Difference between revisions of "Journal Club"
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[https://www.cell.com/cell/fulltext/S0092-8674(24)01479-X Microbial ecosystems and ecological driving forces in the deepest ocean sediments] | [https://www.cell.com/cell/fulltext/S0092-8674(24)01479-X Microbial ecosystems and ecological driving forces in the deepest ocean sediments] | ||
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[https://www.nature.com/articles/s42256-024-00974-9 A machine learning approach to leveraging electronic health records for enhanced omics analysis] | [https://www.nature.com/articles/s42256-024-00974-9 A machine learning approach to leveraging electronic health records for enhanced omics analysis] | ||
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|style="padding:.4em;"|YR Jung | |style="padding:.4em;"|YR Jung | ||
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[https://www.nature.com/articles/s42256-024-00942-3 Moving towards genome-wide data integration for patient stratification with Integrate Any Omics] | [https://www.nature.com/articles/s42256-024-00942-3 Moving towards genome-wide data integration for patient stratification with Integrate Any Omics] | ||
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|style="padding:.4em;"|YR Kim | |style="padding:.4em;"|YR Kim | ||
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[https://www.nature.com/articles/s41592-024-02523-z Nucleotide Transformer: building and evaluating robust foundation models for human genomics] | [https://www.nature.com/articles/s41592-024-02523-z Nucleotide Transformer: building and evaluating robust foundation models for human genomics] | ||
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|style="padding:.4em;"|JY Kim | |style="padding:.4em;"|JY Kim | ||
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[https://www.biorxiv.org/content/10.1101/2024.01.10.575018v2 Previously hidden intraspecies dynamics underlie the apparent stability of two important skin microbiome species] | [https://www.biorxiv.org/content/10.1101/2024.01.10.575018v2 Previously hidden intraspecies dynamics underlie the apparent stability of two important skin microbiome species] | ||
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|style="padding:.4em;"|WJ Kim | |style="padding:.4em;"|WJ Kim | ||
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[https://www.nature.com/articles/s41564-024-01906-4 Longitudinal phage–bacteria dynamics in the early life gut microbiome] | [https://www.nature.com/articles/s41564-024-01906-4 Longitudinal phage–bacteria dynamics in the early life gut microbiome] | ||
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|style="padding:.4em;"|SH Ahn | |style="padding:.4em;"|SH Ahn | ||
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[https://www.biorxiv.org/content/10.1101/2025.03.04.641479v1.full VIRGO2: Unveiling the Functional and Ecological Complexity of the Vaginal Microbiome with an Enhanced Non-Redundant Gene Catalog] | [https://www.biorxiv.org/content/10.1101/2025.03.04.641479v1.full VIRGO2: Unveiling the Functional and Ecological Complexity of the Vaginal Microbiome with an Enhanced Non-Redundant Gene Catalog] | ||
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|style="padding:.4em;"|EJ Sung | |style="padding:.4em;"|EJ Sung | ||
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[https://www.biorxiv.org/content/10.1101/2025.02.25.640181v1 geneRNIB: a living benchmark for gene regulatory network inference] | [https://www.biorxiv.org/content/10.1101/2025.02.25.640181v1 geneRNIB: a living benchmark for gene regulatory network inference] | ||
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|style="padding:.4em;"|HJ Kim | |style="padding:.4em;"|HJ Kim | ||
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[https://www.biorxiv.org/content/10.1101/2025.02.26.640259v1 Highly accurate prophage island detection with PIDE] | [https://www.biorxiv.org/content/10.1101/2025.02.26.640259v1 Highly accurate prophage island detection with PIDE] | ||
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|style="padding:.4em;"|JY Ma | |style="padding:.4em;"|JY Ma | ||
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[https://www.biorxiv.org/content/10.1101/2025.02.18.638918v1 Genome modeling and design across all domains of life with Evo 2] | [https://www.biorxiv.org/content/10.1101/2025.02.18.638918v1 Genome modeling and design across all domains of life with Evo 2] | ||
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|style="padding:.4em;"|JH Cha | |style="padding:.4em;"|JH Cha | ||
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[https://www.cell.com/cell/fulltext/S0092-8674(24)01429-6 Metagenome-informed metaproteomics of the human gut microbiome, host, and dietary exposome uncovers signatures of health and inflammatory bowel disease] | [https://www.cell.com/cell/fulltext/S0092-8674(24)01429-6 Metagenome-informed metaproteomics of the human gut microbiome, host, and dietary exposome uncovers signatures of health and inflammatory bowel disease] | ||
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|style="padding:.4em;"|IS Choi | |style="padding:.4em;"|IS Choi | ||
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[https://www.nature.com/articles/s41586-024-08411-y A cell atlas foundation model for scalable search of similar human cells] | [https://www.nature.com/articles/s41586-024-08411-y A cell atlas foundation model for scalable search of similar human cells] | ||
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|style="padding:.4em;"|NY Kim | |style="padding:.4em;"|NY Kim | ||
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[https://www.cell.com/med/fulltext/S2666-6340(24)00405-7?uuid=uuid%3Af113d914-7ecf-4e5a-b4c8-00c0a90cfcbe Effects of gut microbiota on immune checkpoint inhibitors in multi-cancer and as microbial biomarkers for predicting therapeutic response] | [https://www.cell.com/med/fulltext/S2666-6340(24)00405-7?uuid=uuid%3Af113d914-7ecf-4e5a-b4c8-00c0a90cfcbe Effects of gut microbiota on immune checkpoint inhibitors in multi-cancer and as microbial biomarkers for predicting therapeutic response] | ||
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[https://pubmed.ncbi.nlm.nih.gov/39999841/ Unveiling familial aggregation of nasopharyngeal carcinoma: Insights from oral microbiome dysbiosis] | [https://pubmed.ncbi.nlm.nih.gov/39999841/ Unveiling familial aggregation of nasopharyngeal carcinoma: Insights from oral microbiome dysbiosis] | ||
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[https://www.biorxiv.org/content/10.1101/2024.05.24.595648v1 SaprotHub: Making Protein Modeling Accessible to All Biologists] | [https://www.biorxiv.org/content/10.1101/2024.05.24.595648v1 SaprotHub: Making Protein Modeling Accessible to All Biologists] | ||
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|style="padding:.4em;"|YR Jung | |style="padding:.4em;"|YR Jung | ||
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[https://www.nature.com/articles/s41587-023-02079-x Disentanglement of single-cell data with biolord] | [https://www.nature.com/articles/s41587-023-02079-x Disentanglement of single-cell data with biolord] | ||
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|style="padding:.4em;"|YR Kim | |style="padding:.4em;"|YR Kim | ||
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[https://www.science.org/doi/10.1126/science.ads0018 Simulating 500 million years of evolution with a language model] | [https://www.science.org/doi/10.1126/science.ads0018 Simulating 500 million years of evolution with a language model] | ||
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|style="padding:.4em;"|JY Kim | |style="padding:.4em;"|JY Kim | ||
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[https://www.biorxiv.org/content/10.1101/2025.01.30.635558v1 GenomeOcean: An Efficient Genome Foundation Model Trained on Large-Scale Metagenomic Assemblies] | [https://www.biorxiv.org/content/10.1101/2025.01.30.635558v1 GenomeOcean: An Efficient Genome Foundation Model Trained on Large-Scale Metagenomic Assemblies] | ||
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|style="padding:.4em;"|WJ Kim | |style="padding:.4em;"|WJ Kim | ||
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[https://www.nature.com/articles/s41467-025-56165-6 Predicting metabolite response to dietary intervention using deep learning] | [https://www.nature.com/articles/s41467-025-56165-6 Predicting metabolite response to dietary intervention using deep learning] | ||
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|style="padding:.4em;"|SH Ahn | |style="padding:.4em;"|SH Ahn | ||
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[https://www.biorxiv.org/content/10.1101/2025.03.14.643159v1 Ultra-fast and highly sensitive protein structure alignment with segment-level representations and block-sparse optimization] | [https://www.biorxiv.org/content/10.1101/2025.03.14.643159v1 Ultra-fast and highly sensitive protein structure alignment with segment-level representations and block-sparse optimization] | ||
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|style="padding:.4em;"|EJ Sung | |style="padding:.4em;"|EJ Sung | ||
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[https://www.nature.com/articles/s41590-024-02059-6 Integrating single-cell RNA and T cell/B cell receptor sequencing with mass cytometry reveals dynamic trajectories of human peripheral immune cells from birth to old age] | [https://www.nature.com/articles/s41590-024-02059-6 Integrating single-cell RNA and T cell/B cell receptor sequencing with mass cytometry reveals dynamic trajectories of human peripheral immune cells from birth to old age] | ||
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|style="padding:.4em;"|HJ Kim | |style="padding:.4em;"|HJ Kim | ||
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[https://pubmed.ncbi.nlm.nih.gov/39838963/ GOPhage: protein function annotation for bacteriophages by integrating the genomic context] | [https://pubmed.ncbi.nlm.nih.gov/39838963/ GOPhage: protein function annotation for bacteriophages by integrating the genomic context] | ||
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|style="padding:.4em;"|JY Ma | |style="padding:.4em;"|JY Ma | ||
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[https://www.nature.com/articles/s41592-024-02552-8 Orthology inference at scale with FastOMA] | [https://www.nature.com/articles/s41592-024-02552-8 Orthology inference at scale with FastOMA] | ||
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|style="padding:.4em;"|JH Cha | |style="padding:.4em;"|JH Cha | ||
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[https://www.nature.com/articles/s41588-025-02086-5 ImmuneLENS characterizes systemic immune dysregulation in aging and cancer] | [https://www.nature.com/articles/s41588-025-02086-5 ImmuneLENS characterizes systemic immune dysregulation in aging and cancer] | ||
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|style="padding:.4em;"|IS Choi | |style="padding:.4em;"|IS Choi | ||
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[https://www.nature.com/articles/s41586-024-08453-2 Mapping cells through time and space with moscot] | [https://www.nature.com/articles/s41586-024-08453-2 Mapping cells through time and space with moscot] | ||
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[https://www.biorxiv.org/content/10.1101/2025.02.05.636567v1 Human gut microbiota subspecies carry implicit information for in-depth microbiome research] | [https://www.biorxiv.org/content/10.1101/2025.02.05.636567v1 Human gut microbiota subspecies carry implicit information for in-depth microbiome research] | ||
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[https://www.biorxiv.org/content/10.1101/2025.02.07.636498v1 Intraspecies associations from strain-rich metagenome sample] | [https://www.biorxiv.org/content/10.1101/2025.02.07.636498v1 Intraspecies associations from strain-rich metagenome sample] | ||
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[https://doi.org/10.1101/2025.02.13.638109 Commonly used compositional data analysis implementations are not advantageous in microbial differential abundance analyses benchmarked against biological ground truth] | [https://doi.org/10.1101/2025.02.13.638109 Commonly used compositional data analysis implementations are not advantageous in microbial differential abundance analyses benchmarked against biological ground truth] | ||
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[https://www.biorxiv.org/content/10.1101/2025.03.09.642188v2 Learning the language of protein-protein interactions] | [https://www.biorxiv.org/content/10.1101/2025.03.09.642188v2 Learning the language of protein-protein interactions] | ||
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[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] | [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] | ||
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Revision as of 18:24, 26 March 2025
Date | Team | Paper index |
Presenter | Paper title |
---|---|---|---|---|
2024/06/18 | Single-cell | 24-32 | EB Hong |
Spatial transcriptomics reveal neuron–astrocyte synergy in long-term memory |
2024/06/18 | Single-cell | 24-31 | JJ Heo |
scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses |
2024/06/18 | Single-cell | 24-30 | SM Han |
Spatial transcriptomics of planktonic and sessile bacterial populations at single-cell resolution |
2024/06/18 | Single-cell | 24-29 | HJ Choi | |
2024/06/11 | Single-cell | 24-28 | SA Choi | |
2024/06/11 | Single-cell | 24-27 | HJ Cha |
Cell-type-specific responses to fungal infection in plants revealed by single-cell transcriptomics |
2024/06/11 | Single-cell | 24-26 | YK Jung | |
2024/06/11 | Single-cell | 24-25 | HJ Lee | |
2024/06/04 | Single-cell | 24-24 | HK Lee |
Delineating mouse β-cell identity during lifetime and in diabetes with a single cell atlas |
2024/06/04 | Single-cell | 24-23 | JI Lee |
Multimodal spatiotemporal phenotyping of human retinal organoid development |
2024/06/04 | Single-cell | 24-22 | JH Lee | |
2024/06/04 | Single-cell | 24-21 | JH Lee |
A single-cell analysis of the Arabidopsis vegetative shoot apex |
2024/05/28 | Single-cell | 24-20 | JH Lee |
Droplet-based high-throughput single microbe RNA sequencing by smRandom-seq |
2024/05/28 | Single-cell | 24-19 | YH Lee | |
2024/05/28 | Single-cell | 24-18 | EB Yu | |
2024/05/28 | Single-cell | 24-17 | DY Won |
Spatial metatranscriptomics resolves host–bacteria–fungi interactomes |
2024/05/21 | Single-cell | 24-16 | SG Oh | |
2024/05/21 | Single-cell | 24-15 | SY Park | |
2024/05/21 | Single-cell | 24-14 | HS Moon | |
2024/05/21 | Single-cell | 24-13 | JH Nam |
Spatial cellular architecture predicts prognosis in glioblastoma |
2024/05/14 | Single-cell | 24-12 | HS Na | |
2024/05/14 | Single-cell | 24-11 | PK Kim |
Transcriptional adaptation of olfactory sensory neurons to GPCR identity and activity |
2024/05/14 | Single-cell | 24-10 | SH Kwon | |
2024/05/14 | Single-cell | 24-9 | Q Zhen | |
2024/05/07 | Single-cell | 24-8 | CR Leenaars | |
2024/05/07 | Single-cell | 24-7 | YR Kim | |
2024/05/07 | Single-cell | 24-6 | JY Kim |
Spatial transcriptomics landscape of lesions from non-communicable inflammatory skin diseases |
2024/05/07 | Single-cell | 24-5 | WJ Kim |
Neuregulin 4 suppresses NASH-HCC development by restraining tumor-prone liver microenvironment |
2024/04/23 | Single-cell | 24-4 | G Koh |
Single-nucleus multiregion transcriptomic analysis of brain vasculature in Alzheimer’s disease |
2024/04/23 | Single-cell | 24-3 | SH Ahn | |
2024/04/23 | Single-cell | 24-2 | EJ Sung | |
2024/04/23 | Single-cell | 24-1 | HJ Kim |
Date | Team | Paper index |
Presenter | Paper title |
---|---|---|---|---|
2023/08/30 | Single-cell | 23-24 | JW Yu | |
2023/08/09 | Single-cell | 23-23 | IS Choi |
Major data analysis errors invalidate cancer microbiome findings |
2023/08/02 | Single-cell | 23-22 | EJ Sung | |
2023/07/26 | Single-cell | 23-21 | G Koh | |
2023/07/19 | Single-cell | 23-20 | JW Yu |
Estimation of tumor cell total mRNA expression in 15 cancer types predicts disease progression |
2023/07/12 | Single-cell | 23-19 | JH Cha |
DIALOGUE maps multicellular programs in tissue from single-cell or spatial transcriptomics data |
2023/07/05 | Single-cell | 23-18 | SB Baek |
Pan-cancer T cell atlas links a cellular stress response state to immunotherapy resistance |
2023/06/28 | Single-cell | 23-17 | EJ Sung | |
2023/06/21 | Single-cell | 23-16 | IS Choi | |
2023/06/14 | Single-cell | 23-15 | G Koh | |
2023/05/31 | Single-cell | 23-14 | JW Yu |
Mutated processes predict immune checkpoint inhibitor therapy benefit in metastatic melanoma |
2023/05/24 | Single-cell | 23-13 | JH Cha | |
2023/05/17 | Single-cell | 23-12 | SB Baek | |
2023/05/10 | Single-cell | 23-11 | EJ Sung |
Supervised discovery of interpretable gene programs from single-cell data |
2023/05/03 | Single-cell | 23-10 | IS Choi |
Effect of the intratumoral microbiota on spatial and cellular heterogeneity in cancer |
2023/04/26 | Single-cell | 23-9 | G Koh | |
2023/03/22 | Single-cell | 23-8 | JW Yu |
MetaTiME: Meta-components of the Tumor Immune Microenvironment |
2023/03/08 | Single-cell | 23-7 | JH Cha | |
2023/02/21 | Single-cell | 23-6 | SB Baek | |
2023/02/14 | Single-cell | 23-5 | EJ Sung |
A T cell resilience model associated with response to immunotherapy in multiple tumor types |
2022/01/31 | Single-cell | 23-4 | IS Choi | |
2023/01/25 | Single-cell | 23-3 | G Koh | |
2023/01/17 | Single-cell | 23-2 | JW Yu |
Pan-cancer integrative histology-genomic analysis via multimodal deep learning |
2023/01/11 | Single-cell | 23-1 | JH Cha |
Date | Team | Paper index |
Presenter | Paper title |
---|---|---|---|---|
2021/11/23 | Single-cell | 21-39 | IS Choi | |
2021/11/16 | Single-cell | 21-38 | SB Back | |
2021/11/09 | Single-cell | 21-37 | JH Cha | |
2021/11/02 | Single-cell | 21-36 | SB Baek |
Functional Inference of Gene Regulation using Single-Cell Multi-Omics |
2021/10/26 | Single-cell | 21-35 | IS Choi | |
2021/10/19 | Single-cell | 21-34 | JH Cha | |
2021/10/05 | Single-cell | 21-33 | JH Cha |
Tumor and immune reprogramming during immunotherapy in advanced renal cell carcinoma |
2021/09/28 | Single-cell | 21-32 | SB Baek | |
2021/09/14 | Single-cell | 21-31 | IS Choi | |
2021/09/07 | Single-cell | 21-30 | JH Cha |
A single-cell map of intratumoral changes during anti-PD1 treatment of patients with breast cancer |
2021/08/31 | Single-cell | 21-29 | IS Choi |
Single-cell landscape of the ecosystem in early-relapse hepatocellular carcinoma |
2021/08/24 | Single-cell | 21-28 | SB Baek |
Interpreting type 1 diabetes risk with genetics and single-cell epigenomics |
Date | Team | Paper index |
Presenter | Paper title |
---|---|---|---|---|
2021/02/22 | Single-cell | 21-8 | IS Choi |
Functional CRISPR dissection of gene networks controlling human regulatory T cell identity |
21-7 | JH Cha |
Molecular Pathways of Colon Inflammation Induced by Cancer Immunotherapy | ||
2021/02/15 | Single-cell | 21-6 | SB Baek | |
21-5 | IS Choi |
Trajectory-based differential expression analysis for single-cell sequencing data | ||
2021/02/08 | Single-cell | 21-4 | SB Baek |
Genetic determinants of co-accessible chromatin regions in activated T cells across humans |
21-3 | JH Cha |
Single-Cell Analyses Inform Mechanisms of Myeloid-Targeted Therapies in Colon Cancer | ||
2021/02/01 | Single-cell | 21-2 | JW Cho | |
21-1 | JW Cho |