network_based_elucidation_of_human_disease_similarities_reveals_common_functional_modules_enriched_for_pluripotent_drug_targets.pdf]]
|-
|2010/05/19
|align="center"|2010-19
|[[media:100421_interpreting_metabolomic_profiles_using_unbiased_pathway_models.pdf|interpreting_metabolomic_profiles_using_unbiased_pathway_models.pdf]]
|-
|2010/04/21
|align="center"|2010-18
|[[media:100414_identification_of_networks_of_co_occurring_tumor_related_dna_copy_number_changes_using_a_genome_wide_scoringapproach.pdf|identification_of_networks_of_co_occurring_tumor_related_dna_copy_number_changes_using_a_genome_wide_scoringapproach.pdf]]
|-
|2010/04/14
|align="center"|2010-17
|[http://www.cell.com/retrieve/pii/S0092867410000796 an_atlas_of_combinatorial_transcriptional_regulation_in_mouse_and_man]
|-
|rowspan = "2" |2010/04/07
|align="center"|2010-16
|[http://www.pnas.org/content/early/2010/03/11/0910200107.long systematic_discovery_of_nonobvious_human_disease_models_through_orthologous_phenotypes]
|-
|align="center"|2010-15
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature08800.html genome_side_association_study_of_107_phenotypes_in_Arabidopsis_thaliana_inbred_lines]
|-
|rowspan="2"|2010/03/31
|align="center"|2010-14
|[[media:100331_toward_stem_cell_systems_biology_from_molecules_to_networks_and_landscapes.pdf|toward_stem_cell_systems_biology_from_molecules_to_networks_and_landscapes.pdf]]
|-
|align="center"|2010-13
|[http://www.nature.com/nrm/journal/v10/n10/abs/nrm2766.html systems_biology_of_stem_cell_fate_and_cellular_reprogramming]
|-
|2010/03/24
|align="center"|2010-12
|[http://www.nature.com/nbt/journal/v27/n2/abs/nbt.1522.html dynamic_modularity_in_protein_interaction_networks_predicts_breast_cancer_outcome]
|-
|2010/01/18
|align="center"|2010-11
|JournalClub_100118_a_tutorial_on_statistical_methods_for_population_association_studies
|-
|2010/01/16
|align="center"|2010-10
|systems-level_dinamic_analyses_of_fate_change_in_murine_embryonic_stem_cells
|-
|2010/01/15
|align="center"|2010-09
|distinguishing_direct_versus_indirect_transcription_factor-DNA-interactions
|-
|2010/01/14
|align="center"|2010-08
|chemogenomic_profiling_predicts_antifungal_synergies
|-
|2010/01/13
|align="center"|2010-07
|edgetic_perturbation_models_of_human_inherited_disorders
|-
|2010/01/12
|align="center"|2010-06
|analysis_of_cell_fate_from_single-cell_gene_expression_profiles_in_C.elegans
|-
|2010/01/11
|align="center"|2010-05
|predicting_new_molecular_targets_for_known_drugs.pdf
Reference:SEA(Similarity Ensemble Approach)
|-
|2010/01/09
|align="center"|2010-04
|harnessing_gene_expression_to_identify_the_genetic_basis_of_drug_resistance
|-
|2010/01/08
|align="center"|2010-03
|an_integrative_approach_to_reveal_driver_gene_fusions_from_paired_end_sequencing_data_in_cancer
|-
|2010/01/07
|align="center"|2010-02
|a_phenotypic_profile_of_the_candida_albicans_regulatory_network
|-
|2010/01/06
|align="center"|2010-01
|a_global_view_of_protein_expression_in_human_cells_tissues_and_organs
|}

Journal Club

2023-08-22T03:25:12Z

Bsb0613:

{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2023-1 ADVANCED MICROBIOME DATA ANALYSIS
|-
!scope="col" style="padding:.4em" | Date
!scope="col" style="padding:.4em" | Team
!scope="col" style="padding:.4em" | Paper index
!scope="col" style="padding:.4em" | Presenter
!scope="col" style="padding:.4em" | Paper title
|-
|style="padding:.4em;" rowspan=1|2023/06/13
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-24
|style="padding:.4em;"|JY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.chom.2020.03.005 Structure of the Mucosal and Stool Microbiome in Lynch Syndrome]
|-
|style="padding:.4em;" rowspan=1|2023/06/13
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-23
|style="padding:.4em;"|WJ Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41586-019-1237-9 Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases]
|-
|style="padding:.4em;" rowspan=1|2023/06/13
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-22
|style="padding:.4em;"|SH Ahn
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.jare.2022.03.007 Roles of oral microbiota and oral-gut microbial transmission in hypertension]
|-
|style="padding:.4em;" rowspan=1|2023/05/30
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-21
|style="padding:.4em;"|SY Lim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.chom.2022.08.009 Human gut microbiota stimulate defined innate immune responses that vary from phylum to strain]
|-
|style="padding:.4em;" rowspan=1|2023/05/30
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-20
|style="padding:.4em;"|BS Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41591-023-02217-7 Gut microbial metabolism of 5-ASA diminishes its clinical efficacy in inflammatory bowel disease]
|-
|style="padding:.4em;" rowspan=1|2023/05/30
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-19
|style="padding:.4em;"|JY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.chom.2023.01.004 Deficient butyrate-producing capacity in the gut microbiome is associated with bacterial network disturbances and fatigue symptoms in ME/CFS]
|-
|style="padding:.4em;" rowspan=1|2023/05/23
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-18
|style="padding:.4em;"|EJ Sung
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.ccell.2022.11.013 Gut microbiota-mediated nucleotide synthesis attenuates the response to neoadjuvant chemoradiotherapy in rectal cancer]
|-
|style="padding:.4em;" rowspan=1|2023/05/23
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-17
|style="padding:.4em;"|G Koh
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.chom.2021.06.019 Multi-omics reveal microbial determinants impacting responses to biologic therapies in inflammatory bowel disease]
|-
|style="padding:.4em;" rowspan=1|2023/05/23
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-16
|style="padding:.4em;"|SH Lee
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41586-022-05181-3 Identification of trypsin-degrading commensals in the large intestine]
|-
|style="padding:.4em;" rowspan=1|2023/05/16
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-15
|style="padding:.4em;"|JP Hong
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41586-022-05546-8 Questioning the fetal microbiome illustrates pitfalls of low-biomass microbial studies]
|-
|style="padding:.4em;" rowspan=1|2023/05/16
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-14
|style="padding:.4em;"|MR Jang
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.chom.2023.01.013 Tissue-resident Lachnospiraceae family bacteria protect against colorectal carcinogenesis by promoting tumor immune surveillance]
|-
|style="padding:.4em;" rowspan=1|2023/05/16
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-13
|style="padding:.4em;"|JW Yu
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.cell.2022.09.005 Pan-cancer analyses reveal cancer-type-specific fungal ecologies and bacteriome interactions]
|-
|style="padding:.4em;" rowspan=1|2023/05/09
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-12
|style="padding:.4em;"|NY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.cell.2022.09.015 A pan-cancer mycobiome analysis reveals fungal involvement in gastrointestinal and lung tumors]
|-
|style="padding:.4em;" rowspan=1|2023/05/09
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-11
|style="padding:.4em;"|HR Shin
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41564-021-01030-7 Multi-kingdom microbiota analyses identify bacterial–fungal interactions and biomarkers of colorectal cancer across cohorts]
|-
|style="padding:.4em;" rowspan=1|2023/05/09
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-10
|style="padding:.4em;"|SG Oh
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s42255-022-00716-4 The antitumour effects of caloric restriction are mediated by the gut microbiome]
|-
|style="padding:.4em;" rowspan=1|2023/05/02
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-9
|style="padding:.4em;"|WJ Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41591-022-01964-3 Variability of strain engraftment and predictability of microbiome composition after fecal microbiota transplantation across different diseases]
|-
|style="padding:.4em;" rowspan=1|2023/05/02
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-8
|style="padding:.4em;"|SM Han
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41591-022-01913-0 Drivers and determinants of strain dynamics following fecal microbiota transplantation]
|-
|style="padding:.4em;" rowspan=1|2023/05/02
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-7
|style="padding:.4em;"|YY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.cell.2022.11.023 Mobile genetic elements from the maternal microbiome shape infant gut microbial assembly and metabolism]
|-
|style="padding:.4em;" rowspan=1|2023/04/18
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-6
|style="padding:.4em;"|SH Heo
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.chom.2023.01.018 Mother-to-infant microbiota transmission and infant microbiota development across multiple body sites]
|-
|style="padding:.4em;" rowspan=1|2023/04/18
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-5
|style="padding:.4em;"|SY Yang
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41467-023-36633-7 Population-level impacts of antibiotic usage on the human gut microbiome]
|-
|style="padding:.4em;" rowspan=1|2023/04/18
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-4
|style="padding:.4em;"|YH Yoon
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41586-022-05438-x Enterococci enhance Clostridioides difficile pathogenesis]
|-
|style="padding:.4em;" rowspan=1|2023/04/11
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-3
|style="padding:.4em;"|DH Lee
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1002/imt2.61 Targeting keystone species helps restore the dysbiosis of butyrate‐producing bacteria in nonalcoholic fatty liver disease]
|-
|style="padding:.4em;" rowspan=1|2023/04/11
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-2
|style="padding:.4em;"|YJ Roh
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1002/advs.202203115 Differential Oral Microbial Input Determines Two Microbiota Pneumo-Types Associated with Health Status]
|-
|style="padding:.4em;" rowspan=1|2023/04/11
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-1
|style="padding:.4em;"|SH Ahn
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.cell.2022.08.021 Gut microbiome of multiple sclerosis patients and paired household healthy controls reveal associations with disease risk and course]
|}

{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2023 scOmics
|-
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|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|EJ Sung
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[https://doi.org/10.1038/s41588-022-01273-y MHC II immunogenicity shapes the neoepitope landscape in human tumors]
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|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|IS Choi
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[https://doi.org/10.1038/s41586-023-06130-4 Hallmarks of transcriptional intratumour heterogeneity across a thousand tumours]
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|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-31
|style="padding:.4em;"|JW Yu
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[https://doi.org/10.1038/s41467-023-37353-8 Pan-cancer classification of single cells in the tumour microenvironment]
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|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|JH Cha
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[https://doi.org/10.1038/s41587-023-01686-y Single-cell mapping of combinatorial target antigens for CAR switches using logic gates]
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|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|SB Baek
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[https://doi.org/10.1038/s41587-023-01782-z Comparative analysis of cell–cell communication at single-cell resolution]
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|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|EJ Sung
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[https://doi.org/10.1038/s43018-023-00566-3 Phenotypic diversity of T cells in human primary and metastatic brain tumors revealed by multiomic interrogation]
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|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-27
|style="padding:.4em;"|IS Choi
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[https://doi.org/10.1038/s41591-023-02324-5 An integrated tumor, immune and microbiome atlas of colon cancer]
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|style="padding:.4em;" rowspan=1|2023/09/13
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-26
|style="padding:.4em;"|JW Yu
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[https://doi.org/10.1101/2023.01.17.524482 Decoupling the correlation between cytotoxic and exhausted T lymphocyte transcriptomic signatures enhances melanoma immunotherapy response prediction from tumor expression]
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|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|JH Cha
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[https://doi.org/10.1016/j.immuni.2023.04.010 Recruitment of epitope-specific T cell clones with a low-avidity threshold supports efficacy against mutational escape upon re-infection]
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|style="padding:.4em;"|SB Baek
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[https://doi.org/10.1038/s41587-022-01476-y Multi-omic single-cell velocity models epigenome–transcriptome interactions and improves cell fate prediction]
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|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-23
|style="padding:.4em;"|IS Choi
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[https://doi.org/10.1101/2023.07.28.550993 Major data analysis errors invalidate cancer microbiome findings]
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|style="padding:.4em;" rowspan=1|2023/08/02
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-22
|style="padding:.4em;"|EJ Sung
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[https://doi.org/10.1038/s43018-022-00475-x A single-cell atlas of glioblastoma evolution under therapy reveals cell-intrinsic and cell-extrinsic therapeutic targets]
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|style="padding:.4em;"|23-21
|style="padding:.4em;"|G Koh
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[https://doi.org/10.1038/s43018-022-00433-7 Single-cell meta-analyses reveal responses of tumor-reactive CXCL13+ T cells to immune-checkpoint blockade]
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|style="padding:.4em;"|JW Yu
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[https://doi.org/10.1038/s41587-022-01342-x Estimation of tumor cell total mRNA expression in 15 cancer types predicts disease progression]
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|style="padding:.4em;"|JH Cha
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[https://doi.org/10.1038/s41587-022-01288-0 DIALOGUE maps multicellular programs in tissue from single-cell or spatial transcriptomics data]
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[https://doi.org/10.1038/s41591-023-02371-y Pan-cancer T cell atlas links a cellular stress response state to immunotherapy resistance]
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|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|EJ Sung
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[https://doi.org/10.1016/j.patter.2022.100651 Self-supervised graph representation learning integrates multiple molecular networks and decodes gene-disease relationships]
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|style="padding:.4em;"|IS Choi
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[https://doi.org/10.1016/j.ccell.2022.10.008 High-resolution single-cell atlas reveals diversity and plasticity of tissue-resident neutrophils in non-small cell lung cancer]
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|style="padding:.4em;"|G Koh
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[https://doi.org/10.1186/s13059-022-02828-2 Parallel single-cell and bulk transcriptome analyses reveal key features of the gastric tumor microenvironment]
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|style="padding:.4em;"|JW Yu
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[https://doi.org/10.1038/s41467-022-32838-4 Mutated processes predict immune checkpoint inhibitor therapy benefit in metastatic melanoma]
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|style="padding:.4em;"|JH Cha
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[https://doi.org/10.1038/s43018-021-00292-8 Temporal single-cell tracing reveals clonal revival and expansion of precursor exhausted T cells during anti-PD-1 therapy in lung cancer]
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[https://doi.org/10.1016/j.cell.2022.11.028 Integrative single-cell analysis of cardiogenesis indentifies developmental trajectories and non-conding mutations in congenital heart disease]
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|style="padding:.4em;"|EJ Sung
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[https://doi.org/10.1101/2022.12.20.521311 Supervised discovery of interpretable gene programs from single-cell data]
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|style="padding:.4em;"|IS Choi
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[https://doi.org/10.1038/s41586-022-05435-0 Effect of the intratumoral microbiota on spatial and cellular heterogeneity in cancer]
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|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-9
|style="padding:.4em;"|G Koh
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[https://www.nature.com/articles/s41588-022-01141-9 Cancer cell states recur across tumor types and form specific interactions with the tumor microenvironment]
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|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-8
|style="padding:.4em;"|JW Yu
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[https://www.biorxiv.org/content/10.1101/2022.08.05.502989v1 MetaTiME: Meta-components of the Tumor Immune Microenvironment]
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|style="padding:.4em;"|JH Cha
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[https://www.nature.com/articles/s41590-022-01262-7 Pre-encoded responsiveness to type I interferon in the peripheral immune system defines outcome of PD1 blockade therapy]
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|style="padding:.4em;" rowspan=1|2023/02/21
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|style="padding:.4em;"|SB Baek
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[https://www.biorxiv.org/content/10.1101/2022.03.16.484513v1 Integrated single-cell profiling dissects cell-state-specific enhancer landscapes of human tumor-infiltrating T cells]
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|style="padding:.4em;"|EJ Sung
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[https://www.nature.com/articles/s41591-022-01799-y A T cell resilience model associated with response to immunotherapy in multiple tumor types]
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|style="padding:.4em;"|IS Choi
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[https://www.nature.com/articles/s41588-022-01134-8 Single-nucleus and spatial transcriptome profiling of pancreatic cancer identifies multicellular dynamics associated with neoadjuvant treatment]
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|style="padding:.4em;"|G Koh
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[https://pubmed.ncbi.nlm.nih.gov/35649411/ Cross-tissue, single-cell stromal atlas identifies shared pathological fibroblast phenotypes in four chronic inflammatory diseases]
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|style="padding:.4em;"|JW Yu
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[https://www.sciencedirect.com/science/article/pii/S1535610822003178 Pan-cancer integrative histology-genomic analysis via multimodal deep learning]
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[https://pubmed.ncbi.nlm.nih.gov/35803260/ Tissue-resident memory and circulating T cells are early responders to pre-surgical cancer immunotherapy]
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|+style="text-align:left;font-size:12pt" | 2023 Microbiome
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|style="padding:.4em;" rowspan=1|2023/11/17
|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|SH Ahn
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[https://doi.org/10.1038/s41467-023-39264-0 A data-driven approach for predicting the impact of drugs on the human microbiome]
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[https://doi.org/10.1101/2023.04.06.535777 Activation of programmed cell death and counter-defense functions of phage accessory genes]
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|style="padding:.4em;" rowspan=1|Microbiome
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[https://doi.org/10.1038/s41467-023-39459-5 Top-down identification of keystone taxa in the microbiome]
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[https://doi.org/10.1016/j.cels.2022.12.007 Pitfalls of genotyping microbial communities with rapidly growing genome collections]
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[https://doi.org/10.1186/s13059-023-03028-2 Reconstruction of the last bacterial common ancestor from 183 pangenomes reveals a versatile ancient core genome]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-43
|style="padding:.4em;"|SH Lee
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[https://doi.org/10.1038/s41587-023-01868-8 Generation of accurate, expandable phylogenomic trees with uDance]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-42
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[https://doi.org/10.1016/j.immuni.2023.04.003 Phage display sequencing reveals that genetic, environmental, and intrinsic factors influence variation of human antibody epitope repertoire]
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|style="padding:.4em;"|23-41
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[https://doi.org/10.1016/j.immuni.2023.04.017 Phage-display immunoprecipitation sequencing of the antibody epitope repertoire in inflammatory bowel disease reveals distinct antibody signatures]
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[https://doi.org/10.15252/msb.202311525 Consistency across multi-omics layers in a drug-perturbed gut microbial community]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-39
|style="padding:.4em;"|SH Ahn
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[https://doi.org/10.1038/s41591-023-02407-3 Microbiome-derived cobalamin and succinyl-CoA as biomarkers for improved screening of anal cancer]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-38
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[https://doi.org/10.1101/2023.03.05.531206 You can move, but you can’t hide: identification of mobile genetic elements with geNomad]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-37
|style="padding:.4em;"|JH Cha
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[https://doi.org/10.1038/s41591-023-02424-2 The airway microbiome mediates the interaction between environmental exposure and respiratory health in humans]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-36
|style="padding:.4em;"|JY Ma
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[https://doi.org/10.1080/19490976.2023.2224474 Ordering taxa in image convolution networks improves microbiome-based machine learning accuracy]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-35
|style="padding:.4em;"|NY Kim
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[https://doi.org/10.1101/2023.08.12.553040 The defensome of complex bacterial communities]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-34
|style="padding:.4em;"|SH Lee
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[https://doi.org/10.1016/j.cell.2023.03.011 Dietary tryptophan metabolite released by intratumoral Lactobacillus reuteri facilitates immune checkpoint inhibitor treatment]
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|style="padding:.4em;"|23-33
|style="padding:.4em;"|WJ Kim
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[https://doi.org/10.1038/s43587-022-00306-9 Toward an improved definition of a healthy microbiome for healthy aging]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-32
|style="padding:.4em;"|JY Kim
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[https://doi.org/10.1038/s43587-022-00287-9 Associations of the skin, oral and gut microbiome with aging, frailty and infection risk reservoirs in older adults]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-31
|style="padding:.4em;"|SH Ahn
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[https://doi.org/10.7554/eLife.50240 Adjusting for age improves identification of gut microbiome alterations in multiple diseases]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-30
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[https://doi.org/10.1038/s41564-023-01370-6 Centenarians have a diverse gut virome with the potential to modulate metabolism and promote healthy lifespan]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-29
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[https://doi.org/10.1186/s40168-023-01614-x Statistical modeling of gut microbiota for personalized health status monitoring]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-28
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[https://doi.org/10.1016/j.chom.2023.01.003 Longitudinal comparison of the developing gut virome in infants and their mothers]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-27
|style="padding:.4em;"|JY Ma
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[https://doi.org/10.1016/j.chom.2023.05.024 Enterosignatures define common bacterial guilds in the human gut microbiome]
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|style="padding:.4em;" rowspan=1|Microbiome
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[https://doi.org/10.1186/s13059-023-02902-3 PhyloMed: a phylogeny-based test of mediation effect in microbiome]
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|style="padding:.4em;"|SH Lee
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[https://doi.org/10.1016/j.chom.2023.05.027 The TaxUMAP atlas: Efficient display of large clinical microbiome data reveals ecological competition in protection against bacteremia]
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|style="padding:.4em;"|23-24
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[http://dx.doi.org/10.1038/nbt.3704 Measurement of bacterial replication rates in microbial communities]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-23
|style="padding:.4em;"|JY Kim
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[https://doi.org/10.1186/s40168-023-01564-4 Skin microbiome diferentiates into distinct cutotypes with unique metabolic functions upon exposure to polycyclic aromatic hydrocarbons]
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|style="padding:.4em;"|SH Ahn
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[https://doi.org/10.1016/j.xcrm.2023.100920 Enrichment of oral-derived bacteria in inflamed colorectal tumors and distinct associations of Fusobacterium in the mesenchymal subtype]
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|style="padding:.4em;" rowspan=1|Microbiome
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[https://doi.org/10.1038/s41586-023-05989-7 Profiling the human intestinal environment under physiological conditions]
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[https://doi.org/10.1186/s40168-023-01494-1 Genome-centric metagenomics reveals the host-driven dynamics and ecological role of CPR bacteria in an activated sludge system]
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|style="padding:.4em;"|JY Ma
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[https://doi.org/10.1016/j.patter.2022.100658 Enhanced metagenomic deep learning for disease prediction and consistent signature recognition by restructured microbiome 2D representations]
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[https://doi.org/10.1186/s13059-022-02809-5 Gene fow and introgression are pervasive forces shaping the evolution of bacterial species]
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|style="padding:.4em;"|JH Cha
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[https://doi.org/10.1186/s40168-022-01435-4 Alterations of oral microbiota and impact on the gut microbiome in type 1 diabetes mellitus revealed by integrated multi‑omic analyses]
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[https://doi.org/10.1038/s41467-022-33397-4 Deciphering microbial gene function using natural language processing]
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|style="padding:.4em;"|SH Lee
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[https://doi.org/10.1101/2022.11.28.518265 Rethinking bacterial relationships in light of their molecular abilities]
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|style="padding:.4em;" rowspan=1|2023/06/02
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|style="padding:.4em;"|JY Ma
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[https://doi.org/10.1016/j.immuni.2022.08.016 The CD4+ T cell response to a commensal-derived epitope transitions from a tolerant to an inflammatory state in Crohn’s disease]
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|style="padding:.4em;"|NY Kim
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[https://doi.org/10.1038/s41591-018-0203-7 Antigen discovery and specification of immunodominance hierarchies for MHCIIrestricted epitopes]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|SH Lee
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[https://doi.org/10.1101/2022.10.11.511790 Single Cell Transcriptomics Reveals the Hidden Microbiomes of Human Tissues]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|JY Kim
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[https://doi.org/10.1038/s41564-017-0096-0 Stability of the human faecal microbiome in a cohort of adult men]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-10
|style="padding:.4em;"|WJ Kim
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[https://doi.org/10.1038/s41564-017-0084-4 Metatranscriptome of human faecal microbial communities in a cohort of adult men]
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|style="padding:.4em;" rowspan=1|2023/03/24
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-9
|style="padding:.4em;"|SH Ahn
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[https://doi.org/10.1016/j.ccell.2022.09.009 Tumor microbiome links cellular programs and immunity in pancreatic cancer]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|HJ Kim
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[https://doi.org/10.1038/s41467-022-32832-w Extensive gut virome variation and its associations with host and environmental factors in a population-level cohort]
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|style="padding:.4em;" rowspan=1|2023/03/10
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-7
|style="padding:.4em;"|JH Cha
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[https://doi.org/10.1038/s41586-022-05620-1 The person-to-person transmission landscape of the gut and oral microbiomes]
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|style="padding:.4em;" rowspan=1|2023/02/21
|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|JY Ma
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[https://doi.org/10.1101/2023.01.30.526328 BIRDMAn: A Bayesian differential abundance framework that enables robust inference of host-microbe associations]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|NY Kim
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[https://www.nature.com/articles/s41564-022-01157-1 Phage–host coevolution in natural populations]
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|style="padding:.4em;" rowspan=1|2023/01/31
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-4
|style="padding:.4em;"|SH Lee
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[https://www.nature.com/articles/s41467-022-29968-0 A randomized controlled trial for response of microbiome network to exercise and diet intervention in patients with nonalcoholic fatty liver disease]
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|style="padding:.4em;"|SH Ahn
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[https://www.biorxiv.org/content/10.1101/2022.05.19.492684v1 Scalable power analysis and effect size exploration of microbiome community differences with Evident]
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[https://www.biorxiv.org/content/10.1101/2022.08.05.502982v1 Phanta: Phage-inclusive profiling of human gut metagenomes]
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|style="padding:.4em;"|23-1
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[https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1010373 Computational approach to modeling microbiome landscapes associated with chronic human disease progression]
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[https://www.biorxiv.org/content/10.1101/2022.08.19.504505v1 SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks]
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|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|22-31
|style="padding:.4em;"|JH Cha, SB Baek, IS Choi
|style="padding:.4em;text-align:left"|
[https://www.pnas.org/doi/10.1073/pnas.2105859118 Representation learning of RNA velocity reveals robust cell transitions]
[https://www.nature.com/articles/s41467-022-34188-7 UniTVelo: temporally unified RNA velocity reinforces single-cell trajectory inference]
[https://www.sciencedirect.com/science/article/pii/S0092867421015774 Mapping transcriptomic vector fields of single cells]
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|style="padding:.4em;"|IS Choi
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[https://www.nature.com/articles/s41467-022-31535-6 Network-based machine learning approach to predict immunotherapy response in cancer patients]
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|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|SB Baek
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[https://www.biorxiv.org/content/10.1101/2022.05.04.490536v1 Modeling fragment counts improves single-cell ATAC-seq analysis]
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|style="padding:.4em;" rowspan=1|2022/10/11
|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|G Koh
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[https://www.nature.com/articles/s41586-022-04718-w Extricating human tumour immune alterations from tissue inflammation]
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|style="padding:.4em;" rowspan=1|2022/09/13
|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|JW Yu
|style="padding:.4em;text-align:left"|
[https://www.biorxiv.org/content/10.1101/2022.06.15.495325v1 T cell receptor convergence is an indicator of antigen-specific T cell response in cancer immunotherapies]
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|style="padding:.4em;" rowspan=1|2022/09/06
|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://www.nature.com/articles/s41587-021-01091-3 Identifying phenotype-associated subpopulations by integrating bulk and single-cell sequencing data]
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|style="padding:.4em;" rowspan=1|2022/09/01
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|22-27
|style="padding:.4em;"|SB Baek
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[https://www.biorxiv.org/content/10.1101/2021.12.06.471401v1 MIRA: Joint regulatory modeling of multimodal expression and chromatin accessibility in single cells]
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|style="padding:.4em;"|EJ Sung
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[https://www.sciencedirect.com/science/article/pii/S1535610822000654 Immune phenotypic linkage between colorectal cancer and liver metastasis]
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|style="padding:.4em;"|IS Choi
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[https://doi.org/10.1101/2022.02.05.479217 Biologically informed deep learning to infer gene program activity in single cells]
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[https://www.nature.com/articles/s43018-022-00356-3 Cell type and gene expression deconvolution with BayesPrism enables Bayesian integrative analysis across bulk and single-cell RNA sequencing in oncology]
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|style="padding:.4em;"|JH Cha
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[https://pubmed.ncbi.nlm.nih.gov/34462589/ Mapping single-cell data to reference atlases by transfer learning]
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|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|JW Yu
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[https://doi.org/10.1101/2021.10.31.466532 Pan-cancer mapping of single T cell profiles reveals a TCF1:CXCR6-CXCL16 regulatory axis essential for effective anti-tumor immunity]
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|style="padding:.4em;"|IS Choi
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[https://pubmed.ncbi.nlm.nih.gov/34845454/ Identifying disease-critical cell types and cellular processes across the human body by integration of single-cell profiles and human genetics]
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[https://www.biorxiv.org/content/10.1101/2021.06.07.447430v2 Metacells untangle large and complex single-cell transcriptome networks]
[https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1812-2 MetaCell: analysis of single-cell RNA-seq data using K-nn graph partitions]
[https://genomebiology.biomedcentral.com/articles/10.1186/s13059-022-02667-1 Metacell‑2: a divide‑and‑conquer metacell algorithm for scalable scRNA‑seq analysis]
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|style="padding:.4em;"|SB Baek
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[https://pubmed.ncbi.nlm.nih.gov/34675423/ Co-varying neighborhood analysis identifies cell populations associated with phenotypes of interest from single-cell transcriptomics]
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[https://pubmed.ncbi.nlm.nih.gov/34426704/ Integrating T cell receptor sequences and transcriptional profiles by clonotype neighbor graph analysis (CoNGA)]
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[https://pubmed.ncbi.nlm.nih.gov/34986867/ Hepatocellular carcinoma patients with high circulating cytotoxic T cells and intra-tumoral immune signature benefit from pembrolizumab: results from a single-arm phase 2 trial]
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|style="padding:.4em;"|EJ Sung
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[https://pubmed.ncbi.nlm.nih.gov/35199064/ Effect of imputation on gene network reconstruction from single-cell RNA-seq data]
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|style="padding:.4em;"|IS Choi
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[https://pubmed.ncbi.nlm.nih.gov/35105355/ Diagnostic Evidence GAuge of Single cells (DEGAS): a flexible deep transfer learning framework for prioritizing cells in relation to disease]
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[https://pubmed.ncbi.nlm.nih.gov/35172892/ Integrating single-cell sequencing data with GWAS summary statistics reveals CD16+monocytes and memory CD8+T cells involved in severe COVID-19]
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[https://pubmed.ncbi.nlm.nih.gov/34663807/ Single cell T cell landscape and T cell receptor repertoire profiling of AML in context of PD-1 blockade therapy]
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[https://pubmed.ncbi.nlm.nih.gov/34594031/ Systematic investigation of cytokine signaling activity at the tissue and single-cell levels]
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[https://pubmed.ncbi.nlm.nih.gov/34852236/ Neoantigen-driven B cell and CD4 T follicular helper cell collaboration promotes anti-tumor CD8 T cell responses]
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[https://pubmed.ncbi.nlm.nih.gov/34930412/ MultiMAP: dimensionality reduction and integration of multimodal data]
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|style="padding:.4em;"|IS Choi
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[https://www.biorxiv.org/content/10.1101/2020.10.19.345983v2 CellRank for directed single-cell fate mapping]
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[https://pubmed.ncbi.nlm.nih.gov/34653365/ Single-cell analyses reveal key immune cell subsets associated with response to PD-L1 blockade in triple-negative breast cancer]
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|style="padding:.4em;"|IS Choi
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[https://pubmed.ncbi.nlm.nih.gov/34390642/ Chromatin and gene-regulatory dynamics of the developing human cerebral cortex at single-cell resolution]
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[https://pubmed.ncbi.nlm.nih.gov/34767762/ Single-cell analysis of human non-small cell lung cancer lesions refines tumor classification and patient stratification]
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|style="padding:.4em;"|JH Cha
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[https://pubmed.ncbi.nlm.nih.gov/34914499/ Pan-cancer single-cell landscape of tumor-infiltrating T cells]
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[https://pubmed.ncbi.nlm.nih.gov/34597583/ Atlas of clinically distinct cell states and ecosystems across human solid tumors]
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[https://pubmed.ncbi.nlm.nih.gov/34489465/ Single-cell ATAC and RNA sequencing reveal pre-existing and persistent cells associated with prostate cancer relapse]
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[https://www.biorxiv.org/content/10.1101/2022.08.02.502504v1 A novel in silico method employs chemical and protein similarity algorithms to accurately identify chemical transformations in the human gut microbiome]
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[https://www.biorxiv.org/content/10.1101/2021.09.13.460160v3 Inference of disease-associated microbial biomarkers based on metagenomic and metatranscriptomic data]
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[https://www.sciencedirect.com/science/article/pii/S0092867422009199?via%3Dihub Personalized microbiome-driven effects of non-nutritive sweeteners on human glucose tolerance]
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[https://www.nature.com/articles/s41564-022-01121-z Identification of shared and disease-specific host gene–microbiome associations across human diseases using multi-omic integration]
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[https://www.sciencedirect.com/science/article/pii/S193131282200049X Caudovirales bacteriophages are associated with improved executive function and memory in flies, mice, and humans]
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[https://www.biorxiv.org/content/10.1101/2021.10.06.463341v2.full SynTracker: a synteny based tool for tracking microbial strains]
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[https://www.nature.com/articles/s41587-022-01226-0 Identification of antimicrobial peptides from the human gut microbiome using deep learning]
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|style="padding:.4em;" rowspan=1|Microbiome
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[https://www.nature.com/articles/s41586-022-04648-7 Discovery of bioactive microbial gene products in inflammatory bowel disease]
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[https://www.nature.com/articles/s43588-022-00247-8 Large-scale microbiome data integration enables robust biomarker identification]
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[https://www.nature.com/articles/s41467-022-30512-3 Predicting cancer prognosis and drug response from the tumor microbiome]
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[https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-022-01231-0 MetaPop: a pipeline for macro- and microdiversity analyses and visualization of microbial and viral metagenome-derived populations]
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[https://www.nature.com/articles/s41586-022-04862-3 Biosynthetic potential of the global ocean microbiome]
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[https://www.nature.com/articles/s41596-020-00480-3 Tutorial: assessing metagenomics software with the CAMI benchmarking toolkit disease]
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[https://www.sciencedirect.com/science/article/pii/S2666379121002561 Identification of Faecalibacterium prausnitzii strains for gut microbiome-based intervention in Alzheimer’s-type dementia]
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[https://www.nature.com/articles/s41591-022-01688-4 Microbiome and metabolome features of the cardiometabolic disease spectrum]
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[https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02195-w Functional and genetic markers of niche partitioning among enigmatic members of the human oral microbiome]
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[https://www.biorxiv.org/content/10.1101/2022.02.21.480893v1 Integrating phylogenetic and functional data in microbiome studies]
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[https://genomebiology.biomedcentral.com/articles/10.1186/s13059-021-02473-1 Pandora: nucleotide-resolution bacterial pan-genomics with reference graphs]
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[https://journals.asm.org/doi/10.1128/mSystems.00252-19 Comprehensive Analysis Reveals the Evolution and Pathogenicity of Aeromonas, Viewed from Both Single Isolated Species and Microbial Communities]
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[https://genomebiology.biomedcentral.com/articles/10.1186/s13059-022-02610-4 AGAMEMNON: an Accurate metaGenomics And MEtatranscriptoMics quaNtificatiON analysis suite]
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[https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02200-2 Metapangenomics of the oral microbiome provides insights into habitat adaptation and cultivar diversity]
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[https://genomebiology.biomedcentral.com/articles/10.1186/s13059-021-02576-9 Species-resolved sequencing of low-biomass or degraded microbiomes using 2bRAD-M]
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[https://pubmed.ncbi.nlm.nih.gov/34517888/ Gut microbial determinants of clinically important improvement in patients with rheumatoid arthritis]
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[https://pubmed.ncbi.nlm.nih.gov/34618582/ Commensal bacteria promote endocrine resistance in prostate cancer through androgen biosynthesis]
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[https://pubmed.ncbi.nlm.nih.gov/34551799/ The influence of the gut microbiome on BCG-induced trained immunity]
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[https://pubmed.ncbi.nlm.nih.gov/34912116/ Towards the biogeography of prokaryotic genes]
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[https://www.biorxiv.org/content/10.1101/2022.07.06.499075v1 Maast: genotyping thousands of microbial strains efficiently]
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[https://www.cell.com/cell-host-microbe/fulltext/S1931-3128(19)30041-1?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS1931312819300411%3Fshowall%3Dtrue Distinct Genetic and Functional Traits of Human Intestinal Prevotella copri Strains Are Associated with Different Habitual Diets]
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[https://www.nature.com/articles/s41586-019-1237-9 Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases]
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[https://www.nature.com/articles/s41467-021-21475-y Gastrointestinal microbiota composition predicts peripheral inflammatory state during treatment of human tuberculosis]
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[https://www.sciencedirect.com/science/article/pii/S1931312820301694 Structure of the Mucosal and Stool Microbiome in Lynch Syndrome]
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[https://science.sciencemag.org/content/369/6506/936 Cross-reactivity between tumor MHC class 1-restricted antigens and an enterococcal bacteriophage]
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[https://www.nature.com/articles/s41564-020-00831-6 Bifidobacterium bifidum strains synergize with immune checkpoint inhibitors to reduce tumour burden in mice]
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[https://www.nature.com/articles/s41591-020-01223-3 The gut microbiome modulates the protective association between a Mediterranean diet and cardiometabolic disease risk]
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[https://www.nature.com/articles/s41467-019-14177-z Impact of commonly used drugs on the composition and metabolic function of the gut microbiota]
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[https://www.sciencedirect.com/science/article/pii/S0092867420305638 Personalized Mapping of Drug Metabolism by the Human Gut Microbiome]
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[https://www.cell.com/fulltext/S0092-8674(17)30107-1 Mining the Human Gut Microbiota for Immunomodulatory Organisms]
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[https://www.nature.com/articles/s41586-020-2095-1 Microbiome analyses of blood and tissues suggest cancer diagnostic approach]
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[https://science.sciencemag.org/content/368/6494/973 The human tumor microbiome is composed of tumor type-specific intracellular bacteria]
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[https://www.nature.com/articles/s41590-020-0784-4 Functional CRISPR dissection of gene networks controlling human regulatory T cell identity]
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[https://www.sciencedirect.com/science/article/pii/S0092867420306887 Molecular Pathways of Colon Inflammation Induced by Cancer Immunotherapy]
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[https://www.nature.com/articles/s41588-020-00721-x Single-cell epigenomic analyses implicate candidate causal variants at inherited risk loci for Alzheimer’s and Parkinson’s diseases]
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[https://www.nature.com/articles/s41467-020-14766-3 Trajectory-based differential expression analysis for single-cell sequencing data]
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[https://www.nature.com/articles/s41588-018-0156-2 Genetic determinants of co-accessible chromatin regions in activated T cells across humans]
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[https://www.sciencedirect.com/science/article/pii/S009286742030341X Single-Cell Analyses Inform Mechanisms of Myeloid-Targeted Therapies in Colon Cancer]
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[https://www.sciencedirect.com/science/article/pii/S0092867419308967?via%3Dihub Single-Cell Analysis of Crohn's Disease Lesions Identifies a Pathogenic Cellular Module Associated with Resistance to Anti-TNF Therapy]
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[https://pubmed.ncbi.nlm.nih.gov/32393797/ Multi-omic single-cell snapshots reveal multiple independent trajectories to drug tolerance in a melanoma cell line]
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|style="padding:.4em;"|20-14
|style="padding:.4em;"|JW Choi
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[https://pubmed.ncbi.nlm.nih.gov/30595452/ Dysfunctional CD8 T Cells Form a Proliferative, Dynamically Regulated Compartment Within Human Melanoma]
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[https://pubmed.ncbi.nlm.nih.gov/30388455/ A Cancer Cell Program Promotes T Cell Exclusion and Resistance to Checkpoint Blockade]
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[https://pubmed.ncbi.nlm.nih.gov/31974247/ Single-cell Transcriptional Diversity Is a Hallmark of Developmental Potential]
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|style="padding:.4em;"|JH Kim
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[https://pubmed.ncbi.nlm.nih.gov/31675496/ Landscape and Dynamics of Single Immune Cells in Hepatocellular Carcinoma]
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[https://pubmed.ncbi.nlm.nih.gov/32103181/ Peripheral T Cell Expansion Predicts Tumour Infiltration and Clinical Response]
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[https://pubmed.ncbi.nlm.nih.gov/30388456/ Defining T Cell States Associated With Response to Checkpoint Immunotherapy in Melanoma]
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[https://pubmed.ncbi.nlm.nih.gov/31915379/ Rapid Non-Uniform Adaptation to Conformation-Specific KRAS(G12C) Inhibition]
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[https://pubmed.ncbi.nlm.nih.gov/31959990/ Targeted Therapy Guided by Single-Cell Transcriptomic Analysis in Drug-Induced Hypersensitivity Syndrome: A Case Report]
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[https://pubmed.ncbi.nlm.nih.gov/32066951/ Distinct Microbial and Immune Niches of the Human Colon]
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[https://pubmed.ncbi.nlm.nih.gov/31375813/ Massively Parallel Single-Cell Chromatin Landscapes of Human Immune Cell Development and Intratumoral T Cell Exhaustion]
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[https://pubmed.ncbi.nlm.nih.gov/29434354/ Single-cell Gene Expression Reveals a Landscape of Regulatory T Cell Phenotypes Shaped by the TCR]
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|style="padding:.4em;"|20-3
|style="padding:.4em;"|NY Kim
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[https://pubmed.ncbi.nlm.nih.gov/30078704/ A Single-Cell Atlas of In Vivo Mammalian Chromatin Accessibility]
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[https://pubmed.ncbi.nlm.nih.gov/32014031/ scAI: An Unsupervised Approach for the Integrative Analysis of Parallel Single-Cell Transcriptomic and Epigenomic Profiles]
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[https://pubmed.ncbi.nlm.nih.gov/31792411/ Single-cell Multiomic Analysis Identifies Regulatory Programs in Mixed-Phenotype Acute Leukemia]
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[https://www.sciencedirect.com/science/article/pii/S1931312819303488 Obese Individuals with and without Type 2 Diabetes Show Different Gut Microbial Functional Capacity and Composition]
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[https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-019-0722-6 Clustering co-abundant genes identifies components of the gut microbiome that are reproducibly associated with colorectal cancer and inflammatory bowel disease]
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[https://www.nature.com/articles/s41587-019-0206-z Massively parallel single-cell chromatin landscapes of human immune cell development and intratumoral T cell exhaustion]
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[https://www.biorxiv.org/content/10.1101/739011v1 Assessment of computational methods for the analysis of single-cell ATAC-seq data]
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[https://www.sciencedirect.com/science/article/pii/S1931312819303026 Meta-Analysis Reveals Reproducible Gut Microbiome Alterations in Response to a High-Fat Diet]
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|style="padding:.4em;"|19-46
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[https://www.sciencedirect.com/science/article/pii/S0092867419307731 Tumor Microbiome Diversity and Composition Influence Pancreatic Cancer Outcomes]
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[https://genome.cshlp.org/content/early/2019/04/01/gr.243725.118 The accessible chromatin landscape of the murine hippocampus at single-cell resolution]
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[https://www.sciencedirect.com/science/article/pii/S009286741830446X Integrated Single-Cell Analysis Maps the Continuous Regulatory Landscape of Human Hematopoietic Differentiation]
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[https://science.sciencemag.org/content/365/6449/eaau4735 A sparse covarying unit that describes healthy and impaired human gut microbiota development]
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[https://www.sciencedirect.com/science/article/pii/S0092867419307810 Large-Scale Analyses of Human Microbiomes Reveal Thousands of Small, Novel Genes]
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[https://www.sciencedirect.com/science/article/pii/S0092867419307329?via%3Dihub Intra- and Inter-cellular Rewiring of the Human Colon during Ulcerative Colitis]
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[https://www.nature.com/articles/nbt.4042 Multiplexed droplet single-cell RNA-sequencing using natural genetic variation]
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[https://www.sciencedirect.com/science/article/pii/S193131281930352X?via%3Dihub The Landscape of Genetic Content in the Gut and Oral Human Microbiome]
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[https://www.sciencedirect.com/science/article/pii/S0092867419307755?via%3Dihub Benchmarking Metagenomics Tools for Taxonomic Classification]
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[https://www.biorxiv.org/content/10.1101/719088v1 Coexpression uncovers a unified single-cell transcriptomic landscape]
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[https://www.biorxiv.org/content/10.1101/586859v1 Single-cell interactomes of the human brain reveal cell-type specific convergence of brain disorders]
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|style="padding:.4em;" rowspan=3|2019/08/14
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[https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004075 Proportionality: a valid alternative to correlation for relative data]
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[https://www.nature.com/articles/s41598-017-16520-0 propr: An R-package for Identifying Proportionally Abundant Features Using Compositional Data Analysis]
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[https://www.nature.com/articles/s41591-018-0157-9 Robust prediction of response to immune checkpoint blockade therapy in metastatic melanoma]
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[https://www.nature.com/articles/s41592-018-0254-1 A test metric for assessing single-cell RNA-seq batch correction]
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[https://www.sciencedirect.com/science/article/pii/S0092867419305598 Comprehensive Integration of Single-Cell Data]
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[https://www.sciencedirect.com/science/article/pii/S0092867418313941 Defining T Cell States Associated with Response to Checkpoint Immunotherapy in Melanoma]
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|style="padding:.4em;"|19-29
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[https://www.sciencedirect.com/science/article/pii/S009286741831242X High-Dimensional Analysis Delineates Myeloid and Lymphoid Compartment Remodeling during Successful Immune-Checkpoint Cancer Therapy]
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|style="padding:.4em;" rowspan=2|2019/08/07
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[https://www.biorxiv.org/content/10.1101/555557v1 A single-cell reference map for human blood and tissue T cell activation reveals functional states in health and disease]
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[https://www.sciencedirect.com/science/article/pii/S009286741831568X Dysfunctional CD8 T Cells Form a Proliferative, Dynamically Regulated Compartment within Human Melanoma]
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[https://www.nature.com/articles/nm.4466 High-dimensional single-cell analysis predicts response to anti-PD-1 immunotherapy]
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[https://www.sciencedirect.com/science/article/pii/S0092867418311784 A Cancer Cell Program Promotes T Cell Exclusion and Resistance to Checkpoint Blockade]
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[https://www.ncbi.nlm.nih.gov/pubmed/26006008 COMPASS identifies T-cell subsets correlated with clinical outcomes.]
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[https://www.ncbi.nlm.nih.gov/pubmed/30936547 Meta-analysis of fecal metagenomes reveals global microbial signatures that are specific for colorectal cancer]
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[https://www.ncbi.nlm.nih.gov/pubmed/30936548 Metagenomic analysis of colorectal cancer datasets identifies cross-cohort microbial diagnostic signatures and a link with choline degradation]
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[https://www.ncbi.nlm.nih.gov/pubmed/30664783 Microbial network disturbances in relapsing refractory Crohn's disease.]
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[https://www.ncbi.nlm.nih.gov/pubmed/30867587 New insights from uncultivated genomes of the global human gut microbiome]
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[https://www.ncbi.nlm.nih.gov/pubmed/30661755 Extensive Unexplored Human Microbiome Diversity Revealed by Over 150,000 Genomes from Metagenomes Spanning Age, Geography, and Lifestyle.]
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|style="padding:.4em;" rowspan=2|2019/05/16
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[https://www.ncbi.nlm.nih.gov/pubmed/29311644 Dynamics of metatranscription in the inflammatory bowel disease gut microbiome.]
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[https://www.ncbi.nlm.nih.gov/pubmed/29335555 Metatranscriptome of human faecal microbial communities in a cohort of adult men.]
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[https://www.ncbi.nlm.nih.gov/pubmed/30193113 Post-Antibiotic Gut Mucosal Microbiome Reconstitution Is Impaired by Probiotics and Improved by Autologous FMT.]
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[https://www.ncbi.nlm.nih.gov/pubmed/30193112 Personalized Gut Mucosal Colonization Resistance to Empiric Probiotics Is Associated with Unique Host and Microbiome Features.]
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|style="padding:.4em;" rowspan=2|2019/05/02
|style="padding:.4em;"|19-12
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[https://www.ncbi.nlm.nih.gov/pubmed/30753825 Distinct Immune Cell Populations Define Response to Anti-PD-1 Monotherapy and Anti-PD-1/Anti-CTLA-4 Combined Therapy.]
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|style="padding:.4em;"|19-11
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[https://www.ncbi.nlm.nih.gov/pubmed/30778252 Subsets of exhausted CD8+ T cells differentially mediate tumor control and respond to checkpoint blockade.]
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|style="padding:.4em;" rowspan=2|2019/4/11
|style="padding:.4em;"|19-10
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[https://www.ncbi.nlm.nih.gov/pubmed/30479382 Lineage tracking reveals dynamic relationships of T cells in colorectal cancer.]
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|style="padding:.4em;"|19-9
|style="padding:.4em;"|
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[https://www.ncbi.nlm.nih.gov/pubmed/30523328 Single cell dissection of plasma cell heterogeneity in symptomatic and asymptomatic myeloma.]
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|style="padding:.4em;" rowspan=2|2019/4/4
|style="padding:.4em;"|19-8
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[https://www.ncbi.nlm.nih.gov/pubmed/29942092 Single-cell profiling of breast cancer T cells reveals a tissue-resident memory subset associated with improved prognosis.]
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|style="padding:.4em;"|19-7
|style="padding:.4em;"|
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[https://www.ncbi.nlm.nih.gov/pubmed/29942094 Global characterization of T cells in non-small-cell lung cancer by single-cell sequencing]
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|style="padding:.4em;" rowspan=2|2019/3/28
|style="padding:.4em;"|19-6
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[https://www.ncbi.nlm.nih.gov/pubmed/28319088 Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors.]
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|style="padding:.4em;"|19-5
|style="padding:.4em;"|
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[https://www.ncbi.nlm.nih.gov/pubmed/28622514 Landscape of Infiltrating T Cells in Liver Cancer Revealed by Single-Cell Sequencing.]
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|style="padding:.4em;" rowspan=3|2019/3/21
|style="padding:.4em;"|19-4
|style="padding:.4em;"|
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[https://www.ncbi.nlm.nih.gov/pubmed/29988129 Phenotype molding of stromal cells in the lung tumor microenvironment.]
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|style="padding:.4em;"|19-3-1
|style="padding:.4em;"|
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[https://www.ncbi.nlm.nih.gov/pubmed/30787436 A single-cell molecular map of mouse gastrulation and early organogenesis]
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|style="padding:.4em;"|19-3
|style="padding:.4em;"|
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[https://www.ncbi.nlm.nih.gov/pubmed/30787437 The single-cell transcriptional landscape of mammalian organogenesis]
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|style="padding:.4em;" rowspan=2|2019/3/14
|style="padding:.4em;"|19-2
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[https://www.ncbi.nlm.nih.gov/pubmed/29961579 Single-Cell Map of Diverse Immune Phenotypes in the Breast Tumor Microenvironment]
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|style="padding:.4em;"|19-1
|style="padding:.4em;"|
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[https://www.ncbi.nlm.nih.gov/pubmed/29198524 Single-Cell Transcriptomic Analysis of Primary and Metastatic Tumor Ecosystems in Head and Neck Cancer]
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{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2018
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!scope="col" style="padding:.4em" |Date
!scope="col" style="padding:.4em" | Paper index
!scope="col" style="padding:.4em" | Presenter
!scope="col" style="padding:.4em" | Paper title
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|style="padding:.4em;" rowspan=2|2018/06/14
|style="padding:.4em;"|18-12
|style="padding:.4em;"|
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=A+pan-cancer+analysis+of+enhancer+expression+in+nearly+9000+patient+samples A Pan-Cancer Analysis of Enhancer Expression in Nearly 9000 Patient Samples.]
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|style="padding:.4em;"|18-11
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=Machine+learning+identifies+stemness+features+associated+with+oncogenic+dedifferentiation Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation.]
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|style="padding:.4em;" rowspan=2|2018/06/07
|style="padding:.4em;"|18-10
|style="padding:.4em;"|
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=Developmental+and+oncogenic+programs+in+H3K27M+gliomas+dissected+by+single-cell+RNA-seq Developmental and oncogenic programs in H3K27M gliomas dissected by single-cell RNA-seq.]
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|style="padding:.4em;"|18-9
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=Chemoresistance+evolution+in+triple-negative+breast+cancer+delineated+by+single-cell+sequencing Chemoresistance Evolution in Triple-Negative Breast Cancer Delineated by Single-Cell Sequencing.]
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|style="padding:.4em;" rowspan=2|2018/05/31
|style="padding:.4em;"|18-8
|style="padding:.4em;"|
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=Mapping+human+pluripotent+stem+cell+differentiation+pathways+using+high+throughput+single-cell+RNA-sequencing Mapping human pluripotent stem cell differentiation pathways using high throughput single-cell RNA-sequencing.]
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|style="padding:.4em;"|18-7
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=A+single-cell+RNA-seq+survey+of+the+developmental+landscape+of+the+human+prefrontal+cortex A single-cell RNA-seq survey of the developmental landscape of the human prefrontal cortex.]
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|style="padding:.4em;" rowspan=2|2018/05/24
|style="padding:.4em;"|18-6
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=Single-cell+RNA+sequencing+identifies+celltype-specific+cis-eQTLs+and+co-expression+QTLs Single-cell RNA sequencing identifies celltype-specific cis-eQTLs and co-expression QTLs.]
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|style="padding:.4em;"|18-5
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=FOCS%3A+a+novel+method+for+analyzing+enhancer+and+gene+activity+patterns+infers+an+extensive+enhancer-promoter+map FOCS: a novel method for analyzing enhancer and gene activity patterns infers an extensive enhancer-promoter map.]
|-
|style="padding:.4em;" rowspan=2|2018/05/17
|style="padding:.4em;"|18-4
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/29149608 A Distinct Gene Module for Dysfunction Uncoupled from Activation in Tumor-Infiltrating T Cells.]
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|style="padding:.4em;"|18-3
|style="padding:.4em;"|SH Lee
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/29610481 A global transcriptional network connecting noncoding mutations to changes in tumor gene expression.]
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|style="padding:.4em;" rowspan=2|2018/05/10
|style="padding:.4em;"|18-2
|style="padding:.4em;"|JW Cho
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=Inferring+regulatory+element+landscapes+and+transcription+factor+networks+from+cancer+methylomes Inferring regulatory element landscapes and transcription factor networks from cancer methylomes.]
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|style="padding:.4em;"|18-1
|style="padding:.4em;"|DS Bae
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[https://www.ncbi.nlm.nih.gov/pubmed/28129544 Genomic and Transcriptomic Features of Response to Anti-PD-1 Therapy in Metastatic Melanoma.]
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{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2017
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!scope="col" style="padding:.4em" |Date
!scope="col" style="padding:.4em" | Paper index
!scope="col" style="padding:.4em" | Presenter
!scope="col" style="padding:.4em" | Paper title
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|style="padding:.4em;" rowspan=2|2017/06/28
|style="padding:.4em;"|17-36
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/28104840 Tumor aneuploidy correlates with markers of immune evasion and with reduced response to immunotherapy.]
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|style="padding:.4em;"|17-35
|style="padding:.4em;"|JW Cho
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/27240091 Landscape of tumor-infiltrating T cell repertoire of human cancers.]
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|style="padding:.4em;" rowspan=2|2017/06/14
|style="padding:.4em;"|17-34
|style="padding:.4em;"|JW Cho
|style="padding:.4em;text-align:left"|
[http://biorxiv.org/content/early/2015/09/01/025908 The landscape of T cell infiltration in human cancer and its association with antigen presenting gene expression.]
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|style="padding:.4em;"|17-33
|style="padding:.4em;"|MY Lee
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.08.052 A Distinct Gene Module for Dysfunction Uncoupled from Activation in Tumor-Infiltrating T Cells.]
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|style="padding:.4em;" rowspan=2|2017/06/07
|style="padding:.4em;"|17-32
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[http://www.tandfonline.com/doi/full/10.1080/2162402X.2016.1253654 Identification of genetic determinants of breast cancer immune phenotypes by integrative genome-scale analysis.]
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|style="padding:.4em;"|17-31
|style="padding:.4em;"|MY Lee
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.celrep.2016.03.075 Genomic Correlates of Immune-Cell Infiltrates in Colorectal Carcinoma.]
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|style="padding:.4em;" rowspan=2|2017/05/31
|style="padding:.4em;"|17-30
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.02.065 Genomic and Transcriptomic Features of Response to Anti-PD-1 Therapy in Metastatic Melanoma.]
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|style="padding:.4em;"|17-29
|style="padding:.4em;"|JW Cho
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.celrep.2016.12.019 Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade.]
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|style="padding:.4em;" rowspan=2|2017/05/24
|style="padding:.4em;"|17-28
|style="padding:.4em;"|EB Kim
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.12.022 Systemic Immunity Is Required for Effective Cencer Immunotherapy.]
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|style="padding:.4em;"|17-27
|style="padding:.4em;"|CY Kim
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.10.020 Linking the Human Gut Microbiome to Inflammatory Cytokine Production Capacity.]
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|style="padding:.4em;" rowspan=2|2017/05/17
|style="padding:.4em;"|17-26
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.10.018 Host and Environmental Factors Influencing Individual Human Cytokine Responses.]
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|style="padding:.4em;"|17-25
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.10.017 A Functional Genomics Approach to Understand Variation in Cytokine Production in Humans.]
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|style="padding:.4em;" rowspan=2|2017/04/26
|style="padding:.4em;"|17-24
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1038%2FNMETH.4177 Pooled CRISPR screening with single-cell transcriptome readout.]
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|style="padding:.4em;"|17-23
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.11.039 Dissecting Immune Circuits by Linking CRISPR-Pooled Screens with Single-Cell RNA-Seq.]
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|style="padding:.4em;" rowspan=2|2017/04/12
|style="padding:.4em;"|17-22
|style="padding:.4em;"|SH Lee
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.11.038 Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens.]
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|style="padding:.4em;"|17-21
|style="padding:.4em;"|CY Kim
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1038%2Fnbt.3569 Wishbone identifies bifurcating developmental trajectories from single-cell data.]
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|style="padding:.4em;" rowspan=2|2017/04/05
|style="padding:.4em;"|17-20
|style="padding:.4em;"|KS Kim
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[http://biorxiv.org/content/early/2017/02/21/110668 Reversed graph embedding resolves complex single-cell developmental trajectories.]
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|style="padding:.4em;"|17-19
|style="padding:.4em;"|SH Lee
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1038%2Fnmeth.4150 Single-cell mRNA quantification and differential analysis with Census.]
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|style="padding:.4em;" rowspan=2|2017/03/29
|style="padding:.4em;"|17-18
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.celrep.2016.12.060 Single-Cell Transcriptomic Analysis Defines Heterogeneity and Transcriptional Dynamics in the Adult Neural Stem Cell Lineage.]
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|style="padding:.4em;"|17-17
|style="padding:.4em;"|DS Bae
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[https://www.ncbi.nlm.nih.gov/pubmed/26051941 Single-Cell Network Analysis Identifies DDIT3 as a Nodal Lineage Regulator in Hematopoiesis.]
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|style="padding:.4em;" rowspan=2|2017/03/22
|style="padding:.4em;"|17-16
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[https://www.ncbi.nlm.nih.gov/pubmed/27580035 Single-cell analysis of mixed-lineage states leading to a binary cell fate choice.]
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|style="padding:.4em;"|17-15
|style="padding:.4em;"|EB Kim
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[https://www.ncbi.nlm.nih.gov/pubmed/27281220 Dissecting direct reprogramming from fibroblast to neuron using single-cell RNA-seq.]
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|style="padding:.4em;" rowspan=2|2017/03/15
|style="padding:.4em;"|17-14
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1126%2Fscience.aad0501 Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq.]
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|style="padding:.4em;"|17-13
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[https://www.ncbi.nlm.nih.gov/pubmed/26084335 Single-cell mRNA sequencing identifies subclonal heterogeneity in anti-cancer drug responses of lung adenocarcinoma cells.]
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|style="padding:.4em;" rowspan=2|2017/02/28
|style="padding:.4em;"|17-12
|style="padding:.4em;"|KS Kim
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[https://www.ncbi.nlm.nih.gov/pubmed/27824113 A Comprehensive Characterization of the Function of LincRNAs in Transcriptional Regulation Through Long-Range Chromatin Interactions.]
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|style="padding:.4em;"|17-11
|style="padding:.4em;"|JE Shim
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[//pubmed.gov/27851969 Epigenomic Deconvolution of Breast Tumors Reveals Metabolic Coupling between Constituent Cell Types.]
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|style="padding:.4em;" rowspan=2|2017/02/21
|style="padding:.4em;"|17-10
|style="padding:.4em;"|EB Kim
|style="padding:.4em;text-align:left"|
[http://biorxiv.org/content/early/2016/12/30/097451 Convergence of dispersed regulatory mutations predicts driver genes in prostate cancer.]
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|style="padding:.4em;"|17-09
|style="padding:.4em;"|EB Kim
|style="padding:.4em;text-align:left"|
[//pubmed.gov/27723759 Chromatin structure-based prediction of recurrent noncoding mutations in cancer.]
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|style="padding:.4em;" rowspan=2|2017/02/07
|style="padding:.4em;"|17-08
|style="padding:.4em;"|DS Bae
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[http://biorxiv.org/content/early/2016/11/17/088286 Somatic Mutations and Neoepitope Homology in Melanomas Treated with CTLA-4 Blockade.]
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|style="padding:.4em;"|17-07
|style="padding:.4em;"|MY Lee
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[http://biorxiv.org/content/early/2016/11/28/090134 Chemotherapy weakly contributes to predicted neoantigen expression in ovarian cancer.]
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|style="padding:.4em;" rowspan=1|2017/01/31
|style="padding:.4em;"|17-06
|style="padding:.4em;"|CY Kim
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[//www.ncbi.nlm.nih.gov/pubmed/27912059 Microbiota Diurnal Rhythmicity Programs Host Transcriptome Oscillations.]
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|style="padding:.4em;" rowspan=1|2017/01/24
|style="padding:.4em;"|17-05
|style="padding:.4em;"|JW Cho
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[//www.ncbi.nlm.nih.gov/pubmed/27851914 Transcriptional Landscape of Human Tissue Lymphocytes Unveils Uniqueness of Tumor-Infiltrating T Regulatory Cells.]
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|style="padding:.4em;" rowspan=2|2017/01/17
|style="padding:.4em;"|17-04
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[//www.ncbi.nlm.nih.gov/pubmed/25938943 Mapping long-range promoter contacts in human cells with high-resolution capture Hi-C]
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|style="padding:.4em;"|17-03
|style="padding:.4em;"|EB Kim
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[//www.ncbi.nlm.nih.gov/pubmed/27306882 CHiCAGO: robust detection of DNA looping interactions in Capture Hi-C data.]
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|style="padding:.4em;" rowspan=1|2017/01/10
|style="padding:.4em;"|17-02
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[//www.ncbi.nlm.nih.gov/pubmed/26527291 ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis]
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|style="padding:.4em;" rowspan=1|2017/01/03
|style="padding:.4em;"|17-01
|style="padding:.4em;"|SH Lee
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[//www.ncbi.nlm.nih.gov/pubmed/26287467 Single-cell messenger RNA sequencing reveals rare intestinal cell types]
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{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2016
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|style="padding:.4em;" rowspan=1|2016/12/27
|style="padding:.4em;"|2016-31
|style="padding:.4em;"|EB Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/25664528 Decoding the regulatory network of early blood development from single-cell gene expression measurements.]
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|style="padding:.4em;" rowspan=1|2016/12/6
|style="padding:.4em;"|2016-30
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26299571 Single-Cell RNA-Seq with Waterfall Reveals Molecular Cascades underlying Adult Neurogenesis.]
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|style="padding:.4em;" rowspan=1|2016/11/29
|style="padding:.4em;"|2016-29
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/24658644 The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells.]
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|style="padding:.4em;" rowspan=1|2016/11/22
|style="padding:.4em;"|2016-28
|style="padding:.4em;"|CY Kim
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[http://www.ncbi.nlm.nih.gov/pubmed/26887813 Classification of low quality cells from single-cell RNA-seq data.]
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|style="padding:.4em;" rowspan=1|2016/11/15
|style="padding:.4em;"|2016-27
|style="padding:.4em;"|MY Lee
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[http://www.ncbi.nlm.nih.gov/pubmed/26000487 Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells.]
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|style="padding:.4em;" rowspan=1|2016/11/8
|style="padding:.4em;"|2016-26
|style="padding:.4em;"|JW Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26000488 Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets.]
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|style="padding:.4em;" rowspan=1|2016/11/1
|style="padding:.4em;"|2016-25
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27426982 Causal Mechanistic Regulatory Network for Glioblastoma Deciphered Using Systems Genetics Network Analysis.]
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|style="padding:.4em;" rowspan=1|2016/10/25
|style="padding:.4em;"|2016-24
|style="padding:.4em;"|MY Lee
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27549193 Comprehensive analyses of tumor immunity: implications for cancer immunotherapy.]
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|style="padding:.4em;" rowspan=1|2016/10/11
|style="padding:.4em;"|2016-23
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=sidespread+parainflammation Widespread parainflammation in human cancer]
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|style="padding:.4em;" rowspan=1|2016/9/27
|style="padding:.4em;"|2016-22
|style="padding:.4em;"|ER Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27535533 Analysis of protein-coding genetic variation in 60,706 humans]
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|style="padding:.4em;" rowspan=1|2016/9/20
|style="padding:.4em;"|2016-21
|style="padding:.4em;"|SM Yang
|style="padding:.4em;text-align:left"|
Functional characterization of somatic mutations in cancer using network-based inference of protein activity
[http://www.ncbi.nlm.nih.gov/pubmed/27322546 pubmed]
[http://www.nature.com/ng/journal/v48/n8/full/ng.3593.html fulltext]
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|style="padding:.4em;" rowspan=1|2016/9/13
|style="padding:.4em;"|2016-20
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=exploiting+single-cell+expression+to+characterize Exploiting single-cell expression to characterize co-expression replicability.]
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|style="padding:.4em;" rowspan=1|2016/9/6
|style="padding:.4em;"|2016-19
|style="padding:.4em;"|T Lee
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26940869 Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade]
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|style="padding:.4em;" rowspan=1|2016/8/31
|style="padding:.4em;"|2016-18
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27264179 A Computational Drug Repositioning Approach for Targeting Oncogenic Transcription Factors]
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|style="padding:.4em;" rowspan=1|2016/8/16
|style="padding:.4em;"|2016-17
|style="padding:.4em;"|JW Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27309802 The landscape of accessible chromatin in mammalian preimplantation embryos]
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|style="padding:.4em;" rowspan=1|2016/8/8
|style="padding:.4em;"|2016-16
|style="padding:.4em;"|EB Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27064255 Enhancer-promoter interactions are encoded by complex genomic signatures on looping chromatin]
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|style="padding:.4em;" rowspan=1|2016/8/1
|style="padding:.4em;"|2016-15
|style="padding:.4em;"|MY Lee
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27040498 Personalized Immunomonitoring Uncovers Molecular Networks that Stratify Lupus Patients]
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|style="padding:.4em;" rowspan=1|2016/7/25
|style="padding:.4em;"|2016-14
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=integration+of+summary+data+from+gwas+and+eqtl+studies Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets]
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|style="padding:.4em;" rowspan=1|2016/7/18
|style="padding:.4em;"|2016-13
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/23624555 Identification of transcriptional regulators in the mouse immune system]
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|style="padding:.4em;" rowspan=2|2016/6/8
|style="padding:.4em;"|2016-12
|style="padding:.4em;"|DS Bae, CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26619012 Mapping the effects of drugs on the immune system]
|-
|style="padding:.4em;"| 2016-11
|style="padding:.4em;"|DS Bae, CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26186195 Elucidating compound mechanism of action by network perturbation analysis]
|-
|style="padding:.4em;" rowspan=2|2016/6/1
|style="padding:.4em;"|2016-10
|style="padding:.4em;"|MY Lee,SM Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26854917 Integrative approaches for large-scale transcriptome-wide association studies]
|-
|style="padding:.4em;"| 2016-9
|style="padding:.4em;"|MY Lee,SM Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26950747 Tissue-specific regulatory circuits reveal variable modular perturbations across complex diseases]
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|style="padding:.4em;" rowspan=2|2016/5/18
|style="padding:.4em;"|2016-8
|style="padding:.4em;"|CY Kim,SJ Kwon
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27013732 Survey of variation in human transcription factors reveals prevalent DNA binding changes]
|-
|style="padding:.4em;"| 2016-7
|style="padding:.4em;"| CY Kim,SJ Kwon
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26502339 Large-scale identification of sequence variants influencing human transcription factor occupancy in vivo]
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|style="padding:.4em;" rowspan=2|2016/5/11
|style="padding:.4em;"|2016-6
|style="padding:.4em;"|DS Bae, CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/25171417 Predicting Cancer-specific vulnerability via data-driven detection of synthetic lethality]
|-
|style="padding:.4em;"| 2016-5
|style="padding:.4em;"|DS Bae, CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/23467089 Dynamic regulatory network controlling Th17 cell differentiation]
|-
|style="padding:.4em;" rowspan=2|2016/5/4
|style="padding:.4em;"|2016-4
|style="padding:.4em;"|MY Lee,SM Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/25853550 Characterization of the immunophenotypes and antigenomes of colorectal cancers reveals distinct tumor escape mechanisms and novel targets for immunotherapy]
|-
|style="padding:.4em;"| 2016-3
|style="padding:.4em;"|MY Lee,SM Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26618344 Regulators of genetic risk of breast cancer identified by integrative network analysis]
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|style="padding:.4em;" rowspan=2|2016/4/27
|style="padding:.4em;"|2016-2
|style="padding:.4em;"|CY Kim,SJ Kwon
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26780608 A predictive computational framework for direct reprogramming between human cell types]
|-
|style="padding:.4em;"| 2016-1
|style="padding:.4em;"| CY Kim,SJ Kwon
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/25126793 CellNet: Network biology applied to stem cell engineering]
|}

{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2015
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!scope="col" style="padding:.4em" | Date
!scope="col" style="padding:.4em" | Paper index
!scope="col" style="padding:.4em" | Presenter
!scope="col" style="padding:.4em" | Paper title
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|style="padding:.4em;" rowspan=2|2015/06/11
|style="padding:.4em;"|2015-55
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://genome.cshlp.org/content/24/2/340.long Improved exome prioritization of disease genes through cross-species phenotype comparison.]
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|style="padding:.4em;"|2015-54
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0002929714001128 Phevor combines multiple biomedical ontologies for accurate identification of disease-causing alleles in single individuals and small nuclear families.]
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|style="padding:.4em;" rowspan=2|2015/06/04
|style="padding:.4em;"|2015-53
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nmeth/journal/v10/n11/full/nmeth.2656.html eXtasy: variant prioritization by genomic data fusion.]
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|style="padding:.4em;"|2015-52
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://genome.cshlp.org/content/21/9/1529.long A probabilistic disease-gene finder for personal genomes.]
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|style="padding:.4em;" rowspan=2|2015/05/28
|style="padding:.4em;"|2015-51
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/ng/journal/v46/n12/full/ng.3141.html Systematic analysis of noncoding somatic mutations and gene expression alterations across 14 tumor types.]
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|style="padding:.4em;"|2015-50
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nbt/journal/v28/n5/full/nbt.1630.html GREAT improves functional interpretation of cis-regulatory regions.]
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|style="padding:.4em;" rowspan=2|2015/05/21
|style="padding:.4em;"|2015-49
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nmeth/journal/v11/n3/full/nmeth.2832.html Functional annotation of noncoding sequence variants.]
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|style="padding:.4em;"|2015-48
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://genomebiology.com/content/15/10/480 FunSeq2: A framework for prioritizing noncoding regulatory variants in cancer.]
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|style="padding:.4em;" rowspan=2|2015/05/14
|style="padding:.4em;"|2015-47
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nmeth/journal/v12/n2/full/nmeth.3215.html Selecting causal genes from genome-wide association studies via functionally coherent subnetworks.]
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|style="padding:.4em;"|2015-46
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/ncomms/2015/150119/ncomms6890/full/ncomms6890.html Biological interpretation of genome-wide association studies using predicted gene functions.]
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|style="padding:.4em;" rowspan=3|2015/05/07
|style="padding:.4em;"|2015-45
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/ncomms/2014/140626/ncomms5212/full/ncomms5212.html Human symptoms-disease network.]
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|style="padding:.4em;"|2015-44
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867413010246 A nondegenerate code of deleterious variants in Mendelian loci contributes to complex disease risk.]
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|style="padding:.4em;"|2015-43
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.sciencemag.org/content/347/6224/1257601.long Uncovering disease-disease relationships through the incomplete interactome.]

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|style="padding:.4em;" rowspan=2|2015/04/30
|style="padding:.4em;"|2015-42
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://genome.cshlp.org/content/25/1/142.long The discovery of integrated gene networks for autism and related disorders.]
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|style="padding:.4em;"|2015-41
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://msb.embopress.org/content/10/12/774.long Integrated systems analysis reveals a molecular network underlying autism spectrum disorders.]
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|style="padding:.4em;" rowspan=3|2015/04/23
|style="padding:.4em;"|2015-40
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v518/n7539/full/nature13990.html Dissecting neural differentiation regulatory networks through epigenetic footprinting.]
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|style="padding:.4em;"|2015-39
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v518/n7539/full/nature14221.html Cell-of-origin chromatin organization shapes the mutational landscape of cancer.]
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|style="padding:.4em;"|2015-38
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v518/n7539/full/nature14248.html Integrative analysis of 111 reference human epigenomes.]
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|style="padding:.4em;" rowspan=2|2015/04/09
|style="padding:.4em;"|2015-37
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://genome.cshlp.org/content/25/2/246.long Genome-wide analysis of local chromatin packing in Arabidopsis thaliana.]
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|style="padding:.4em;"|2015-36
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414014974 A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping.]
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|style="padding:.4em;" rowspan=2|2015/04/02
|style="padding:.4em;"|2015-35
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nrg/journal/v14/n6/full/nrg3454.html Exploring the three-dimensional organization of genomes: interpreting chromatin interaction data.]
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|style="padding:.4em;"|2015-34
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.sciencemag.org/content/347/6225/1010.long Transcribed enhancers lead waves of coordinated transcription in transitioning mammalian cells.]
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|style="padding:.4em;" rowspan=2|2015/03/26
|style="padding:.4em;"|2015-33
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://genome.cshlp.org/content/25/2/257.long Cupid: simultaneous reconstruction of microRNA-target and ceRNA networks.]
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|style="padding:.4em;"|2015-32
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://genome.cshlp.org/content/25/1/41.long Characterization of the neural stem cell gene regulatory network identifies OLIG2 as a multifunctional regulator of self-renewal.]
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|style="padding:.4em;" rowspan=2|2015/03/19
|style="padding:.4em;"|2015-31
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nbt/journal/vaop/ncurrent/full/nbt.3154.html Decoding the regulatory network of early blood development from single-cell gene expression measurements.]
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|style="padding:.4em;"|2015-30
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nrg/journal/v16/n3/full/nrg3833.html Computational and analytical challenges in single-cell transcriptomics.]
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|style="padding:.4em;" rowspan=3|2015/03/12
|style="padding:.4em;"|2015-29
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867415000136 Extensive Strain-Level Copy-Number Variation across Human Gut Microbiome Species.]
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|style="padding:.4em;"|2015-28
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v500/n7464/full/nature12506.html Richness of human gut microbiome correlates with metabolic markers.]
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|style="padding:.4em;"|2015-27
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v490/n7418/full/nature11450.html A metagenome-wide association study of gut microbiota in type 2 diabetes.]
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|style="padding:.4em;" rowspan=3|2015/03/09
|style="padding:.4em;"|2015-26
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003326 Practical guidelines for the comprehensive analysis of ChIP-seq data.]
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|style="padding:.4em;"|2015-25
|style="padding:.4em;"|T Lee
|style="padding:.4em;text-align:left"|
[http://genome.cshlp.org/content/23/5/777.long Patterns of regulatory activity across diverse human cell types predict tissue identity, transcription factor binding, and long-range interactions.]
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|style="padding:.4em;"|2015-24
|style="padding:.4em;"|ER Kim
|style="padding:.4em;text-align:left"|
[http://msb.embopress.org/content/10/11/760.long Rapid neurogenesis through transcriptional activation in human stem cells.]
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|style="padding:.4em;" rowspan=3|2015/03/02
|style="padding:.4em;"|2015-23
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414011787 Genome-Scale CRISPR-Mediated Control of Gene Repression and Activation.]
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|style="padding:.4em;"|2015-22
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1004237 Integrating multiple genomic data to predict disease-causing nonsynonymous single nucleotide variants in exome sequencing studies.]
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|style="padding:.4em;"|2015-21
|style="padding:.4em;"|ER Kim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/ng/journal/v45/n6/full/ng.2653.html The Genotype-Tissue Expression (GTEx) project.]
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|style="padding:.4em;" rowspan=3|2015/02/24
|style="padding:.4em;"|2015-20
|style="padding:.4em;"|ER Kim
|style="padding:.4em;text-align:left"|
[http://www.sciencemag.org/content/346/6212/1007.long Mouse regulatory DNA landscapes reveal global principles of cis-regulatory evolution.]
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|style="padding:.4em;"|2015-19
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v515/n7527/full/nature13985.html Principles of regulatory information conservation between mouse and human.]
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|style="padding:.4em;"|2015-18
|style="padding:.4em;"|BH Kang
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v515/n7527/full/nature13972.html Conservation of trans-acting circuitry during mammalian regulatory evolution.]
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|style="padding:.4em;" rowspan=3|2015/02/16
|style="padding:.4em;"|2015-17
|style="padding:.4em;"|T Lee
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature13835.html Genetic and epigenetic fine mapping of causal autoimmune disease variants.]
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|style="padding:.4em;"|2015-16
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/ng/journal/v46/n3/full/ng.2892.html A general framework for estimating the relative pathogenicity of human genetic variants.]
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|style="padding:.4em;"|2015-15
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003825 A probabilistic model to predict clinical phenotypic traits from genome sequencing.]
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|style="padding:.4em;" rowspan=4|2015/02/02
|style="padding:.4em;"|2015-14
|style="padding:.4em;"|BH Kang
|style="padding:.4em;text-align:left"|
[http://www.pnas.org/content/111/22/E2329.long Relating the metatranscriptome and metagenome of the human gut.]
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|style="padding:.4em;"|2015-13
|style="padding:.4em;"|BH Kang
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v513/n7516/full/nature13568.html Alterations of the human gut microbiome in liver cirrhosis.]
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|style="padding:.4em;"|2015-12
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nbt/journal/v32/n8/full/nbt.2942.html An integrated catalog of reference genes in the human gut microbiome.]
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|style="padding:.4em;"|2015-11
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nbt/journal/v32/n8/full/nbt.2939.html Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes.]
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|style="padding:.4em;" rowspan=5|2015/01/26
|style="padding:.4em;"|2015-10
|style="padding:.4em;"|HH Kim
|style="padding:.4em;text-align:left"|
[http://msb.embopress.org/content/9/1/666.long Computational meta'omics for microbial community studies.]
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|style="padding:.4em;"|2015-09
|style="padding:.4em;"|HH Kim
|style="padding:.4em;text-align:left"|
[http://genomemedicine.com/content/5/7/65 Functional profiling of the gut microbiome in disease-associated inflammation.]
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|style="padding:.4em;"|2015-08
|style="padding:.4em;"|BH Kang
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S016895251200145X Biodiversity and functional genomics in the human microbiome.]
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|style="padding:.4em;"|2015-07
|style="padding:.4em;"|BH Kang
|style="padding:.4em;text-align:left"|
[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002808 Chapter 12: Human Microbiome Analysis.]
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|style="padding:.4em;"|2015-06
|style="padding:.4em;"|BH Kang
|style="padding:.4em;text-align:left"|
[http://www.cell.com/cell/abstract/S0092-8674%2814%2900864-2 Conducting a Microbiome Study.]
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|style="padding:.4em;" rowspan=3|2015/01/12
|style="padding:.4em;"|2015-05
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/ncomms/2014/141210/ncomms6522/full/ncomms6522.html Small RNA changes en route to distinct cellular states of induced pluripotency.]
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|style="padding:.4em;"|2015-04
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v516/n7530/full/nature14046.html Genome-wide characterization of the routes to pluripotency.]
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|style="padding:.4em;"|2015-03
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v516/n7530/full/nature14047.html Divergent reprogramming routes lead to alternative stem-cell states.]
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|style="padding:.4em;" rowspan=2|2015/01/05
|style="padding:.4em;"|2015-02
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.pnas.org/content/111/21/E2191.long Global view of enhancer-promoter interactome in human cells.]
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|style="padding:.4em;"|2015-01
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://nar.oxfordjournals.org/content/41/22/10391.long Combining Hi-C data with phylogenetic correlation to predict the target genes of distal regulatory elements in human genome.]
|}
{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2014
|-
!scope="col" style="padding:.4em" | Date
!scope="col" style="padding:.4em" | Paper index
!scope="col" style="padding:.4em" | Presenter
!scope="col" style="padding:.4em" | Paper title
|-
|style="padding:.4em;" rowspan=2|2014/12/23
|style="padding:.4em;"|2014-41
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867413012270 Super-enhancers in the control of cell identity and disease.]
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|style="padding:.4em;"|2014-40
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867413003929 Master transcription factors and mediator establish super-enhancers at key cell identity genes.]
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|style="padding:.4em;" rowspan=4|2014/12/09
|style="padding:.4em;"|2014-39
|style="padding:.4em;"|HH Kim
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414013713 Unraveling the biology of a fungal meningitis pathogen using chemical genetics.]
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|style="padding:.4em;"|2014-38
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414014226 A proteome-scale map of the human interactome network.]
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|style="padding:.4em;"|2014-37
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[http://msb.embopress.org/content/10/9/752.long The role of the interactome in the maintenance of deleterious variability in human populations.]
|-
|style="padding:.4em;"|2014-36
|style="padding:.4em;"|HS Shim
|style="padding:.4em;text-align:left"|
[http://www.sciencemag.org/content/342/6154/1235587.long Integrative annotation of variants from 1092 humans: application to cancer genomics.]
|-
|style="padding:.4em;" rowspan=2|2014/12/02
|style="padding:.4em;"|2014-35
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://genome.cshlp.org/content/23/8/1319.long Mapping functional transcription factor networks from gene expression data.]
|-
|style="padding:.4em;"|2014-34
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nrg/journal/v15/n7/full/nrg3684.html In pursuit of design principles of regulatory sequences.]
|-
|style="padding:.4em;" rowspan=2|2014/11/25
|style="padding:.4em;"|2014-33
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[http://genomemedicine.com/content/3/6/36 Epigenomics of human embryonic stem cells and induced pluripotent stem cells: insights into pluripotency and implications for disease.]
|-
|style="padding:.4em;"|2014-32
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S009286741300891X Developmental fate and cellular maturity encoded in human regulatory DNA landscapes.]
|-
|style="padding:.4em;" rowspan=2|2014/11/18
|style="padding:.4em;"|2014-31
|style="padding:.4em;"|SM Yang
|style="padding:.4em;text-align:left"|
[http://genomemedicine.com/content/6/8/64 The 'dnet' approach promotes emerging research on cancer patient survival.]
|-
|style="padding:.4em;"|2014-30
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414010368 Determination and inference of eukaryotic transcription factor sequence specificity.]
|-
|style="padding:.4em;" rowspan=3|2014/11/11
|style="padding:.4em;"|2014-29
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[http://www.pnas.org/content/110/16/6412.long Transcription factors interfering with dedifferentiation induce cell type-specific transcriptional profiles.]
|-
|style="padding:.4em;"|2014-28
|style="padding:.4em;"|HH Kim
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414009350 Dissecting engineered cell types and enhancing cell fate conversion via CellNet.]
|-
|style="padding:.4em;"|2014-27
|style="padding:.4em;"|HH Kim
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414009349 CellNet: network biology applied to stem cell engineering.]
|-
|style="padding:.4em;" rowspan=2|2014/11/04
|style="padding:.4em;"|2014-26
|style="padding:.4em;"|HS Shim
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414009775 Predicting Cancer-Specific Vulnerability via Data-Driven Detection of Synthetic Lethality.]
|-
|style="padding:.4em;"|2014-25
|style="padding:.4em;"|JH Shin
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nmeth/journal/v11/n9/full/nmeth.3046.html Phen-Gen: combining phenotype and genotype to analyze rare disorders.]
|-
|style="padding:.4em;" rowspan=2|2014/10/28
|style="padding:.4em;"|2014-24
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nbt/journal/vaop/ncurrent/full/nbt.2877.html A community effort to assess and improve drug sensitivity prediction algorithms.]
|-
|style="padding:.4em;"|2014-23
|style="padding:.4em;"|HS Shim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/ng/journal/v46/n9/full/ng.3051.html Multi-tiered genomic analysis of head and neck cancer ties TP53 mutation to 3p loss.]
|-
|style="padding:.4em;" rowspan=2|2014/09/30
|style="padding:.4em;"|2014-22
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nmeth/journal/v10/n11/full/nmeth.2651.html Network-based stratification of tumor mutations.]
|-
|style="padding:.4em;"|2014-21
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414001457 Synonymous mutations frequently act as driver mutations in human cancers.]
|-
|style="padding:.4em;" rowspan=2|2014/09/23
|style="padding:.4em;"|2014-20
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003460 VarWalker: Personalized Mutation Network Analysis of Putative Cancer Genes from Next-Generation Sequencing Data.]
|-
|style="padding:.4em;"|2014-19
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://genomebiology.com/content/13/12/R124 DriverNet: uncovering the impact of somatic driver mutations on transcriptional networks in cancer.]
|-
|style="padding:.4em;" rowspan=2|2014/09/16
|style="padding:.4em;"|2014-18
|style="padding:.4em;"|JH Shin
|style="padding:.4em;text-align:left"|
[http://genomebiology.com/content/14/5/R52 Integrated analysis of recurrent properties of cancer genes to identify novel drivers.]
|-
|style="padding:.4em;"|2014-17
|style="padding:.4em;"|AR Cho
|style="padding:.4em;text-align:left"|
[http://genomemedicine.com/content/4/11/89 Improving the prediction of the functional impact of cancer mutations by baseline tolerance transformation.]
|-
|style="padding:.4em;" rowspan=2|2014/09/02
|style="padding:.4em;"|2014-16
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=23228031 A network module-based method for identifying cancer prognostic signatures.]
|-
|style="padding:.4em;"|2014-15
|style="padding:.4em;"|AR Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=24429628 Realizing the promise of cancer predisposition genes.]
|-
|style="padding:.4em;" rowspan=2|2014/08/19
|style="padding:.4em;"|2014-14
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=24952901 Assessing the clinical utility of cancer genomic and proteomic data across tumor types.]
|-
|style="padding:.4em;"|2014-13
|style="padding:.4em;"|HS Shim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=24132290 Mutational landscape and significance across 12 major cancer types.]
|-
|style="padding:.4em;" rowspan=2|2014/08/12
|style="padding:.4em;"|2014-12
|style="padding:.4em;"|HS Shim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/23945592 Signatures of mutational processes in human cancer.]
|-
|style="padding:.4em;"|2014-11
|style="padding:.4em;"|HJ Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/24390350 Discovery and saturation analysis of cancer genes across 21 tumour types.]
|-
|style="padding:.4em;" rowspan=2|2014/08/05
|style="padding:.4em;"|2014-10
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=24084849 Comprehensive identification of mutational cancer driver genes across 12 tumor types.]
|-
|style="padding:.4em;"|2014-9
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=24037244 IntOGen-mutations identifies cancer drivers across tumor types.]
|-
|style="padding:.4em;" rowspan=3|2014/07/29
|style="padding:.4em;"|2014-8
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/23900255 Computational approaches to identify functional genetic variants in cancer genomes.]
|-
|style="padding:.4em;"|2014-7
|style="padding:.4em;"|AR Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=23770567 Mutational heterogeneity in cancer and the search for new cancer-associated genes.]
|-
|style="padding:.4em;"|2014-6
|style="padding:.4em;"|AR Cho
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nmeth/journal/v11/n4/abs/nmeth.2891.html Cancer genomes: discerning drivers from passengers]
|-
|style="padding:.4em;" rowspan=3|2014/07/22
|style="padding:.4em;"|2014-5
|style="padding:.4em;"|AR Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/24071849 The Cancer Genome Atlas Pan-Cancer analysis project.]
|-
|style="padding:.4em;"|2014-4
|style="padding:.4em;"|AR Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/23539594 Cancer genome landscapes.]
|-
|style="padding:.4em;"|2014-3
|style="padding:.4em;"|AR Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=21900272 Distinguishing driver and passenger mutations in an evolutionary history categorized by interference.]
|-
|style="padding:.4em;" rowspan=2|2014/07/15
|style="padding:.4em;"|2014-2
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=24670764 A promoter-level mammalian expression atlas.]
|-
|style="padding:.4em;"|2014-1
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=24670763 An atlas of active enhancers across human cell types and tissues.]
|}
{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2013
|-
!scope="col" style="padding:.4em" | Date
!scope="col" style="padding:.4em" | Paper index
!scope="col" style="padding:.4em" | Presenter
!scope="col" style="padding:.4em" | Paper title
|-
|style="padding:.4em;" rowspan=2|2013/06/26
|style="padding:.4em;"|2013-31
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|[http://nar.oxfordjournals.org/content/40/16/7690.long Integration of Hi-C and ChIP-seq data reveals distinct types of chromatin linkages]
|-
|style="padding:.4em;"|2013-30
|style="padding:.4em;"|YH Ko
|style="padding:.4em;text-align:left"|[http://www.nature.com/nbt/journal/v31/n4/full/nbt.2519.html Deciphering molecular circuits from genetic variation underlying transcriptional responsiveness to stimuli]
|-
|style="padding:.4em;" rowspan=2|2013/06/04
|style="padding:.4em;"|2013-29
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|[https://www.cell.com/abstract/S0092-8674(13)00439-X Mapping the Human miRNA Interactome by CLASH Reveals Frequent Noncanonical Binding]
|-
|style="padding:.4em;"|2013-28
|style="padding:.4em;"|ER Kim
|style="padding:.4em;text-align:left"|[http://www.nature.com/nbt/journal/v31/n3/full/nbt.2514.html Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples]
|-
|style="padding:.4em;" rowspan=2|2013/05/28
|style="padding:.4em;"|2013-27
|style="padding:.4em;"|YH Ko
|style="padding:.4em;text-align:left"|[http://www.pnas.org/content/102/38/13544.long Discovering statistically significant pathways in expression profiling studies]
|-
|style="padding:.4em;"|2013-26
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|[http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0058977 Prediction and Validation of Gene-Disease Associations Using Methods Inspired by Social Network Analyses]
|-
|style="padding:.4em;" rowspan=2|2013/05/21
|style="padding:.4em;"|2013-25
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|[http://www.nature.com/nature/journal/v496/n7446/full/nature11981.html Dynamic regulatory network controlling TH17 cell differentiation]
|-
|style="padding:.4em;"|2013-24
|style="padding:.4em;"|T Lee
|style="padding:.4em;text-align:left"|[http://www.sciencedirect.com/science/article/pii/S0092867412013529 Deciphering and Prediction of Transcriptome Dynamics under Fluctuating Field Conditions]

|-
|style="padding:.4em;" rowspan=2|2013/05/14
|style="padding:.4em;"|2013-23
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|[http://www.sciencedirect.com/science/article/pii/S0092867412015565 Integrative eQTL-Based Analyses Reveal the Biology of Breast Cancer Risk Loci]

|-
|style="padding:.4em;"|2013-22
|style="padding:.4em;"|HJ Kim
|style="padding:.4em;text-align:left"|[http://www.nature.com/ng/journal/v45/n2/full/ng.2504.html Chromatin marks identify critical cell types for fine mapping complex trait variants]
|-
|style="padding:.4em;" rowspan=2|'''2013/05/07'''
|style="padding:.4em;"|2013-21
|style="padding:.4em;"|ER Kim
|style="padding:.4em;text-align:left"|[http://www.cell.com/abstract/S0092-8674(12)01555-3 Genome-wide Chromatin State Transitions Associated with Developmental and Environmental Cues]
|-
|style="padding:.4em;"|2013-20
|style="padding:.4em;"|HS Shim
|style="padding:.4em;text-align:left"|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1003201 Human Disease-Associated Genetic Variation Impacts Large Intergenic Non-Coding RNA Expression]
|-
|style="padding:.4em;"|2013/04/09
|style="padding:.4em;"|2013-19
|style="padding:.4em;"|HJ Kim
|style="padding:.4em;text-align:left"|[http://genome.cshlp.org/content/22/9/1790.long Annotation of functional variation in personal genomes using RegulomeDB]
|-
|style="padding:.4em;"|2013/04/02
|style="padding:.4em;"|2013-18
|style="padding:.4em;"|HJ Kim
|style="padding:.4em;text-align:left"|[http://genome.cshlp.org/content/22/9/1775.long The GENCODE v7 catalog of human long noncoding RNAs: Analysis of their gene structure, evolution, and expression]
|-
|style="padding:.4em;" |2013/03/12
|style="padding:.4em;"|2013-17
|style="padding:.4em;"| ER Kim
|style="padding:.4em;text-align:left"|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002311 Global Mapping of Cell Type–Specific Open Chromatin by FAIRE-seq Reveals the Regulatory Role of the NFI Family in Adipocyte Differentiation]
|-
|style="padding:.4em;" rowspan=1|2013/03/05
|style="padding:.4em;"|2013-16
|style="padding:.4em;"| ER Kim
|style="padding:.4em;text-align:left"|[http://www.nature.com/nrg/journal/v10/n10/full/nrg2641.html ChIP–seq: advantages and challenges of a maturing technology]
|-
|style="padding:.4em;" rowspan=3|2013/02/21
|style="padding:.4em;"|2013-15
|style="padding:.4em;"| KS Kim
|style="padding:.4em;text-align:left"|[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002830 Novel Modeling of Combinatorial miRNA Targeting Identifies SNP with Potential Role in Bone Density]
|-
|style="padding:.4em;"|2013-14
|style="padding:.4em;"| KS Kim
|style="padding:.4em;text-align:left"|[http://www.cell.com/abstract/S0092-8674(12)01424-9 A Molecular Roadmap of Reprogramming Somatic Cells into iPS Cells]
|-
|style="padding:.4em;"|2013-13
|style="padding:.4em;"| HJ Han
|style="padding:.4em;text-align:left"|[http://genome.cshlp.org/content/22/6/1015.long Differential DNase I hypersensitivity reveals factor-dependent chromatin dynamics.]
|-
|style="padding:.4em;" rowspan=3|2013/02/15
|style="padding:.4em;"|2013-12
|style="padding:.4em;"| HJ Han
|style="padding:.4em;text-align:left"|[http://www.nature.com/ng/journal/v43/n3/full/ng.759.html Chromatin accessibility pre-determines glucocorticoid receptor binding patterns]
|-
|style="padding:.4em;"|2013-11
|style="padding:.4em;"| JE Shim, CY Kim
|style="padding:.4em;text-align:left"|[http://www.nature.com/nbt/journal/v30/n11/full/nbt.2422.html Interpreting noncoding genetic variation in complex traits and human disease]
|-
|style="padding:.4em;"|2013-10
|style="padding:.4em;"|HJ Han, JH Kim
|style="padding:.4em;text-align:left"|[http://genome.cshlp.org/content/21/3/447.full.pdf+html Accurate inference of transcription factor binding from DNA sequence and chromatin accessibility data]
|-
|style="padding:.4em;" rowspan=3|2013/02/08
|style="padding:.4em;"|2013-09
|style="padding:.4em;"|HJ Kim
|style="padding:.4em;text-align:left"|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002599 Widespread Site-Dependent Buffering of Human Regulatory Polymorphism]
|-
|style="padding:.4em;"|2013-08
|style="padding:.4em;"|JE Shim, CY Kim
|style="padding:.4em;text-align:left;"|[http://genome.cshlp.org/content/22/9/1748.long Linking disease associations with regulatory information in the human genome]
|-
|style="padding:.4em;"|2013-07
|style="padding:.4em;"|HJ Han, JH Kim
|style="padding:.4em;text-align:left"|[http://genome.cshlp.org/content/22/9/1658.long Understanding transcriptional regulation by integrative analysis of transcription factor binding data]
|-
|style="padding:.4em;" rowspan=3|2013/01/25
|style="padding:.4em;"|2013-06
|style="padding:.4em;"|HJ Han, '''JH Kim'''
|style="padding:.4em;text-align:left;"|[http://www.nature.com/nature/journal/v489/n7414/full/nature11279.html The long-range interaction landscape of gene promoters]
|-
|style="padding:.4em;"|2013-05
|style="padding:.4em;"|'''ER Kim''', HS Shim
|style="padding:.4em;text-align:left;"|[http://www.nature.com/nature/journal/v489/n7414/full/nature11233.html Landscape of transcription in human cells]
|-
|style="padding:.4em;"|2013-04
|style="padding:.4em;"|'''HJ Han''', JH Kim
|style="padding:.4em;text-align:left;"|[http://www.nature.com/nature/journal/v489/n7414/full/nature11245.html Architecture of the human regulatory network derived from ENCODE data]

|-
|style="padding:.4em;" rowspan=2|2013/01/18
|style="padding:.4em;"|2013-03
|style="padding:.4em;"|KS Kim, '''TH Kim'''
|style="padding:.4em;text-align:left;"|[http://www.nature.com/nature/journal/v489/n7414/full/nature11212.html An expansive human regulatory lexicon encoded in transcription factor footprints]
|-
|style="padding:.4em;"|2013-02
|style="padding:.4em;"|'''HJ Han''', JH Kim
|style="padding:.4em;text-align:left;"|[http://www.nature.com/nature/journal/v489/n7414/full/nature11232.html The accessible chromatin landscape of the human genome]
|-
|style="padding:.4em;" rowspan=1|2013/01/11
|style="padding:.4em;"|2013-01
|style="padding:.4em;"| '''JE Shim''', CY Kim
|style="padding:.4em;text-align:left"|[http://www.nature.com/nature/journal/v489/n7414/full/nature11247.html An integrated encyclopedia of DNA elements in the human genome]
|}

2012
{|border ="1"
|style="color:black; background-color:#dcdcdc; padding:5px;" align="center" |Date
|style="color:black; background-color:#dcdcdc;" align="center"|Paper index
|style="color:black; background-color:#dcdcdc;" align="center"|Paper title
|-
|rowspan = "1"|2013/01/11
|align ="center"|2012-81
|[http://genome.cshlp.org/content/22/8/1589.full (TH Kim) MuSiC: identifying mutational significance in cancer genomes.]
|-
|rowspan = "1"|2012/12/04
|align ="center"|2012-80
|[http://genome.cshlp.org/content/22/8/1383 (CY KIM) Human genomic disease variants: A neutral evolutionary explanation]
|-
|rowspan = "2"|2012/11/20
| align="center"|2012-79
|[http://www.cell.com/abstract/S0092-8674(12)00639-3 (HS Shim) Circuitry and Dynamics of Human Transcription Factor Regulatory Networks]
|-
| align="center"|2012-78
|[http://www.nature.com/nature/journal/v487/n7408/full/nature11288.html (HJ Kim) Interpreting cancer genomes using systematic host network perturbations by tumour virus proteins]
|-
|rowspan = "2"|2012/11/06
| align="center"|2012-77
|[http://www.sciencemag.org/content/337/6099/1190.short (HJ Han) Systematic Localization of Common Disease-Associated Variation in Regulatory DNA]
|-
| align="center"|2012-76
|[http://www.pnas.org/cgi/doi/10.1073/pnas.1201904109 (KS Kim) A public resource facilitating clinical use of genomes]
|-
|rowspan = "6" |2012/07/19
| align="center"|2012-75
|[http://genome.cshlp.org/content/22/7/1334 (HJ Han & YH Ko) Predictive regulatory models in Drosophila melanogaster by integrative inference of transcriptional networks]
|-
| align="center"|2012-74
|[http://www.sciencemag.org/content/337/6090/100.full (JE Shim) An Abundance of Rare Functional Variants in 202 Drug Target Genes Sequenced in 14,002 People]
|-
| align="center"|2012-73
|[http://www.sciencemag.org/content/337/6090/64.abstract (JE Shim) Evolution and Functional Impact of Rare Coding Variation from Deep Sequencing of Human Exomes]
|-
| align="center"|2012-72
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2063581/ (SH Hwang) Network-based classification of breast cancer metastasis]
|-
| align="center"|2012-71
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002707 (T Lee&CY Kim)Brain Expression Genome-Wide Association Study (eGWAS) Identifies Human Disease-Associated Variants]
|-
| align="center"|2012-70
|[http://genome.cshlp.org/content/20/9/1297 (ER Kim&TH Kim)The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data]
|-
|rowspan = "8" |2012/07/16
| align="center"|2012-69
|[http://www.nature.com/ng/journal/v43/n5/full/ng.806.html (ER Kim&TH Kim)A framework for variation discovery and genotyping using next-generation DNA sequencing data]
|-
| align="center"|2012-66
|[http://bioinformatics.oxfordjournals.org/content/25/16/2078.long (ER Kim&TH Kim)The Sequence Alignment/Map format and SAMtools]
|-
| align="center"|2012-65
|[http://bioinformatics.oxfordjournals.org/content/early/2011/06/07/bioinformatics.btr330 (ER Kim&TH Kim)The Variant Call Format and VCFtools]
|-
| align="center"|2012-64
|[http://www.cell.com/abstract/S0092-8674(12)00104-3 (YH Go&HJ Han)The Impact of the Gut Microbiota on Human Health: An Integrative View]
|-
|align="center"|2012-63
|[http://www.sciencemag.org/content/336/6086/1262.abstract (T Lee&CY Kim)Host-Gut Microbiota Metabolic Interactions]
|-
|align="center"|2012-62
|[http://www.sciencemag.org/content/336/6086/1268.full (AR Cho,JH Ju)Interactions Between the Microbiota and the Immune System]
|-
|align="center"|2012-61
|[http://www.sciencemag.org/content/336/6086/1255.abstract (SH Hwang&HJ Cho)The Application of Ecological Theory Toward an Understanding of the Human Microbiome]
|-
|align="center"|2012-60
|[http://stm.sciencemag.org/content/4/137/137rv5 (SH Hwang&HJ Cho)Microbiota-Targeted Therapies: An Ecological Perspective]
|-
|rowspan = "3" |2012/07/13
|align="center"|2012-59
|[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002358 (JH Shin&HJ Kim)Metabolic Reconstruction for Metagenomic Data and Its Application to the Human Microbiome]
|-
| align="center"|2012-58
|[http://www.nature.com/nature/journal/v486/n7402/full/nature11209.html (JH Shin&HJ Kim)A framework for human microbiome research]
|-
|align="center"|2012-57
|[http://www.nature.com/nature/journal/v486/n7402/full/nature11234.html (JH Shin&HJ Kim)Structure, function and diversity of the healthy human microbiome]
|-
|rowspan = "4" |2012/07/12
|align="center"|2012-56
|[http://nar.oxfordjournals.org/content/40/D1/D957.long (AR Cho&JH Ju)COLT-Cancer: functional genetic screening resource for essential genes in human cancer cell lines]
|-
|align="center"|2012-55
|[http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002511 (YH Go&HJ Han)Google Goes Cancer: Improving Outcome Prediction for Cancer Patients by Network-Based Ranking of Marker Genes]
|-
| align="center"|2012-54
|[http://www.nature.com/msb/journal/v7/n1/full/msb201147.html (YH Go&HJ Han)A pharmacogenomic method for individualized prediction of drug sensitivity]
|-
|align="center"|2012-53
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature10983.html (JH Soh)The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups]
|-
|rowspan = "6" |2012/07/09
|align="center"|2012-52
|[http://www.nature.com/nature/journal/v483/n7391/full/nature11003.html (ER Kim&TH Kim)The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity]
|-
|align="center"|2012-51
|[http://www.nature.com/nature/journal/v483/n7391/full/nature11005.html (ER Kim&TH Kim)Systematic identification of genomic markers of drug sensitivity in cancer cells]
|-
| align="center"|2012-50
|[http://www.pnas.org/content/early/2011/10/13/1018854108.abstract (ER Kim&TH Kim)Subtype and pathway specific responses to anticancer compounds in breast cancer]
|-
|align="center"|2012-49
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature10945.html (JE Shim&KS Kim)De novo mutations revealed by whole-exome sequencing are strongly associated with autism]
|-
|align="center"|2012-48
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature11011.html (JE Shim&KS Kim)Patterns and rates of exonic de novo mutations in autism spectrum disorders]
|-
|align="center"|2012-47
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature10989.html (JE Shim&KS Kim)Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations]
|-
|rowspan = "6" |2012/07/06
| align="center"|2012-46
|[http://www.g3journal.org/content/1/3/233.full (T Lee&CY Kim)Integrating Rare-Variant Testing, Function Prediction, and Gene Network in Composite Resequencing-Based Genome-Wide Association Studies (CR-GWAS)]
|-
|align="center"|2012-45
|[http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002621 (T Lee&CY Kim)Type 2 Diabetes Risk Alleles Demonstrate Extreme Directional Differentiation among Human Populations, Compared to Other Diseases]
|-
|align="center"|2012-44
|[http://www.nature.com/nature/journal/v480/n7376/full/nature10665.html (AR Cho&JH Ju)Predicting mutation outcome from early stochastic variation in genetic interaction partners]
|-
|align="center"|2012-43
|[http://www.sciencemag.org/content/335/6064/82 (AR Cho&JH Ju)Fitness Trade-Offs and Environmentally Induced Mutation Buffering in Isogenic C. elegans]
|-
| align="center"|2012-42
|[http://genome.cshlp.org/content/22/6/1163 (JE Shim&KS Kim)Identification of microRNA-regulated gene networks by expression analysis of target genes]
|-
|align="center"|2012-41
|[http://www.nature.com/ng/journal/v44/n6/full/ng.2303.html (JE Shim&KS Kim)Exome sequencing and the genetic basis of complex traits]
|-
|rowspan = "6" |2012/07/02
|align="center"|2012-40
|[http://www.cell.com/abstract/S0092-8674(12)00573-9 (JH Soh)Functional Repurposing Revealed by Comparing S. pombe and S. cerevisiae Genetic Interactions]
|-
|align="center"|2012-39
|[http://genome.cshlp.org/content/22/2/375.long (ER Kim&TH Kim)De novo discovery of mutated driver pathways in cancer]
|-
| align="center"|2012-38
|[http://www.nature.com/msb/journal/v4/n1/full/msb20082.html (YH Go&HJ Han)A systems biology approach to prediction of oncogenes and molecular perturbation targets in B-cell lymphomas]
|-
|align="center"|2012-37
|[http://www.nature.com/msb/journal/v7/n1/full/msb201126.html (SH Hwang&HJ Cho)PREDICT: a method for inferring novel drug indications with application to personalized medicine]
|-
|align="center"|2012-36
|[http://www.nature.com/nature/journal/v453/n7198/full/nature06973.html (SH Hwang&HJ Cho)Synergistic response to oncogenic mutations defines gene class critical to cancer phenotype]
|-
|align="center"|2012-35
|[http://www.sciencemag.org/content/334/6062/1518.full (JH Shin&HJ Kim)Detecting Novel Associations in Large Data Sets]
|-
|rowspan = "3" |2012/03/05
| align="center"|2012-34
|[http://www.ncbi.nlm.nih.gov/pubmed/18516045 (8,HH Kim)Mapping and quantifying mammalian transcriptomes by RNA-Seq.]
|-
|align="center"|2012-33
|[http://genomebiology.com/2010/11/10/R106 (11,Go&Ju)Differential expression analysis for sequence count data]
|-
|align="center"|2012-32
|[http://bioinformatics.oxfordjournals.org/content/26/1/139.short (12,Go&Ju)edgeR: a Bioconductor package for differential expression analysis of digital gene expression data ]
|-
|rowspan = "7" |2012/02/27 2012/02/28
| align="center"|2012-31
|[http://www.nature.com/nrg/journal/v10/n1/full/nrg2484.html (1,JW Song)RNA-Seq: a revolutionary tool for transcriptomics]
|-
|align="center"|2012-30
|[http://www.nature.com/nmeth/journal/v8/n6/full/nmeth.1613.html (2,JW Song)Computational methods for transcriptome annotation and quantification using RNA-seq]
|-
|align="center"|2012-29
|[http://genomebiology.com/2010/11/12/220 (3,HJ Han)From RNA-seq reads to differential expression results]
|-
|align="center"|2012-28
|[http://www.nature.com/nmeth/journal/v7/n9/full/nmeth.1491.html (4,AR Cho)Comprehensive comparative analysis of strand-specific RNA sequencing methods]
|-
|align="center"|2012-27
|[http://www.plosone.org/article/info:doi%2F10.1371%2Fjournal.pone.0026426 (5,T Lee)A Low-Cost Library Construction Protocol and Data Analysis Pipeline for Illumina-Based Strand-Specific Multiplex RNA-Seq]
|-
|align="center"|2012-26
|[http://www.nature.com/nbt/journal/v28/n5/abs/nbt.1621.html (6,So&Shin)Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation]
|-
|align="center"|2012-25
|[http://bioinformatics.oxfordjournals.org/content/25/9/1105.abstract (7,So&Shin)TopHat: discovering splice junctions with RNA-Seq]
|-
|rowspan = "5" |2012/02/06
| align="center"|2012-24
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3125733/ mirConnX: condition-specific mRNA-microRNA network integrator]
|-
|align="center"|2012-23
|[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002190 Construction and Analysis of an Integrated Regulatory Network Derived from High-Throughput Sequencing Data]
|-
|align="center"|2012-22
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002415 A Densely Interconnected Genome-Wide Network of MicroRNAs and Oncogenic Pathways Revealed Using Gene Expression Signatures]
|-
|align="center"|2012-21
|[http://genome.cshlp.org/content/20/5/589.short Reprogramming of miRNA networks in cancer and leukemia]
|-
|align="center"|2012-20
|[http://www.cell.com/abstract/S0092-8674(11)01152-4 An Extensive MicroRNA-Mediated Network of RNA-RNA Interactions Regulates Established Oncogenic Pathways in Glioblastoma]
|-
|rowspan = "5" |2012/01/30
|align="center"|2012-19
|[http://www.cell.com/abstract/S0092-8674(11)00237-6 Principles and Strategies for Developing Network Models in Cancer]
|-
|align="center"|2012-18
|[http://www.nature.com/ng/journal/v37/n4/full/ng1532.html Reverse engineering of regulatory networks in human B cells]
|-
|align="center"|2012-17
|[http://www.nature.com/nature/journal/v452/n7186/abs/nature06757.html Variations in DNA elucidate molecular networks that cause disease]
|-
|align="center"|2012-16
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2779083/ Harnessing gene expression to identify the genetic basis of drug resistance]
|-
|align="center"|2012-15
|[http://www.sciencedirect.com/science/article/pii/S0092867410012936 An Integrated Approach to Uncover Drivers of Cancer]
|-
|rowspan = "3" |2012/01/09
|align="center"|2012-14
|[http://www.ncbi.nlm.nih.gov/pubmed/18704161 Genetic variation in an individual human exome.]
|-
|align="center"|2012-13
|[http://www.ncbi.nlm.nih.gov/pubmed/22081227 Predicting phenotypic variation in yeast from individual genome sequences.]
|-
|align="center"|2012-12
|[http://www.ncbi.nlm.nih.gov/pubmed/20435227 Clinical assessment incorporating a personal genome.]
|-
|rowspan = "6" |2012/01/09 2012/01/16
| align="center"|2012-11
|[http://www.ncbi.nlm.nih.gov/pubmed/20399638 Human allelic variation: perspective from protein function, structure, and evolution.]
|-
|align="center"|2012-10
|[http://www.ncbi.nlm.nih.gov/pubmed/19561590 Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm.]
|-
|align="center"|2012-09
|[http://www.ncbi.nlm.nih.gov/pubmed/11230178 Prediction of deleterious human alleles.]
|-
|align="center"|2012-08
|[http://www.ncbi.nlm.nih.gov/pubmed/12202775 Human non-synonymous SNPs: server and survey.]
|-
|align="center"|2012-07
|[http://www.ncbi.nlm.nih.gov/pubmed/20354512 A method and server for predicting damaging missense mutations.]
|-
|align="center"|2012-06
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1920242/ SNAP: predict effect of non-synonymous polymorphisms on function]
|-
|rowspan = "3" |2012/01/09 2012/01/16
|align="center"|2012-05
|[http://www.ncbi.nlm.nih.gov/pubmed/21920052 Computational and statistical approaches to analyzing variants identified by exome sequencing.]
|-
|align="center"|2012-04
|[http://www.ncbi.nlm.nih.gov/pubmed/18179889 Shifting paradigm of association studies: value of rare single-nucleotide polymorphisms.]
|-
|align="center"|2012-03
|[http://www.ncbi.nlm.nih.gov/pubmed/19684571 Targeted capture and massively parallel sequencing of 12 human exomes.]
|-
|rowspan = "2" |2012/01/09 2012/01/16
|align="center"|2012-02
|[http://www.ncbi.nlm.nih.gov/pubmed/17637733 The distribution of fitness effects of new mutations.]
|-
|align="center"|2012-01
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1852724/ Most Rare Missense Alleles Are Deleterious in Humans: Implications for Complex Disease and Association Studies]
|}
2011
{|border ="1"
|style="color:black; background-color:#dcdcdc;" align="center" |Date
|style="color:black; background-color:#dcdcdc;" align="center"|Paper_index
|style="color:black; background-color:#dcdcdc;" align="center"|Paper_title
|-
|rowspan = "6" |2011/11/28
|align="center"|2011-49
|[http://bmir.stanford.edu/file_asset/index.php/1407/BMIR-2009-1355.pdf (Shin)Data-Driven Methods to Discover Molecular Determinants of Serious Adverse Drug Events]
|-
|align="center"|2011-48
|[http://www.nature.com/nchembio/journal/v4/n11/abs/nchembio.118.html (Shin)Network pharmacology: the next paradigm in drug discovery]
|-
|align="center"|2011-47
|[http://www.nature.com/msb/journal/v7/n1/full/msb201171.html (Oh)Systematic exploration of synergistic drug pairs]
|-
|align="center"|2011-46
|[http://www.nature.com/msb/journal/v5/n1/full/msb200995.html (Shim)Chemogenomic profiling predicts antifungal synergies]
|-
|align="center"|2011-45
|[http://www.pnas.org/content/104/32/13086 (Beck)A strategy for predicting the chemosensitivity of human cancers and its application to drug discovery]
|-
|align="center"|2011-44
|[http://www.nature.com/nchembio/journal/v1/n7/full/nchembio747.html (Hwang)Analysis of drug-induced effect patterns to link structure and side effects of medicines]
|-
|rowspan = "3" |2011/11/14
|align="center"|2011-43
|[http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000662 Network-Based Elucidation of Human Disease Similarities Reveals Common Functional Modules Enriched for Pluripotent Drug Targets]
|-
|align="center"|2011-42
|[http://www.nature.com/msb/journal/v7/n1/full/msb201131.html Cross-species discovery of syncretic drug combinations that potentiate the antifungal fluconazole]
|-
|align="center"|2011-41
|[http://www.nature.com/msb/journal/v7/n1/full/msb20115.html Analysis of multiple compound–protein interactions reveals novel bioactive molecules]
|-
|rowspan = "4" |2011/11/07
|align="center"|2011-40
|[http://www.nature.com/nbt/journal/v25/n10/abs/nbt1338.html Drug—target network]
|-
|align="center"|2011-39
|[http://www.nature.com/msb/journal/v6/n1/full/msb200998.html A side effect resource to capture phenotypic effects of drugs]
|-
|align="center"|2011-38
|[http://genome.cshlp.org/content/18/2/206.long Quantitative systems-level determinants of human genes targeted by successful drugs]
|-
|align="center"|2011-37
|[http://www.sciencemag.org/content/321/5886/263.short Drug Target Identification Using Side-Effect Similarity]
|-
|rowspan = "3" |2011/11/07
|align="center"|2011-36
|[http://stm.sciencemag.org/content/3/96/96ra77.short Discovery and Preclinical Validation of Drug Indications Using Compendia of Public Gene Expression Data]
|-
|align="center"|2011-35
|[http://bib.oxfordjournals.org/content/12/4/303.short Exploiting drug–disease relationships for computational drug repositioning]
|-
|align="center"|2011-34
|[http://www.springerlink.com/content/4489r051nu2t0ul1/ Drug Discovery in a Multidimensional World: Systems, Patterns, and Networks]
|-
|rowspan = "3" |2011/10/05
|align="center"|2011-33
|[http://genome.cshlp.org/content/20/7/960.full Analysis of membrane proteins in metagenomics: Networks of correlated environmental features and protein families]
|-
|align="center"|2011-32
|[http://www.pnas.org/content/106/5/1374.full Quantifying environmental adaptation of metabolic pathways in metagenomics]
|-
|align="center"|2011-31
|[http://www.pnas.org/content/104/35/13913.abstract Quantitative assessment of protein function prediction from metagenomics shotgun sequences]
|-
|rowspan = "4" |2011/10/04
|align="center"|2011-30
|[http://www.nature.com/nature/journal/v464/n7285/full/nature08821.html A human gut microbial gene catalogue established by metagenomic sequencing]
|-
|align="center"|2011-29
|[http://www.nature.com/nrmicro/journal/v6/n9/abs/nrmicro1935.html Molecular eco-systems biology: towards an understanding of community function]
|-
|align="center"|2011-28
|[http://genome.cshlp.org/content/early/2009/04/20/gr.085464.108 Microbial community profiling for human microbiome projects: Tools, techniques, and challenges]
|-
|align="center"|2011-27
|[http://www.nature.com/nrmicro/journal/v9/n4/full/nrmicro2540.html Unravelling the effects of the environment and host genotype on the gut microbiome]
|-
|rowspan = "2" |2011/09/19
|align="center"|2011-26
|[http://www.sciencemag.org/content/333/6042/596.full independently evolved virulence effectors converge onto hubs in a plant immune system Network]
|-
|align="center"|2011-25
|[http://www.sciencemag.org/content/333/6042/601.full Evidence for network evolution in an arabidopsis interactome Map]
|-
|rowspan = "2" |2011/09/05
|align="center"|2011-24
|[http://www.sciencedirect.com/science/article/pii/S0092867411005861 Exome Sequencing of Ion Channel Genes Reveals Complex Profiles Confounding Personal Risk Assessment in Epilepsy]
|-
|align="center"|2011-23
|[http://www.cell.com/abstract/S0092-8674(11)00543-5 Pluripotency factors in Embryonic stem cells Regulate Differentiation into Germ Layers]
|-
|rowspan = "2" |2011/09/22
|align="center"|2011-22
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002077 Integrated Genome-scale predition of Detrimental Mutations in Transcription Networks]
|-
|align="center"|2011-21
|[http://www.nature.com/nrg/journal/v12/n4/abs/nrg2969.html From expression QTLs to personalized transcriptomics]
|-
|2011/06/20
|align="center"|2011-20
|[http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001046 a user's guide to the encyclopedia of DNA elements(ENCODE)]
|-
|rowspan = "2" |2011/03/30
|align="center"|2011-19
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature09944.html enterotypes of the human gut microbiome]
|-
|align="center"|2011-18
|[http://www.nature.com/msb/journal/v7/n1/full/msb20116.html toward molecular trait-based ecology, through intergration of biogeochemical, geographical and metagenomic data]
|-
|rowspan = "2" |2011/03/16
|align="center"|2011-17
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002047 variable pathogenicity determines individual lifespan in ''caenorhabditis elegans'']
|-
|align="center"|2011-16
|[http://www.cell.com/abstract/S0092-8674(11)00371-0 a high-resolution ''c.elegans'' essential gene network based on phenotypic profiling of a complex tissue]
|-
|rowspan = "3" |2011/04/25
|align="center"|2011-15
|[http://www.ncbi.nlm.nih.gov/pubmed/21376230 Hallmarks of Cancer : The next generation]
|-
|align="center"|2011-14
|[http://www.cell.com/abstract/S0092-8674(11)00296-0 Mapping Cancer Origins]
|-
|align="center"|2011-13
|[http://www.cell.com/abstract/S0092-8674(11)00297-2 Genetic Interactions in Cancer Progression and Treatment]
|-
|rowspan = "2" |2011/04/11
|align="center"|2011-12
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1001273 Proteins encoded in genomic regions associated with immune-mediated disease physically interact and suggest underlying biology]
|-
|align="center"|2011-11
|***Changed!***
[http://www.ncbi.nlm.nih.gov/pubmed/19557189 Identifying relationships among genomic disease regions: predicting genes at pathogenic SNP associations and rare deletions]
|-
|rowspan = "2"|2011/03/28
|align="center"|2011-10
|[http://www.pnas.org/content/106/44/18843.long profiling translatomes of discrete cell populations resolves altered cellular priorities during hypoxia in Arabidopsis]
|-
|align="center"|2011-09
|[http://www.nature.com/msb/journal/v6/n1/full/msb201076.html cell-type specific analysis of translating RNAs in developing flowers reveals new levels of control]
|-
|rowspan = "2"|2011/03/14
|align="center"|2011-08
|[http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6WSN-51SFHJD-1&_user=44062&_coverDate=01%2F07%2F2011&_rdoc=1&_fmt=high&_orig=search&_origin=search&_sort=d&_docanchor=&view=c&_acct=C000004738&_version=1&_urlVersion=0&_userid=44062&md5=2f6017aab4c794ed7e78fbbd8447077a&searchtype=a phenotypic landscape of a bacterial cell]
|-
|align="center"|2011-07
|[http://www.nature.com/msb/journal/v6/n1/full/msb2010107.html cross-species chemogenomic profiling reveals evolutionarily conserved drug mode of action]
|-
|rowspan = "2"|2011/02/28
|align="center"|2011-06
|[http://www.pnas.org/content/early/2010/09/23/1004666107.abstract genomic patterns of pleiotropy and the evolution of complexity]
|-
|align="center"|2011-05
|[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1001009 simultaneous genome-wide inference of physical, genetic, regulatory, and functional pathway]
|-
|2011/02/21
|align="center"|2011-04
|[http://www.nature.com/msb/journal/v6/n1/full/msb201071.html dynamic interaction networks in a hierarchically organized tissue]
|-
|2011/02/14
|align="center"|2011-03
|[http://www.sciencemag.org/content/330/6009/1385.abstract rewiring of genetic networks in response to DNA damage]
|-
|rowspan = "2"|2011/01/31
|align="center"|2011-02
|[http://www.nature.com/nrg/journal/v10/n9/abs/nrg2633.html Applying mass spectrometry-based proteomics to genetics, genomics and network biology]
|-
|align="center"|2011-01
|[http://physiolgenomics.physiology.org/content/33/1/18.long Data analysis and bioinformatics tools for tandem mass spectrometry in proteomics]
|}
2010
{|border ="1"
|style="color:black; background-color:#dcdcdc;" align="center" |Date
|style="color:black; background-color:#dcdcdc;" align="center"|Paper_index
|style="color:black; background-color:#dcdcdc;" align="center"|Paper_title
|-
|rowspan = "2"|2010/12/27
|align="center"|2010-29
|[http://www.nature.com/nature/journal/v467/n7312/full/nature09326.html Functional_roles_fornoise_in_genetic_circuits]
|-
|align="center"|2010-28
|[http://www.nature.com/ng/journal/v42/n7/full/ng.610.html Estimation_of_effect_size_distribution_from_genome-wide_association_studies_and_implications_for_future_discoveries]
|-
|rowspan = "2"|2010/11/15
|align="center"|2010-27
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1000932 Liver_and_Adipose_Expression_associated_SNPs_are_enriched_for_association_to_type_2_diabetes]
|-
|align="center"|2010-26
|[http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B8JDC-50J4MRJ-2&_user=44062&_coverDate=10%2F10%2F2010&_rdoc=1&_fmt=high&_orig=search&_origin=search&_sort=d&_docanchor=&view=c&_acct=C000004738&_version=1&_urlVersion=0&_userid=44062&md5=7667a67cf8ba3f68117b8d0ff85a8887&searchtype=a It's_the_machine_that_matters:_predicting_gene_function_and_phenotype_from_protein_networks]
|-
|rowspan = "2"|2010/11/01
|align="center"|2010-25
|[http://genome.cshlp.org/content/early/2010/06/15/gr.104216.109 A_genome-wide_map_of_human_genetic_interactions_inferred_from_radiation_hybrid_genotypes]
|-
|align="center"|2010-24
|[http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0012139 A_Genome-Wide_Gene_Function_Prediction_Resource_for_Drosophila_melanogaster]
|-
|2010/10/11
|align="center"|2010-23
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2913399/?tool=pubmed Dissecting_spatio-temporal_protein_networks_driving_human_heart_development_and_related_disorders]
|-
|rowspan = "2"|2010/09/13
|align="center"|2010-22
|[http://www.nature.com/ng/journal/v42/n7/full/ng.600.html Transposable_elements_have_rewired_the_core_regulatory_network_of_human_embryonic_stem_cells]
|-
|align="center"|2010-21
|[http://www.nature.com/ng/journal/v42/n7/full/ng0710-557.html Limits_of_sequence_and_functional_conservation]
|-
|2010/05/26
|align="center"|2010-20
|[[media:100428_network_based_elucidation_of_human_disease_similarities_reveals_common_functional_modules_enriched_for_pluripotent_drug_targets.pdf|network_based_elucidation_of_human_disease_similarities_reveals_common_functional_modules_enriched_for_pluripotent_drug_targets.pdf]]
|-
|2010/05/19
|align="center"|2010-19
|[[media:100421_interpreting_metabolomic_profiles_using_unbiased_pathway_models.pdf|interpreting_metabolomic_profiles_using_unbiased_pathway_models.pdf]]
|-
|2010/04/21
|align="center"|2010-18
|[[media:100414_identification_of_networks_of_co_occurring_tumor_related_dna_copy_number_changes_using_a_genome_wide_scoringapproach.pdf|identification_of_networks_of_co_occurring_tumor_related_dna_copy_number_changes_using_a_genome_wide_scoringapproach.pdf]]
|-
|2010/04/14
|align="center"|2010-17
|[http://www.cell.com/retrieve/pii/S0092867410000796 an_atlas_of_combinatorial_transcriptional_regulation_in_mouse_and_man]
|-
|rowspan = "2" |2010/04/07
|align="center"|2010-16
|[http://www.pnas.org/content/early/2010/03/11/0910200107.long systematic_discovery_of_nonobvious_human_disease_models_through_orthologous_phenotypes]
|-
|align="center"|2010-15
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature08800.html genome_side_association_study_of_107_phenotypes_in_Arabidopsis_thaliana_inbred_lines]
|-
|rowspan="2"|2010/03/31
|align="center"|2010-14
|[[media:100331_toward_stem_cell_systems_biology_from_molecules_to_networks_and_landscapes.pdf|toward_stem_cell_systems_biology_from_molecules_to_networks_and_landscapes.pdf]]
|-
|align="center"|2010-13
|[http://www.nature.com/nrm/journal/v10/n10/abs/nrm2766.html systems_biology_of_stem_cell_fate_and_cellular_reprogramming]
|-
|2010/03/24
|align="center"|2010-12
|[http://www.nature.com/nbt/journal/v27/n2/abs/nbt.1522.html dynamic_modularity_in_protein_interaction_networks_predicts_breast_cancer_outcome]
|-
|2010/01/18
|align="center"|2010-11
|JournalClub_100118_a_tutorial_on_statistical_methods_for_population_association_studies
|-
|2010/01/16
|align="center"|2010-10
|systems-level_dinamic_analyses_of_fate_change_in_murine_embryonic_stem_cells
|-
|2010/01/15
|align="center"|2010-09
|distinguishing_direct_versus_indirect_transcription_factor-DNA-interactions
|-
|2010/01/14
|align="center"|2010-08
|chemogenomic_profiling_predicts_antifungal_synergies
|-
|2010/01/13
|align="center"|2010-07
|edgetic_perturbation_models_of_human_inherited_disorders
|-
|2010/01/12
|align="center"|2010-06
|analysis_of_cell_fate_from_single-cell_gene_expression_profiles_in_C.elegans
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|2010/01/11
|align="center"|2010-05
|predicting_new_molecular_targets_for_known_drugs.pdf
Reference:SEA(Similarity Ensemble Approach)
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|2010/01/09
|align="center"|2010-04
|harnessing_gene_expression_to_identify_the_genetic_basis_of_drug_resistance
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|2010/01/08
|align="center"|2010-03
|an_integrative_approach_to_reveal_driver_gene_fusions_from_paired_end_sequencing_data_in_cancer
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|2010/01/07
|align="center"|2010-02
|a_phenotypic_profile_of_the_candida_albicans_regulatory_network
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|2010/01/06
|align="center"|2010-01
|a_global_view_of_protein_expression_in_human_cells_tissues_and_organs
|}

Journal Club

2023-07-31T00:54:37Z

Bsb0613:

{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2023-1 ADVANCED MICROBIOME DATA ANALYSIS
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!scope="col" style="padding:.4em" | Date
!scope="col" style="padding:.4em" | Team
!scope="col" style="padding:.4em" | Paper index
!scope="col" style="padding:.4em" | Presenter
!scope="col" style="padding:.4em" | Paper title
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|style="padding:.4em;" rowspan=1|2023/06/13
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-24
|style="padding:.4em;"|JY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.chom.2020.03.005 Structure of the Mucosal and Stool Microbiome in Lynch Syndrome]
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|style="padding:.4em;" rowspan=1|2023/06/13
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-23
|style="padding:.4em;"|WJ Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41586-019-1237-9 Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases]
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|style="padding:.4em;" rowspan=1|2023/06/13
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-22
|style="padding:.4em;"|SH Ahn
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.jare.2022.03.007 Roles of oral microbiota and oral-gut microbial transmission in hypertension]
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|style="padding:.4em;" rowspan=1|2023/05/30
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-21
|style="padding:.4em;"|SY Lim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.chom.2022.08.009 Human gut microbiota stimulate defined innate immune responses that vary from phylum to strain]
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|style="padding:.4em;" rowspan=1|2023/05/30
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-20
|style="padding:.4em;"|BS Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41591-023-02217-7 Gut microbial metabolism of 5-ASA diminishes its clinical efficacy in inflammatory bowel disease]
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|style="padding:.4em;" rowspan=1|2023/05/30
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-19
|style="padding:.4em;"|JY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.chom.2023.01.004 Deficient butyrate-producing capacity in the gut microbiome is associated with bacterial network disturbances and fatigue symptoms in ME/CFS]
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|style="padding:.4em;" rowspan=1|2023/05/23
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-18
|style="padding:.4em;"|EJ Sung
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.ccell.2022.11.013 Gut microbiota-mediated nucleotide synthesis attenuates the response to neoadjuvant chemoradiotherapy in rectal cancer]
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|style="padding:.4em;" rowspan=1|2023/05/23
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-17
|style="padding:.4em;"|G Koh
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.chom.2021.06.019 Multi-omics reveal microbial determinants impacting responses to biologic therapies in inflammatory bowel disease]
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|style="padding:.4em;" rowspan=1|2023/05/23
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-16
|style="padding:.4em;"|SH Lee
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41586-022-05181-3 Identification of trypsin-degrading commensals in the large intestine]
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|style="padding:.4em;" rowspan=1|2023/05/16
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-15
|style="padding:.4em;"|JP Hong
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41586-022-05546-8 Questioning the fetal microbiome illustrates pitfalls of low-biomass microbial studies]
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|style="padding:.4em;" rowspan=1|2023/05/16
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-14
|style="padding:.4em;"|MR Jang
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.chom.2023.01.013 Tissue-resident Lachnospiraceae family bacteria protect against colorectal carcinogenesis by promoting tumor immune surveillance]
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|style="padding:.4em;" rowspan=1|2023/05/16
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-13
|style="padding:.4em;"|JW Yu
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.cell.2022.09.005 Pan-cancer analyses reveal cancer-type-specific fungal ecologies and bacteriome interactions]
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|style="padding:.4em;" rowspan=1|2023/05/09
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-12
|style="padding:.4em;"|NY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.cell.2022.09.015 A pan-cancer mycobiome analysis reveals fungal involvement in gastrointestinal and lung tumors]
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|style="padding:.4em;" rowspan=1|2023/05/09
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-11
|style="padding:.4em;"|HR Shin
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41564-021-01030-7 Multi-kingdom microbiota analyses identify bacterial–fungal interactions and biomarkers of colorectal cancer across cohorts]
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|style="padding:.4em;" rowspan=1|2023/05/09
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-10
|style="padding:.4em;"|SG Oh
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s42255-022-00716-4 The antitumour effects of caloric restriction are mediated by the gut microbiome]
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|style="padding:.4em;" rowspan=1|2023/05/02
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-9
|style="padding:.4em;"|WJ Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41591-022-01964-3 Variability of strain engraftment and predictability of microbiome composition after fecal microbiota transplantation across different diseases]
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|style="padding:.4em;" rowspan=1|2023/05/02
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-8
|style="padding:.4em;"|SM Han
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41591-022-01913-0 Drivers and determinants of strain dynamics following fecal microbiota transplantation]
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|style="padding:.4em;" rowspan=1|2023/05/02
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-7
|style="padding:.4em;"|YY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.cell.2022.11.023 Mobile genetic elements from the maternal microbiome shape infant gut microbial assembly and metabolism]
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|style="padding:.4em;" rowspan=1|2023/04/18
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-6
|style="padding:.4em;"|SH Heo
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.chom.2023.01.018 Mother-to-infant microbiota transmission and infant microbiota development across multiple body sites]
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|style="padding:.4em;" rowspan=1|2023/04/18
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-5
|style="padding:.4em;"|SY Yang
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41467-023-36633-7 Population-level impacts of antibiotic usage on the human gut microbiome]
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|style="padding:.4em;" rowspan=1|2023/04/18
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-4
|style="padding:.4em;"|YH Yoon
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41586-022-05438-x Enterococci enhance Clostridioides difficile pathogenesis]
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|style="padding:.4em;" rowspan=1|2023/04/11
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-3
|style="padding:.4em;"|DH Lee
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1002/imt2.61 Targeting keystone species helps restore the dysbiosis of butyrate‐producing bacteria in nonalcoholic fatty liver disease]
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|style="padding:.4em;" rowspan=1|2023/04/11
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-2
|style="padding:.4em;"|YJ Roh
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1002/advs.202203115 Differential Oral Microbial Input Determines Two Microbiota Pneumo-Types Associated with Health Status]
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|style="padding:.4em;" rowspan=1|2023/04/11
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-1
|style="padding:.4em;"|SH Ahn
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.cell.2022.08.021 Gut microbiome of multiple sclerosis patients and paired household healthy controls reveal associations with disease risk and course]
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{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2023 scOmics
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!scope="col" style="padding:.4em" | Date
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|style="padding:.4em;" rowspan=1|2023/10/25
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-33
|style="padding:.4em;"|IS Choi
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41588-022-01273-y MHC II immunogenicity shapes the neoepitope landscape in human tumors]
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|style="padding:.4em;" rowspan=1|2023/10/18
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-32
|style="padding:.4em;"|G Koh
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41586-023-06130-4 Hallmarks of transcriptional intratumour heterogeneity across a thousand tumours]
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|style="padding:.4em;" rowspan=1|2023/10/11
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-31
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41587-023-01686-y Single-cell mapping of combinatorial target antigens for CAR switches using logic gates]
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|style="padding:.4em;" rowspan=1|2023/10/04
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-30
|style="padding:.4em;"|SB Baek
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41587-023-01782-z Comparative analysis of cell–cell communication at single-cell resolution]
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|style="padding:.4em;" rowspan=1|2023/09/27
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-29
|style="padding:.4em;"|EJ Sung
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s43018-023-00566-3 Phenotypic diversity of T cells in human primary and metastatic brain tumors revealed by multiomic interrogation]
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|style="padding:.4em;" rowspan=1|2023/09/20
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-28
|style="padding:.4em;"|IS Choi
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41591-023-02324-5 An integrated tumor, immune and microbiome atlas of colon cancer]
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|style="padding:.4em;" rowspan=1|2023/09/13
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-27
|style="padding:.4em;"|G Koh
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41467-023-37353-8 Pan-cancer classification of single cells in the tumour microenvironment]
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|style="padding:.4em;" rowspan=1|2023/09/06
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-26
|style="padding:.4em;"|JW Yu
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2023.01.17.524482 Decoupling the correlation between cytotoxic and exhausted T lymphocyte transcriptomic signatures enhances melanoma immunotherapy response prediction from tumor expression]
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|style="padding:.4em;" rowspan=1|2023/08/30
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-25
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.immuni.2023.04.010 Recruitment of epitope-specific T cell clones with a low-avidity threshold supports efficacy against mutational escape upon re-infection]
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|style="padding:.4em;" rowspan=1|2023/08/23
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-24
|style="padding:.4em;"|SB Baek
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41587-022-01476-y Multi-omic single-cell velocity models epigenome–transcriptome interactions and improves cell fate prediction]
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|style="padding:.4em;" rowspan=1|2023/08/09
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-23
|style="padding:.4em;"|EJ Sung
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s43018-022-00475-x A single-cell atlas of glioblastoma evolution under therapy reveals cell-intrinsic and cell-extrinsic therapeutic targets]
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|style="padding:.4em;" rowspan=1|2023/08/02
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-22
|style="padding:.4em;"|IS Choi
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41587-022-01467-z Modeling intercellular communication in tissues using spatial graphs of cells]
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|style="padding:.4em;" rowspan=1|2023/07/26
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-21
|style="padding:.4em;"|G Koh
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s43018-022-00433-7 Single-cell meta-analyses reveal responses of tumor-reactive CXCL13+ T cells to immune-checkpoint blockade]
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|style="padding:.4em;" rowspan=1|2023/07/19
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-20
|style="padding:.4em;"|JW Yu
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41587-022-01342-x Estimation of tumor cell total mRNA expression in 15 cancer types predicts disease progression]
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|style="padding:.4em;" rowspan=1|2023/07/12
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-19
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41587-022-01288-0 DIALOGUE maps multicellular programs in tissue from single-cell or spatial transcriptomics data]
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|style="padding:.4em;" rowspan=1|2023/07/05
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-18
|style="padding:.4em;"|SB Baek
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41591-023-02371-y Pan-cancer T cell atlas links a cellular stress response state to immunotherapy resistance]
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|style="padding:.4em;" rowspan=1|2023/06/28
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-17
|style="padding:.4em;"|EJ Sung
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.patter.2022.100651 Self-supervised graph representation learning integrates multiple molecular networks and decodes gene-disease relationships]
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|style="padding:.4em;" rowspan=1|2023/06/21
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-16
|style="padding:.4em;"|IS Choi
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.ccell.2022.10.008 High-resolution single-cell atlas reveals diversity and plasticity of tissue-resident neutrophils in non-small cell lung cancer]
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|style="padding:.4em;" rowspan=1|2023/06/14
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-15
|style="padding:.4em;"|G Koh
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1186/s13059-022-02828-2 Parallel single-cell and bulk transcriptome analyses reveal key features of the gastric tumor microenvironment]
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|style="padding:.4em;" rowspan=1|2023/05/31
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-14
|style="padding:.4em;"|JW Yu
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41467-022-32838-4 Mutated processes predict immune checkpoint inhibitor therapy benefit in metastatic melanoma]
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|style="padding:.4em;" rowspan=1|2023/05/24
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-13
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s43018-021-00292-8 Temporal single-cell tracing reveals clonal revival and expansion of precursor exhausted T cells during anti-PD-1 therapy in lung cancer]
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|style="padding:.4em;" rowspan=1|2023/05/17
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-12
|style="padding:.4em;"|SB Baek
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.cell.2022.11.028 Integrative single-cell analysis of cardiogenesis indentifies developmental trajectories and non-conding mutations in congenital heart disease]
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|style="padding:.4em;" rowspan=1|2023/05/10
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-11
|style="padding:.4em;"|EJ Sung
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2022.12.20.521311 Supervised discovery of interpretable gene programs from single-cell data]
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|style="padding:.4em;" rowspan=1|2023/05/03
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-10
|style="padding:.4em;"|IS Choi
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41586-022-05435-0 Effect of the intratumoral microbiota on spatial and cellular heterogeneity in cancer]
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|style="padding:.4em;" rowspan=1|2023/04/26
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-9
|style="padding:.4em;"|G Koh
|style="padding:.4em;text-align:left"|
[https://www.nature.com/articles/s41588-022-01141-9 Cancer cell states recur across tumor types and form specific interactions with the tumor microenvironment]
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|style="padding:.4em;" rowspan=1|2023/03/22
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-8
|style="padding:.4em;"|JW Yu
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[https://www.biorxiv.org/content/10.1101/2022.08.05.502989v1 MetaTiME: Meta-components of the Tumor Immune Microenvironment]
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|style="padding:.4em;" rowspan=1|2023/03/08
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-7
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://www.nature.com/articles/s41590-022-01262-7 Pre-encoded responsiveness to type I interferon in the peripheral immune system defines outcome of PD1 blockade therapy]
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|style="padding:.4em;" rowspan=1|2023/02/21
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-6
|style="padding:.4em;"|SB Baek
|style="padding:.4em;text-align:left"|
[https://www.biorxiv.org/content/10.1101/2022.03.16.484513v1 Integrated single-cell profiling dissects cell-state-specific enhancer landscapes of human tumor-infiltrating T cells]
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|style="padding:.4em;" rowspan=1|2023/02/14
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-5
|style="padding:.4em;"|EJ Sung
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[https://www.nature.com/articles/s41591-022-01799-y A T cell resilience model associated with response to immunotherapy in multiple tumor types]
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|style="padding:.4em;" rowspan=1|2022/01/31
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-4
|style="padding:.4em;"|IS Choi
|style="padding:.4em;text-align:left"|
[https://www.nature.com/articles/s41588-022-01134-8 Single-nucleus and spatial transcriptome profiling of pancreatic cancer identifies multicellular dynamics associated with neoadjuvant treatment]
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|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-3
|style="padding:.4em;"|G Koh
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[https://pubmed.ncbi.nlm.nih.gov/35649411/ Cross-tissue, single-cell stromal atlas identifies shared pathological fibroblast phenotypes in four chronic inflammatory diseases]
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|style="padding:.4em;" rowspan=1|2023/01/17
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-2
|style="padding:.4em;"|JW Yu
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[https://www.sciencedirect.com/science/article/pii/S1535610822003178 Pan-cancer integrative histology-genomic analysis via multimodal deep learning]
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|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-1
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://pubmed.ncbi.nlm.nih.gov/35803260/ Tissue-resident memory and circulating T cells are early responders to pre-surgical cancer immunotherapy]
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|style="padding:.4em;" rowspan=1|Microbiome
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[https://doi.org/10.1038/s41564-023-01370-6 Centenarians have a diverse gut virome with the potential to modulate metabolism and promote healthy lifespan]
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|style="padding:.4em;" rowspan=1|2023/09/22
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-28
|style="padding:.4em;"|JH Cha
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[https://doi.org/10.1016/j.chom.2023.01.003 Longitudinal comparison of the developing gut virome in infants and their mothers]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|JY Ma
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[https://doi.org/10.1016/j.chom.2023.05.024 Enterosignatures define common bacterial guilds in the human gut microbiome]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|NY Kim
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[https://doi.org/10.1186/s13059-023-02902-3 PhyloMed: a phylogeny-based test of mediation effect in microbiome]
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|style="padding:.4em;" rowspan=1|2023/09/01
|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|SH Lee
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[https://doi.org/10.1016/j.chom.2023.05.027 The TaxUMAP atlas: Efficient display of large clinical microbiome data reveals ecological competition in protection against bacteremia]
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[https://doi.org/10.1186/s40168-023-01564-4 Skin microbiome diferentiates into distinct cutotypes with unique metabolic functions upon exposure to polycyclic aromatic hydrocarbons]
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|style="padding:.4em;"|WJ Kim
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[http://dx.doi.org/10.1038/nbt.3704 Measurement of bacterial replication rates in microbial communities]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|SH Ahn
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[https://doi.org/10.1016/j.xcrm.2023.100920 Enrichment of oral-derived bacteria in inflamed colorectal tumors and distinct associations of Fusobacterium in the mesenchymal subtype]
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[https://doi.org/10.1101/2023.03.05.531206 You can move, but you can’t hide: identification of mobile genetic elements with geNomad]
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|style="padding:.4em;"|JH Cha
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[https://doi.org/10.1186/s40168-023-01494-1 Genome-centric metagenomics reveals the host-driven dynamics and ecological role of CPR bacteria in an activated sludge system]
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[https://doi.org/10.1016/j.patter.2022.100658 Enhanced metagenomic deep learning for disease prediction and consistent signature recognition by restructured microbiome 2D representations]
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|style="padding:.4em;"|NY Kim
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[https://doi.org/10.1186/s13059-022-02809-5 Gene fow and introgression are pervasive forces shaping the evolution of bacterial species]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|JH Cha
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[https://doi.org/10.1186/s40168-022-01435-4 Alterations of oral microbiota and impact on the gut microbiome in type 1 diabetes mellitus revealed by integrated multi‑omic analyses]
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[https://doi.org/10.1038/s41467-022-33397-4 Deciphering microbial gene function using natural language processing]
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|style="padding:.4em;"|SH Lee
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[https://doi.org/10.1101/2022.11.28.518265 Rethinking bacterial relationships in light of their molecular abilities]
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|style="padding:.4em;" rowspan=1|2023/06/02
|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|JY Ma
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[https://doi.org/10.1016/j.immuni.2022.08.016 The CD4+ T cell response to a commensal-derived epitope transitions from a tolerant to an inflammatory state in Crohn’s disease]
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|style="padding:.4em;"|NY Kim
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[https://doi.org/10.1038/s41591-018-0203-7 Antigen discovery and specification of immunodominance hierarchies for MHCIIrestricted epitopes]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|SH Lee
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[https://doi.org/10.1101/2022.10.11.511790 Single Cell Transcriptomics Reveals the Hidden Microbiomes of Human Tissues]
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|style="padding:.4em;"|JY Kim
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[https://doi.org/10.1038/s41564-017-0096-0 Stability of the human faecal microbiome in a cohort of adult men]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|WJ Kim
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[https://doi.org/10.1038/s41564-017-0084-4 Metatranscriptome of human faecal microbial communities in a cohort of adult men]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|SH Ahn
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[https://doi.org/10.1016/j.ccell.2022.09.009 Tumor microbiome links cellular programs and immunity in pancreatic cancer]
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|style="padding:.4em;"|HJ Kim
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[https://doi.org/10.1038/s41467-022-32832-w Extensive gut virome variation and its associations with host and environmental factors in a population-level cohort]
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|style="padding:.4em;" rowspan=1|2023/03/10
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-7
|style="padding:.4em;"|JH Cha
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[https://doi.org/10.1038/s41586-022-05620-1 The person-to-person transmission landscape of the gut and oral microbiomes]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|JY Ma
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[https://doi.org/10.1101/2023.01.30.526328 BIRDMAn: A Bayesian differential abundance framework that enables robust inference of host-microbe associations]
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|style="padding:.4em;"|NY Kim
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[https://www.nature.com/articles/s41564-022-01157-1 Phage–host coevolution in natural populations]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-4
|style="padding:.4em;"|SH Lee
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[https://www.nature.com/articles/s41467-022-29968-0 A randomized controlled trial for response of microbiome network to exercise and diet intervention in patients with nonalcoholic fatty liver disease]
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|style="padding:.4em;"|SH Ahn
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[https://www.biorxiv.org/content/10.1101/2022.05.19.492684v1 Scalable power analysis and effect size exploration of microbiome community differences with Evident]
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[https://www.biorxiv.org/content/10.1101/2022.08.05.502982v1 Phanta: Phage-inclusive profiling of human gut metagenomes]
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[https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1010373 Computational approach to modeling microbiome landscapes associated with chronic human disease progression]
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[https://www.biorxiv.org/content/10.1101/2022.08.19.504505v1 SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks]
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|style="padding:.4em;"|22-31
|style="padding:.4em;"|JH Cha, SB Baek, IS Choi
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[https://www.pnas.org/doi/10.1073/pnas.2105859118 Representation learning of RNA velocity reveals robust cell transitions]
[https://www.nature.com/articles/s41467-022-34188-7 UniTVelo: temporally unified RNA velocity reinforces single-cell trajectory inference]
[https://www.sciencedirect.com/science/article/pii/S0092867421015774 Mapping transcriptomic vector fields of single cells]
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[https://www.nature.com/articles/s41467-022-31535-6 Network-based machine learning approach to predict immunotherapy response in cancer patients]
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[https://www.biorxiv.org/content/10.1101/2022.05.04.490536v1 Modeling fragment counts improves single-cell ATAC-seq analysis]
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|style="padding:.4em;"|G Koh
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[https://www.nature.com/articles/s41586-022-04718-w Extricating human tumour immune alterations from tissue inflammation]
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|style="padding:.4em;"|JW Yu
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[https://www.biorxiv.org/content/10.1101/2022.06.15.495325v1 T cell receptor convergence is an indicator of antigen-specific T cell response in cancer immunotherapies]
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|style="padding:.4em;" rowspan=1|2022/09/06
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|style="padding:.4em;"|JH Cha
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[https://www.nature.com/articles/s41587-021-01091-3 Identifying phenotype-associated subpopulations by integrating bulk and single-cell sequencing data]
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|style="padding:.4em;" rowspan=1|2022/09/01
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|22-27
|style="padding:.4em;"|SB Baek
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[https://www.biorxiv.org/content/10.1101/2021.12.06.471401v1 MIRA: Joint regulatory modeling of multimodal expression and chromatin accessibility in single cells]
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[https://www.sciencedirect.com/science/article/pii/S1535610822000654 Immune phenotypic linkage between colorectal cancer and liver metastasis]
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[https://doi.org/10.1101/2022.02.05.479217 Biologically informed deep learning to infer gene program activity in single cells]
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[https://www.nature.com/articles/s43018-022-00356-3 Cell type and gene expression deconvolution with BayesPrism enables Bayesian integrative analysis across bulk and single-cell RNA sequencing in oncology]
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|style="padding:.4em;"|JH Cha
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[https://pubmed.ncbi.nlm.nih.gov/34462589/ Mapping single-cell data to reference atlases by transfer learning]
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|style="padding:.4em;"|JW Yu
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[https://doi.org/10.1101/2021.10.31.466532 Pan-cancer mapping of single T cell profiles reveals a TCF1:CXCR6-CXCL16 regulatory axis essential for effective anti-tumor immunity]
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[https://pubmed.ncbi.nlm.nih.gov/34845454/ Identifying disease-critical cell types and cellular processes across the human body by integration of single-cell profiles and human genetics]
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[https://www.biorxiv.org/content/10.1101/2021.06.07.447430v2 Metacells untangle large and complex single-cell transcriptome networks]
[https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1812-2 MetaCell: analysis of single-cell RNA-seq data using K-nn graph partitions]
[https://genomebiology.biomedcentral.com/articles/10.1186/s13059-022-02667-1 Metacell‑2: a divide‑and‑conquer metacell algorithm for scalable scRNA‑seq analysis]
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[https://pubmed.ncbi.nlm.nih.gov/34675423/ Co-varying neighborhood analysis identifies cell populations associated with phenotypes of interest from single-cell transcriptomics]
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[https://pubmed.ncbi.nlm.nih.gov/34426704/ Integrating T cell receptor sequences and transcriptional profiles by clonotype neighbor graph analysis (CoNGA)]
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[https://pubmed.ncbi.nlm.nih.gov/34986867/ Hepatocellular carcinoma patients with high circulating cytotoxic T cells and intra-tumoral immune signature benefit from pembrolizumab: results from a single-arm phase 2 trial]
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[https://pubmed.ncbi.nlm.nih.gov/35199064/ Effect of imputation on gene network reconstruction from single-cell RNA-seq data]
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[https://pubmed.ncbi.nlm.nih.gov/35105355/ Diagnostic Evidence GAuge of Single cells (DEGAS): a flexible deep transfer learning framework for prioritizing cells in relation to disease]
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[https://pubmed.ncbi.nlm.nih.gov/35172892/ Integrating single-cell sequencing data with GWAS summary statistics reveals CD16+monocytes and memory CD8+T cells involved in severe COVID-19]
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[https://pubmed.ncbi.nlm.nih.gov/34663807/ Single cell T cell landscape and T cell receptor repertoire profiling of AML in context of PD-1 blockade therapy]
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[https://pubmed.ncbi.nlm.nih.gov/34594031/ Systematic investigation of cytokine signaling activity at the tissue and single-cell levels]
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[https://pubmed.ncbi.nlm.nih.gov/34852236/ Neoantigen-driven B cell and CD4 T follicular helper cell collaboration promotes anti-tumor CD8 T cell responses]
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[https://pubmed.ncbi.nlm.nih.gov/34930412/ MultiMAP: dimensionality reduction and integration of multimodal data]
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[https://www.biorxiv.org/content/10.1101/2020.10.19.345983v2 CellRank for directed single-cell fate mapping]
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[https://pubmed.ncbi.nlm.nih.gov/34653365/ Single-cell analyses reveal key immune cell subsets associated with response to PD-L1 blockade in triple-negative breast cancer]
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[https://pubmed.ncbi.nlm.nih.gov/34390642/ Chromatin and gene-regulatory dynamics of the developing human cerebral cortex at single-cell resolution]
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[https://pubmed.ncbi.nlm.nih.gov/34767762/ Single-cell analysis of human non-small cell lung cancer lesions refines tumor classification and patient stratification]
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[https://pubmed.ncbi.nlm.nih.gov/34914499/ Pan-cancer single-cell landscape of tumor-infiltrating T cells]
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[https://pubmed.ncbi.nlm.nih.gov/34597583/ Atlas of clinically distinct cell states and ecosystems across human solid tumors]
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[https://www.cell.com/cell-host-microbe/fulltext/S1931-3128(19)30041-1?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS1931312819300411%3Fshowall%3Dtrue Distinct Genetic and Functional Traits of Human Intestinal Prevotella copri Strains Are Associated with Different Habitual Diets]
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[https://www.biorxiv.org/content/10.1101/2021.02.09.430114v2 Single-cell ATAC and RNA sequencing reveal pre-existing and persistent subpopulations of cells associated with relapse of prostate cancer]
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[https://www.nature.com/articles/s41586-021-03552-w Interpreting type 1 diabetes risk with genetics and single-cell epigenomics]
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[https://www.sciencedirect.com/science/article/pii/S0092867421000726 Massive expansion of human gut bacteriophage diversity]
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[https://www.sciencedirect.com/science/article/pii/S1931312821001451 The infant gut resistome associates with E. coli, environmental exposures, gut microbiome maturity, and asthma-associated bacterial composition]
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[https://www.sciencedirect.com/science/article/pii/S1931312820306703?dgcid=rss_sd_all Methotrexate impacts conserved pathways in diverse human gut bacteria leading to decreased host immune activation]
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[https://science.sciencemag.org/content/366/6471/eaax9176 A metagenomic strategy for harnessing the chemical repertoire of the human microbiome]
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[https://www.nature.com/articles/s41467-019-10927-1 Predictive metabolomic profiling of microbial communities using amplicon or metagenomic sequences]
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[https://www.nature.com/articles/s41564-018-0306-4 Gut microbiome structure and metabolic activity in inflammatory bowel disease]
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[https://www.nature.com/articles/s41591-020-01183-8 Microbiome connections with host metabolism and habitual diet from 1,098 deeply phenotyped individuals]
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[https://www.nature.com/articles/s41467-020-18476-8 A predictive index for health status using species-level gut microbiome profiling]
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|style="padding:.4em;"|JH Cha
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[https://www.nature.com/articles/s41586-019-1237-9 Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases]
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[https://www.nature.com/articles/s41467-021-21475-y Gastrointestinal microbiota composition predicts peripheral inflammatory state during treatment of human tuberculosis]
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[https://www.sciencedirect.com/science/article/pii/S1931312820301694 Structure of the Mucosal and Stool Microbiome in Lynch Syndrome]
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[https://science.sciencemag.org/content/369/6506/936 Cross-reactivity between tumor MHC class 1-restricted antigens and an enterococcal bacteriophage]
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[https://www.nature.com/articles/s41564-020-00831-6 Bifidobacterium bifidum strains synergize with immune checkpoint inhibitors to reduce tumour burden in mice]
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[https://www.nature.com/articles/s41591-020-01223-3 The gut microbiome modulates the protective association between a Mediterranean diet and cardiometabolic disease risk]
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[https://www.nature.com/articles/s41467-019-14177-z Impact of commonly used drugs on the composition and metabolic function of the gut microbiota]
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[https://www.sciencedirect.com/science/article/pii/S0092867420305638 Personalized Mapping of Drug Metabolism by the Human Gut Microbiome]
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[https://www.cell.com/fulltext/S0092-8674(17)30107-1 Mining the Human Gut Microbiota for Immunomodulatory Organisms]
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[https://www.nature.com/articles/s41586-020-2095-1 Microbiome analyses of blood and tissues suggest cancer diagnostic approach]
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[https://science.sciencemag.org/content/368/6494/973 The human tumor microbiome is composed of tumor type-specific intracellular bacteria]
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[https://www.nature.com/articles/s41590-020-0784-4 Functional CRISPR dissection of gene networks controlling human regulatory T cell identity]
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[https://www.sciencedirect.com/science/article/pii/S0092867420306887 Molecular Pathways of Colon Inflammation Induced by Cancer Immunotherapy]
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[https://www.nature.com/articles/s41588-020-00721-x Single-cell epigenomic analyses implicate candidate causal variants at inherited risk loci for Alzheimer’s and Parkinson’s diseases]
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[https://www.nature.com/articles/s41467-020-14766-3 Trajectory-based differential expression analysis for single-cell sequencing data]
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[https://www.nature.com/articles/s41588-018-0156-2 Genetic determinants of co-accessible chromatin regions in activated T cells across humans]
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[https://www.sciencedirect.com/science/article/pii/S009286742030341X Single-Cell Analyses Inform Mechanisms of Myeloid-Targeted Therapies in Colon Cancer]
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[https://www.sciencedirect.com/science/article/pii/S0092867419308967?via%3Dihub Single-Cell Analysis of Crohn's Disease Lesions Identifies a Pathogenic Cellular Module Associated with Resistance to Anti-TNF Therapy]
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[https://www.nature.com/articles/s41467-020-15956-9 Multi-omic single-cell snapshots reveal multiple independent trajectories to drug tolerance in a melanoma cell line]
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[https://pubmed.ncbi.nlm.nih.gov/32393797/ Multi-omic single-cell snapshots reveal multiple independent trajectories to drug tolerance in a melanoma cell line]
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|style="padding:.4em;"|20-14
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[https://pubmed.ncbi.nlm.nih.gov/30595452/ Dysfunctional CD8 T Cells Form a Proliferative, Dynamically Regulated Compartment Within Human Melanoma]
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[https://pubmed.ncbi.nlm.nih.gov/30388455/ A Cancer Cell Program Promotes T Cell Exclusion and Resistance to Checkpoint Blockade]
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[https://pubmed.ncbi.nlm.nih.gov/31974247/ Single-cell Transcriptional Diversity Is a Hallmark of Developmental Potential]
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[https://pubmed.ncbi.nlm.nih.gov/31675496/ Landscape and Dynamics of Single Immune Cells in Hepatocellular Carcinoma]
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[https://pubmed.ncbi.nlm.nih.gov/32103181/ Peripheral T Cell Expansion Predicts Tumour Infiltration and Clinical Response]
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[https://pubmed.ncbi.nlm.nih.gov/30388456/ Defining T Cell States Associated With Response to Checkpoint Immunotherapy in Melanoma]
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[https://pubmed.ncbi.nlm.nih.gov/31915379/ Rapid Non-Uniform Adaptation to Conformation-Specific KRAS(G12C) Inhibition]
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[https://pubmed.ncbi.nlm.nih.gov/31959990/ Targeted Therapy Guided by Single-Cell Transcriptomic Analysis in Drug-Induced Hypersensitivity Syndrome: A Case Report]
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[https://pubmed.ncbi.nlm.nih.gov/32066951/ Distinct Microbial and Immune Niches of the Human Colon]
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[https://pubmed.ncbi.nlm.nih.gov/31375813/ Massively Parallel Single-Cell Chromatin Landscapes of Human Immune Cell Development and Intratumoral T Cell Exhaustion]
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[https://pubmed.ncbi.nlm.nih.gov/29434354/ Single-cell Gene Expression Reveals a Landscape of Regulatory T Cell Phenotypes Shaped by the TCR]
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|style="padding:.4em;"|20-3
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[https://pubmed.ncbi.nlm.nih.gov/30078704/ A Single-Cell Atlas of In Vivo Mammalian Chromatin Accessibility]
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[https://pubmed.ncbi.nlm.nih.gov/32014031/ scAI: An Unsupervised Approach for the Integrative Analysis of Parallel Single-Cell Transcriptomic and Epigenomic Profiles]
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[https://pubmed.ncbi.nlm.nih.gov/31792411/ Single-cell Multiomic Analysis Identifies Regulatory Programs in Mixed-Phenotype Acute Leukemia]
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[https://www.sciencedirect.com/science/article/pii/S1931312819303488 Obese Individuals with and without Type 2 Diabetes Show Different Gut Microbial Functional Capacity and Composition]
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|style="padding:.4em;"|19-50
|style="padding:.4em;"|SH Lee
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[https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-019-0722-6 Clustering co-abundant genes identifies components of the gut microbiome that are reproducibly associated with colorectal cancer and inflammatory bowel disease]
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[https://www.nature.com/articles/s41587-019-0206-z Massively parallel single-cell chromatin landscapes of human immune cell development and intratumoral T cell exhaustion]
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[https://www.biorxiv.org/content/10.1101/739011v1 Assessment of computational methods for the analysis of single-cell ATAC-seq data]
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|style="padding:.4em;" rowspan=2|Microbiome
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[https://www.sciencedirect.com/science/article/pii/S1931312819303026 Meta-Analysis Reveals Reproducible Gut Microbiome Alterations in Response to a High-Fat Diet]
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|style="padding:.4em;"|19-46
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[https://www.sciencedirect.com/science/article/pii/S0092867419307731 Tumor Microbiome Diversity and Composition Influence Pancreatic Cancer Outcomes]
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[https://genome.cshlp.org/content/early/2019/04/01/gr.243725.118 The accessible chromatin landscape of the murine hippocampus at single-cell resolution]
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[https://www.sciencedirect.com/science/article/pii/S009286741830446X Integrated Single-Cell Analysis Maps the Continuous Regulatory Landscape of Human Hematopoietic Differentiation]
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|style="padding:.4em;" rowspan=2|2019/09/17
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[https://science.sciencemag.org/content/365/6449/eaau4735 A sparse covarying unit that describes healthy and impaired human gut microbiota development]
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[https://www.sciencedirect.com/science/article/pii/S0092867419307810 Large-Scale Analyses of Human Microbiomes Reveal Thousands of Small, Novel Genes]
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|style="padding:.4em;" rowspan=2|2019/09/10
|style="padding:.4em;" rowspan=2|Single-cell
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[https://www.sciencedirect.com/science/article/pii/S0092867419307329?via%3Dihub Intra- and Inter-cellular Rewiring of the Human Colon during Ulcerative Colitis]
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|style="padding:.4em;"|19-40
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[https://www.nature.com/articles/nbt.4042 Multiplexed droplet single-cell RNA-sequencing using natural genetic variation]
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[https://www.sciencedirect.com/science/article/pii/S193131281930352X?via%3Dihub The Landscape of Genetic Content in the Gut and Oral Human Microbiome]
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|style="padding:.4em;"|19-38
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[https://www.sciencedirect.com/science/article/pii/S0092867419307755?via%3Dihub Benchmarking Metagenomics Tools for Taxonomic Classification]
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[https://www.biorxiv.org/content/10.1101/719088v1 Coexpression uncovers a unified single-cell transcriptomic landscape]
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|style="padding:.4em;"|19-36
|style="padding:.4em;"|MY Lee
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[https://www.biorxiv.org/content/10.1101/586859v1 Single-cell interactomes of the human brain reveal cell-type specific convergence of brain disorders]
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|style="padding:.4em;" rowspan=3|2019/08/14
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[https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004075 Proportionality: a valid alternative to correlation for relative data]
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|style="padding:.4em;"|19-34
|style="padding:.4em;"|SH Lee
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[https://www.nature.com/articles/s41598-017-16520-0 propr: An R-package for Identifying Proportionally Abundant Features Using Compositional Data Analysis]
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[https://www.nature.com/articles/s41591-018-0157-9 Robust prediction of response to immune checkpoint blockade therapy in metastatic melanoma]
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[https://www.nature.com/articles/s41592-018-0254-1 A test metric for assessing single-cell RNA-seq batch correction]
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[https://www.sciencedirect.com/science/article/pii/S0092867419305598 Comprehensive Integration of Single-Cell Data]
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[https://www.sciencedirect.com/science/article/pii/S0092867418313941 Defining T Cell States Associated with Response to Checkpoint Immunotherapy in Melanoma]
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[https://www.sciencedirect.com/science/article/pii/S009286741831242X High-Dimensional Analysis Delineates Myeloid and Lymphoid Compartment Remodeling during Successful Immune-Checkpoint Cancer Therapy]
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[https://www.biorxiv.org/content/10.1101/555557v1 A single-cell reference map for human blood and tissue T cell activation reveals functional states in health and disease]
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[https://www.sciencedirect.com/science/article/pii/S009286741831568X Dysfunctional CD8 T Cells Form a Proliferative, Dynamically Regulated Compartment within Human Melanoma]
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[https://www.nature.com/articles/nm.4466 High-dimensional single-cell analysis predicts response to anti-PD-1 immunotherapy]
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[https://www.sciencedirect.com/science/article/pii/S0092867418311784 A Cancer Cell Program Promotes T Cell Exclusion and Resistance to Checkpoint Blockade]
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|style="padding:.4em;" rowspan=2|2019/07/23
|style="padding:.4em;"|19-24
|style="padding:.4em;"|HJ Han
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[https://www.ncbi.nlm.nih.gov/pubmed/26006008 COMPASS identifies T-cell subsets correlated with clinical outcomes.]
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|style="padding:.4em;"|19-23
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[https://www.nature.com/articles/ncomms14825 Sensitive detection of rare disease-associated cell subsets via representation learning]
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|style="padding:.4em;" rowspan=3|2019/05/30
|style="padding:.4em;"|19-22
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[https://www.ncbi.nlm.nih.gov/pubmed/30936547 Meta-analysis of fecal metagenomes reveals global microbial signatures that are specific for colorectal cancer]
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|style="padding:.4em;"|19-21
|style="padding:.4em;"|
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[https://www.ncbi.nlm.nih.gov/pubmed/30936548 Metagenomic analysis of colorectal cancer datasets identifies cross-cohort microbial diagnostic signatures and a link with choline degradation]
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|style="padding:.4em;"|19-20
|style="padding:.4em;"|
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[https://www.ncbi.nlm.nih.gov/pubmed/30664783 Microbial network disturbances in relapsing refractory Crohn's disease.]
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|style="padding:.4em;" rowspan=3|2019/05/23
|style="padding:.4em;"|19-19
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[https://www.ncbi.nlm.nih.gov/pubmed/30867587 New insights from uncultivated genomes of the global human gut microbiome]
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|style="padding:.4em;"|19-18
|style="padding:.4em;"|
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[https://www.ncbi.nlm.nih.gov/pubmed/30745586 A new genomic blueprint of the human gut microbiota]
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|style="padding:.4em;"|19-17
|style="padding:.4em;"|
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[https://www.ncbi.nlm.nih.gov/pubmed/30661755 Extensive Unexplored Human Microbiome Diversity Revealed by Over 150,000 Genomes from Metagenomes Spanning Age, Geography, and Lifestyle.]
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|style="padding:.4em;" rowspan=2|2019/05/16
|style="padding:.4em;"|19-16
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[https://www.ncbi.nlm.nih.gov/pubmed/29311644 Dynamics of metatranscription in the inflammatory bowel disease gut microbiome.]
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|style="padding:.4em;"|19-15
|style="padding:.4em;"|
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[https://www.ncbi.nlm.nih.gov/pubmed/29335555 Metatranscriptome of human faecal microbial communities in a cohort of adult men.]
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|style="padding:.4em;" rowspan=2|2019/05/09
|style="padding:.4em;"|19-14
|style="padding:.4em;"|
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[https://www.ncbi.nlm.nih.gov/pubmed/30193113 Post-Antibiotic Gut Mucosal Microbiome Reconstitution Is Impaired by Probiotics and Improved by Autologous FMT.]
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|style="padding:.4em;"|19-13
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[https://www.ncbi.nlm.nih.gov/pubmed/30193112 Personalized Gut Mucosal Colonization Resistance to Empiric Probiotics Is Associated with Unique Host and Microbiome Features.]
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|style="padding:.4em;" rowspan=2|2019/05/02
|style="padding:.4em;"|19-12
|style="padding:.4em;"|
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[https://www.ncbi.nlm.nih.gov/pubmed/30753825 Distinct Immune Cell Populations Define Response to Anti-PD-1 Monotherapy and Anti-PD-1/Anti-CTLA-4 Combined Therapy.]
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|style="padding:.4em;"|19-11
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[https://www.ncbi.nlm.nih.gov/pubmed/30778252 Subsets of exhausted CD8+ T cells differentially mediate tumor control and respond to checkpoint blockade.]
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|style="padding:.4em;" rowspan=2|2019/4/11
|style="padding:.4em;"|19-10
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[https://www.ncbi.nlm.nih.gov/pubmed/30479382 Lineage tracking reveals dynamic relationships of T cells in colorectal cancer.]
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|style="padding:.4em;"|19-9
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[https://www.ncbi.nlm.nih.gov/pubmed/30523328 Single cell dissection of plasma cell heterogeneity in symptomatic and asymptomatic myeloma.]
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|style="padding:.4em;" rowspan=2|2019/4/4
|style="padding:.4em;"|19-8
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[https://www.ncbi.nlm.nih.gov/pubmed/29942092 Single-cell profiling of breast cancer T cells reveals a tissue-resident memory subset associated with improved prognosis.]
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|style="padding:.4em;"|19-7
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[https://www.ncbi.nlm.nih.gov/pubmed/29942094 Global characterization of T cells in non-small-cell lung cancer by single-cell sequencing]
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|style="padding:.4em;" rowspan=2|2019/3/28
|style="padding:.4em;"|19-6
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[https://www.ncbi.nlm.nih.gov/pubmed/28319088 Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors.]
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|style="padding:.4em;"|19-5
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[https://www.ncbi.nlm.nih.gov/pubmed/28622514 Landscape of Infiltrating T Cells in Liver Cancer Revealed by Single-Cell Sequencing.]
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|style="padding:.4em;" rowspan=3|2019/3/21
|style="padding:.4em;"|19-4
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[https://www.ncbi.nlm.nih.gov/pubmed/29988129 Phenotype molding of stromal cells in the lung tumor microenvironment.]
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|style="padding:.4em;"|19-3-1
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[https://www.ncbi.nlm.nih.gov/pubmed/30787436 A single-cell molecular map of mouse gastrulation and early organogenesis]
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|style="padding:.4em;"|19-3
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[https://www.ncbi.nlm.nih.gov/pubmed/30787437 The single-cell transcriptional landscape of mammalian organogenesis]
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|style="padding:.4em;" rowspan=2|2019/3/14
|style="padding:.4em;"|19-2
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[https://www.ncbi.nlm.nih.gov/pubmed/29961579 Single-Cell Map of Diverse Immune Phenotypes in the Breast Tumor Microenvironment]
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|style="padding:.4em;"|19-1
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[https://www.ncbi.nlm.nih.gov/pubmed/29198524 Single-Cell Transcriptomic Analysis of Primary and Metastatic Tumor Ecosystems in Head and Neck Cancer]
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{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2018
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|style="padding:.4em;" rowspan=2|2018/06/14
|style="padding:.4em;"|18-12
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=A+pan-cancer+analysis+of+enhancer+expression+in+nearly+9000+patient+samples A Pan-Cancer Analysis of Enhancer Expression in Nearly 9000 Patient Samples.]
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|style="padding:.4em;"|18-11
|style="padding:.4em;"|
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=Machine+learning+identifies+stemness+features+associated+with+oncogenic+dedifferentiation Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation.]
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|style="padding:.4em;" rowspan=2|2018/06/07
|style="padding:.4em;"|18-10
|style="padding:.4em;"|
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=Developmental+and+oncogenic+programs+in+H3K27M+gliomas+dissected+by+single-cell+RNA-seq Developmental and oncogenic programs in H3K27M gliomas dissected by single-cell RNA-seq.]
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|style="padding:.4em;"|18-9
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=Chemoresistance+evolution+in+triple-negative+breast+cancer+delineated+by+single-cell+sequencing Chemoresistance Evolution in Triple-Negative Breast Cancer Delineated by Single-Cell Sequencing.]
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|style="padding:.4em;" rowspan=2|2018/05/31
|style="padding:.4em;"|18-8
|style="padding:.4em;"|
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=Mapping+human+pluripotent+stem+cell+differentiation+pathways+using+high+throughput+single-cell+RNA-sequencing Mapping human pluripotent stem cell differentiation pathways using high throughput single-cell RNA-sequencing.]
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|style="padding:.4em;"|18-7
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=A+single-cell+RNA-seq+survey+of+the+developmental+landscape+of+the+human+prefrontal+cortex A single-cell RNA-seq survey of the developmental landscape of the human prefrontal cortex.]
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|style="padding:.4em;" rowspan=2|2018/05/24
|style="padding:.4em;"|18-6
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=Single-cell+RNA+sequencing+identifies+celltype-specific+cis-eQTLs+and+co-expression+QTLs Single-cell RNA sequencing identifies celltype-specific cis-eQTLs and co-expression QTLs.]
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|style="padding:.4em;"|18-5
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=FOCS%3A+a+novel+method+for+analyzing+enhancer+and+gene+activity+patterns+infers+an+extensive+enhancer-promoter+map FOCS: a novel method for analyzing enhancer and gene activity patterns infers an extensive enhancer-promoter map.]
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|style="padding:.4em;" rowspan=2|2018/05/17
|style="padding:.4em;"|18-4
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/29149608 A Distinct Gene Module for Dysfunction Uncoupled from Activation in Tumor-Infiltrating T Cells.]
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|style="padding:.4em;"|18-3
|style="padding:.4em;"|SH Lee
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/29610481 A global transcriptional network connecting noncoding mutations to changes in tumor gene expression.]
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|style="padding:.4em;" rowspan=2|2018/05/10
|style="padding:.4em;"|18-2
|style="padding:.4em;"|JW Cho
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=Inferring+regulatory+element+landscapes+and+transcription+factor+networks+from+cancer+methylomes Inferring regulatory element landscapes and transcription factor networks from cancer methylomes.]
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|style="padding:.4em;"|18-1
|style="padding:.4em;"|DS Bae
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[https://www.ncbi.nlm.nih.gov/pubmed/28129544 Genomic and Transcriptomic Features of Response to Anti-PD-1 Therapy in Metastatic Melanoma.]
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{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2017
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!scope="col" style="padding:.4em" |Date
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!scope="col" style="padding:.4em" | Presenter
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|style="padding:.4em;" rowspan=2|2017/06/28
|style="padding:.4em;"|17-36
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/28104840 Tumor aneuploidy correlates with markers of immune evasion and with reduced response to immunotherapy.]
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|style="padding:.4em;"|17-35
|style="padding:.4em;"|JW Cho
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/27240091 Landscape of tumor-infiltrating T cell repertoire of human cancers.]
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|style="padding:.4em;" rowspan=2|2017/06/14
|style="padding:.4em;"|17-34
|style="padding:.4em;"|JW Cho
|style="padding:.4em;text-align:left"|
[http://biorxiv.org/content/early/2015/09/01/025908 The landscape of T cell infiltration in human cancer and its association with antigen presenting gene expression.]
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|style="padding:.4em;"|17-33
|style="padding:.4em;"|MY Lee
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.08.052 A Distinct Gene Module for Dysfunction Uncoupled from Activation in Tumor-Infiltrating T Cells.]
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|style="padding:.4em;" rowspan=2|2017/06/07
|style="padding:.4em;"|17-32
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[http://www.tandfonline.com/doi/full/10.1080/2162402X.2016.1253654 Identification of genetic determinants of breast cancer immune phenotypes by integrative genome-scale analysis.]
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|style="padding:.4em;"|17-31
|style="padding:.4em;"|MY Lee
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.celrep.2016.03.075 Genomic Correlates of Immune-Cell Infiltrates in Colorectal Carcinoma.]
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|style="padding:.4em;" rowspan=2|2017/05/31
|style="padding:.4em;"|17-30
|style="padding:.4em;"|DS Bae
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.02.065 Genomic and Transcriptomic Features of Response to Anti-PD-1 Therapy in Metastatic Melanoma.]
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|style="padding:.4em;"|17-29
|style="padding:.4em;"|JW Cho
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.celrep.2016.12.019 Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade.]
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|style="padding:.4em;" rowspan=2|2017/05/24
|style="padding:.4em;"|17-28
|style="padding:.4em;"|EB Kim
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.12.022 Systemic Immunity Is Required for Effective Cencer Immunotherapy.]
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|style="padding:.4em;"|17-27
|style="padding:.4em;"|CY Kim
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.10.020 Linking the Human Gut Microbiome to Inflammatory Cytokine Production Capacity.]
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|style="padding:.4em;" rowspan=2|2017/05/17
|style="padding:.4em;"|17-26
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.10.018 Host and Environmental Factors Influencing Individual Human Cytokine Responses.]
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|style="padding:.4em;"|17-25
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.10.017 A Functional Genomics Approach to Understand Variation in Cytokine Production in Humans.]
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|style="padding:.4em;" rowspan=2|2017/04/26
|style="padding:.4em;"|17-24
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1038%2FNMETH.4177 Pooled CRISPR screening with single-cell transcriptome readout.]
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|style="padding:.4em;"|17-23
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.11.039 Dissecting Immune Circuits by Linking CRISPR-Pooled Screens with Single-Cell RNA-Seq.]
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|style="padding:.4em;" rowspan=2|2017/04/12
|style="padding:.4em;"|17-22
|style="padding:.4em;"|SH Lee
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.11.038 Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens.]
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|style="padding:.4em;"|17-21
|style="padding:.4em;"|CY Kim
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1038%2Fnbt.3569 Wishbone identifies bifurcating developmental trajectories from single-cell data.]
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|style="padding:.4em;" rowspan=2|2017/04/05
|style="padding:.4em;"|17-20
|style="padding:.4em;"|KS Kim
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[http://biorxiv.org/content/early/2017/02/21/110668 Reversed graph embedding resolves complex single-cell developmental trajectories.]
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|style="padding:.4em;"|17-19
|style="padding:.4em;"|SH Lee
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1038%2Fnmeth.4150 Single-cell mRNA quantification and differential analysis with Census.]
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|style="padding:.4em;" rowspan=2|2017/03/29
|style="padding:.4em;"|17-18
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.celrep.2016.12.060 Single-Cell Transcriptomic Analysis Defines Heterogeneity and Transcriptional Dynamics in the Adult Neural Stem Cell Lineage.]
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|style="padding:.4em;"|17-17
|style="padding:.4em;"|DS Bae
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[https://www.ncbi.nlm.nih.gov/pubmed/26051941 Single-Cell Network Analysis Identifies DDIT3 as a Nodal Lineage Regulator in Hematopoiesis.]
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|style="padding:.4em;" rowspan=2|2017/03/22
|style="padding:.4em;"|17-16
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[https://www.ncbi.nlm.nih.gov/pubmed/27580035 Single-cell analysis of mixed-lineage states leading to a binary cell fate choice.]
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|style="padding:.4em;"|17-15
|style="padding:.4em;"|EB Kim
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[https://www.ncbi.nlm.nih.gov/pubmed/27281220 Dissecting direct reprogramming from fibroblast to neuron using single-cell RNA-seq.]
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|style="padding:.4em;" rowspan=2|2017/03/15
|style="padding:.4em;"|17-14
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1126%2Fscience.aad0501 Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq.]
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|style="padding:.4em;"|17-13
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[https://www.ncbi.nlm.nih.gov/pubmed/26084335 Single-cell mRNA sequencing identifies subclonal heterogeneity in anti-cancer drug responses of lung adenocarcinoma cells.]
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|style="padding:.4em;" rowspan=2|2017/02/28
|style="padding:.4em;"|17-12
|style="padding:.4em;"|KS Kim
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[https://www.ncbi.nlm.nih.gov/pubmed/27824113 A Comprehensive Characterization of the Function of LincRNAs in Transcriptional Regulation Through Long-Range Chromatin Interactions.]
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|style="padding:.4em;"|17-11
|style="padding:.4em;"|JE Shim
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[//pubmed.gov/27851969 Epigenomic Deconvolution of Breast Tumors Reveals Metabolic Coupling between Constituent Cell Types.]
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|style="padding:.4em;" rowspan=2|2017/02/21
|style="padding:.4em;"|17-10
|style="padding:.4em;"|EB Kim
|style="padding:.4em;text-align:left"|
[http://biorxiv.org/content/early/2016/12/30/097451 Convergence of dispersed regulatory mutations predicts driver genes in prostate cancer.]
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|style="padding:.4em;"|17-09
|style="padding:.4em;"|EB Kim
|style="padding:.4em;text-align:left"|
[//pubmed.gov/27723759 Chromatin structure-based prediction of recurrent noncoding mutations in cancer.]
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|style="padding:.4em;" rowspan=2|2017/02/07
|style="padding:.4em;"|17-08
|style="padding:.4em;"|DS Bae
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[http://biorxiv.org/content/early/2016/11/17/088286 Somatic Mutations and Neoepitope Homology in Melanomas Treated with CTLA-4 Blockade.]
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|style="padding:.4em;"|17-07
|style="padding:.4em;"|MY Lee
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[http://biorxiv.org/content/early/2016/11/28/090134 Chemotherapy weakly contributes to predicted neoantigen expression in ovarian cancer.]
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|style="padding:.4em;" rowspan=1|2017/01/31
|style="padding:.4em;"|17-06
|style="padding:.4em;"|CY Kim
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[//www.ncbi.nlm.nih.gov/pubmed/27912059 Microbiota Diurnal Rhythmicity Programs Host Transcriptome Oscillations.]
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|style="padding:.4em;" rowspan=1|2017/01/24
|style="padding:.4em;"|17-05
|style="padding:.4em;"|JW Cho
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[//www.ncbi.nlm.nih.gov/pubmed/27851914 Transcriptional Landscape of Human Tissue Lymphocytes Unveils Uniqueness of Tumor-Infiltrating T Regulatory Cells.]
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|style="padding:.4em;" rowspan=2|2017/01/17
|style="padding:.4em;"|17-04
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[//www.ncbi.nlm.nih.gov/pubmed/25938943 Mapping long-range promoter contacts in human cells with high-resolution capture Hi-C]
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|style="padding:.4em;"|17-03
|style="padding:.4em;"|EB Kim
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[//www.ncbi.nlm.nih.gov/pubmed/27306882 CHiCAGO: robust detection of DNA looping interactions in Capture Hi-C data.]
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|style="padding:.4em;" rowspan=1|2017/01/10
|style="padding:.4em;"|17-02
|style="padding:.4em;"|JE Shim
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[//www.ncbi.nlm.nih.gov/pubmed/26527291 ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis]
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|style="padding:.4em;" rowspan=1|2017/01/03
|style="padding:.4em;"|17-01
|style="padding:.4em;"|SH Lee
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[//www.ncbi.nlm.nih.gov/pubmed/26287467 Single-cell messenger RNA sequencing reveals rare intestinal cell types]
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{|class=wikitable style="text-align:center;"
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|style="padding:.4em;" rowspan=1|2016/12/27
|style="padding:.4em;"|2016-31
|style="padding:.4em;"|EB Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/25664528 Decoding the regulatory network of early blood development from single-cell gene expression measurements.]
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|style="padding:.4em;" rowspan=1|2016/12/6
|style="padding:.4em;"|2016-30
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26299571 Single-Cell RNA-Seq with Waterfall Reveals Molecular Cascades underlying Adult Neurogenesis.]
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|style="padding:.4em;" rowspan=1|2016/11/29
|style="padding:.4em;"|2016-29
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/24658644 The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells.]
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|style="padding:.4em;" rowspan=1|2016/11/22
|style="padding:.4em;"|2016-28
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26887813 Classification of low quality cells from single-cell RNA-seq data.]
|-
|style="padding:.4em;" rowspan=1|2016/11/15
|style="padding:.4em;"|2016-27
|style="padding:.4em;"|MY Lee
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26000487 Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells.]
|-
|style="padding:.4em;" rowspan=1|2016/11/8
|style="padding:.4em;"|2016-26
|style="padding:.4em;"|JW Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26000488 Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets.]
|-
|style="padding:.4em;" rowspan=1|2016/11/1
|style="padding:.4em;"|2016-25
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27426982 Causal Mechanistic Regulatory Network for Glioblastoma Deciphered Using Systems Genetics Network Analysis.]
|-
|style="padding:.4em;" rowspan=1|2016/10/25
|style="padding:.4em;"|2016-24
|style="padding:.4em;"|MY Lee
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27549193 Comprehensive analyses of tumor immunity: implications for cancer immunotherapy.]
|-
|style="padding:.4em;" rowspan=1|2016/10/11
|style="padding:.4em;"|2016-23
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=sidespread+parainflammation Widespread parainflammation in human cancer]
|-
|style="padding:.4em;" rowspan=1|2016/9/27
|style="padding:.4em;"|2016-22
|style="padding:.4em;"|ER Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27535533 Analysis of protein-coding genetic variation in 60,706 humans]
|-
|style="padding:.4em;" rowspan=1|2016/9/20
|style="padding:.4em;"|2016-21
|style="padding:.4em;"|SM Yang
|style="padding:.4em;text-align:left"|
Functional characterization of somatic mutations in cancer using network-based inference of protein activity
[http://www.ncbi.nlm.nih.gov/pubmed/27322546 pubmed]
[http://www.nature.com/ng/journal/v48/n8/full/ng.3593.html fulltext]
|-
|style="padding:.4em;" rowspan=1|2016/9/13
|style="padding:.4em;"|2016-20
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=exploiting+single-cell+expression+to+characterize Exploiting single-cell expression to characterize co-expression replicability.]
|-
|style="padding:.4em;" rowspan=1|2016/9/6
|style="padding:.4em;"|2016-19
|style="padding:.4em;"|T Lee
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26940869 Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade]
|-
|style="padding:.4em;" rowspan=1|2016/8/31
|style="padding:.4em;"|2016-18
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27264179 A Computational Drug Repositioning Approach for Targeting Oncogenic Transcription Factors]
|-
|style="padding:.4em;" rowspan=1|2016/8/16
|style="padding:.4em;"|2016-17
|style="padding:.4em;"|JW Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27309802 The landscape of accessible chromatin in mammalian preimplantation embryos]
|-
|style="padding:.4em;" rowspan=1|2016/8/8
|style="padding:.4em;"|2016-16
|style="padding:.4em;"|EB Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27064255 Enhancer-promoter interactions are encoded by complex genomic signatures on looping chromatin]
|-
|style="padding:.4em;" rowspan=1|2016/8/1
|style="padding:.4em;"|2016-15
|style="padding:.4em;"|MY Lee
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27040498 Personalized Immunomonitoring Uncovers Molecular Networks that Stratify Lupus Patients]
|-
|style="padding:.4em;" rowspan=1|2016/7/25
|style="padding:.4em;"|2016-14
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=integration+of+summary+data+from+gwas+and+eqtl+studies Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets]
|-
|style="padding:.4em;" rowspan=1|2016/7/18
|style="padding:.4em;"|2016-13
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/23624555 Identification of transcriptional regulators in the mouse immune system]
|-
|style="padding:.4em;" rowspan=2|2016/6/8
|style="padding:.4em;"|2016-12
|style="padding:.4em;"|DS Bae, CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26619012 Mapping the effects of drugs on the immune system]
|-
|style="padding:.4em;"| 2016-11
|style="padding:.4em;"|DS Bae, CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26186195 Elucidating compound mechanism of action by network perturbation analysis]
|-
|style="padding:.4em;" rowspan=2|2016/6/1
|style="padding:.4em;"|2016-10
|style="padding:.4em;"|MY Lee,SM Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26854917 Integrative approaches for large-scale transcriptome-wide association studies]
|-
|style="padding:.4em;"| 2016-9
|style="padding:.4em;"|MY Lee,SM Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26950747 Tissue-specific regulatory circuits reveal variable modular perturbations across complex diseases]
|-
|style="padding:.4em;" rowspan=2|2016/5/18
|style="padding:.4em;"|2016-8
|style="padding:.4em;"|CY Kim,SJ Kwon
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27013732 Survey of variation in human transcription factors reveals prevalent DNA binding changes]
|-
|style="padding:.4em;"| 2016-7
|style="padding:.4em;"| CY Kim,SJ Kwon
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26502339 Large-scale identification of sequence variants influencing human transcription factor occupancy in vivo]
|-
|style="padding:.4em;" rowspan=2|2016/5/11
|style="padding:.4em;"|2016-6
|style="padding:.4em;"|DS Bae, CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/25171417 Predicting Cancer-specific vulnerability via data-driven detection of synthetic lethality]
|-
|style="padding:.4em;"| 2016-5
|style="padding:.4em;"|DS Bae, CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/23467089 Dynamic regulatory network controlling Th17 cell differentiation]
|-
|style="padding:.4em;" rowspan=2|2016/5/4
|style="padding:.4em;"|2016-4
|style="padding:.4em;"|MY Lee,SM Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/25853550 Characterization of the immunophenotypes and antigenomes of colorectal cancers reveals distinct tumor escape mechanisms and novel targets for immunotherapy]
|-
|style="padding:.4em;"| 2016-3
|style="padding:.4em;"|MY Lee,SM Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26618344 Regulators of genetic risk of breast cancer identified by integrative network analysis]
|-
|style="padding:.4em;" rowspan=2|2016/4/27
|style="padding:.4em;"|2016-2
|style="padding:.4em;"|CY Kim,SJ Kwon
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26780608 A predictive computational framework for direct reprogramming between human cell types]
|-
|style="padding:.4em;"| 2016-1
|style="padding:.4em;"| CY Kim,SJ Kwon
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/25126793 CellNet: Network biology applied to stem cell engineering]
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{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2015
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!scope="col" style="padding:.4em" | Date
!scope="col" style="padding:.4em" | Paper index
!scope="col" style="padding:.4em" | Presenter
!scope="col" style="padding:.4em" | Paper title
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|style="padding:.4em;" rowspan=2|2015/06/11
|style="padding:.4em;"|2015-55
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://genome.cshlp.org/content/24/2/340.long Improved exome prioritization of disease genes through cross-species phenotype comparison.]
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|style="padding:.4em;"|2015-54
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0002929714001128 Phevor combines multiple biomedical ontologies for accurate identification of disease-causing alleles in single individuals and small nuclear families.]
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|style="padding:.4em;" rowspan=2|2015/06/04
|style="padding:.4em;"|2015-53
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nmeth/journal/v10/n11/full/nmeth.2656.html eXtasy: variant prioritization by genomic data fusion.]
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|style="padding:.4em;"|2015-52
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://genome.cshlp.org/content/21/9/1529.long A probabilistic disease-gene finder for personal genomes.]
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|style="padding:.4em;" rowspan=2|2015/05/28
|style="padding:.4em;"|2015-51
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/ng/journal/v46/n12/full/ng.3141.html Systematic analysis of noncoding somatic mutations and gene expression alterations across 14 tumor types.]
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|style="padding:.4em;"|2015-50
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nbt/journal/v28/n5/full/nbt.1630.html GREAT improves functional interpretation of cis-regulatory regions.]
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|style="padding:.4em;" rowspan=2|2015/05/21
|style="padding:.4em;"|2015-49
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nmeth/journal/v11/n3/full/nmeth.2832.html Functional annotation of noncoding sequence variants.]
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|style="padding:.4em;"|2015-48
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://genomebiology.com/content/15/10/480 FunSeq2: A framework for prioritizing noncoding regulatory variants in cancer.]
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|style="padding:.4em;" rowspan=2|2015/05/14
|style="padding:.4em;"|2015-47
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nmeth/journal/v12/n2/full/nmeth.3215.html Selecting causal genes from genome-wide association studies via functionally coherent subnetworks.]
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|style="padding:.4em;"|2015-46
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/ncomms/2015/150119/ncomms6890/full/ncomms6890.html Biological interpretation of genome-wide association studies using predicted gene functions.]
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|style="padding:.4em;" rowspan=3|2015/05/07
|style="padding:.4em;"|2015-45
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/ncomms/2014/140626/ncomms5212/full/ncomms5212.html Human symptoms-disease network.]
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|style="padding:.4em;"|2015-44
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867413010246 A nondegenerate code of deleterious variants in Mendelian loci contributes to complex disease risk.]
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|style="padding:.4em;"|2015-43
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.sciencemag.org/content/347/6224/1257601.long Uncovering disease-disease relationships through the incomplete interactome.]

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|style="padding:.4em;" rowspan=2|2015/04/30
|style="padding:.4em;"|2015-42
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://genome.cshlp.org/content/25/1/142.long The discovery of integrated gene networks for autism and related disorders.]
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|style="padding:.4em;"|2015-41
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://msb.embopress.org/content/10/12/774.long Integrated systems analysis reveals a molecular network underlying autism spectrum disorders.]
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|style="padding:.4em;" rowspan=3|2015/04/23
|style="padding:.4em;"|2015-40
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v518/n7539/full/nature13990.html Dissecting neural differentiation regulatory networks through epigenetic footprinting.]
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|style="padding:.4em;"|2015-39
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v518/n7539/full/nature14221.html Cell-of-origin chromatin organization shapes the mutational landscape of cancer.]
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|style="padding:.4em;"|2015-38
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v518/n7539/full/nature14248.html Integrative analysis of 111 reference human epigenomes.]
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|style="padding:.4em;" rowspan=2|2015/04/09
|style="padding:.4em;"|2015-37
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://genome.cshlp.org/content/25/2/246.long Genome-wide analysis of local chromatin packing in Arabidopsis thaliana.]
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|style="padding:.4em;"|2015-36
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414014974 A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping.]
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|style="padding:.4em;" rowspan=2|2015/04/02
|style="padding:.4em;"|2015-35
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nrg/journal/v14/n6/full/nrg3454.html Exploring the three-dimensional organization of genomes: interpreting chromatin interaction data.]
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|style="padding:.4em;"|2015-34
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.sciencemag.org/content/347/6225/1010.long Transcribed enhancers lead waves of coordinated transcription in transitioning mammalian cells.]
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|style="padding:.4em;" rowspan=2|2015/03/26
|style="padding:.4em;"|2015-33
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://genome.cshlp.org/content/25/2/257.long Cupid: simultaneous reconstruction of microRNA-target and ceRNA networks.]
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|style="padding:.4em;"|2015-32
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://genome.cshlp.org/content/25/1/41.long Characterization of the neural stem cell gene regulatory network identifies OLIG2 as a multifunctional regulator of self-renewal.]
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|style="padding:.4em;" rowspan=2|2015/03/19
|style="padding:.4em;"|2015-31
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nbt/journal/vaop/ncurrent/full/nbt.3154.html Decoding the regulatory network of early blood development from single-cell gene expression measurements.]
|-
|style="padding:.4em;"|2015-30
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nrg/journal/v16/n3/full/nrg3833.html Computational and analytical challenges in single-cell transcriptomics.]
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|style="padding:.4em;" rowspan=3|2015/03/12
|style="padding:.4em;"|2015-29
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867415000136 Extensive Strain-Level Copy-Number Variation across Human Gut Microbiome Species.]
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|style="padding:.4em;"|2015-28
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v500/n7464/full/nature12506.html Richness of human gut microbiome correlates with metabolic markers.]
|-
|style="padding:.4em;"|2015-27
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v490/n7418/full/nature11450.html A metagenome-wide association study of gut microbiota in type 2 diabetes.]
|-
|style="padding:.4em;" rowspan=3|2015/03/09
|style="padding:.4em;"|2015-26
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003326 Practical guidelines for the comprehensive analysis of ChIP-seq data.]
|-
|style="padding:.4em;"|2015-25
|style="padding:.4em;"|T Lee
|style="padding:.4em;text-align:left"|
[http://genome.cshlp.org/content/23/5/777.long Patterns of regulatory activity across diverse human cell types predict tissue identity, transcription factor binding, and long-range interactions.]
|-
|style="padding:.4em;"|2015-24
|style="padding:.4em;"|ER Kim
|style="padding:.4em;text-align:left"|
[http://msb.embopress.org/content/10/11/760.long Rapid neurogenesis through transcriptional activation in human stem cells.]
|-
|style="padding:.4em;" rowspan=3|2015/03/02
|style="padding:.4em;"|2015-23
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414011787 Genome-Scale CRISPR-Mediated Control of Gene Repression and Activation.]
|-
|style="padding:.4em;"|2015-22
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1004237 Integrating multiple genomic data to predict disease-causing nonsynonymous single nucleotide variants in exome sequencing studies.]
|-
|style="padding:.4em;"|2015-21
|style="padding:.4em;"|ER Kim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/ng/journal/v45/n6/full/ng.2653.html The Genotype-Tissue Expression (GTEx) project.]
|-
|style="padding:.4em;" rowspan=3|2015/02/24
|style="padding:.4em;"|2015-20
|style="padding:.4em;"|ER Kim
|style="padding:.4em;text-align:left"|
[http://www.sciencemag.org/content/346/6212/1007.long Mouse regulatory DNA landscapes reveal global principles of cis-regulatory evolution.]
|-
|style="padding:.4em;"|2015-19
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v515/n7527/full/nature13985.html Principles of regulatory information conservation between mouse and human.]
|-
|style="padding:.4em;"|2015-18
|style="padding:.4em;"|BH Kang
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v515/n7527/full/nature13972.html Conservation of trans-acting circuitry during mammalian regulatory evolution.]
|-
|style="padding:.4em;" rowspan=3|2015/02/16
|style="padding:.4em;"|2015-17
|style="padding:.4em;"|T Lee
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature13835.html Genetic and epigenetic fine mapping of causal autoimmune disease variants.]
|-
|style="padding:.4em;"|2015-16
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/ng/journal/v46/n3/full/ng.2892.html A general framework for estimating the relative pathogenicity of human genetic variants.]
|-
|style="padding:.4em;"|2015-15
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003825 A probabilistic model to predict clinical phenotypic traits from genome sequencing.]
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|style="padding:.4em;" rowspan=4|2015/02/02
|style="padding:.4em;"|2015-14
|style="padding:.4em;"|BH Kang
|style="padding:.4em;text-align:left"|
[http://www.pnas.org/content/111/22/E2329.long Relating the metatranscriptome and metagenome of the human gut.]
|-
|style="padding:.4em;"|2015-13
|style="padding:.4em;"|BH Kang
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v513/n7516/full/nature13568.html Alterations of the human gut microbiome in liver cirrhosis.]
|-
|style="padding:.4em;"|2015-12
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nbt/journal/v32/n8/full/nbt.2942.html An integrated catalog of reference genes in the human gut microbiome.]
|-
|style="padding:.4em;"|2015-11
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nbt/journal/v32/n8/full/nbt.2939.html Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes.]
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|style="padding:.4em;" rowspan=5|2015/01/26
|style="padding:.4em;"|2015-10
|style="padding:.4em;"|HH Kim
|style="padding:.4em;text-align:left"|
[http://msb.embopress.org/content/9/1/666.long Computational meta'omics for microbial community studies.]
|-
|style="padding:.4em;"|2015-09
|style="padding:.4em;"|HH Kim
|style="padding:.4em;text-align:left"|
[http://genomemedicine.com/content/5/7/65 Functional profiling of the gut microbiome in disease-associated inflammation.]
|-
|style="padding:.4em;"|2015-08
|style="padding:.4em;"|BH Kang
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S016895251200145X Biodiversity and functional genomics in the human microbiome.]
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|style="padding:.4em;"|2015-07
|style="padding:.4em;"|BH Kang
|style="padding:.4em;text-align:left"|
[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002808 Chapter 12: Human Microbiome Analysis.]
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|style="padding:.4em;"|2015-06
|style="padding:.4em;"|BH Kang
|style="padding:.4em;text-align:left"|
[http://www.cell.com/cell/abstract/S0092-8674%2814%2900864-2 Conducting a Microbiome Study.]
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|style="padding:.4em;" rowspan=3|2015/01/12
|style="padding:.4em;"|2015-05
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/ncomms/2014/141210/ncomms6522/full/ncomms6522.html Small RNA changes en route to distinct cellular states of induced pluripotency.]
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|style="padding:.4em;"|2015-04
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v516/n7530/full/nature14046.html Genome-wide characterization of the routes to pluripotency.]
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|style="padding:.4em;"|2015-03
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v516/n7530/full/nature14047.html Divergent reprogramming routes lead to alternative stem-cell states.]
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|style="padding:.4em;" rowspan=2|2015/01/05
|style="padding:.4em;"|2015-02
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.pnas.org/content/111/21/E2191.long Global view of enhancer-promoter interactome in human cells.]
|-
|style="padding:.4em;"|2015-01
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://nar.oxfordjournals.org/content/41/22/10391.long Combining Hi-C data with phylogenetic correlation to predict the target genes of distal regulatory elements in human genome.]
|}
{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2014
|-
!scope="col" style="padding:.4em" | Date
!scope="col" style="padding:.4em" | Paper index
!scope="col" style="padding:.4em" | Presenter
!scope="col" style="padding:.4em" | Paper title
|-
|style="padding:.4em;" rowspan=2|2014/12/23
|style="padding:.4em;"|2014-41
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867413012270 Super-enhancers in the control of cell identity and disease.]
|-
|style="padding:.4em;"|2014-40
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867413003929 Master transcription factors and mediator establish super-enhancers at key cell identity genes.]
|-
|style="padding:.4em;" rowspan=4|2014/12/09
|style="padding:.4em;"|2014-39
|style="padding:.4em;"|HH Kim
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414013713 Unraveling the biology of a fungal meningitis pathogen using chemical genetics.]
|-
|style="padding:.4em;"|2014-38
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414014226 A proteome-scale map of the human interactome network.]
|-
|style="padding:.4em;"|2014-37
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[http://msb.embopress.org/content/10/9/752.long The role of the interactome in the maintenance of deleterious variability in human populations.]
|-
|style="padding:.4em;"|2014-36
|style="padding:.4em;"|HS Shim
|style="padding:.4em;text-align:left"|
[http://www.sciencemag.org/content/342/6154/1235587.long Integrative annotation of variants from 1092 humans: application to cancer genomics.]
|-
|style="padding:.4em;" rowspan=2|2014/12/02
|style="padding:.4em;"|2014-35
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://genome.cshlp.org/content/23/8/1319.long Mapping functional transcription factor networks from gene expression data.]
|-
|style="padding:.4em;"|2014-34
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nrg/journal/v15/n7/full/nrg3684.html In pursuit of design principles of regulatory sequences.]
|-
|style="padding:.4em;" rowspan=2|2014/11/25
|style="padding:.4em;"|2014-33
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[http://genomemedicine.com/content/3/6/36 Epigenomics of human embryonic stem cells and induced pluripotent stem cells: insights into pluripotency and implications for disease.]
|-
|style="padding:.4em;"|2014-32
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S009286741300891X Developmental fate and cellular maturity encoded in human regulatory DNA landscapes.]
|-
|style="padding:.4em;" rowspan=2|2014/11/18
|style="padding:.4em;"|2014-31
|style="padding:.4em;"|SM Yang
|style="padding:.4em;text-align:left"|
[http://genomemedicine.com/content/6/8/64 The 'dnet' approach promotes emerging research on cancer patient survival.]
|-
|style="padding:.4em;"|2014-30
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414010368 Determination and inference of eukaryotic transcription factor sequence specificity.]
|-
|style="padding:.4em;" rowspan=3|2014/11/11
|style="padding:.4em;"|2014-29
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[http://www.pnas.org/content/110/16/6412.long Transcription factors interfering with dedifferentiation induce cell type-specific transcriptional profiles.]
|-
|style="padding:.4em;"|2014-28
|style="padding:.4em;"|HH Kim
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414009350 Dissecting engineered cell types and enhancing cell fate conversion via CellNet.]
|-
|style="padding:.4em;"|2014-27
|style="padding:.4em;"|HH Kim
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414009349 CellNet: network biology applied to stem cell engineering.]
|-
|style="padding:.4em;" rowspan=2|2014/11/04
|style="padding:.4em;"|2014-26
|style="padding:.4em;"|HS Shim
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414009775 Predicting Cancer-Specific Vulnerability via Data-Driven Detection of Synthetic Lethality.]
|-
|style="padding:.4em;"|2014-25
|style="padding:.4em;"|JH Shin
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nmeth/journal/v11/n9/full/nmeth.3046.html Phen-Gen: combining phenotype and genotype to analyze rare disorders.]
|-
|style="padding:.4em;" rowspan=2|2014/10/28
|style="padding:.4em;"|2014-24
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nbt/journal/vaop/ncurrent/full/nbt.2877.html A community effort to assess and improve drug sensitivity prediction algorithms.]
|-
|style="padding:.4em;"|2014-23
|style="padding:.4em;"|HS Shim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/ng/journal/v46/n9/full/ng.3051.html Multi-tiered genomic analysis of head and neck cancer ties TP53 mutation to 3p loss.]
|-
|style="padding:.4em;" rowspan=2|2014/09/30
|style="padding:.4em;"|2014-22
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nmeth/journal/v10/n11/full/nmeth.2651.html Network-based stratification of tumor mutations.]
|-
|style="padding:.4em;"|2014-21
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414001457 Synonymous mutations frequently act as driver mutations in human cancers.]
|-
|style="padding:.4em;" rowspan=2|2014/09/23
|style="padding:.4em;"|2014-20
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003460 VarWalker: Personalized Mutation Network Analysis of Putative Cancer Genes from Next-Generation Sequencing Data.]
|-
|style="padding:.4em;"|2014-19
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://genomebiology.com/content/13/12/R124 DriverNet: uncovering the impact of somatic driver mutations on transcriptional networks in cancer.]
|-
|style="padding:.4em;" rowspan=2|2014/09/16
|style="padding:.4em;"|2014-18
|style="padding:.4em;"|JH Shin
|style="padding:.4em;text-align:left"|
[http://genomebiology.com/content/14/5/R52 Integrated analysis of recurrent properties of cancer genes to identify novel drivers.]
|-
|style="padding:.4em;"|2014-17
|style="padding:.4em;"|AR Cho
|style="padding:.4em;text-align:left"|
[http://genomemedicine.com/content/4/11/89 Improving the prediction of the functional impact of cancer mutations by baseline tolerance transformation.]
|-
|style="padding:.4em;" rowspan=2|2014/09/02
|style="padding:.4em;"|2014-16
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=23228031 A network module-based method for identifying cancer prognostic signatures.]
|-
|style="padding:.4em;"|2014-15
|style="padding:.4em;"|AR Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=24429628 Realizing the promise of cancer predisposition genes.]
|-
|style="padding:.4em;" rowspan=2|2014/08/19
|style="padding:.4em;"|2014-14
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=24952901 Assessing the clinical utility of cancer genomic and proteomic data across tumor types.]
|-
|style="padding:.4em;"|2014-13
|style="padding:.4em;"|HS Shim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=24132290 Mutational landscape and significance across 12 major cancer types.]
|-
|style="padding:.4em;" rowspan=2|2014/08/12
|style="padding:.4em;"|2014-12
|style="padding:.4em;"|HS Shim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/23945592 Signatures of mutational processes in human cancer.]
|-
|style="padding:.4em;"|2014-11
|style="padding:.4em;"|HJ Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/24390350 Discovery and saturation analysis of cancer genes across 21 tumour types.]
|-
|style="padding:.4em;" rowspan=2|2014/08/05
|style="padding:.4em;"|2014-10
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=24084849 Comprehensive identification of mutational cancer driver genes across 12 tumor types.]
|-
|style="padding:.4em;"|2014-9
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=24037244 IntOGen-mutations identifies cancer drivers across tumor types.]
|-
|style="padding:.4em;" rowspan=3|2014/07/29
|style="padding:.4em;"|2014-8
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/23900255 Computational approaches to identify functional genetic variants in cancer genomes.]
|-
|style="padding:.4em;"|2014-7
|style="padding:.4em;"|AR Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=23770567 Mutational heterogeneity in cancer and the search for new cancer-associated genes.]
|-
|style="padding:.4em;"|2014-6
|style="padding:.4em;"|AR Cho
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nmeth/journal/v11/n4/abs/nmeth.2891.html Cancer genomes: discerning drivers from passengers]
|-
|style="padding:.4em;" rowspan=3|2014/07/22
|style="padding:.4em;"|2014-5
|style="padding:.4em;"|AR Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/24071849 The Cancer Genome Atlas Pan-Cancer analysis project.]
|-
|style="padding:.4em;"|2014-4
|style="padding:.4em;"|AR Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/23539594 Cancer genome landscapes.]
|-
|style="padding:.4em;"|2014-3
|style="padding:.4em;"|AR Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=21900272 Distinguishing driver and passenger mutations in an evolutionary history categorized by interference.]
|-
|style="padding:.4em;" rowspan=2|2014/07/15
|style="padding:.4em;"|2014-2
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=24670764 A promoter-level mammalian expression atlas.]
|-
|style="padding:.4em;"|2014-1
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=24670763 An atlas of active enhancers across human cell types and tissues.]
|}
{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2013
|-
!scope="col" style="padding:.4em" | Date
!scope="col" style="padding:.4em" | Paper index
!scope="col" style="padding:.4em" | Presenter
!scope="col" style="padding:.4em" | Paper title
|-
|style="padding:.4em;" rowspan=2|2013/06/26
|style="padding:.4em;"|2013-31
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|[http://nar.oxfordjournals.org/content/40/16/7690.long Integration of Hi-C and ChIP-seq data reveals distinct types of chromatin linkages]
|-
|style="padding:.4em;"|2013-30
|style="padding:.4em;"|YH Ko
|style="padding:.4em;text-align:left"|[http://www.nature.com/nbt/journal/v31/n4/full/nbt.2519.html Deciphering molecular circuits from genetic variation underlying transcriptional responsiveness to stimuli]
|-
|style="padding:.4em;" rowspan=2|2013/06/04
|style="padding:.4em;"|2013-29
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|[https://www.cell.com/abstract/S0092-8674(13)00439-X Mapping the Human miRNA Interactome by CLASH Reveals Frequent Noncanonical Binding]
|-
|style="padding:.4em;"|2013-28
|style="padding:.4em;"|ER Kim
|style="padding:.4em;text-align:left"|[http://www.nature.com/nbt/journal/v31/n3/full/nbt.2514.html Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples]
|-
|style="padding:.4em;" rowspan=2|2013/05/28
|style="padding:.4em;"|2013-27
|style="padding:.4em;"|YH Ko
|style="padding:.4em;text-align:left"|[http://www.pnas.org/content/102/38/13544.long Discovering statistically significant pathways in expression profiling studies]
|-
|style="padding:.4em;"|2013-26
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|[http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0058977 Prediction and Validation of Gene-Disease Associations Using Methods Inspired by Social Network Analyses]
|-
|style="padding:.4em;" rowspan=2|2013/05/21
|style="padding:.4em;"|2013-25
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|[http://www.nature.com/nature/journal/v496/n7446/full/nature11981.html Dynamic regulatory network controlling TH17 cell differentiation]
|-
|style="padding:.4em;"|2013-24
|style="padding:.4em;"|T Lee
|style="padding:.4em;text-align:left"|[http://www.sciencedirect.com/science/article/pii/S0092867412013529 Deciphering and Prediction of Transcriptome Dynamics under Fluctuating Field Conditions]

|-
|style="padding:.4em;" rowspan=2|2013/05/14
|style="padding:.4em;"|2013-23
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|[http://www.sciencedirect.com/science/article/pii/S0092867412015565 Integrative eQTL-Based Analyses Reveal the Biology of Breast Cancer Risk Loci]

|-
|style="padding:.4em;"|2013-22
|style="padding:.4em;"|HJ Kim
|style="padding:.4em;text-align:left"|[http://www.nature.com/ng/journal/v45/n2/full/ng.2504.html Chromatin marks identify critical cell types for fine mapping complex trait variants]
|-
|style="padding:.4em;" rowspan=2|'''2013/05/07'''
|style="padding:.4em;"|2013-21
|style="padding:.4em;"|ER Kim
|style="padding:.4em;text-align:left"|[http://www.cell.com/abstract/S0092-8674(12)01555-3 Genome-wide Chromatin State Transitions Associated with Developmental and Environmental Cues]
|-
|style="padding:.4em;"|2013-20
|style="padding:.4em;"|HS Shim
|style="padding:.4em;text-align:left"|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1003201 Human Disease-Associated Genetic Variation Impacts Large Intergenic Non-Coding RNA Expression]
|-
|style="padding:.4em;"|2013/04/09
|style="padding:.4em;"|2013-19
|style="padding:.4em;"|HJ Kim
|style="padding:.4em;text-align:left"|[http://genome.cshlp.org/content/22/9/1790.long Annotation of functional variation in personal genomes using RegulomeDB]
|-
|style="padding:.4em;"|2013/04/02
|style="padding:.4em;"|2013-18
|style="padding:.4em;"|HJ Kim
|style="padding:.4em;text-align:left"|[http://genome.cshlp.org/content/22/9/1775.long The GENCODE v7 catalog of human long noncoding RNAs: Analysis of their gene structure, evolution, and expression]
|-
|style="padding:.4em;" |2013/03/12
|style="padding:.4em;"|2013-17
|style="padding:.4em;"| ER Kim
|style="padding:.4em;text-align:left"|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002311 Global Mapping of Cell Type–Specific Open Chromatin by FAIRE-seq Reveals the Regulatory Role of the NFI Family in Adipocyte Differentiation]
|-
|style="padding:.4em;" rowspan=1|2013/03/05
|style="padding:.4em;"|2013-16
|style="padding:.4em;"| ER Kim
|style="padding:.4em;text-align:left"|[http://www.nature.com/nrg/journal/v10/n10/full/nrg2641.html ChIP–seq: advantages and challenges of a maturing technology]
|-
|style="padding:.4em;" rowspan=3|2013/02/21
|style="padding:.4em;"|2013-15
|style="padding:.4em;"| KS Kim
|style="padding:.4em;text-align:left"|[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002830 Novel Modeling of Combinatorial miRNA Targeting Identifies SNP with Potential Role in Bone Density]
|-
|style="padding:.4em;"|2013-14
|style="padding:.4em;"| KS Kim
|style="padding:.4em;text-align:left"|[http://www.cell.com/abstract/S0092-8674(12)01424-9 A Molecular Roadmap of Reprogramming Somatic Cells into iPS Cells]
|-
|style="padding:.4em;"|2013-13
|style="padding:.4em;"| HJ Han
|style="padding:.4em;text-align:left"|[http://genome.cshlp.org/content/22/6/1015.long Differential DNase I hypersensitivity reveals factor-dependent chromatin dynamics.]
|-
|style="padding:.4em;" rowspan=3|2013/02/15
|style="padding:.4em;"|2013-12
|style="padding:.4em;"| HJ Han
|style="padding:.4em;text-align:left"|[http://www.nature.com/ng/journal/v43/n3/full/ng.759.html Chromatin accessibility pre-determines glucocorticoid receptor binding patterns]
|-
|style="padding:.4em;"|2013-11
|style="padding:.4em;"| JE Shim, CY Kim
|style="padding:.4em;text-align:left"|[http://www.nature.com/nbt/journal/v30/n11/full/nbt.2422.html Interpreting noncoding genetic variation in complex traits and human disease]
|-
|style="padding:.4em;"|2013-10
|style="padding:.4em;"|HJ Han, JH Kim
|style="padding:.4em;text-align:left"|[http://genome.cshlp.org/content/21/3/447.full.pdf+html Accurate inference of transcription factor binding from DNA sequence and chromatin accessibility data]
|-
|style="padding:.4em;" rowspan=3|2013/02/08
|style="padding:.4em;"|2013-09
|style="padding:.4em;"|HJ Kim
|style="padding:.4em;text-align:left"|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002599 Widespread Site-Dependent Buffering of Human Regulatory Polymorphism]
|-
|style="padding:.4em;"|2013-08
|style="padding:.4em;"|JE Shim, CY Kim
|style="padding:.4em;text-align:left;"|[http://genome.cshlp.org/content/22/9/1748.long Linking disease associations with regulatory information in the human genome]
|-
|style="padding:.4em;"|2013-07
|style="padding:.4em;"|HJ Han, JH Kim
|style="padding:.4em;text-align:left"|[http://genome.cshlp.org/content/22/9/1658.long Understanding transcriptional regulation by integrative analysis of transcription factor binding data]
|-
|style="padding:.4em;" rowspan=3|2013/01/25
|style="padding:.4em;"|2013-06
|style="padding:.4em;"|HJ Han, '''JH Kim'''
|style="padding:.4em;text-align:left;"|[http://www.nature.com/nature/journal/v489/n7414/full/nature11279.html The long-range interaction landscape of gene promoters]
|-
|style="padding:.4em;"|2013-05
|style="padding:.4em;"|'''ER Kim''', HS Shim
|style="padding:.4em;text-align:left;"|[http://www.nature.com/nature/journal/v489/n7414/full/nature11233.html Landscape of transcription in human cells]
|-
|style="padding:.4em;"|2013-04
|style="padding:.4em;"|'''HJ Han''', JH Kim
|style="padding:.4em;text-align:left;"|[http://www.nature.com/nature/journal/v489/n7414/full/nature11245.html Architecture of the human regulatory network derived from ENCODE data]

|-
|style="padding:.4em;" rowspan=2|2013/01/18
|style="padding:.4em;"|2013-03
|style="padding:.4em;"|KS Kim, '''TH Kim'''
|style="padding:.4em;text-align:left;"|[http://www.nature.com/nature/journal/v489/n7414/full/nature11212.html An expansive human regulatory lexicon encoded in transcription factor footprints]
|-
|style="padding:.4em;"|2013-02
|style="padding:.4em;"|'''HJ Han''', JH Kim
|style="padding:.4em;text-align:left;"|[http://www.nature.com/nature/journal/v489/n7414/full/nature11232.html The accessible chromatin landscape of the human genome]
|-
|style="padding:.4em;" rowspan=1|2013/01/11
|style="padding:.4em;"|2013-01
|style="padding:.4em;"| '''JE Shim''', CY Kim
|style="padding:.4em;text-align:left"|[http://www.nature.com/nature/journal/v489/n7414/full/nature11247.html An integrated encyclopedia of DNA elements in the human genome]
|}

2012
{|border ="1"
|style="color:black; background-color:#dcdcdc; padding:5px;" align="center" |Date
|style="color:black; background-color:#dcdcdc;" align="center"|Paper index
|style="color:black; background-color:#dcdcdc;" align="center"|Paper title
|-
|rowspan = "1"|2013/01/11
|align ="center"|2012-81
|[http://genome.cshlp.org/content/22/8/1589.full (TH Kim) MuSiC: identifying mutational significance in cancer genomes.]
|-
|rowspan = "1"|2012/12/04
|align ="center"|2012-80
|[http://genome.cshlp.org/content/22/8/1383 (CY KIM) Human genomic disease variants: A neutral evolutionary explanation]
|-
|rowspan = "2"|2012/11/20
| align="center"|2012-79
|[http://www.cell.com/abstract/S0092-8674(12)00639-3 (HS Shim) Circuitry and Dynamics of Human Transcription Factor Regulatory Networks]
|-
| align="center"|2012-78
|[http://www.nature.com/nature/journal/v487/n7408/full/nature11288.html (HJ Kim) Interpreting cancer genomes using systematic host network perturbations by tumour virus proteins]
|-
|rowspan = "2"|2012/11/06
| align="center"|2012-77
|[http://www.sciencemag.org/content/337/6099/1190.short (HJ Han) Systematic Localization of Common Disease-Associated Variation in Regulatory DNA]
|-
| align="center"|2012-76
|[http://www.pnas.org/cgi/doi/10.1073/pnas.1201904109 (KS Kim) A public resource facilitating clinical use of genomes]
|-
|rowspan = "6" |2012/07/19
| align="center"|2012-75
|[http://genome.cshlp.org/content/22/7/1334 (HJ Han & YH Ko) Predictive regulatory models in Drosophila melanogaster by integrative inference of transcriptional networks]
|-
| align="center"|2012-74
|[http://www.sciencemag.org/content/337/6090/100.full (JE Shim) An Abundance of Rare Functional Variants in 202 Drug Target Genes Sequenced in 14,002 People]
|-
| align="center"|2012-73
|[http://www.sciencemag.org/content/337/6090/64.abstract (JE Shim) Evolution and Functional Impact of Rare Coding Variation from Deep Sequencing of Human Exomes]
|-
| align="center"|2012-72
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2063581/ (SH Hwang) Network-based classification of breast cancer metastasis]
|-
| align="center"|2012-71
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002707 (T Lee&CY Kim)Brain Expression Genome-Wide Association Study (eGWAS) Identifies Human Disease-Associated Variants]
|-
| align="center"|2012-70
|[http://genome.cshlp.org/content/20/9/1297 (ER Kim&TH Kim)The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data]
|-
|rowspan = "8" |2012/07/16
| align="center"|2012-69
|[http://www.nature.com/ng/journal/v43/n5/full/ng.806.html (ER Kim&TH Kim)A framework for variation discovery and genotyping using next-generation DNA sequencing data]
|-
| align="center"|2012-66
|[http://bioinformatics.oxfordjournals.org/content/25/16/2078.long (ER Kim&TH Kim)The Sequence Alignment/Map format and SAMtools]
|-
| align="center"|2012-65
|[http://bioinformatics.oxfordjournals.org/content/early/2011/06/07/bioinformatics.btr330 (ER Kim&TH Kim)The Variant Call Format and VCFtools]
|-
| align="center"|2012-64
|[http://www.cell.com/abstract/S0092-8674(12)00104-3 (YH Go&HJ Han)The Impact of the Gut Microbiota on Human Health: An Integrative View]
|-
|align="center"|2012-63
|[http://www.sciencemag.org/content/336/6086/1262.abstract (T Lee&CY Kim)Host-Gut Microbiota Metabolic Interactions]
|-
|align="center"|2012-62
|[http://www.sciencemag.org/content/336/6086/1268.full (AR Cho,JH Ju)Interactions Between the Microbiota and the Immune System]
|-
|align="center"|2012-61
|[http://www.sciencemag.org/content/336/6086/1255.abstract (SH Hwang&HJ Cho)The Application of Ecological Theory Toward an Understanding of the Human Microbiome]
|-
|align="center"|2012-60
|[http://stm.sciencemag.org/content/4/137/137rv5 (SH Hwang&HJ Cho)Microbiota-Targeted Therapies: An Ecological Perspective]
|-
|rowspan = "3" |2012/07/13
|align="center"|2012-59
|[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002358 (JH Shin&HJ Kim)Metabolic Reconstruction for Metagenomic Data and Its Application to the Human Microbiome]
|-
| align="center"|2012-58
|[http://www.nature.com/nature/journal/v486/n7402/full/nature11209.html (JH Shin&HJ Kim)A framework for human microbiome research]
|-
|align="center"|2012-57
|[http://www.nature.com/nature/journal/v486/n7402/full/nature11234.html (JH Shin&HJ Kim)Structure, function and diversity of the healthy human microbiome]
|-
|rowspan = "4" |2012/07/12
|align="center"|2012-56
|[http://nar.oxfordjournals.org/content/40/D1/D957.long (AR Cho&JH Ju)COLT-Cancer: functional genetic screening resource for essential genes in human cancer cell lines]
|-
|align="center"|2012-55
|[http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002511 (YH Go&HJ Han)Google Goes Cancer: Improving Outcome Prediction for Cancer Patients by Network-Based Ranking of Marker Genes]
|-
| align="center"|2012-54
|[http://www.nature.com/msb/journal/v7/n1/full/msb201147.html (YH Go&HJ Han)A pharmacogenomic method for individualized prediction of drug sensitivity]
|-
|align="center"|2012-53
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature10983.html (JH Soh)The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups]
|-
|rowspan = "6" |2012/07/09
|align="center"|2012-52
|[http://www.nature.com/nature/journal/v483/n7391/full/nature11003.html (ER Kim&TH Kim)The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity]
|-
|align="center"|2012-51
|[http://www.nature.com/nature/journal/v483/n7391/full/nature11005.html (ER Kim&TH Kim)Systematic identification of genomic markers of drug sensitivity in cancer cells]
|-
| align="center"|2012-50
|[http://www.pnas.org/content/early/2011/10/13/1018854108.abstract (ER Kim&TH Kim)Subtype and pathway specific responses to anticancer compounds in breast cancer]
|-
|align="center"|2012-49
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature10945.html (JE Shim&KS Kim)De novo mutations revealed by whole-exome sequencing are strongly associated with autism]
|-
|align="center"|2012-48
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature11011.html (JE Shim&KS Kim)Patterns and rates of exonic de novo mutations in autism spectrum disorders]
|-
|align="center"|2012-47
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature10989.html (JE Shim&KS Kim)Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations]
|-
|rowspan = "6" |2012/07/06
| align="center"|2012-46
|[http://www.g3journal.org/content/1/3/233.full (T Lee&CY Kim)Integrating Rare-Variant Testing, Function Prediction, and Gene Network in Composite Resequencing-Based Genome-Wide Association Studies (CR-GWAS)]
|-
|align="center"|2012-45
|[http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002621 (T Lee&CY Kim)Type 2 Diabetes Risk Alleles Demonstrate Extreme Directional Differentiation among Human Populations, Compared to Other Diseases]
|-
|align="center"|2012-44
|[http://www.nature.com/nature/journal/v480/n7376/full/nature10665.html (AR Cho&JH Ju)Predicting mutation outcome from early stochastic variation in genetic interaction partners]
|-
|align="center"|2012-43
|[http://www.sciencemag.org/content/335/6064/82 (AR Cho&JH Ju)Fitness Trade-Offs and Environmentally Induced Mutation Buffering in Isogenic C. elegans]
|-
| align="center"|2012-42
|[http://genome.cshlp.org/content/22/6/1163 (JE Shim&KS Kim)Identification of microRNA-regulated gene networks by expression analysis of target genes]
|-
|align="center"|2012-41
|[http://www.nature.com/ng/journal/v44/n6/full/ng.2303.html (JE Shim&KS Kim)Exome sequencing and the genetic basis of complex traits]
|-
|rowspan = "6" |2012/07/02
|align="center"|2012-40
|[http://www.cell.com/abstract/S0092-8674(12)00573-9 (JH Soh)Functional Repurposing Revealed by Comparing S. pombe and S. cerevisiae Genetic Interactions]
|-
|align="center"|2012-39
|[http://genome.cshlp.org/content/22/2/375.long (ER Kim&TH Kim)De novo discovery of mutated driver pathways in cancer]
|-
| align="center"|2012-38
|[http://www.nature.com/msb/journal/v4/n1/full/msb20082.html (YH Go&HJ Han)A systems biology approach to prediction of oncogenes and molecular perturbation targets in B-cell lymphomas]
|-
|align="center"|2012-37
|[http://www.nature.com/msb/journal/v7/n1/full/msb201126.html (SH Hwang&HJ Cho)PREDICT: a method for inferring novel drug indications with application to personalized medicine]
|-
|align="center"|2012-36
|[http://www.nature.com/nature/journal/v453/n7198/full/nature06973.html (SH Hwang&HJ Cho)Synergistic response to oncogenic mutations defines gene class critical to cancer phenotype]
|-
|align="center"|2012-35
|[http://www.sciencemag.org/content/334/6062/1518.full (JH Shin&HJ Kim)Detecting Novel Associations in Large Data Sets]
|-
|rowspan = "3" |2012/03/05
| align="center"|2012-34
|[http://www.ncbi.nlm.nih.gov/pubmed/18516045 (8,HH Kim)Mapping and quantifying mammalian transcriptomes by RNA-Seq.]
|-
|align="center"|2012-33
|[http://genomebiology.com/2010/11/10/R106 (11,Go&Ju)Differential expression analysis for sequence count data]
|-
|align="center"|2012-32
|[http://bioinformatics.oxfordjournals.org/content/26/1/139.short (12,Go&Ju)edgeR: a Bioconductor package for differential expression analysis of digital gene expression data ]
|-
|rowspan = "7" |2012/02/27 2012/02/28
| align="center"|2012-31
|[http://www.nature.com/nrg/journal/v10/n1/full/nrg2484.html (1,JW Song)RNA-Seq: a revolutionary tool for transcriptomics]
|-
|align="center"|2012-30
|[http://www.nature.com/nmeth/journal/v8/n6/full/nmeth.1613.html (2,JW Song)Computational methods for transcriptome annotation and quantification using RNA-seq]
|-
|align="center"|2012-29
|[http://genomebiology.com/2010/11/12/220 (3,HJ Han)From RNA-seq reads to differential expression results]
|-
|align="center"|2012-28
|[http://www.nature.com/nmeth/journal/v7/n9/full/nmeth.1491.html (4,AR Cho)Comprehensive comparative analysis of strand-specific RNA sequencing methods]
|-
|align="center"|2012-27
|[http://www.plosone.org/article/info:doi%2F10.1371%2Fjournal.pone.0026426 (5,T Lee)A Low-Cost Library Construction Protocol and Data Analysis Pipeline for Illumina-Based Strand-Specific Multiplex RNA-Seq]
|-
|align="center"|2012-26
|[http://www.nature.com/nbt/journal/v28/n5/abs/nbt.1621.html (6,So&Shin)Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation]
|-
|align="center"|2012-25
|[http://bioinformatics.oxfordjournals.org/content/25/9/1105.abstract (7,So&Shin)TopHat: discovering splice junctions with RNA-Seq]
|-
|rowspan = "5" |2012/02/06
| align="center"|2012-24
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3125733/ mirConnX: condition-specific mRNA-microRNA network integrator]
|-
|align="center"|2012-23
|[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002190 Construction and Analysis of an Integrated Regulatory Network Derived from High-Throughput Sequencing Data]
|-
|align="center"|2012-22
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002415 A Densely Interconnected Genome-Wide Network of MicroRNAs and Oncogenic Pathways Revealed Using Gene Expression Signatures]
|-
|align="center"|2012-21
|[http://genome.cshlp.org/content/20/5/589.short Reprogramming of miRNA networks in cancer and leukemia]
|-
|align="center"|2012-20
|[http://www.cell.com/abstract/S0092-8674(11)01152-4 An Extensive MicroRNA-Mediated Network of RNA-RNA Interactions Regulates Established Oncogenic Pathways in Glioblastoma]
|-
|rowspan = "5" |2012/01/30
|align="center"|2012-19
|[http://www.cell.com/abstract/S0092-8674(11)00237-6 Principles and Strategies for Developing Network Models in Cancer]
|-
|align="center"|2012-18
|[http://www.nature.com/ng/journal/v37/n4/full/ng1532.html Reverse engineering of regulatory networks in human B cells]
|-
|align="center"|2012-17
|[http://www.nature.com/nature/journal/v452/n7186/abs/nature06757.html Variations in DNA elucidate molecular networks that cause disease]
|-
|align="center"|2012-16
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2779083/ Harnessing gene expression to identify the genetic basis of drug resistance]
|-
|align="center"|2012-15
|[http://www.sciencedirect.com/science/article/pii/S0092867410012936 An Integrated Approach to Uncover Drivers of Cancer]
|-
|rowspan = "3" |2012/01/09
|align="center"|2012-14
|[http://www.ncbi.nlm.nih.gov/pubmed/18704161 Genetic variation in an individual human exome.]
|-
|align="center"|2012-13
|[http://www.ncbi.nlm.nih.gov/pubmed/22081227 Predicting phenotypic variation in yeast from individual genome sequences.]
|-
|align="center"|2012-12
|[http://www.ncbi.nlm.nih.gov/pubmed/20435227 Clinical assessment incorporating a personal genome.]
|-
|rowspan = "6" |2012/01/09 2012/01/16
| align="center"|2012-11
|[http://www.ncbi.nlm.nih.gov/pubmed/20399638 Human allelic variation: perspective from protein function, structure, and evolution.]
|-
|align="center"|2012-10
|[http://www.ncbi.nlm.nih.gov/pubmed/19561590 Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm.]
|-
|align="center"|2012-09
|[http://www.ncbi.nlm.nih.gov/pubmed/11230178 Prediction of deleterious human alleles.]
|-
|align="center"|2012-08
|[http://www.ncbi.nlm.nih.gov/pubmed/12202775 Human non-synonymous SNPs: server and survey.]
|-
|align="center"|2012-07
|[http://www.ncbi.nlm.nih.gov/pubmed/20354512 A method and server for predicting damaging missense mutations.]
|-
|align="center"|2012-06
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1920242/ SNAP: predict effect of non-synonymous polymorphisms on function]
|-
|rowspan = "3" |2012/01/09 2012/01/16
|align="center"|2012-05
|[http://www.ncbi.nlm.nih.gov/pubmed/21920052 Computational and statistical approaches to analyzing variants identified by exome sequencing.]
|-
|align="center"|2012-04
|[http://www.ncbi.nlm.nih.gov/pubmed/18179889 Shifting paradigm of association studies: value of rare single-nucleotide polymorphisms.]
|-
|align="center"|2012-03
|[http://www.ncbi.nlm.nih.gov/pubmed/19684571 Targeted capture and massively parallel sequencing of 12 human exomes.]
|-
|rowspan = "2" |2012/01/09 2012/01/16
|align="center"|2012-02
|[http://www.ncbi.nlm.nih.gov/pubmed/17637733 The distribution of fitness effects of new mutations.]
|-
|align="center"|2012-01
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1852724/ Most Rare Missense Alleles Are Deleterious in Humans: Implications for Complex Disease and Association Studies]
|}
2011
{|border ="1"
|style="color:black; background-color:#dcdcdc;" align="center" |Date
|style="color:black; background-color:#dcdcdc;" align="center"|Paper_index
|style="color:black; background-color:#dcdcdc;" align="center"|Paper_title
|-
|rowspan = "6" |2011/11/28
|align="center"|2011-49
|[http://bmir.stanford.edu/file_asset/index.php/1407/BMIR-2009-1355.pdf (Shin)Data-Driven Methods to Discover Molecular Determinants of Serious Adverse Drug Events]
|-
|align="center"|2011-48
|[http://www.nature.com/nchembio/journal/v4/n11/abs/nchembio.118.html (Shin)Network pharmacology: the next paradigm in drug discovery]
|-
|align="center"|2011-47
|[http://www.nature.com/msb/journal/v7/n1/full/msb201171.html (Oh)Systematic exploration of synergistic drug pairs]
|-
|align="center"|2011-46
|[http://www.nature.com/msb/journal/v5/n1/full/msb200995.html (Shim)Chemogenomic profiling predicts antifungal synergies]
|-
|align="center"|2011-45
|[http://www.pnas.org/content/104/32/13086 (Beck)A strategy for predicting the chemosensitivity of human cancers and its application to drug discovery]
|-
|align="center"|2011-44
|[http://www.nature.com/nchembio/journal/v1/n7/full/nchembio747.html (Hwang)Analysis of drug-induced effect patterns to link structure and side effects of medicines]
|-
|rowspan = "3" |2011/11/14
|align="center"|2011-43
|[http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000662 Network-Based Elucidation of Human Disease Similarities Reveals Common Functional Modules Enriched for Pluripotent Drug Targets]
|-
|align="center"|2011-42
|[http://www.nature.com/msb/journal/v7/n1/full/msb201131.html Cross-species discovery of syncretic drug combinations that potentiate the antifungal fluconazole]
|-
|align="center"|2011-41
|[http://www.nature.com/msb/journal/v7/n1/full/msb20115.html Analysis of multiple compound–protein interactions reveals novel bioactive molecules]
|-
|rowspan = "4" |2011/11/07
|align="center"|2011-40
|[http://www.nature.com/nbt/journal/v25/n10/abs/nbt1338.html Drug—target network]
|-
|align="center"|2011-39
|[http://www.nature.com/msb/journal/v6/n1/full/msb200998.html A side effect resource to capture phenotypic effects of drugs]
|-
|align="center"|2011-38
|[http://genome.cshlp.org/content/18/2/206.long Quantitative systems-level determinants of human genes targeted by successful drugs]
|-
|align="center"|2011-37
|[http://www.sciencemag.org/content/321/5886/263.short Drug Target Identification Using Side-Effect Similarity]
|-
|rowspan = "3" |2011/11/07
|align="center"|2011-36
|[http://stm.sciencemag.org/content/3/96/96ra77.short Discovery and Preclinical Validation of Drug Indications Using Compendia of Public Gene Expression Data]
|-
|align="center"|2011-35
|[http://bib.oxfordjournals.org/content/12/4/303.short Exploiting drug–disease relationships for computational drug repositioning]
|-
|align="center"|2011-34
|[http://www.springerlink.com/content/4489r051nu2t0ul1/ Drug Discovery in a Multidimensional World: Systems, Patterns, and Networks]
|-
|rowspan = "3" |2011/10/05
|align="center"|2011-33
|[http://genome.cshlp.org/content/20/7/960.full Analysis of membrane proteins in metagenomics: Networks of correlated environmental features and protein families]
|-
|align="center"|2011-32
|[http://www.pnas.org/content/106/5/1374.full Quantifying environmental adaptation of metabolic pathways in metagenomics]
|-
|align="center"|2011-31
|[http://www.pnas.org/content/104/35/13913.abstract Quantitative assessment of protein function prediction from metagenomics shotgun sequences]
|-
|rowspan = "4" |2011/10/04
|align="center"|2011-30
|[http://www.nature.com/nature/journal/v464/n7285/full/nature08821.html A human gut microbial gene catalogue established by metagenomic sequencing]
|-
|align="center"|2011-29
|[http://www.nature.com/nrmicro/journal/v6/n9/abs/nrmicro1935.html Molecular eco-systems biology: towards an understanding of community function]
|-
|align="center"|2011-28
|[http://genome.cshlp.org/content/early/2009/04/20/gr.085464.108 Microbial community profiling for human microbiome projects: Tools, techniques, and challenges]
|-
|align="center"|2011-27
|[http://www.nature.com/nrmicro/journal/v9/n4/full/nrmicro2540.html Unravelling the effects of the environment and host genotype on the gut microbiome]
|-
|rowspan = "2" |2011/09/19
|align="center"|2011-26
|[http://www.sciencemag.org/content/333/6042/596.full independently evolved virulence effectors converge onto hubs in a plant immune system Network]
|-
|align="center"|2011-25
|[http://www.sciencemag.org/content/333/6042/601.full Evidence for network evolution in an arabidopsis interactome Map]
|-
|rowspan = "2" |2011/09/05
|align="center"|2011-24
|[http://www.sciencedirect.com/science/article/pii/S0092867411005861 Exome Sequencing of Ion Channel Genes Reveals Complex Profiles Confounding Personal Risk Assessment in Epilepsy]
|-
|align="center"|2011-23
|[http://www.cell.com/abstract/S0092-8674(11)00543-5 Pluripotency factors in Embryonic stem cells Regulate Differentiation into Germ Layers]
|-
|rowspan = "2" |2011/09/22
|align="center"|2011-22
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002077 Integrated Genome-scale predition of Detrimental Mutations in Transcription Networks]
|-
|align="center"|2011-21
|[http://www.nature.com/nrg/journal/v12/n4/abs/nrg2969.html From expression QTLs to personalized transcriptomics]
|-
|2011/06/20
|align="center"|2011-20
|[http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001046 a user's guide to the encyclopedia of DNA elements(ENCODE)]
|-
|rowspan = "2" |2011/03/30
|align="center"|2011-19
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature09944.html enterotypes of the human gut microbiome]
|-
|align="center"|2011-18
|[http://www.nature.com/msb/journal/v7/n1/full/msb20116.html toward molecular trait-based ecology, through intergration of biogeochemical, geographical and metagenomic data]
|-
|rowspan = "2" |2011/03/16
|align="center"|2011-17
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002047 variable pathogenicity determines individual lifespan in ''caenorhabditis elegans'']
|-
|align="center"|2011-16
|[http://www.cell.com/abstract/S0092-8674(11)00371-0 a high-resolution ''c.elegans'' essential gene network based on phenotypic profiling of a complex tissue]
|-
|rowspan = "3" |2011/04/25
|align="center"|2011-15
|[http://www.ncbi.nlm.nih.gov/pubmed/21376230 Hallmarks of Cancer : The next generation]
|-
|align="center"|2011-14
|[http://www.cell.com/abstract/S0092-8674(11)00296-0 Mapping Cancer Origins]
|-
|align="center"|2011-13
|[http://www.cell.com/abstract/S0092-8674(11)00297-2 Genetic Interactions in Cancer Progression and Treatment]
|-
|rowspan = "2" |2011/04/11
|align="center"|2011-12
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1001273 Proteins encoded in genomic regions associated with immune-mediated disease physically interact and suggest underlying biology]
|-
|align="center"|2011-11
|***Changed!***
[http://www.ncbi.nlm.nih.gov/pubmed/19557189 Identifying relationships among genomic disease regions: predicting genes at pathogenic SNP associations and rare deletions]
|-
|rowspan = "2"|2011/03/28
|align="center"|2011-10
|[http://www.pnas.org/content/106/44/18843.long profiling translatomes of discrete cell populations resolves altered cellular priorities during hypoxia in Arabidopsis]
|-
|align="center"|2011-09
|[http://www.nature.com/msb/journal/v6/n1/full/msb201076.html cell-type specific analysis of translating RNAs in developing flowers reveals new levels of control]
|-
|rowspan = "2"|2011/03/14
|align="center"|2011-08
|[http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6WSN-51SFHJD-1&_user=44062&_coverDate=01%2F07%2F2011&_rdoc=1&_fmt=high&_orig=search&_origin=search&_sort=d&_docanchor=&view=c&_acct=C000004738&_version=1&_urlVersion=0&_userid=44062&md5=2f6017aab4c794ed7e78fbbd8447077a&searchtype=a phenotypic landscape of a bacterial cell]
|-
|align="center"|2011-07
|[http://www.nature.com/msb/journal/v6/n1/full/msb2010107.html cross-species chemogenomic profiling reveals evolutionarily conserved drug mode of action]
|-
|rowspan = "2"|2011/02/28
|align="center"|2011-06
|[http://www.pnas.org/content/early/2010/09/23/1004666107.abstract genomic patterns of pleiotropy and the evolution of complexity]
|-
|align="center"|2011-05
|[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1001009 simultaneous genome-wide inference of physical, genetic, regulatory, and functional pathway]
|-
|2011/02/21
|align="center"|2011-04
|[http://www.nature.com/msb/journal/v6/n1/full/msb201071.html dynamic interaction networks in a hierarchically organized tissue]
|-
|2011/02/14
|align="center"|2011-03
|[http://www.sciencemag.org/content/330/6009/1385.abstract rewiring of genetic networks in response to DNA damage]
|-
|rowspan = "2"|2011/01/31
|align="center"|2011-02
|[http://www.nature.com/nrg/journal/v10/n9/abs/nrg2633.html Applying mass spectrometry-based proteomics to genetics, genomics and network biology]
|-
|align="center"|2011-01
|[http://physiolgenomics.physiology.org/content/33/1/18.long Data analysis and bioinformatics tools for tandem mass spectrometry in proteomics]
|}
2010
{|border ="1"
|style="color:black; background-color:#dcdcdc;" align="center" |Date
|style="color:black; background-color:#dcdcdc;" align="center"|Paper_index
|style="color:black; background-color:#dcdcdc;" align="center"|Paper_title
|-
|rowspan = "2"|2010/12/27
|align="center"|2010-29
|[http://www.nature.com/nature/journal/v467/n7312/full/nature09326.html Functional_roles_fornoise_in_genetic_circuits]
|-
|align="center"|2010-28
|[http://www.nature.com/ng/journal/v42/n7/full/ng.610.html Estimation_of_effect_size_distribution_from_genome-wide_association_studies_and_implications_for_future_discoveries]
|-
|rowspan = "2"|2010/11/15
|align="center"|2010-27
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1000932 Liver_and_Adipose_Expression_associated_SNPs_are_enriched_for_association_to_type_2_diabetes]
|-
|align="center"|2010-26
|[http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B8JDC-50J4MRJ-2&_user=44062&_coverDate=10%2F10%2F2010&_rdoc=1&_fmt=high&_orig=search&_origin=search&_sort=d&_docanchor=&view=c&_acct=C000004738&_version=1&_urlVersion=0&_userid=44062&md5=7667a67cf8ba3f68117b8d0ff85a8887&searchtype=a It's_the_machine_that_matters:_predicting_gene_function_and_phenotype_from_protein_networks]
|-
|rowspan = "2"|2010/11/01
|align="center"|2010-25
|[http://genome.cshlp.org/content/early/2010/06/15/gr.104216.109 A_genome-wide_map_of_human_genetic_interactions_inferred_from_radiation_hybrid_genotypes]
|-
|align="center"|2010-24
|[http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0012139 A_Genome-Wide_Gene_Function_Prediction_Resource_for_Drosophila_melanogaster]
|-
|2010/10/11
|align="center"|2010-23
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2913399/?tool=pubmed Dissecting_spatio-temporal_protein_networks_driving_human_heart_development_and_related_disorders]
|-
|rowspan = "2"|2010/09/13
|align="center"|2010-22
|[http://www.nature.com/ng/journal/v42/n7/full/ng.600.html Transposable_elements_have_rewired_the_core_regulatory_network_of_human_embryonic_stem_cells]
|-
|align="center"|2010-21
|[http://www.nature.com/ng/journal/v42/n7/full/ng0710-557.html Limits_of_sequence_and_functional_conservation]
|-
|2010/05/26
|align="center"|2010-20
|[[media:100428_network_based_elucidation_of_human_disease_similarities_reveals_common_functional_modules_enriched_for_pluripotent_drug_targets.pdf|network_based_elucidation_of_human_disease_similarities_reveals_common_functional_modules_enriched_for_pluripotent_drug_targets.pdf]]
|-
|2010/05/19
|align="center"|2010-19
|[[media:100421_interpreting_metabolomic_profiles_using_unbiased_pathway_models.pdf|interpreting_metabolomic_profiles_using_unbiased_pathway_models.pdf]]
|-
|2010/04/21
|align="center"|2010-18
|[[media:100414_identification_of_networks_of_co_occurring_tumor_related_dna_copy_number_changes_using_a_genome_wide_scoringapproach.pdf|identification_of_networks_of_co_occurring_tumor_related_dna_copy_number_changes_using_a_genome_wide_scoringapproach.pdf]]
|-
|2010/04/14
|align="center"|2010-17
|[http://www.cell.com/retrieve/pii/S0092867410000796 an_atlas_of_combinatorial_transcriptional_regulation_in_mouse_and_man]
|-
|rowspan = "2" |2010/04/07
|align="center"|2010-16
|[http://www.pnas.org/content/early/2010/03/11/0910200107.long systematic_discovery_of_nonobvious_human_disease_models_through_orthologous_phenotypes]
|-
|align="center"|2010-15
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature08800.html genome_side_association_study_of_107_phenotypes_in_Arabidopsis_thaliana_inbred_lines]
|-
|rowspan="2"|2010/03/31
|align="center"|2010-14
|[[media:100331_toward_stem_cell_systems_biology_from_molecules_to_networks_and_landscapes.pdf|toward_stem_cell_systems_biology_from_molecules_to_networks_and_landscapes.pdf]]
|-
|align="center"|2010-13
|[http://www.nature.com/nrm/journal/v10/n10/abs/nrm2766.html systems_biology_of_stem_cell_fate_and_cellular_reprogramming]
|-
|2010/03/24
|align="center"|2010-12
|[http://www.nature.com/nbt/journal/v27/n2/abs/nbt.1522.html dynamic_modularity_in_protein_interaction_networks_predicts_breast_cancer_outcome]
|-
|2010/01/18
|align="center"|2010-11
|JournalClub_100118_a_tutorial_on_statistical_methods_for_population_association_studies
|-
|2010/01/16
|align="center"|2010-10
|systems-level_dinamic_analyses_of_fate_change_in_murine_embryonic_stem_cells
|-
|2010/01/15
|align="center"|2010-09
|distinguishing_direct_versus_indirect_transcription_factor-DNA-interactions
|-
|2010/01/14
|align="center"|2010-08
|chemogenomic_profiling_predicts_antifungal_synergies
|-
|2010/01/13
|align="center"|2010-07
|edgetic_perturbation_models_of_human_inherited_disorders
|-
|2010/01/12
|align="center"|2010-06
|analysis_of_cell_fate_from_single-cell_gene_expression_profiles_in_C.elegans
|-
|2010/01/11
|align="center"|2010-05
|predicting_new_molecular_targets_for_known_drugs.pdf
Reference:SEA(Similarity Ensemble Approach)
|-
|2010/01/09
|align="center"|2010-04
|harnessing_gene_expression_to_identify_the_genetic_basis_of_drug_resistance
|-
|2010/01/08
|align="center"|2010-03
|an_integrative_approach_to_reveal_driver_gene_fusions_from_paired_end_sequencing_data_in_cancer
|-
|2010/01/07
|align="center"|2010-02
|a_phenotypic_profile_of_the_candida_albicans_regulatory_network
|-
|2010/01/06
|align="center"|2010-01
|a_global_view_of_protein_expression_in_human_cells_tissues_and_organs
|}