Difference between revisions of "Journal Club"
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| + | |+style="text-align:left;font-size:12pt" | 2025-2 Journal Club  | ||
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| + | !scope="col" style="padding:.4em" | Date  | ||
| + | !scope="col" style="padding:.4em" | Paper<br/>index  | ||
| + | !scope="col" style="padding:.4em" | Presenter  | ||
| + | !scope="col" style="padding:.4em" | Paper title  | ||
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| + | |style="padding:.4em;" rowspan=1|2025/11/18  | ||
| + | |style="padding:.4em;"|25-81  | ||
| + | |style="padding:.4em;"|HJ Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1186/s40168-025-02159-x Bidirectional subsethood of shared marker profiles enables accurate virus classification]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/11/11  | ||
| + | |style="padding:.4em;"|25-80  | ||
| + | |style="padding:.4em;"|JY Ma  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1101/2025.10.19.683269 Self-supervised learning enables robust 1 microbiome predictions in data-limited and 2 cross-cohort settings]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/11/11  | ||
| + | |style="padding:.4em;"|25-79  | ||
| + | |style="padding:.4em;"|JH Cha  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.21203/rs.3.rs-5063726/v1 Microbiome-wide PheWAS links gut microbial SNVs to human health and exposures]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/11/04  | ||
| + | |style="padding:.4em;"|25-78  | ||
| + | |style="padding:.4em;"|IS Choi  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1016/j.patter.2025.101326 BioLLM: A standardized framework for integrating and benchmarking single-cell foundation models]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/11/04  | ||
| + | |style="padding:.4em;"|25-77  | ||
| + | |style="padding:.4em;"|SB Lim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41587-025-02813-7 Predicting functions of uncharacterized gene products from microbial communities]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/10/28  | ||
| + | |style="padding:.4em;"|25-76-2  | ||
| + | |style="padding:.4em;"|HB Lee  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.biorxiv.org/content/10.1101/2025.07.31.667797v1.full OmniCellAgent: Towards AI Co-Scientists for Scientific Discovery in Precision Medicine]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/10/28  | ||
| + | |style="padding:.4em;"|25-76-1  | ||
| + | |style="padding:.4em;"|HB Lee  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.nature.com/articles/s41586-023-06792-0 Autonomous chemical research with large language models]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/10/28  | ||
| + | |style="padding:.4em;"|25-75  | ||
| + | |style="padding:.4em;"|YR Jung  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41587-025-02777-8 Systema: a framework for evaluating genetic perturbation response prediction beyond systematic variation]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/9/30  | ||
| + | |style="padding:.4em;"|25-74  | ||
| + | |style="padding:.4em;"|YR Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.biorxiv.org/content/10.1101/2025.06.29.662198v1 GLM-Prior: a nucleotide transformer model reveals prior knowledge as the driver of GRN inference performance]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/9/30  | ||
| + | |style="padding:.4em;"|25-73-2  | ||
| + | |style="padding:.4em;"|JY Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41467-025-60131-7 Yanomami skin microbiome complexity challenges prevailing concepts of healthy skin]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/9/30  | ||
| + | |style="padding:.4em;"|25-73-1  | ||
| + | |style="padding:.4em;"|JY Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.biorxiv.org/content/10.1101/2025.04.24.650393v2 Large-scale skin metagenomics reveals extensive prevalence, coordination, and functional adaptation of skin microbiome dermotypes across body sites]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/9/23  | ||
| + | |style="padding:.4em;"|25-72  | ||
| + | |style="padding:.4em;"|SH Ahn  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.biorxiv.org/content/10.1101/2025.06.04.656517v1 Generanno: A Genomic Foundation Model for Metagenomic Annotation]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/9/23  | ||
| + | |style="padding:.4em;"|25-71  | ||
| + | |style="padding:.4em;"|EJ Sung  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.nature.com/articles/s41587-024-02182-7 Inferring gene regulatory networks from single-cell multiome data using atlas-scale external data]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/9/16  | ||
| + | |style="padding:.4em;"|25-70  | ||
| + | |style="padding:.4em;"|JH Cha  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.biorxiv.org/content/10.1101/2025.06.26.661544v1 Interpreting Attention Mechanisms in Genomic Transformer Models: A Framework for Biological Insights]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/9/16  | ||
| + | |style="padding:.4em;"|25-69  | ||
| + | |style="padding:.4em;"|IS Choi  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.biorxiv.org/content/10.1101/2025.06.11.659222v1 A large-scale foundation model for bulk transcriptomes]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/9/9  | ||
| + | |style="padding:.4em;"|25-68  | ||
| + | |style="padding:.4em;"|HJ Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.nature.com/articles/s41592-025-02636-z xTrimoPGLM: unified 100-billion-parameter pretrained transformer for deciphering the language of proteins]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/9/9  | ||
| + | |style="padding:.4em;"|25-67  | ||
| + | |style="padding:.4em;"|JY Ma  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.nature.com/articles/s42256-025-01044-4 Generalized biological foundation model with unified nucleic acid and protein language]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/9/2  | ||
| + | |style="padding:.4em;"|25-66  | ||
| + | |style="padding:.4em;"|NY Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.biorxiv.org/content/10.1101/2025.06.25.661532v1 AlphaGenome: advancing regulatory variant effect prediction with a unified DNA sequence model]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/9/2  | ||
| + | |style="padding:.4em;"|25-65  | ||
| + | |style="padding:.4em;"|YR Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.nature.com/articles/s41591-025-03610-0 Gut microbiome evolution from infancy to 8 years of age]  | ||
| + | |}  | ||
| + | |||
| + | {|class=wikitable style="text-align:center;"  | ||
| + | |+style="text-align:left;font-size:12pt" | 2025-1 Journal Club  | ||
| + | |-  | ||
| + | !scope="col" style="padding:.4em" | Date  | ||
| + | !scope="col" style="padding:.4em" | Paper<br/>index  | ||
| + | !scope="col" style="padding:.4em" | Presenter  | ||
| + | !scope="col" style="padding:.4em" | Paper title  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/8/29  | ||
| + | |style="padding:.4em;"|25-64  | ||
| + | |style="padding:.4em;"|HB Lee  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.nature.com/articles/s41592-025-02723-1 Sliding Window Interaction Grammar (SWING): a generalized interaction language model for peptide and protein interactions]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/8/29  | ||
| + | |style="padding:.4em;"|25-63  | ||
| + | |style="padding:.4em;"|SH Ahn  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.biorxiv.org/content/10.1101/2025.07.03.662911v1 SSAlign: Ultrafast and Sensitive Protein Structure Search at Scale]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/8/29  | ||
| + | |style="padding:.4em;"|25-62  | ||
| + | |style="padding:.4em;"|YR Jung  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.biorxiv.org/content/10.1101/2025.06.14.659567v2 Foundation Model Attributions Reveal Shared Inflammatory Program Across]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/8/19  | ||
| + | |style="padding:.4em;"|25-61  | ||
| + | |style="padding:.4em;"|SB Lim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://pubmed.ncbi.nlm.nih.gov/39809266/ Gut microbial GABA imbalance emerges as a metabolic signature in mild autism spectrum disorder linked to overrepresented Escherichia]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/8/19  | ||
| + | |style="padding:.4em;"|25-60  | ||
| + | |style="padding:.4em;"|JY Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.nature.com/articles/s42255-025-01318-6 Multi-omic analysis reveals transkingdom gut dysbiosis in metabolic dysfunction-associated steatotic liver disease]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/8/12  | ||
| + | |style="padding:.4em;"|25-59  | ||
| + | |style="padding:.4em;"|EJ Sung  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.nature.com/articles/s41592-025-02627-0 scNET: learning context-specific gene and cell embeddings by integrating single-cell gene expression data with protein–protein interactions]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/8/12  | ||
| + | |style="padding:.4em;"|25-58  | ||
| + | |style="padding:.4em;"|HJ Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.biorxiv.org/content/10.1101/2025.04.17.649224v1 Fine-Tuning Protein Language Models Unlocks the Potential of Underrepresented Viral Proteomes]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/8/12  | ||
| + | |style="padding:.4em;"|25-57  | ||
| + | |style="padding:.4em;"|JY Ma  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.nature.com/articles/s41592-024-02552-8 Orthology inference at scale with FastOMA]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/8/8  | ||
| + | |style="padding:.4em;"|25-56  | ||
| + | |style="padding:.4em;"|JH Cha  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.nature.com/articles/s41467-025-58442-w Lineage-specific microbial protein prediction enables large-scale exploration of protein ecology within the human gut]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/8/8  | ||
| + | |style="padding:.4em;"|25-55  | ||
| + | |style="padding:.4em;"|IS Choi  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://pubmed.ncbi.nlm.nih.gov/39554079/ Cell2Sentence: Teaching Large Language Models the Language of Biology]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/8/8  | ||
| + | |style="padding:.4em;"|25-54  | ||
| + | |style="padding:.4em;"|NY Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-023-01615-w High‑resolution strain‑level microbiome composition analysis from short reads]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/8/1  | ||
| + | |style="padding:.4em;"|25-53  | ||
| + | |style="padding:.4em;"|SB Lim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.nature.com/articles/s41564-025-01963-3 Gut metagenomes reveal interactions between dietary restriction, ageing and the microbiome in genetically diverse mice]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/8/1  | ||
| + | |style="padding:.4em;"|25-52  | ||
| + | |style="padding:.4em;"|HB Lee  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.nature.com/articles/s42256-024-00974-9 A machine learning approach to leveraging electronic health records for enhanced omics analysis]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/8/1  | ||
| + | |style="padding:.4em;"|25-51  | ||
| + | |style="padding:.4em;"|YR Jung  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.nature.com/articles/s41587-023-01905-6 Predicting transcriptional outcomes of novel multigene perturbations with GEARS]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/7/25  | ||
| + | |style="padding:.4em;"|25-50  | ||
| + | |style="padding:.4em;"|YR Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.nature.com/articles/s41592-024-02523-z Nucleotide Transformer: building and evaluating robust foundation models for human genomics]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/7/25  | ||
| + | |style="padding:.4em;"|25-49  | ||
| + | |style="padding:.4em;"|JY Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/7/25  | ||
| + | |style="padding:.4em;"|25-48  | ||
| + | |style="padding:.4em;"|SH Ahn  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/7/18  | ||
| + | |style="padding:.4em;"|25-47  | ||
| + | |style="padding:.4em;"|NY Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/7/18  | ||
| + | |style="padding:.4em;"|25-46  | ||
| + | |style="padding:.4em;"|HJ Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.biorxiv.org/content/10.1101/2025.02.26.640259v1 Highly accurate prophage island detection with PIDE]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/7/18  | ||
| + | |style="padding:.4em;"|25-45  | ||
| + | |style="padding:.4em;"|JY Ma  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.biorxiv.org/content/10.1101/2025.02.18.638918v1 Genome modeling and design across all domains of life with Evo 2]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/7/11  | ||
| + | |style="padding:.4em;"|25-44  | ||
| + | |style="padding:.4em;"|JH Cha  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/7/11  | ||
| + | |style="padding:.4em;"|25-43  | ||
| + | |style="padding:.4em;"|IS Choi  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.nature.com/articles/s41586-024-08411-y A cell atlas foundation model for scalable search of similar human cells]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/7/11  | ||
| + | |style="padding:.4em;"|25-42  | ||
| + | |style="padding:.4em;"|EJ Sung  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.biorxiv.org/content/10.1101/2025.02.25.640181v1 geneRNIB: a living benchmark for gene regulatory network inference]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/7/4  | ||
| + | |style="padding:.4em;"|25-41  | ||
| + | |style="padding:.4em;"|JY Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.biorxiv.org/content/10.1101/2025.01.30.635558v1 GenomeOcean: An Efficient Genome Foundation Model Trained on Large-Scale Metagenomic Assemblies]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/7/4  | ||
| + | |style="padding:.4em;"|25-40  | ||
| + | |style="padding:.4em;"|HB Lee  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.biorxiv.org/content/10.1101/2024.05.24.595648v1 SaprotHub: Making Protein Modeling Accessible to All Biologists]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/7/4  | ||
| + | |style="padding:.4em;"|25-39  | ||
| + | |style="padding:.4em;"|YR Jung  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.nature.com/articles/s41587-023-02079-x Disentanglement of single-cell data with biolord]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/6/27  | ||
| + | |style="padding:.4em;"|25-38  | ||
| + | |style="padding:.4em;"|YR Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.science.org/doi/10.1126/science.ads0018 Simulating 500 million years of evolution with a language model]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/6/27  | ||
| + | |style="padding:.4em;"|25-37  | ||
| + | |style="padding:.4em;"|SB Lim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://pubmed.ncbi.nlm.nih.gov/39999841/ Unveiling familial aggregation of nasopharyngeal carcinoma: Insights from oral microbiome dysbiosis]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/6/27  | ||
| + | |style="padding:.4em;"|25-36  | ||
| + | |style="padding:.4em;"|WJ Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.nature.com/articles/s41467-025-56165-6 Predicting metabolite response to dietary intervention using deep learning]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/6/13  | ||
| + | |style="padding:.4em;"|25-35  | ||
| + | |style="padding:.4em;"|SH Ahn  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/6/13  | ||
| + | |style="padding:.4em;"|25-34  | ||
| + | |style="padding:.4em;"|EJ Sung  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/6/13  | ||
| + | |style="padding:.4em;"|25-33  | ||
| + | |style="padding:.4em;"|HJ Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://pubmed.ncbi.nlm.nih.gov/39838963/ GOPhage: protein function annotation for bacteriophages by integrating the genomic context]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/5/30  | ||
| + | |style="padding:.4em;"|25-32  | ||
| + | |style="padding:.4em;"|JY Ma  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://pubmed.ncbi.nlm.nih.gov/39123049/ Fast, sensitive detection of protein homologs using deep dense retrieval]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/5/30  | ||
| + | |style="padding:.4em;"|25-31  | ||
| + | |style="padding:.4em;"|JH Cha  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.nature.com/articles/s41588-025-02086-5 ImmuneLENS characterizes systemic immune dysregulation in aging and cancer]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/5/23  | ||
| + | |style="padding:.4em;"|25-30  | ||
| + | |style="padding:.4em;"|IS Choi  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.nature.com/articles/s41586-024-08453-2 Mapping cells through time and space with moscot]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/5/23  | ||
| + | |style="padding:.4em;"|25-29  | ||
| + | |style="padding:.4em;"|NY Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.biorxiv.org/content/10.1101/2025.02.05.636567v1 Human gut microbiota subspecies carry implicit information for in-depth microbiome research]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/5/23  | ||
| + | |style="padding:.4em;"|25-28  | ||
| + | |style="padding:.4em;"|NY Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.biorxiv.org/content/10.1101/2025.02.07.636498v1 Intraspecies associations from strain-rich metagenome sample]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/5/9  | ||
| + | |style="padding:.4em;"|25-27  | ||
| + | |style="padding:.4em;"|SB Lim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/5/9  | ||
| + | |style="padding:.4em;"|25-26  | ||
| + | |style="padding:.4em;"|HB Lee  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.nature.com/articles/s42256-024-00942-3 Moving towards genome-wide data integration for patient stratification with Integrate Any Omics]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/4/11  | ||
| + | |style="padding:.4em;"|25-25  | ||
| + | |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|2025/4/11  | ||
| + | |style="padding:.4em;"|25-24  | ||
| + | |style="padding:.4em;"|YR Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1016/j.cell.2024.08.037 Emergence of community behaviors in the gut microbiota upon drug treatment]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/3/28  | ||
| + | |style="padding:.4em;"|25-23  | ||
| + | |style="padding:.4em;"|JY Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41564-024-01832-5 Prediction of strain level phage–host interactions across the Escherichia genus using only genomic information]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/3/28  | ||
| + | |style="padding:.4em;"|25-22  | ||
| + | |style="padding:.4em;"|WJ Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1101/2025.01.07.631807 Metagenomic estimation of absolute bacterial biomass in the mammalian gut through host-derived read normalization]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/3/21  | ||
| + | |style="padding:.4em;"|25-21  | ||
| + | |style="padding:.4em;"|SH Ahn  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1101/2024.10.02.616292 De novo discovery of conserved gene clusters in microbial genomes with Spacedust]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/3/21  | ||
| + | |style="padding:.4em;"|25-20  | ||
| + | |style="padding:.4em;"|SH Ahn  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1101/2024.04.14.589414 Rapid and Sensitive Protein Complex Alignment with Foldseek-Multimer]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/3/21  | ||
| + | |style="padding:.4em;"|25-19  | ||
| + | |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|2025/3/14  | ||
| + | |style="padding:.4em;"|25-18  | ||
| + | |style="padding:.4em;"|HJ Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1101/2024.12.30.630825 MGM as a large-scale pretrained foundation model for microbiome analyses in diverse contexts ]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/3/14  | ||
| + | |style="padding:.4em;"|25-17  | ||
| + | |style="padding:.4em;"|JY Ma  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1126/science.ado9336 Sequence modeling and design from molecular to genome scale with Evo]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/3/7  | ||
| + | |style="padding:.4em;"|25-16  | ||
| + | |style="padding:.4em;"|JH Cha  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/3/7  | ||
| + | |style="padding:.4em;"|25-15  | ||
| + | |style="padding:.4em;"|NY Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1101/2025.01.06.631550 Quantifying Metagenomic Strain Associations from Microbiomes with Anpan]  | ||
| + | |}  | ||
| + | |||
| + | {|class=wikitable style="text-align:center;"  | ||
| + | |+style="text-align:left;font-size:12pt" | 2024-2 scOmics  | ||
| + | |-  | ||
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| + | !scope="col" style="padding:.4em" | Paper title  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/2/24  | ||
| + | |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|2025/2/19  | ||
| + | |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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/1/15  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|25-4  | ||
| + | |style="padding:.4em;"|JH Cha  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1101/2024.09.24.614685 scEMB: Learning context representation of genes based on large-scale single-cell transcriptomics]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/1/8  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|25-3  | ||
| + | |style="padding:.4em;"|HB Lee  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1093/bioinformatics/btad663 TT3D: Leveraging precomputed protein 3D sequence models to predict protein–protein interactions]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/1/8  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|25-2  | ||
| + | |style="padding:.4em;"|HB Lee  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1093/bioinformatics/btac258 Topsy-Turvy: integrating a global view into sequence-based PPI prediction]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/1/8  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|25-1  | ||
| + | |style="padding:.4em;"|HB Lee  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/12/17  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-30  | ||
| + | |style="padding:.4em;"|YR Jung  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1101/2024.08.16.608180 Quantized multi-task learning for context-specific representations of gene network dynamics]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/12/3  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-29  | ||
| + | |style="padding:.4em;"|EJ Sung  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1101/2023.11.21.568145 ANDES: a novel best-match approach for enhancing gene set analysis in embedding spaces]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/11/26  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-28  | ||
| + | |style="padding:.4em;"|IS Choi  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41592-024-02303-9 CellRank 2: unified fate mapping in multiview single-cell data]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/11/19  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-27  | ||
| + | |style="padding:.4em;"|SB Baek  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1101/2024.07.29.605556 scPRINT: pre-training on 50 million cells allows robust gene network predictions]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/11/12  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-26  | ||
| + | |style="padding:.4em;"|JH Cha  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41467-024-46440-3 Bidirectional generation of structure and properties through a single molecular foundation model]  | ||
| + | |}  | ||
| + | |||
| + | {|class=wikitable style="text-align:center;"  | ||
| + | |+style="text-align:left;font-size:12pt" | 2024-2 Microbiome  | ||
| + | |-  | ||
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| + | !scope="col" style="padding:.4em" | Paper<br/>index  | ||
| + | !scope="col" style="padding:.4em" | Presenter  | ||
| + | !scope="col" style="padding:.4em" | Paper title  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/2/24  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|25-14  | ||
| + | |style="padding:.4em;"|SB Lim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1101/2024.12.13.628459 MaAsLin 3: Refining and extending generalized multivariable linear models for meta-omic association discovery]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/2/24  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|25-13  | ||
| + | |style="padding:.4em;"|HB Lee  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41564-024-01881-w Profiling lateral gene transfer events in the human microbiome using WAAFLE]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/2/17  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|25-12  | ||
| + | |style="padding:.4em;"|JY Ma  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1101/2024.05.30.596740 ProTrek: Navigating the Protein Universe through Tri-Modal Contrastive Learning]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/2/17  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|25-11  | ||
| + | |style="padding:.4em;"|JY Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1101/2024.12.30.630844 The genetic diversity and populational specificity of the human gut virome at single nucleotide resolution]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/2/3  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|25-10  | ||
| + | |style="padding:.4em;"|WJ Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1186/s13059-024-03425-1 pan-Draft: automated reconstruction of species-representative metabolic models from multiple genomes]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/2/3  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|25-9  | ||
| + | |style="padding:.4em;"|SH Ahn  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1016/j.cell.2024.09.019 A core microbiome signature as an indicator of health]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/1/20  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|25-8  | ||
| + | |style="padding:.4em;"|HJ Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1016/j.cell.2024.09.027 Using artificial intelligence to document the hidden RNA virosphere]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/1/20  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|25-7  | ||
| + | |style="padding:.4em;"|YR Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1101/2023.07.17.549267 Learning a deep language model for microbiomes: the power of large scale unlabeled microbiome data]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/1/13  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|25-6  | ||
| + | |style="padding:.4em;"|JH Cha  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1126/science.adg7492 Accurate proteome-wide missense variant effect prediction with AlphaMissense]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/1/13  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|25-5  | ||
| + | |style="padding:.4em;"|NY Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41591-024-02823-z A gut microbial signature for combination immune checkpoint blockade across cancer types]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/1/6  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|25-4  | ||
| + | |style="padding:.4em;"|YR Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1080/19490976.2024.2418984 Fecal microbial marker panel for aiding diagnosis of autism spectrum disorders]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/1/6  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|25-3  | ||
| + | |style="padding:.4em;"|YR Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41564-024-01739-1 Multikingdom and functional gut microbiota markers for autism spectrum disorder]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2025/1/6  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|25-2  | ||
| + | |style="padding:.4em;"|JY Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1186/s13059-024-03390-9 A realistic benchmark for differential abundance testing and confounder adjustment in human microbiome studies]  | ||
| + | |-  | ||
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| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|25-1  | ||
| + | |style="padding:.4em;"|WJ Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41564-024-01728-4 Microbial community-scale metabolic modelling predicts personalized short-chain fatty acid production profiles in the human gut]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/12/30  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-65  | ||
| + | |style="padding:.4em;"|SH Ahn  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41467-024-52561-6 Gut microbiota wellbeing index predicts overall health in a cohort of 1000 infants]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/12/18  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-64  | ||
| + | |style="padding:.4em;"|HJ Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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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| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-63  | ||
| + | |style="padding:.4em;"|HJ Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1101/2024.06.27.601020 Ultrafast and accurate sequence alignment and clustering of viral genomes]  | ||
| + | |-  | ||
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| + | |style="padding:.4em;"|24-62  | ||
| + | |style="padding:.4em;"|JY Ma  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1101/2024.08.14.607850 The OMG dataset: An Open MetaGenomic corpus for mixed-modality genomic language modeling]  | ||
| + | |-  | ||
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| + | |style="padding:.4em;"|24-61  | ||
| + | |style="padding:.4em;"|JH Cha  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41467-024-46947-9 Genomic language model predicts protein co-regulation and function]  | ||
| + | |-  | ||
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| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-60  | ||
| + | |style="padding:.4em;"|NY Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1101/2024.07.26.605391 Protein Set 1 Transformer: A protein-based genome language model to power high diversity viromics]  | ||
| + | |-  | ||
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| + | |style="padding:.4em;"|YR Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1101/2024.07.11.603044 Prophage-DB: A comprehensive database to explore diversity,distribution, and ecology of prophages]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/11/20  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-58  | ||
| + | |style="padding:.4em;"|JY Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1186/s40168-024-01904-y Strain‑resolved de‑novo metagenomic assembly of viral genomes and microbial 16S rRNAs]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/11/13  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-57  | ||
| + | |style="padding:.4em;"|WJ Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1186/s40168-024-01876-z Prokaryotic‑virus‑encoded auxiliary metabolic genes throughout the global oceans]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/11/13  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-56  | ||
| + | |style="padding:.4em;"|G Koh  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1016/j.cell.2024.07.039 Unexplored microbial diversity from 2,500 food metagenomes and links with the human microbiome]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/11/6  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-55  | ||
| + | |style="padding:.4em;"|SH Ahn  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1101/2024.04.17.589959 Pangenomes of Human Gut Microbiota Uncover Links Between Genetic Diversity and Stress Response]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/11/6  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-54  | ||
| + | |style="padding:.4em;"|HJ Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1101/2024.05.28.596318 vClassifier: a toolkit for species-level classification of prokaryotic viruses]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/11/6  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-53  | ||
| + | |style="padding:.4em;"|HJ Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1101/2024.07.26.605250 GRAViTy-V2: a grounded viral taxonomy application]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/10/16  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-52  | ||
| + | |style="padding:.4em;"|JY Ma  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41467-024-52533-w Accurately predicting enzyme functions through geometric graph learning on ESMFold-predicted structures]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/10/16  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-51  | ||
| + | |style="padding:.4em;"|JH Cha  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1101/2024.06.27.600934 Improved detection of microbiome-disease associations via population structure-aware generalized linear mixed effects models (microSLAM)]  | ||
| + | |}  | ||
| + | {|class=wikitable style="text-align:center;"  | ||
| + | |+style="text-align:left;font-size:12pt" | 2024-1 scOmics  | ||
| + | |-  | ||
| + | !scope="col" style="padding:.4em" | Date  | ||
| + | !scope="col" style="padding:.4em" | Team  | ||
| + | !scope="col" style="padding:.4em" | Paper<br/>index  | ||
| + | !scope="col" style="padding:.4em" | Presenter  | ||
| + | !scope="col" style="padding:.4em" | Paper title  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/11/12  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-25  | ||
| + | |style="padding:.4em;"|HB Lee  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1126/science.adj4857 A blueprint for tumor-infiltrating B cells across human cancers]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/10/29  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-24  | ||
| + | |style="padding:.4em;"|YR Jung  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41467-024-48310-4 Systematic dissection of tumor-normal single-cell ecosystems across a thousand tumors of 30 cancer types]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/10/08  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-23  | ||
| + | |style="padding:.4em;"|EJ Sung  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://genomemedicine.biomedcentral.com/articles/10.1186/s13073-024-01314-7 scDrugPrio: a framework for the analysis of single‑cell transcriptomics to address multiple problems in precision medicine in immune‑mediated inflammatory diseases]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/09/24  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-22  | ||
| + | |style="padding:.4em;"|IS Choi  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41591-024-02856-4 A visual-language foundation model for computational pathology]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/09/10  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-21  | ||
| + | |style="padding:.4em;"|SB Baek  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41592-024-02175-z SLIDE: Significant Latent Factor Interaction Discovery and Exploration across biological domains]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/09/03  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-20  | ||
| + | |style="padding:.4em;"|HB Lee  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1016/j.ccell.2023.12.013 Clinical and molecular features of acquired resistance to immunotherapy in non-small cell lungcancer]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/08/30  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-19  | ||
| + | |style="padding:.4em;"|JH Cha  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1101/2024.06.04.597354 Cell-Graph Compass: Modeling Single Cells with Graph Structure Foundation Model]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/08/16  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-18  | ||
| + | |style="padding:.4em;"|YR Jung  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1016/j.xgen.2023.100473 Single-cell transcriptome landscape of circulating CD4+ T cell populations in autoimmune diseases]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/08/09  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-17  | ||
| + | |style="padding:.4em;"|EJ Sung  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s43588-024-00597-5 Population-level comparisons of gene regulatory networks modeled on highthroughput single-cell transcriptomics data]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/08/02  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-16  | ||
| + | |style="padding:.4em;"|IS Choi  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1101/2024.06.16.599201 node2vec2rank: Large Scale and Stable Graph Differential Analysis via Multi-Layer Node Embeddings and Ranking]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/07/26  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-15  | ||
| + | |style="padding:.4em;"|SB Baek  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1016/j.xgen.2024.100553 Unified cross-modality integration and analysis of T cell receptors and T cell transcriptomes by low-resource-aware representation learning]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/07/19  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-14  | ||
| + | |style="padding:.4em;"|JH Cha  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1101/2023.07.18.549602 Contextual AI models for single-cell protein biology]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/07/12  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-13  | ||
| + | |style="padding:.4em;"|EJ Sung  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1101/2024.04.15.589472 Nicheformer: a foundation model for single-cell and spatial omics]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/07/05  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-12  | ||
| + | |style="padding:.4em;"|IS Choi  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1101/2023.05.29.542705 Large Scale Foundation Model on Single-cell Transcriptomics]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/06/28  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-11  | ||
| + | |style="padding:.4em;"|SB Baek  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41592-024-02201-0 scGPT: toward building a foundation modelfor single-cell multi-omics using generative AI]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/06/21  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-10  | ||
| + | |style="padding:.4em;"|JH Cha  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41586-023-06139-9 Transfer learning enables predictions in network biology]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/06/07  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-9  | ||
| + | |style="padding:.4em;"|EJ Sung  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1158/0008-5472.CAN-23-2650 The Web-Based Portal SpatialTME Integrates Histological Images with Single-Cell and Spatial Transcriptomics to Explore the Tumor Microenvironment]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/05/17  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-8  | ||
| + | |style="padding:.4em;"|IS Choi  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41592-023-02117-1 SEVtras delineates small extracellular vesicles at droplet resolution from single-cell transcriptomes]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/05/10  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-7  | ||
| + | |style="padding:.4em;"|SB Baek  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41587-023-01728-5 A relay velocity model infers cell-dependent RNA velocity]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/05/03  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-5  | ||
| + | |style="padding:.4em;"|EJ Sung  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41467-023-44206-x Pathway centric analysis for single-cell RNA-seq and spatial transcriptomics data with GSDensity]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/04/26  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-6  | ||
| + | |style="padding:.4em;"|JH Cha  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41587-023-01734-7 Dandelion uses the single-cell adaptive immune receptor repertoire to explore lymphocyte developmental origins]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/04/05  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-4  | ||
| + | |style="padding:.4em;"|IS Choi  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1016/j.cell.2023.11.026 Automatic cell-type harmonization and integration across Human Cell Atlas datasets]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/03/22  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-3  | ||
| + | |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|2024/03/15  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-2  | ||
| + | |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|2024/03/08  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-1  | ||
| + | |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]  | ||
| + | |}  | ||
| + | |||
| + | |||
| + | |||
| + | {|class=wikitable style="text-align:center;"  | ||
| + | |+style="text-align:left;font-size:12pt" | 2024-1 Microbiome  | ||
| + | |-  | ||
| + | !scope="col" style="padding:.4em" | Date  | ||
| + | !scope="col" style="padding:.4em" | Team  | ||
| + | !scope="col" style="padding:.4em" | Paper<br/>index  | ||
| + | !scope="col" style="padding:.4em" | Presenter  | ||
| + | !scope="col" style="padding:.4em" | Paper title  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/10/02  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-51  | ||
| + | |style="padding:.4em;"|NY Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/10/02  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-50  | ||
| + | |style="padding:.4em;"|YR Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1186/s40168-024-01832-x Gut virome-wide association analysis identifes cross-population viral signatures for infammatory bowel disease]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/09/25  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-48-2  | ||
| + | |style="padding:.4em;"|JY Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.48550/arXiv.1806.00064 Efficient Low-rank Multimodal Fusion with Modality-Specific Factors]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/09/25  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-48-1  | ||
| + | |style="padding:.4em;"|JY Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.48550/arXiv.1707.07250 Tensor Fusion Network for Multimodal Sentiment Analysis]  | ||
| + | |-  | ||
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| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-49  | ||
| + | |style="padding:.4em;"|WJ Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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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| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-47  | ||
| + | |style="padding:.4em;"|G Koh  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/09/11  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-46  | ||
| + | |style="padding:.4em;"|SH Ahn  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://pubmed.ncbi.nlm.nih.gov/31510656 Deep learning with multimodal representation for pancancer prognosis prediction]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/09/04  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-45-2  | ||
| + | |style="padding:.4em;"|HJ Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://pubmed.ncbi.nlm.nih.gov/32881682 Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosis]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/09/04  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-45-1  | ||
| + | |style="padding:.4em;"|HJ Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1016/j.ccell.2022.07.004 Pan-cancer integrative histology-genomic analysis via multimodal deep learning]  | ||
| + | |-  | ||
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| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-44  | ||
| + | |style="padding:.4em;"|NY Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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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| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-43-2  | ||
| + | |style="padding:.4em;"|JH Cha  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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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| + | |style="padding:.4em;"|24-43-1  | ||
| + | |style="padding:.4em;"|JH Cha  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://arxiv.org/abs/2103.00020 Learning Transferable Visual Models From Natural Language Supervision]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/08/21  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-42  | ||
| + | |style="padding:.4em;"|JY Ma  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41592-022-01616-x BIONIC: biological network integration using convolutions]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/08/14  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-41  | ||
| + | |style="padding:.4em;"|G Koh  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41587-023-01917-2 Protein remote homology detection and structural alignment using deep learning]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/08/14  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-41  | ||
| + | |style="padding:.4em;"|YR Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41586-024-07487-w Accurate structure prediction of biomolecular interactions with AlphaFold 3]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/08/07  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-39  | ||
| + | |style="padding:.4em;"|WJ Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1186/s40168-023-01737-1 Gut microbiome-metabolome interactions predict host condition]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/08/07  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-38  | ||
| + | |style="padding:.4em;"|JY kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41591-024-02963-2 Microbiome confounders and quantitative profiling challenge predicted microbial targets in colorectal cancer development]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/07/31  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-37  | ||
| + | |style="padding:.4em;"|SH Ahn  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41564-024-01751-5 A multi-kingdom collection of 33,804 reference genomes for the human vaginal microbiome]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/07/31  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-36  | ||
| + | |style="padding:.4em;"|HJ Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1101/2023.12.11.571168 Efficient and accurate detection of viral sequences at single-cell resolution reveals putative novel viruses perturbing host gene expression]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/07/24  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-35  | ||
| + | |style="padding:.4em;"|JY Ma  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1101/2024.06.04.596112 Compositional Differential Abundance Testing: Defining and Finding a New Type of Health-Microbiome Associations]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/07/24  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-34  | ||
| + | |style="padding:.4em;"|JH Cha  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1016/j.cell.2024.05.013 Discovery of antimicrobial peptides in the global microbiome with machine learning]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/07/17  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-33  | ||
| + | |style="padding:.4em;"|NY Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1016/j.cell.2024.05.029 Custom scoring based on ecological topology of gut microbiota associated with cancer immunotherapy outcome]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/07/17  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-32  | ||
| + | |style="padding:.4em;"|YR Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41586-024-07336-w Paternal microbiome perturbations impact offspring fitness]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/07/10  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-31  | ||
| + | |style="padding:.4em;"|JY Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1016/j.crmeth.2024.100775 Interactions-based classification of a single microbial sample]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/07/10  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-30  | ||
| + | |style="padding:.4em;"|NY Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1101/2024.04.10.588779 Accurate estimation of intraspecificmicrobial gene content variation inmetagenomic data with MIDAS v3 andStrainPGC]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/07/03  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-29  | ||
| + | |style="padding:.4em;"|WJ Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1016/j.cell.2024.03.021  A pan-cancer analysis of the microbiome inmetastatic cancer]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/07/03  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-28  | ||
| + | |style="padding:.4em;"|G Koh  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1016/j.chom.2024.03.002  A specific enterotype derived from gut microbiomeof older individuals enables favorable responses toimmune checkpoint blockade therapy]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/06/26  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-27  | ||
| + | |style="padding:.4em;"|SH Ahn  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/06/26  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-26  | ||
| + | |style="padding:.4em;"|HJ Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1080/19490976.2024.2309684 A universe of human gut-derived bacterialprophages: unveiling the hidden viral players inintestinal microecology]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/06/19  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-25  | ||
| + | |style="padding:.4em;"|JY Ma  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41388-024-02974-w Robustness of cancer microbiome signals over a broad range of methodological variation]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/06/19  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-24  | ||
| + | |style="padding:.4em;"|JY Cha  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41586-024-07182-w A distinct Fusobacterium nucleatum clade dominates the colorectal cancer niche]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/06/05  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-22  | ||
| + | |style="padding:.4em;"|YR Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1016/j.cell.2024.01.039 A cryptic plasmid is among the most numerous genetic elements in the human gut]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/06/05  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-21  | ||
| + | |style="padding:.4em;"|JY Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1016/j.cell.2024.03.014 Gut microbiome and metabolome profiling in Framingham heart study reveals cholesterol-metabolizing bacteria]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/05/29  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-20  | ||
| + | |style="padding:.4em;"|WJ Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1101/2024.03.18.584290 Fecal microbial load is a major determinant of gut microbiome variation and aconfounder for disease associations]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/05/29  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-19  | ||
| + | |style="padding:.4em;"|G Koh  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41586-024-07162-0 A host-microbiota interactome reveals extensive transkingdom connectivity]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/05/22  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-18  | ||
| + | |style="padding:.4em;"|SH Ahn  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1101/2024.02.02.578701 Metagenomic estimation of dietary intake from human stool]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/05/22  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-17  | ||
| + | |style="padding:.4em;"|HJ Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41467-024-45793-z A metagenomic catalog of the early-life human gut virome]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/05/08  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-16  | ||
| + | |style="padding:.4em;"|JY Ma  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1101/2024.01.08.574624 Large-scale computational analyses of gut microbial CAZyme repertoires enabled by Cayman]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/05/08  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-15  | ||
| + | |style="padding:.4em;"|JH Cha  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41467-024-44720-6 Defining the biogeographical map and potential bacterial translocation of microbiome in human ‘surface organs’]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/05/01  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-14  | ||
| + | |style="padding:.4em;"|NY Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41467-023-42997-7 Gut microbial structural variation associates with immune checkpoint inhibitor response]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/05/01  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-13  | ||
| + | |style="padding:.4em;"|YR Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1080/19490976.2024.2307586 Fungal signature differentiates alcohol-associated liver disease from nonalcoholic fatty liver disease]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/04/24  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-12  | ||
| + | |style="padding:.4em;"|JY Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1080/19490976.2024.2302076 Incorporating metabolic activity, taxonomy and community structure to improve microbiome based predictive models for host phenotype prediction]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/04/24  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-11  | ||
| + | |style="padding:.4em;"|WJ Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41467-023-42112-w Disease-specific loss of microbial cross feeding interactions in the human gut]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/04/03  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-7  | ||
| + | |style="padding:.4em;"|JY Ma  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41592-023-02092-7 Multigroup analysis of compositions of microbiomes with covariate adjustments and repeated measures]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/04/03  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-9  | ||
| + | |style="padding:.4em;"|SH Ahn  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41467-023-40719-7 Microdiversity of the vaginal microbiome is associated with preterm birth]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/03/27  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-8  | ||
| + | |style="padding:.4em;"|HJ Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41564-023-01584-8 Large language models improve annotation of prokaryotic viral proteins]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/03/27  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-10  | ||
| + | |style="padding:.4em;"|G Koh  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41467-023-42998-6 Clinically relevant antibiotic resistance genes are linked to a limited set of taxa within gut microbiome worldwide]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/03/20  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-6-2  | ||
| + | |style="padding:.4em;"|JH Cha  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://academic.oup.com/nargab/article/2/2/lqaa023/5826153 Visualizing ’omic feature rankings and log-ratios using Qurro]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/03/20  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-6-1  | ||
| + | |style="padding:.4em;"|JH Cha  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41467-019-10656-5 Establishing microbial composition measurement standards with reference frames]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/03/20  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-5  | ||
| + | |style="padding:.4em;"|NY Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1186/s13059-024-03166-1 AnnoPRO: a strategy for protein function annotation based on multi-scale protein representation and a hybrid deep learning of dual-path encoding]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/03/13  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-4  | ||
| + | |style="padding:.4em;"|YR Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41467-023-44289-6 Differential responses of the gut microbiome and resistome to antibiotic exposures in infants and adults]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/03/13  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-3  | ||
| + | |style="padding:.4em;"|JY Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41467-023-44290-z Effective binning of metagenomic contigs using contrastive multi-view representation learning]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/03/06  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-2  | ||
| + | |style="padding:.4em;"|WJ Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41559-020-01353-4 Polarization of microbial communities between competitive and cooperative metabolism]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/03/06  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-1-2  | ||
| + | |style="padding:.4em;"|G Koh  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://onlinelibrary.wiley.com/doi/full/10.1002/advs.202303925 Metagenomic Insight into The Global Dissemination of The Antibiotic Resistome]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/03/06  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|24-1-1  | ||
| + | |style="padding:.4em;"|G Koh  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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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| + | |||
| + | {|class=wikitable style="text-align:center;"  | ||
| + | |+style="text-align:left;font-size:12pt" | 2024-1 Advanced scOmics Data Analysis  | ||
| + | |-  | ||
| + | !scope="col" style="padding:.4em" | Date  | ||
| + | !scope="col" style="padding:.4em" | Team  | ||
| + | !scope="col" style="padding:.4em" | Paper<br/>index  | ||
| + | !scope="col" style="padding:.4em" | Presenter  | ||
| + | !scope="col" style="padding:.4em" | Paper title  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/06/18  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-32  | ||
| + | |style="padding:.4em;"|EB Hong  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41586-023-07011-6 Spatial transcriptomics reveal neuron–astrocyte synergy in long-term memory]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/06/18  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-31  | ||
| + | |style="padding:.4em;"|JJ Heo  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41467-021-22197-x scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/06/18  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-30  | ||
| + | |style="padding:.4em;"|SM Han  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1126/science.abi4882 Spatial transcriptomics of planktonic and sessile bacterial populations at single-cell resolution]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/06/18  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-29  | ||
| + | |style="padding:.4em;"|HJ Choi  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41590-024-01792-2 Human lung cancer harbors spatially organized stem-immunity hubs associated with response to immunotherapy]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/06/11  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-28  | ||
| + | |style="padding:.4em;"|SA Choi  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/06/11  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-27  | ||
| + | |style="padding:.4em;"|HJ Cha  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1016/j.chom.2023.08.019 Cell-type-specific responses to fungal infection in plants revealed by single-cell transcriptomics]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/06/11  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-26  | ||
| + | |style="padding:.4em;"|YK Jung  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/06/11  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-25  | ||
| + | |style="padding:.4em;"|HJ Lee  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/06/04  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-24  | ||
| + | |style="padding:.4em;"|HK Lee  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s42255-023-00876-x Delineating mouse β-cell identity during lifetime and in diabetes with a single cell atlas]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/06/04  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-23  | ||
| + | |style="padding:.4em;"|JI Lee  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41587-023-01747-2 Multimodal spatiotemporal phenotyping of human retinal organoid development]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/06/04  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-22  | ||
| + | |style="padding:.4em;"|JH Lee  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41586-024-07251-0 Immune microniches shape intestinal Treg function]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/06/04  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-21  | ||
| + | |style="padding:.4em;"|JH Lee  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1016/j.devcel.2021.02.021 A single-cell analysis of the Arabidopsis vegetative shoot apex]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/05/28  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-20  | ||
| + | |style="padding:.4em;"|JH Lee  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41467-023-40137-9 Droplet-based high-throughput single microbe RNA sequencing by smRandom-seq]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/05/28  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-19  | ||
| + | |style="padding:.4em;"|YH Lee  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/05/28  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-18  | ||
| + | |style="padding:.4em;"|EB Yu  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/05/28  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-17  | ||
| + | |style="padding:.4em;"|DY Won  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41587-023-01979-2 Spatial metatranscriptomics resolves host–bacteria–fungi interactomes]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/05/21  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-16  | ||
| + | |style="padding:.4em;"|SG Oh  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/05/21  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-15  | ||
| + | |style="padding:.4em;"|SY Park  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/05/21  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-14  | ||
| + | |style="padding:.4em;"|HS Moon  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/05/21  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-13  | ||
| + | |style="padding:.4em;"|JH Nam  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41467-023-39933-0 Spatial cellular architecture predicts prognosis in glioblastoma]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/05/14  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-12  | ||
| + | |style="padding:.4em;"|HS Na  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1016/j.celrep.2024.113784 Single-cell spatial transcriptomic and translatomic profiling of dopaminergic neurons in health, aging, and disease]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/05/14  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-11  | ||
| + | |style="padding:.4em;"|PK Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41467-022-30511-4 Transcriptional adaptation of olfactory sensory neurons to GPCR identity and activity]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/05/14  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-10  | ||
| + | |style="padding:.4em;"|SH Kwon  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41467-021-26271-2 Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/05/14  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-9  | ||
| + | |style="padding:.4em;"|Q Zhen  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1021/acscentsci.3c01169 Single-Cell Analysis Reveals Cxcl14+ Fibroblast Accumulation in Regenerating Diabetic Wounds Treated by Hydrogel-Delivering Carbon Monoxide]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/05/07  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-8  | ||
| + | |style="padding:.4em;"|CR Leenaars  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/05/07  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-7  | ||
| + | |style="padding:.4em;"|YR Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/05/07  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-6  | ||
| + | |style="padding:.4em;"|JY Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41467-022-35319-w Spatial transcriptomics landscape of lesions from non-communicable inflammatory skin diseases]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/05/07  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-5  | ||
| + | |style="padding:.4em;"|WJ Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1016/j.cmet.2022.07.010 Neuregulin 4 suppresses NASH-HCC development by restraining tumor-prone liver microenvironment]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/04/23  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-4  | ||
| + | |style="padding:.4em;"|G Koh  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41593-023-01334-3 Single-nucleus multiregion transcriptomic analysis of brain vasculature in Alzheimer’s disease]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/04/23  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-3  | ||
| + | |style="padding:.4em;"|SH Ahn  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/04/23  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-2  | ||
| + | |style="padding:.4em;"|EJ Sung  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41467-022-31519-6 Single cell sequencing identifies clonally expanded synovial CD4+ TPH cells expressing GPR56 in rheumatoid arthritis]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/04/23  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|24-1  | ||
| + | |style="padding:.4em;"|HJ Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |}  | ||
| + | |||
| + | |||
| + | |||
| + | {|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<br/>index  | ||
| + | !scope="col" style="padding:.4em" | Presenter  | ||
| + | !scope="col" style="padding:.4em" | Paper title  | ||
| + | |-  | ||
| + | |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]  | ||
| + | |}  | ||
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| + | {|class=wikitable style="text-align:center;"  | ||
| + | |+style="text-align:left;font-size:12pt" | 2023-2 Microbiome  | ||
| + | |-  | ||
| + | !scope="col" style="padding:.4em" | Date  | ||
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| + | !scope="col" style="padding:.4em" | Paper<br/>index  | ||
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| + | !scope="col" style="padding:.4em" | Paper title  | ||
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| + | |-  | ||
| + | |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://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-023-01692-x Integrating compositional and functional content to describe vaginal microbiomes in health and disease]  | ||
| + | |-  | ||
| + | |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  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1101/2023.07.03.547607 Multi-view integration of microbiome data for identifying disease-associated modules]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/01/24  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|23-60  | ||
| + | |style="padding:.4em;"|JY Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/01/24  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|23-59  | ||
| + | |style="padding:.4em;"|WJ Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/01/17  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|23-58  | ||
| + | |style="padding:.4em;"|G Koh  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/01/17  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|23-57  | ||
| + | |style="padding:.4em;"|SH Ahn  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41467-023-41042-x Impact of dietary interventions on pre-diabetic oral and gut microbiome, metabolites and cytokines]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/01/10  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|23-56  | ||
| + | |style="padding:.4em;"|HJ Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41592-023-02018-3 Fast and robust metagenomic sequence comparison through sparse chaining with skani]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2024/01/10  | ||
| + | |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]  | ||
| + | |-  | ||
| + | |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]  | ||
| + | |-  | ||
| + | |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]  | ||
| + | |-  | ||
| + | |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]  | ||
| + | |-  | ||
| + | |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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2023/12/13  | ||
| + | |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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2023/12/13  | ||
| + | |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]  | ||
| + | |-  | ||
| + | |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]  | ||
| + | |}  | ||
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| + | {|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<br/>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" | 2023-1 scOmics  | ||
| + | |-  | ||
| + | !scope="col" style="padding:.4em" | Date  | ||
| + | !scope="col" style="padding:.4em" | Team  | ||
| + | !scope="col" style="padding:.4em" | Paper<br/>index  | ||
| + | !scope="col" style="padding:.4em" | Presenter  | ||
| + | !scope="col" style="padding:.4em" | Paper title  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2023/08/30  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|23-24  | ||
| + | |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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2023/08/09  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|23-23  | ||
| + | |style="padding:.4em;"|IS Choi  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1101/2023.07.28.550993 Major data analysis errors invalidate cancer microbiome findings]  | ||
| + | |-  | ||
| + | |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  | ||
| + | |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]  | ||
| + | |-  | ||
| + | |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]  | ||
| + | |-  | ||
| + | |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]  | ||
| + | |-  | ||
| + | |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]  | ||
| + | |-  | ||
| + | |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]  | ||
| + | |-  | ||
| + | |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]  | ||
| + | |-  | ||
| + | |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]  | ||
| + | |-  | ||
| + | |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]  | ||
| + | |-  | ||
| + | |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]  | ||
| + | |-  | ||
| + | |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]  | ||
| + | |-  | ||
| + | |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]  | ||
| + | |-  | ||
| + | |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]  | ||
| + | |-  | ||
| + | |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]  | ||
| + | |-  | ||
| + | |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]  | ||
| + | |-  | ||
| + | |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  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.biorxiv.org/content/10.1101/2022.08.05.502989v1 MetaTiME: Meta-components of the Tumor Immune Microenvironment]  | ||
| + | |-  | ||
| + | |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]  | ||
| + | |-  | ||
| + | |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]  | ||
| + | |-  | ||
| + | |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  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.nature.com/articles/s41591-022-01799-y A T cell resilience model associated with response to immunotherapy in multiple tumor types]  | ||
| + | |-  | ||
| + | |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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2023/01/25  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|23-3  | ||
| + | |style="padding:.4em;"|G Koh  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://pubmed.ncbi.nlm.nih.gov/35649411/ Cross-tissue, single-cell stromal atlas identifies shared pathological fibroblast phenotypes in four chronic inflammatory diseases]  | ||
| + | |-  | ||
| + | |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  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.sciencedirect.com/science/article/pii/S1535610822003178 Pan-cancer integrative histology-genomic analysis via multimodal deep learning]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2023/01/11  | ||
| + | |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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| + | |||
| + | {|class=wikitable style="text-align:center;"  | ||
| + | |+style="text-align:left;font-size:12pt" | 2023-1 Microbiome  | ||
| + | |-  | ||
| + | !scope="col" style="padding:.4em" | Date  | ||
| + | !scope="col" style="padding:.4em" | Team  | ||
| + | !scope="col" style="padding:.4em" | Paper<br/>index  | ||
| + | !scope="col" style="padding:.4em" | Presenter  | ||
| + | !scope="col" style="padding:.4em" | Paper title  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2023/08/25  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|23-27  | ||
| + | |style="padding:.4em;"|JY Ma  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1016/j.chom.2023.05.024 Enterosignatures define common bacterial guilds in the human gut microbiome]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2023/08/25  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|23-26  | ||
| + | |style="padding:.4em;"|NY Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1186/s13059-023-02902-3 PhyloMed: a phylogeny-based test of mediation effect in microbiome]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2023/08/18  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|23-25  | ||
| + | |style="padding:.4em;"|SH Lee  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2023/08/18  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|23-24  | ||
| + | |style="padding:.4em;"|WJ Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [http://dx.doi.org/10.1038/nbt.3704 Measurement of bacterial replication rates in microbial communities]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2023/08/11  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|23-23  | ||
| + | |style="padding:.4em;"|JY Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2023/08/11  | ||
| + | |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.xcrm.2023.100920 Enrichment of oral-derived bacteria in inflamed colorectal tumors and distinct associations of Fusobacterium in the mesenchymal subtype]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2023/08/04  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|23-21  | ||
| + | |style="padding:.4em;"|HJ Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41586-023-05989-7 Profiling the human intestinal environment under physiological conditions]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2023/07/28  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|23-20  | ||
| + | |style="padding:.4em;"|JH Cha  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2023/07/14  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|23-19  | ||
| + | |style="padding:.4em;"|JY Ma  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2023/07/07  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|23-18  | ||
| + | |style="padding:.4em;"|NY Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1186/s13059-022-02809-5 Gene fow and introgression are pervasive forces shaping the evolution of bacterial species]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2023/06/30  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|23-17  | ||
| + | |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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2023/06/23  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|23-16  | ||
| + | |style="padding:.4em;"|HJ Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41467-022-33397-4 Deciphering microbial gene function using natural language processing]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2023/06/16  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|23-15  | ||
| + | |style="padding:.4em;"|SH Lee  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1101/2022.11.28.518265 Rethinking bacterial relationships in light of their molecular abilities]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2023/06/02  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|23-14  | ||
| + | |style="padding:.4em;"|JY Ma  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2023/05/26  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|23-13  | ||
| + | |style="padding:.4em;"|NY Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41591-018-0203-7 Antigen discovery and specification of immunodominance hierarchies for MHCIIrestricted epitopes]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2023/05/19  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|23-12  | ||
| + | |style="padding:.4em;"|SH Lee  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1101/2022.10.11.511790 Single Cell Transcriptomics Reveals the Hidden Microbiomes of Human Tissues]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2023/05/12  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|23-11  | ||
| + | |style="padding:.4em;"|JY Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41564-017-0096-0 Stability of the human faecal microbiome in a cohort of adult men]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2023/04/28  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|23-10  | ||
| + | |style="padding:.4em;"|WJ Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41564-017-0084-4 Metatranscriptome of human faecal microbial communities in a cohort of adult men]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2023/03/24  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|23-9  | ||
| + | |style="padding:.4em;"|SH Ahn  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1016/j.ccell.2022.09.009 Tumor microbiome links cellular programs and immunity in pancreatic cancer]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2023/03/17  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|23-8  | ||
| + | |style="padding:.4em;"|HJ Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2023/03/10  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|23-7  | ||
| + | |style="padding:.4em;"|JH Cha  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1038/s41586-022-05620-1 The person-to-person transmission landscape of the gut and oral microbiomes]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2023/02/21  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|23-6  | ||
| + | |style="padding:.4em;"|JY Ma  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1101/2023.01.30.526328 BIRDMAn: A Bayesian differential abundance framework that enables robust inference of host-microbe associations]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2023/02/14  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|23-5  | ||
| + | |style="padding:.4em;"|NY Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.nature.com/articles/s41564-022-01157-1 Phage–host coevolution in natural populations]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2023/01/31  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|23-4  | ||
| + | |style="padding:.4em;"|SH Lee  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2023/01/25  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|23-3  | ||
| + | |style="padding:.4em;"|SH Ahn  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.biorxiv.org/content/10.1101/2022.05.19.492684v1 Scalable power analysis and effect size exploration of microbiome community differences with Evident]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2023/01/17  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|23-2  | ||
| + | |style="padding:.4em;"|HJ Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.biorxiv.org/content/10.1101/2022.08.05.502982v1 Phanta: Phage-inclusive profiling of human gut metagenomes]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2023/01/11  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|23-1  | ||
| + | |style="padding:.4em;"|JH Cha  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1010373 Computational approach to modeling microbiome landscapes associated with chronic human disease progression]  | ||
| + | |}  | ||
| + | |||
| + | |||
| + | |||
| + | {|class=wikitable style="text-align:center;"  | ||
| + | |+style="text-align:left;font-size:12pt" | 2022 scOmics  | ||
| + | |-  | ||
| + | !scope="col" style="padding:.4em" | Date  | ||
| + | !scope="col" style="padding:.4em" | Team  | ||
| + | !scope="col" style="padding:.4em" | Paper<br/>index  | ||
| + | !scope="col" style="padding:.4em" | Presenter  | ||
| + | !scope="col" style="padding:.4em" | Paper title  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/12/28  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|22-32  | ||
| + | |style="padding:.4em;"|EJ Sung  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.biorxiv.org/content/10.1101/2022.08.19.504505v1 SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/11/29  | ||
| + | |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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/11/22  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|22-30  | ||
| + | |style="padding:.4em;"|IS Choi  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.nature.com/articles/s41467-022-31535-6 Network-based machine learning approach to predict immunotherapy response in cancer patients]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/11/08  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|22-29  | ||
| + | |style="padding:.4em;"|SB Baek  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.biorxiv.org/content/10.1101/2022.05.04.490536v1 Modeling fragment counts improves single-cell ATAC-seq analysis]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/10/11  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|22-28  | ||
| + | |style="padding:.4em;"|G Koh  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.nature.com/articles/s41586-022-04718-w Extricating human tumour immune alterations from tissue inflammation]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/09/13  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|22-25  | ||
| + | |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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/09/06  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|22-26  | ||
| + | |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]  | ||
| + | |-  | ||
| + | |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  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.biorxiv.org/content/10.1101/2021.12.06.471401v1 MIRA: Joint regulatory modeling of multimodal expression and chromatin accessibility in single cells]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/08/25  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|22-24  | ||
| + | |style="padding:.4em;"|EJ Sung  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.sciencedirect.com/science/article/pii/S1535610822000654 Immune phenotypic linkage between colorectal cancer and liver metastasis]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/08/18  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|22-23  | ||
| + | |style="padding:.4em;"|IS Choi  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://doi.org/10.1101/2022.02.05.479217 Biologically informed deep learning to infer gene program activity in single cells]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/08/11  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|22-22  | ||
| + | |style="padding:.4em;"|SB Baek  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/07/28  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|22-21  | ||
| + | |style="padding:.4em;"|JH Cha  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://pubmed.ncbi.nlm.nih.gov/34462589/ Mapping single-cell data to reference atlases by transfer learning]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/07/21  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|22-20  | ||
| + | |style="padding:.4em;"|JW Yu  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/07/14  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|22-19  | ||
| + | |style="padding:.4em;"|IS Choi  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/07/07  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|22-18  | ||
| + | |style="padding:.4em;"|EJ Sung	  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/06/23  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|22-17  | ||
| + | |style="padding:.4em;"|SB Baek	  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://pubmed.ncbi.nlm.nih.gov/34675423/ Co-varying neighborhood analysis identifies cell populations associated with phenotypes of interest from single-cell transcriptomics]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/06/09  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|22-16  | ||
| + | |style="padding:.4em;"|JH Cha	  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://pubmed.ncbi.nlm.nih.gov/34426704/ Integrating T cell receptor sequences and transcriptional profiles by clonotype neighbor graph analysis (CoNGA)]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/06/02  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|22-15  | ||
| + | |style="padding:.4em;"|JW Yu		  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/05/19  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|22-14  | ||
| + | |style="padding:.4em;"|EJ Sung		  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://pubmed.ncbi.nlm.nih.gov/35199064/ Effect of imputation on gene network reconstruction from single-cell RNA-seq data]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/05/12  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|22-13  | ||
| + | |style="padding:.4em;"|IS Choi		  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/04/07  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|22-12  | ||
| + | |style="padding:.4em;"|SB Baek		  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/03/25  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|22-11  | ||
| + | |style="padding:.4em;"|JH Cha		  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/03/18  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|22-10  | ||
| + | |style="padding:.4em;"|JW Yu			  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://pubmed.ncbi.nlm.nih.gov/34594031/ Systematic investigation of cytokine signaling activity at the tissue and single-cell levels]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/03/04  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|22-9  | ||
| + | |style="padding:.4em;"|EJ Sung		  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/02/25  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|22-8  | ||
| + | |style="padding:.4em;"|SB Baek		  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://pubmed.ncbi.nlm.nih.gov/34930412/ MultiMAP: dimensionality reduction and integration of multimodal data]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/02/18  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|22-7  | ||
| + | |style="padding:.4em;"|IS Choi		  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.biorxiv.org/content/10.1101/2020.10.19.345983v2 CellRank for directed single-cell fate mapping]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/02/11  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|22-6  | ||
| + | |style="padding:.4em;"|JH Cha		  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/02/04  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|22-5  | ||
| + | |style="padding:.4em;"|IS Choi  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://pubmed.ncbi.nlm.nih.gov/34390642/ Chromatin and gene-regulatory dynamics of the developing human cerebral cortex at single-cell resolution]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/01/28  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|22-4  | ||
| + | |style="padding:.4em;"|EJ Sung		  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://pubmed.ncbi.nlm.nih.gov/34767762/ Single-cell analysis of human non-small cell lung cancer lesions refines tumor classification and patient stratification]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/01/28  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|22-3  | ||
| + | |style="padding:.4em;"|JH Cha		  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://pubmed.ncbi.nlm.nih.gov/34914499/ Pan-cancer single-cell landscape of tumor-infiltrating T cells]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/01/14  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|22-2  | ||
| + | |style="padding:.4em;"|JW Yu		  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://pubmed.ncbi.nlm.nih.gov/34597583/ Atlas of clinically distinct cell states and ecosystems across human solid tumors]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/01/07  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|22-1  | ||
| + | |style="padding:.4em;"|SB Baek		  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://pubmed.ncbi.nlm.nih.gov/34489465/ Single-cell ATAC and RNA sequencing reveal pre-existing and persistent cells associated with prostate cancer relapse]  | ||
| + | |}  | ||
| + | |||
| + | |||
| + | {|class=wikitable style="text-align:center;"  | ||
| + | |+style="text-align:left;font-size:12pt" | 2022 Microbiome  | ||
| + | |-  | ||
| + | !scope="col" style="padding:.4em" | Date  | ||
| + | !scope="col" style="padding:.4em" | Team  | ||
| + | !scope="col" style="padding:.4em" | Paper<br/>index  | ||
| + | !scope="col" style="padding:.4em" | Presenter  | ||
| + | !scope="col" style="padding:.4em" | Paper title  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/12/28  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|22-34  | ||
| + | |style="padding:.4em;"|JY Ma  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/11/29  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|22-33  | ||
| + | |style="padding:.4em;"|NY Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.biorxiv.org/content/10.1101/2021.09.13.460160v3 Inference of disease-associated microbial biomarkers based on metagenomic and metatranscriptomic data]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/11/22  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|22-32  | ||
| + | |style="padding:.4em;"|SH Lee  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.sciencedirect.com/science/article/pii/S0092867422009199?via%3Dihub Personalized microbiome-driven effects of non-nutritive sweeteners on human glucose tolerance]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/10/11  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|22-31  | ||
| + | |style="padding:.4em;"|SH Ahn  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/09/27  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|22-30  | ||
| + | |style="padding:.4em;"|HJ Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.sciencedirect.com/science/article/pii/S193131282200049X Caudovirales bacteriophages are associated with improved executive function and memory in flies, mice, and humans]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/09/13  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|22-29  | ||
| + | |style="padding:.4em;"|JH Cha  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.biorxiv.org/content/10.1101/2021.10.06.463341v2.full SynTracker: a synteny based tool for tracking microbial strains]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/09/06  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|22-28  | ||
| + | |style="padding:.4em;"|JY Ma  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.nature.com/articles/s41587-022-01226-0 Identification of antimicrobial peptides from the human gut microbiome using deep learning]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/09/01  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|22-27  | ||
| + | |style="padding:.4em;"|NY Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.nature.com/articles/s41586-022-04648-7 Discovery of bioactive microbial gene products in inflammatory bowel disease]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/08/25  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|22-26  | ||
| + | |style="padding:.4em;"|SH Lee  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.nature.com/articles/s43588-022-00247-8 Large-scale microbiome data integration enables robust biomarker identification]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/08/18  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|22-25  | ||
| + | |style="padding:.4em;"|SH Ahn  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.nature.com/articles/s41467-022-30512-3 Predicting cancer prognosis and drug response from the tumor microbiome]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/08/11  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|22-24  | ||
| + | |style="padding:.4em;"|HJ Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.cell.com/cell-reports/pdf/S2211-1247(22)00770-7.pdf Thousands of small, novel genes predicted in global phage genomes]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/08/04  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|22-23  | ||
| + | |style="padding:.4em;"|JH Cha  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/07/28  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|22-22  | ||
| + | |style="padding:.4em;"|NY Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.nature.com/articles/s41586-022-04862-3 Biosynthetic potential of the global ocean microbiome]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/07/14  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|22-21  | ||
| + | |style="padding:.4em;"|JY Ma  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://journals.asm.org/doi/10.1128/msystems.00050-22 Compositionally Aware Phylogenetic Beta-Diversity Measures Better Resolve Microbiomes Associated with Phenotype]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/07/07  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|22-20  | ||
| + | |style="padding:.4em;"|SH Lee  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/06/24  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|22-19  | ||
| + | |style="padding:.4em;"|SH Ann  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.sciencedirect.com/science/article/pii/S2666379121002561 Identification of Faecalibacterium prausnitzii strains for gut microbiome-based intervention in Alzheimer’s-type dementia]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/06/10  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|22-18  | ||
| + | |style="padding:.4em;"|HJ Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.nature.com/articles/s41591-022-01688-4 Microbiome and metabolome features of the cardiometabolic disease spectrum]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/06/03  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|22-17  | ||
| + | |style="padding:.4em;"|JH Cha	  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/05/27  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|22-16  | ||
| + | |style="padding:.4em;"|NY Kim	  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.biorxiv.org/content/10.1101/2022.02.21.480893v1 Integrating phylogenetic and functional data in microbiome studies]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/05/20  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|22-15  | ||
| + | |style="padding:.4em;"|MY Ma		  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://genomebiology.biomedcentral.com/articles/10.1186/s13059-021-02473-1 Pandora: nucleotide-resolution bacterial pan-genomics with reference graphs]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/05/13  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|22-14  | ||
| + | |style="padding:.4em;"|SH Lee	  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009442 Multivariable association discovery in population-scale meta-omics studies]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/04/08  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|22-13  | ||
| + | |style="padding:.4em;"|SH Ahn		  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/04/01  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|22-12  | ||
| + | |style="padding:.4em;"|HJ Kim		  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://genomebiology.biomedcentral.com/articles/10.1186/s13059-022-02610-4 AGAMEMNON: an Accurate metaGenomics And MEtatranscriptoMics quaNtificatiON analysis suite]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/03/18  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|22-11  | ||
| + | |style="padding:.4em;"|JH Cha		  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02200-2 Metapangenomics of the oral microbiome provides insights into habitat adaptation and cultivar diversity]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/03/04  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|22-10  | ||
| + | |style="padding:.4em;"|JY Ma		  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://genomemedicine.biomedcentral.com/articles/10.1186/s13073-022-01011-3 Microbiota of the prostate tumor environment investigated by whole-transcriptome profiling]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/02/25  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|22-9  | ||
| + | |style="padding:.4em;"|NY Kim		  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://genomebiology.biomedcentral.com/articles/10.1186/s13059-021-02576-9 Species-resolved sequencing of low-biomass or degraded microbiomes using 2bRAD-M]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/02/18  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|22-8  | ||
| + | |style="padding:.4em;"|SH Lee		  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://pubmed.ncbi.nlm.nih.gov/34893089/ Microbial co-occurrence complicates associations of gut microbiome with US immigration, dietary intake and obesity]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/02/11  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|22-7  | ||
| + | |style="padding:.4em;"|SH Ahn		  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://pubmed.ncbi.nlm.nih.gov/34517888/ Gut microbial determinants of clinically important improvement in patients with rheumatoid arthritis]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/02/04  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|22-6  | ||
| + | |style="padding:.4em;"|JH Cha		  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://pubmed.ncbi.nlm.nih.gov/34880502/ Gut microbiota modulates weight gain in mice after discontinued smoke exposure]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/01/28  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|22-5  | ||
| + | |style="padding:.4em;"|JY Ma			  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://pubmed.ncbi.nlm.nih.gov/34819672/ The human microbiome encodes resistance to the antidiabetic drug acarbose]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/01/28  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|22-4  | ||
| + | |style="padding:.4em;"|SH Lee		  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://pubmed.ncbi.nlm.nih.gov/34618582/ Commensal bacteria promote endocrine resistance in prostate cancer through androgen biosynthesis]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/01/14  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|22-3  | ||
| + | |style="padding:.4em;"|HJ Kim		  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://pubmed.ncbi.nlm.nih.gov/34551799/ The influence of the gut microbiome on BCG-induced trained immunity]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/01/07  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|22-2  | ||
| + | |style="padding:.4em;"|JY Ma		  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://pubmed.ncbi.nlm.nih.gov/34912116/ Towards the biogeography of prokaryotic genes]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/01/07  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|22-1  | ||
| + | |style="padding:.4em;"|NY Kim	  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://pubmed.ncbi.nlm.nih.gov/29347966/ ReprDB and panDB: minimalist databases with maximal microbial representation]  | ||
| + | |}  | ||
| + | |||
| + | |||
| + | {|class=wikitable style="text-align:center;"  | ||
| + | |+style="text-align:left;font-size:12pt" | 2022 Microbiome Special JC  | ||
| + | |-  | ||
| + | !scope="col" style="padding:.4em" | Date  | ||
| + | !scope="col" style="padding:.4em" | Team  | ||
| + | !scope="col" style="padding:.4em" | Paper<br/>index  | ||
| + | !scope="col" style="padding:.4em" | Presenter  | ||
| + | !scope="col" style="padding:.4em" | Paper title  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/08/30  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|22-15  | ||
| + | |style="padding:.4em;"|HY Kang  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.biorxiv.org/content/10.1101/2022.07.06.499075v1 Maast: genotyping thousands of microbial strains efficiently]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/08/30  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|22-14  | ||
| + | |style="padding:.4em;"|YJ Roh  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.biorxiv.org/content/10.1101/2022.06.16.496510v2 MIDAS2: Metagenomic Intra-species Diversity Analysis System]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/08/30  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|22-13  | ||
| + | |style="padding:.4em;"|SC Yang  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.biorxiv.org/content/10.1101/2022.02.01.478746v2 Scalable microbial strain inference in metagenomic data using StrainFacts]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/08/26  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|22-12  | ||
| + | |style="padding:.4em;"|SH Ahn  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/08/26  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|22-11  | ||
| + | |style="padding:.4em;"|HJ Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/08/26  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|22-10  | ||
| + | |style="padding:.4em;"|JY Ma  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.nature.com/articles/s41587-021-01102-3 Fast and accurate metagenotyping of the human gut microbiome with GT-Pro]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/08/19  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|22-9  | ||
| + | |style="padding:.4em;"|JH Cha  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.nature.com/articles/s41587-020-00797-0 inStrain profiles population microdiversity from metagenomic data and sensitively detects shared microbial strains]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/08/19  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|22-8  | ||
| + | |style="padding:.4em;"|NY Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/08/19  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|22-7  | ||
| + | |style="padding:.4em;"|SH Lee  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.sciencedirect.com/science/article/pii/S1931312821002365 Dispersal strategies shape persistence and evolution of human gut bacteria]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/08/09  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|22-6  | ||
| + | |style="padding:.4em;"|SH Ahn  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.sciencedirect.com/science/article/pii/S0092867421003524 The long-term genetic stability and individual specificity of the human gut microbiome]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/08/09  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|22-5  | ||
| + | |style="padding:.4em;"|HJ Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/08/09  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|22-4  | ||
| + | |style="padding:.4em;"|JY Ma  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3000102 Evolutionary dynamics of bacteria in the gut microbiome within and across hosts]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/07/29  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|22-3  | ||
| + | |style="padding:.4em;"|JH Cha  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://elifesciences.org/articles/42693 Extensive transmission of microbes along the gastrointestinal tract]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/07/29  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|22-2  | ||
| + | |style="padding:.4em;"|NY Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2022/07/29  | ||
| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
| + | |style="padding:.4em;"|22-1  | ||
| + | |style="padding:.4em;"|SH Lee  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.nature.com/articles/nmeth.3802 Strain-level microbial epidemiology and population genomics from shotgun metagenomics]  | ||
| + | |}  | ||
| + | |||
| + | |||
| + | |||
| + | {|class=wikitable style="text-align:center;"  | ||
| + | |+style="text-align:left;font-size:12pt" | 2021-2nd semester  | ||
| + | |-  | ||
| + | !scope="col" style="padding:.4em" |Date  | ||
| + | !scope="col" stype="padding:.4em" | Team  | ||
| + | !scope="col" style="padding:.4em" | Paper<br/>index  | ||
| + | !scope="col" style="padding:.4em" | Presenter  | ||
| + | !scope="col" style="padding:.4em" | Paper title  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2021/11/23  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|21-39  | ||
| + | |style="padding:.4em;"|IS Choi  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2021/11/16  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|21-38  | ||
| + | |style="padding:.4em;"|SB Back  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2021/11/09  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|21-37  | ||
| + | |style="padding:.4em;"|JH Cha  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2021/11/02  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|21-36  | ||
| + | |style="padding:.4em;"|SB Baek  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.biorxiv.org/content/10.1101/2021.07.28.453784v1 Functional Inference of Gene Regulation using Single-Cell Multi-Omics]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2021/10/26  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|21-35  | ||
| + | |style="padding:.4em;"|IS Choi  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2021/10/19  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|21-34  | ||
| + | |style="padding:.4em;"|JH Cha  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2021/10/05  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|21-33  | ||
| + | |style="padding:.4em;"|JH Cha  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.sciencedirect.com/science/article/pii/S1535610821001173 Tumor and immune reprogramming during immunotherapy in advanced renal cell carcinoma]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2021/09/28  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|21-32  | ||
| + | |style="padding:.4em;"|SB Baek  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.sciencedirect.com/science/article/pii/S1074761321001199 Single-cell chromatin accessibility landscape identifies tissue repair program in human regulatory T cells]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2021/09/14  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|21-31  | ||
| + | |style="padding:.4em;"|IS Choi   | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2021/09/07  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|21-30  | ||
| + | |style="padding:.4em;"|JH Cha  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2021/08/31  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|21-29  | ||
| + | |style="padding:.4em;"|IS Choi   | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.sciencedirect.com/science/article/abs/pii/S0092867420316135 Single-cell landscape of the ecosystem in early-relapse hepatocellular carcinoma]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2021/08/24  | ||
| + | |style="padding:.4em;" rowspan=1|Single-cell  | ||
| + | |style="padding:.4em;"|21-28  | ||
| + | |style="padding:.4em;"|SB Baek  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.nature.com/articles/s41586-021-03552-w Interpreting type 1 diabetes risk with genetics and single-cell epigenomics]  | ||
| + | |}  | ||
| + | {|class=wikitable style="text-align:center;"  | ||
| + | |+style="text-align:left;font-size:12pt" | 2021-1st semester  | ||
| + | |-  | ||
| + | !scope="col" style="padding:.4em" |Date  | ||
| + | !scope="col" stype="padding:.4em" | Team  | ||
| + | !scope="col" style="padding:.4em" | Paper<br/>index  | ||
| + | !scope="col" style="padding:.4em" | Presenter  | ||
| + | !scope="col" style="padding:.4em" | Paper title  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=2|2021/06/03  | ||
| + | |style="padding:.4em;" rowspan=2|-  | ||
| + | |style="padding:.4em;"|21-27  | ||
| + | |style="padding:.4em;"|HJ Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.sciencedirect.com/science/article/pii/S0092867421000726 Massive expansion of human gut bacteriophage diversity]  | ||
| + | |-  | ||
| + | |style="padding:.4em;"|21-26  | ||
| + | |style="padding:.4em;"|JY Ma  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2021/05/27  | ||
| + | |style="padding:.4em;" rowspan=1|-  | ||
| + | |style="padding:.4em;"|21-25  | ||
| + | |style="padding:.4em;"|JK Yoon  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=2|2021/05/20  | ||
| + | |style="padding:.4em;" rowspan=2|-  | ||
| + | |style="padding:.4em;"|21-24  | ||
| + | |style="padding:.4em;"|NY Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://science.sciencemag.org/content/366/6471/eaax9176 A metagenomic strategy for harnessing the chemical repertoire of the human microbiome]  | ||
| + | |-  | ||
| + | |style="padding:.4em;"|21-23  | ||
| + | |style="padding:.4em;"|IS Choi  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.nature.com/articles/s41467-019-10927-1 Predictive metabolomic profiling of microbial communities using amplicon or metagenomic sequences]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=2|2021/05/13  | ||
| + | |style="padding:.4em;" rowspan=2|-  | ||
| + | |style="padding:.4em;"|21-22  | ||
| + | |style="padding:.4em;"|SA Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.nature.com/articles/s41564-018-0306-4 Gut microbiome structure and metabolic activity in inflammatory bowel disease]  | ||
| + | |-  | ||
| + | |style="padding:.4em;"|21-21  | ||
| + | |style="padding:.4em;"|HJ Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.nature.com/articles/s41591-020-01183-8 Microbiome connections with host metabolism and habitual diet from 1,098 deeply phenotyped individuals]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=2|2021/05/06  | ||
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| + | |style="padding:.4em;"|21-20  | ||
| + | |style="padding:.4em;"|JY Ma  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.nature.com/articles/s41467-020-18476-8 A predictive index for health status using species-level gut microbiome profiling]  | ||
| + | |-  | ||
| + | |style="padding:.4em;"|21-19  | ||
| + | |style="padding:.4em;"|JH Cha  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.nature.com/articles/s41586-019-1237-9 Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=2|2021/04/29  | ||
| + | |style="padding:.4em;" rowspan=2|-  | ||
| + | |style="padding:.4em;"|21-18  | ||
| + | |style="padding:.4em;"|SB Baek  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.nature.com/articles/s41467-021-21475-y Gastrointestinal microbiota composition predicts peripheral inflammatory state during treatment of human tuberculosis]  | ||
| + | |-  | ||
| + | |style="padding:.4em;"|21-17  | ||
| + | |style="padding:.4em;"|SR You  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.sciencedirect.com/science/article/pii/S1931312820301694 Structure of the Mucosal and Stool Microbiome in Lynch Syndrome]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=2|2021/04/22  | ||
| + | |style="padding:.4em;" rowspan=2|-  | ||
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| + | |style="padding:.4em;"|HH  Eom  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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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| + | |style="padding:.4em;"|JH Park  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.nature.com/articles/s41564-020-00831-6 Bifidobacterium bifidum strains synergize with immune checkpoint inhibitors to reduce tumour burden in mice]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2021/04/15  | ||
| + | |style="padding:.4em;" rowspan=1|-  | ||
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| + | |style="padding:.4em;"|MH Lee  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.nature.com/articles/s41591-020-01223-3 The gut microbiome modulates the protective association between a Mediterranean diet and cardiometabolic disease risk]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2021/04/08  | ||
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| + | |style="padding:.4em;"|YY Jang  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.nature.com/articles/s41467-019-14177-z Impact of commonly used drugs on the composition and metabolic function of the gut microbiota]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2021/04/01  | ||
| + | |style="padding:.4em;" rowspan=1|-  | ||
| + | |style="padding:.4em;"|21-12  | ||
| + | |style="padding:.4em;"|NY Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.sciencedirect.com/science/article/pii/S0092867420305638 Personalized Mapping of Drug Metabolism by the Human Gut Microbiome]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2021/03/25  | ||
| + | |style="padding:.4em;" rowspan=1|-  | ||
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| + | |style="padding:.4em;"|JM Lee  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.cell.com/fulltext/S0092-8674(17)30107-1 Mining the Human Gut Microbiota for Immunomodulatory Organisms]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2021/03/18  | ||
| + | |style="padding:.4em;" rowspan=1|-  | ||
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| + | |style="padding:.4em;"|JH Cha  | ||
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| + | [https://www.nature.com/articles/s41586-020-2095-1 Microbiome analyses of blood and tissues suggest cancer diagnostic approach]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=1|2021/03/11  | ||
| + | |style="padding:.4em;" rowspan=1|-  | ||
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| + | |style="padding:.4em;"|JH Cha  | ||
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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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| + | !scope="col" style="padding:.4em" | Paper title  | ||
| + | |-  | ||
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| + | |style="padding:.4em;"|IS Choi  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.nature.com/articles/s41590-020-0784-4 Functional CRISPR dissection of gene networks controlling human regulatory T cell identity]  | ||
| + | |-  | ||
| + | |style="padding:.4em;"|21-7  | ||
| + | |style="padding:.4em;"|JH Cha  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.sciencedirect.com/science/article/pii/S0092867420306887 Molecular Pathways of Colon Inflammation Induced by Cancer Immunotherapy]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=2|2021/02/15  | ||
| + | |style="padding:.4em;" rowspan=2|Single-cell  | ||
| + | |style="padding:.4em;"|21-6  | ||
| + | |style="padding:.4em;"|SB Baek  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;"|21-5  | ||
| + | |style="padding:.4em;"|IS Choi	  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.nature.com/articles/s41467-020-14766-3 Trajectory-based differential expression analysis for single-cell sequencing data]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=2|2021/02/08  | ||
| + | |style="padding:.4em;" rowspan=2|Single-cell  | ||
| + | |style="padding:.4em;"|21-4  | ||
| + | |style="padding:.4em;"|SB Baek	  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.nature.com/articles/s41588-018-0156-2 Genetic determinants of co-accessible chromatin regions in activated T cells across humans]  | ||
| + | |-  | ||
| + | |style="padding:.4em;"|21-3  | ||
| + | |style="padding:.4em;"|JH Cha  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.sciencedirect.com/science/article/pii/S009286742030341X Single-Cell Analyses Inform Mechanisms of Myeloid-Targeted Therapies in Colon Cancer]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=2|2021/02/01  | ||
| + | |style="padding:.4em;" rowspan=2|Single-cell  | ||
| + | |style="padding:.4em;"|21-2  | ||
| + | |style="padding:.4em;"|JW Cho  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;"|21-1  | ||
| + | |style="padding:.4em;"|JW Cho  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |}  | ||
| + | |||
| + | {|class=wikitable style="text-align:center;"  | ||
| + | |+style="text-align:left;font-size:12pt" | 2020-1st semester   | ||
| + | |-  | ||
| + | !scope="col" style="padding:.4em" |Date  | ||
| + | !scope="col" stype="padding:.4em" | Team  | ||
| + | !scope="col" style="padding:.4em" | Paper<br/>index  | ||
| + | !scope="col" style="padding:.4em" | Presenter  | ||
| + | !scope="col" style="padding:.4em" | Paper title  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=3|2021/02/01  | ||
| + | |style="padding:.4em;" rowspan=3|-  | ||
| + | |style="padding:.4em;"|20-15  | ||
| + | |style="padding:.4em;"|JW Cho  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://pubmed.ncbi.nlm.nih.gov/32393797/ Multi-omic single-cell snapshots reveal multiple independent trajectories to drug tolerance in a melanoma cell line]  | ||
| + | |-  | ||
| + | |style="padding:.4em;"|20-14  | ||
| + | |style="padding:.4em;"|JW Choi  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://pubmed.ncbi.nlm.nih.gov/30595452/ Dysfunctional CD8 T Cells Form a Proliferative, Dynamically Regulated Compartment Within Human Melanoma]  | ||
| + | |-  | ||
| + | |style="padding:.4em;"|20-13  | ||
| + | |style="padding:.4em;"|JW Seo  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://pubmed.ncbi.nlm.nih.gov/30388455/ A Cancer Cell Program Promotes T Cell Exclusion and Resistance to Checkpoint Blockade]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=2|2020/06/09  | ||
| + | |style="padding:.4em;" rowspan=2|-  | ||
| + | |style="padding:.4em;"|20-12  | ||
| + | |style="padding:.4em;"|JY Lee  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://pubmed.ncbi.nlm.nih.gov/31974247/ Single-cell Transcriptional Diversity Is a Hallmark of Developmental Potential]  | ||
| + | |-  | ||
| + | |style="padding:.4em;"|20-11  | ||
| + | |style="padding:.4em;"|JH Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://pubmed.ncbi.nlm.nih.gov/31675496/ Landscape and Dynamics of Single Immune Cells in Hepatocellular Carcinoma]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=2|2020/06/02  | ||
| + | |style="padding:.4em;" rowspan=2|-  | ||
| + | |style="padding:.4em;"|20-10  | ||
| + | |style="padding:.4em;"|HY Seo  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://pubmed.ncbi.nlm.nih.gov/32103181/ Peripheral T Cell Expansion Predicts Tumour Infiltration and Clinical Response]  | ||
| + | |-  | ||
| + | |style="padding:.4em;"|20-9  | ||
| + | |style="padding:.4em;"|KH Hong  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://pubmed.ncbi.nlm.nih.gov/30388456/ Defining T Cell States Associated With Response to Checkpoint Immunotherapy in Melanoma]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=2|2020/05/26  | ||
| + | |style="padding:.4em;" rowspan=2|-  | ||
| + | |style="padding:.4em;"|20-8  | ||
| + | |style="padding:.4em;"|JY Seong  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://pubmed.ncbi.nlm.nih.gov/31915379/ Rapid Non-Uniform Adaptation to Conformation-Specific KRAS(G12C) Inhibition]  | ||
| + | |-  | ||
| + | |style="padding:.4em;"|20-7  | ||
| + | |style="padding:.4em;"|OY Min  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://pubmed.ncbi.nlm.nih.gov/31959990/ Targeted Therapy Guided by Single-Cell Transcriptomic Analysis in Drug-Induced Hypersensitivity Syndrome: A Case Report]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=2|2020/05/19  | ||
| + | |style="padding:.4em;" rowspan=2|-  | ||
| + | |style="padding:.4em;"|20-6  | ||
| + | |style="padding:.4em;"|SN Lee  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://pubmed.ncbi.nlm.nih.gov/32066951/ Distinct Microbial and Immune Niches of the Human Colon]  | ||
| + | |-  | ||
| + | |style="padding:.4em;"|20-5  | ||
| + | |style="padding:.4em;"|DJ Park  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://pubmed.ncbi.nlm.nih.gov/31375813/ Massively Parallel Single-Cell Chromatin Landscapes of Human Immune Cell Development and Intratumoral T Cell Exhaustion]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=2|2020/05/12  | ||
| + | |style="padding:.4em;" rowspan=2|-  | ||
| + | |style="padding:.4em;"|20-4  | ||
| + | |style="padding:.4em;"|SY Park  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://pubmed.ncbi.nlm.nih.gov/29434354/ Single-cell Gene Expression Reveals a Landscape of Regulatory T Cell Phenotypes Shaped by the TCR]  | ||
| + | |-  | ||
| + | |style="padding:.4em;"|20-3  | ||
| + | |style="padding:.4em;"|NY Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://pubmed.ncbi.nlm.nih.gov/30078704/ A Single-Cell Atlas of In Vivo Mammalian Chromatin Accessibility]  | ||
| + | |-  | ||
| + | |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  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://pubmed.ncbi.nlm.nih.gov/32014031/ scAI: An Unsupervised Approach for the Integrative Analysis of Parallel Single-Cell Transcriptomic and Epigenomic Profiles]  | ||
| + | |-  | ||
| + | |style="padding:.4em;"|20-1  | ||
| + | |style="padding:.4em;"|JH Cha  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://pubmed.ncbi.nlm.nih.gov/31792411/ Single-cell Multiomic Analysis Identifies Regulatory Programs in Mixed-Phenotype Acute Leukemia]  | ||
| + | |-  | ||
| + | {|class=wikitable style="text-align:center;"  | ||
| + | |+style="text-align:left;font-size:12pt" | 2019-2nd semester  | ||
| + | |-  | ||
| + | !scope="col" style="padding:.4em" |Date  | ||
| + | !scope="col" stype="padding:.4em" | Team  | ||
| + | !scope="col" style="padding:.4em" | Paper<br/>index  | ||
| + | !scope="col" style="padding:.4em" | Presenter  | ||
| + | !scope="col" style="padding:.4em" | Paper title  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=2|2019/10/15  | ||
| + | |style="padding:.4em;" rowspan=2|Microbiome  | ||
| + | |style="padding:.4em;"|19-51  | ||
| + | |style="padding:.4em;"|CY Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.sciencedirect.com/science/article/pii/S1931312819303488 Obese Individuals with and without Type 2 Diabetes Show Different Gut Microbial Functional Capacity and Composition]  | ||
| + | |-  | ||
| + | |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]  | ||
| + | |-  | ||
| + | |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]  | ||
| + | |-  | ||
| + | |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]  | ||
| + | |-  | ||
| + | |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]  | ||
| + | |-  | ||
| + | |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]  | ||
| + | |-  | ||
| + | |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]  | ||
| + | |-  | ||
| + | |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]  | ||
| + | |-  | ||
| + | |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]  | ||
| + | |-  | ||
| + | |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]  | ||
| + | |-  | ||
| + | |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]  | ||
| + | |-  | ||
| + | |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]  | ||
| + | |-  | ||
| + | |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]  | ||
| + | |-  | ||
| + | |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]  | ||
| + | |}  | ||
| + | {|class=wikitable style="text-align:center;"  | ||
| + | |+style="text-align:left;font-size:12pt" | 2019  | ||
| + | |-  | ||
| + | !scope="col" style="padding:.4em" |Date  | ||
| + | !scope="col" style="padding:.4em" | Paper<br/>index  | ||
| + | !scope="col" style="padding:.4em" | Presenter  | ||
| + | !scope="col" style="padding:.4em" | Paper title  | ||
| + | |-  | ||
| + | |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]  | ||
| + | |-  | ||
| + | |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]  | ||
| + | |-  | ||
| + | |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]  | ||
| + | |-  | ||
| + | |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]  | ||
| + | |-  | ||
| + | |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]  | ||
| + | |-  | ||
| + | |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]  | ||
| + | |-  | ||
| + | |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]  | ||
| + | |-  | ||
| + | |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]  | ||
| + | |-  | ||
| + | |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]  | ||
| + | |-  | ||
| + | |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]  | ||
| + | |-  | ||
| + | |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]  | ||
| + | |-  | ||
| + | |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]  | ||
| + | |-  | ||
| + | |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]  | ||
| + | |-  | ||
| + | |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.]  | ||
| + | |-  | ||
| + | |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]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=3|2019/05/30  | ||
| + | |style="padding:.4em;"|19-22  | ||
| + | |style="padding:.4em;"|  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.ncbi.nlm.nih.gov/pubmed/30936547 Meta-analysis of fecal metagenomes reveals global microbial signatures that are specific for colorectal cancer]  | ||
| + | |-  | ||
| + | |style="padding:.4em;"|19-21  | ||
| + | |style="padding:.4em;"|  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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]  | ||
| + | |-  | ||
| + | |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.]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=3|2019/05/23  | ||
| + | |style="padding:.4em;"|19-19  | ||
| + | |style="padding:.4em;"|  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.ncbi.nlm.nih.gov/pubmed/30867587 New insights from uncultivated genomes of the global human gut microbiome]  | ||
| + | |-  | ||
| + | |style="padding:.4em;"|19-18  | ||
| + | |style="padding:.4em;"|  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.ncbi.nlm.nih.gov/pubmed/30745586 A new genomic blueprint of the human gut microbiota]  | ||
| + | |-  | ||
| + | |style="padding:.4em;"|19-17  | ||
| + | |style="padding:.4em;"|  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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.]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=2|2019/05/16  | ||
| + | |style="padding:.4em;"|19-16  | ||
| + | |style="padding:.4em;"|  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.ncbi.nlm.nih.gov/pubmed/29311644 Dynamics of metatranscription in the inflammatory bowel disease gut microbiome.]  | ||
| + | |-  | ||
| + | |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.]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=2|2019/05/09  | ||
| + | |style="padding:.4em;"|19-14  | ||
| + | |style="padding:.4em;"|  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.ncbi.nlm.nih.gov/pubmed/30193113 Post-Antibiotic Gut Mucosal Microbiome Reconstitution Is Impaired by Probiotics and Improved by Autologous FMT.]  | ||
| + | |-  | ||
| + | |style="padding:.4em;"|19-13  | ||
| + | |style="padding:.4em;"|  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.ncbi.nlm.nih.gov/pubmed/30193112 Personalized Gut Mucosal Colonization Resistance to Empiric Probiotics Is Associated with Unique Host and Microbiome Features.]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=2|2019/05/02  | ||
| + | |style="padding:.4em;"|19-12  | ||
| + | |style="padding:.4em;"|  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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.]  | ||
| + | |-  | ||
| + | |style="padding:.4em;"|19-11  | ||
| + | |style="padding:.4em;"|  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.ncbi.nlm.nih.gov/pubmed/30778252 Subsets of exhausted CD8+ T cells differentially mediate tumor control and respond to checkpoint blockade.]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=2|2019/4/11  | ||
| + | |style="padding:.4em;"|19-10  | ||
| + | |style="padding:.4em;"|  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.ncbi.nlm.nih.gov/pubmed/30479382 Lineage tracking reveals dynamic relationships of T cells in colorectal cancer.]  | ||
| + | |-  | ||
| + | |style="padding:.4em;"|19-9  | ||
| + | |style="padding:.4em;"|  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.ncbi.nlm.nih.gov/pubmed/30523328 Single cell dissection of plasma cell heterogeneity in symptomatic and asymptomatic myeloma.]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=2|2019/4/4  | ||
| + | |style="padding:.4em;"|19-8  | ||
| + | |style="padding:.4em;"|  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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.]  | ||
| + | |-  | ||
| + | |style="padding:.4em;"|19-7  | ||
| + | |style="padding:.4em;"|  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.ncbi.nlm.nih.gov/pubmed/29942094 Global characterization of T cells in non-small-cell lung cancer by single-cell sequencing]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=2|2019/3/28  | ||
| + | |style="padding:.4em;"|19-6  | ||
| + | |style="padding:.4em;"|  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.ncbi.nlm.nih.gov/pubmed/28319088 Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors.]  | ||
| + | |-  | ||
| + | |style="padding:.4em;"|19-5  | ||
| + | |style="padding:.4em;"|  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.ncbi.nlm.nih.gov/pubmed/28622514 Landscape of Infiltrating T Cells in Liver Cancer Revealed by Single-Cell Sequencing.]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=3|2019/3/21  | ||
| + | |style="padding:.4em;"|19-4  | ||
| + | |style="padding:.4em;"|  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.ncbi.nlm.nih.gov/pubmed/29988129 Phenotype molding of stromal cells in the lung tumor microenvironment.]  | ||
| + | |-  | ||
| + | |style="padding:.4em;"|19-3-1  | ||
| + | |style="padding:.4em;"|  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.ncbi.nlm.nih.gov/pubmed/30787436 A single-cell molecular map of mouse gastrulation and early organogenesis]  | ||
| + | |-  | ||
| + | |style="padding:.4em;"|19-3  | ||
| + | |style="padding:.4em;"|  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.ncbi.nlm.nih.gov/pubmed/30787437 The single-cell transcriptional landscape of mammalian organogenesis]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=2|2019/3/14  | ||
| + | |style="padding:.4em;"|19-2  | ||
| + | |style="padding:.4em;"|  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.ncbi.nlm.nih.gov/pubmed/29961579 Single-Cell Map of Diverse Immune Phenotypes in the Breast Tumor Microenvironment]  | ||
| + | |-  | ||
| + | |style="padding:.4em;"|19-1  | ||
| + | |style="padding:.4em;"|  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.ncbi.nlm.nih.gov/pubmed/29198524 Single-Cell Transcriptomic Analysis of Primary and Metastatic Tumor Ecosystems in Head and Neck Cancer]  | ||
| + | |}  | ||
| + | {|class=wikitable style="text-align:center;"  | ||
| + | |+style="text-align:left;font-size:12pt" | 2018  | ||
| + | |-  | ||
| + | !scope="col" style="padding:.4em" |Date  | ||
| + | !scope="col" style="padding:.4em" | Paper<br/>index  | ||
| + | !scope="col" style="padding:.4em" | Presenter  | ||
| + | !scope="col" style="padding:.4em" | Paper title  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=2|2018/06/14  | ||
| + | |style="padding:.4em;"|18-12  | ||
| + | |style="padding:.4em;"|  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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.]  | ||
| + | |-  | ||
| + | |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.]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=2|2018/06/07  | ||
| + | |style="padding:.4em;"|18-10  | ||
| + | |style="padding:.4em;"|  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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.]  | ||
| + | |-  | ||
| + | |style="padding:.4em;"|18-9  | ||
| + | |style="padding:.4em;"|  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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.]  | ||
| + | |-  | ||
| + | |style="padding:.4em;" rowspan=2|2018/05/31  | ||
| + | |style="padding:.4em;"|18-8  | ||
| + | |style="padding:.4em;"|  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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.]  | ||
| + | |-  | ||
| + | |style="padding:.4em;"|18-7  | ||
| + | |style="padding:.4em;"|  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [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.]  | ||
| + | |-  | ||
| + | |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.]  | ||
| + | |-  | ||
| + | |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.]  | ||
| + | |-  | ||
| + | |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.]  | ||
| + | |-  | ||
| + | |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.]  | ||
| + | |-  | ||
| + | |style="padding:.4em;"|18-1  | ||
| + | |style="padding:.4em;"|DS Bae  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
| + | [https://www.ncbi.nlm.nih.gov/pubmed/28129544 Genomic and Transcriptomic Features of Response to Anti-PD-1 Therapy in Metastatic Melanoma.]  | ||
| + | |}  | ||
| + | {|class=wikitable style="text-align:center;"  | ||
| + | |+style="text-align:left;font-size:12pt" | 2017  | ||
| + | |-  | ||
| + | !scope="col" style="padding:.4em" |Date  | ||
| + | !scope="col" style="padding:.4em" | Paper<br/>index  | ||
| + | !scope="col" style="padding:.4em" | Presenter  | ||
| + | !scope="col" style="padding:.4em" | Paper title  | ||
| + | |-  | ||
| + | |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.]  | ||
| + | |-  | ||
| + | |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.]  | ||
| + | |-  | ||
| + | |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.]  | ||
| + | |-  | ||
| + | |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.]  | ||
| + | |-  | ||
| + | |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.]  | ||
| + | |-  | ||
| + | |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.]  | ||
| + | |-  | ||
| + | |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.]  | ||
| + | |-  | ||
| + | |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.]  | ||
| + | |-  | ||
| + | |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.]  | ||
| + | |-  | ||
| + | |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.]  | ||
| + | |-  | ||
| + | |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.]  | ||
| + | |-  | ||
| + | |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.]  | ||
| + | |-  | ||
| + | |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.]  | ||
| + | |-  | ||
| + | |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.]  | ||
| + | |-  | ||
| + | |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.]  | ||
| + | |-  | ||
| + | |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.]  | ||
| + | |-  | ||
| + | |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.]  | ||
| + | |-  | ||
| + | |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.]  | ||
| + | |-  | ||
| + | |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.]  | ||
| + | |-  | ||
| + | |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.]  | ||
| + | |-  | ||
| + | |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.]  | ||
| + | |-  | ||
| + | |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]  | ||
| + | |-  | ||
| + | |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.]  | ||
| + | |-  | ||
| + | |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]  | ||
| + | |-  | ||
| + | |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]  | ||
| + | |-  | ||
| + | |}  | ||
| + | {|class=wikitable style="text-align:center;"  | ||
| + | |+style="text-align:left;font-size:12pt" | 2016  | ||
| + | |-  | ||
| + | !scope="col" style="padding:.4em" |Date  | ||
| + | !scope="col" style="padding:.4em" | Paper<br/>index  | ||
| + | !scope="col" style="padding:.4em" | Presenter  | ||
| + | !scope="col" style="padding:.4em" | Paper title  | ||
| + | |-  | ||
| + | |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.]  | ||
| + | |-  | ||
| + | |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.]  | ||
| + | |-  | ||
| + | |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.]  | ||
| + | |-  | ||
| + | |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]  | ||
| + | |}  | ||
| + | |||
| + | {|class=wikitable style="text-align:center;"  | ||
| + | |+style="text-align:left;font-size:12pt" | 2015  | ||
| + | |-  | ||
| + | !scope="col" style="padding:.4em" | Date  | ||
| + | !scope="col" style="padding:.4em" | Paper<br/>index  | ||
| + | !scope="col" style="padding:.4em" | Presenter  | ||
| + | !scope="col" style="padding:.4em" | Paper title  | ||
| + | |-  | ||
| + | |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.]  | ||
| + | |-  | ||
| + | |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.]  | ||
| + | |-  | ||
| + | |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.]  | ||
| + | |-  | ||
| + | |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.]  | ||
| + | |-  | ||
| + | |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.]  | ||
| + | |-  | ||
| + | |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.]  | ||
| + | |-  | ||
| + | |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.]  | ||
| + | |-  | ||
| + | |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.]  | ||
| + | |-  | ||
| + | |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.]  | ||
| + | |-  | ||
| + | |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.]  | ||
| + | |-  | ||
| + | |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.]  | ||
| + | |-  | ||
| + | |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.]  | ||
| + | |-  | ||
| + | |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.]  | ||
| + | |||
| + | |-  | ||
| + | |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.]  | ||
| + | |-  | ||
| + | |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.]  | ||
| + | |-  | ||
| + | |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.]  | ||
| + | |-  | ||
| + | |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.]  | ||
| + | |-  | ||
| + | |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.]  | ||
| + | |-  | ||
| + | |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.]  | ||
| + | |-  | ||
| + | |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.]  | ||
| + | |-  | ||
| + | |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.]  | ||
| + | |-  | ||
| + | |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.]  | ||
| + | |-  | ||
| + | |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.]  | ||
| + | |-  | ||
| + | |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.]  | ||
| + | |-  | ||
| + | |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.]  | ||
| + | |-  | ||
| + | |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.]  | ||
| + | |-  | ||
| + | |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;"  | {|class=wikitable style="text-align:center;"  | ||
|+style="text-align:left;font-size:12pt" | 2014  | |+style="text-align:left;font-size:12pt" | 2014  | ||
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| − | |style="padding:.4em;" rowspan=  | + | |style="padding:.4em;" rowspan=2|2014/12/23  | 
| − | |style="padding:.4em;"|2014-  | + | |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;"|HJ Han  | ||
|style="padding:.4em;text-align:left"|  | |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.]  | [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;"|2014-  | + | |
|style="padding:.4em;"|HS Shim  | |style="padding:.4em;"|HS Shim  | ||
|style="padding:.4em;text-align:left"|  | |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.]  | [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=  | + | |style="padding:.4em;" rowspan=2|2014/09/30  | 
| − | |style="padding:.4em;"|2014-  | + | |style="padding:.4em;"|2014-22  | 
|style="padding:.4em;"|JE Shim  | |style="padding:.4em;"|JE Shim  | ||
|style="padding:.4em;text-align:left"|  | |style="padding:.4em;text-align:left"|  | ||
[http://www.nature.com/nmeth/journal/v10/n11/full/nmeth.2651.html Network-based stratification of tumor mutations.]  | [http://www.nature.com/nmeth/journal/v10/n11/full/nmeth.2651.html Network-based stratification of tumor mutations.]  | ||
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|style="padding:.4em;"|2014-21  | |style="padding:.4em;"|2014-21  | ||
|style="padding:.4em;"|KS Kim  | |style="padding:.4em;"|KS Kim  | ||
Latest revision as of 02:19, 27 October 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 |