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| − | |+style="text-align:left;font-size:12pt" | 2024-1   | + | |+style="text-align:left;font-size:12pt" | 2025-2 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|2025/11/18  | ||
| + | |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]  | ||
| + | |-  | ||
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| − | [https://doi.org/10.1038/  | + | [https://doi.org/10.1038/s41564-024-01739-1 Multikingdom and functional gut microbiota markers for autism spectrum disorder]  | 
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| − | [https://doi.org/10.  | + | [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]  | 
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| − | [https://doi.org/10.1038/s41467-  | + | [https://doi.org/10.1038/s41467-024-52561-6 Gut microbiota wellbeing index predicts overall health in a cohort of 1000 infants]  | 
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| − | [https://doi.org/10.  | + | [https://doi.org/10.1186/s13059-024-03320-9 VirRep: a hybrid language representation learning framework for identifying viruses from human gut metagenomes]  | 
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| − | [https://doi.org/10.  | + | [https://doi.org/10.1101/2024.06.27.601020 Ultrafast and accurate sequence alignment and clustering of viral genomes]  | 
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| − | [https://doi.org/10.  | + | [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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| + | [https://doi.org/10.1038/s41467-024-46947-9 Genomic language model predicts protein co-regulation and function]  | ||
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| + | [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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| − | [https://doi.org/10.  | + | [https://doi.org/10.1101/2024.07.11.603044 Prophage-DB: A comprehensive database to explore diversity,distribution, and ecology of prophages]  | 
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| − | [https://doi.org/10.  | + | [https://doi.org/10.1186/s40168-024-01904-y Strain‑resolved de‑novo metagenomic assembly of viral genomes and microbial 16S rRNAs]  | 
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| − | [https://doi.org/10.  | + | [https://doi.org/10.1186/s40168-024-01876-z Prokaryotic‑virus‑encoded auxiliary metabolic genes throughout the global oceans]  | 
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| − | [https://doi.org/10.  | + | [https://doi.org/10.1016/j.cell.2024.07.039 Unexplored microbial diversity from 2,500 food metagenomes and links with the human microbiome]  | 
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| − | [https://doi.org/10.  | + | [https://doi.org/10.1101/2024.04.17.589959 Pangenomes of Human Gut Microbiota Uncover Links Between Genetic Diversity and Stress Response]  | 
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| − | [https://doi.org/10.  | + | [https://doi.org/10.1101/2024.05.28.596318 vClassifier: a toolkit for species-level classification of prokaryotic viruses]  | 
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| − | [https://doi.org/10.  | + | [https://doi.org/10.1101/2024.07.26.605250 GRAViTy-V2: a grounded viral taxonomy application]  | 
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| − | [https://doi.org/10.  | + | [https://doi.org/10.1126/science.adj4857 A blueprint for tumor-infiltrating B cells across human cancers]  | 
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[https://doi.org/10.1038/s41592-024-02175-z SLIDE: Significant Latent Factor Interaction Discovery and Exploration across biological domains]  | [https://doi.org/10.1038/s41592-024-02175-z SLIDE: Significant Latent Factor Interaction Discovery and Exploration across biological domains]  | ||
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[https://doi.org/10.1016/j.xgen.2023.100473 Single-cell transcriptome landscape of circulating CD4+ T cell populations in autoimmune diseases]  | [https://doi.org/10.1016/j.xgen.2023.100473 Single-cell transcriptome landscape of circulating CD4+ T cell populations in autoimmune diseases]  | ||
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| − | [https://doi.org/10.  | + | [https://doi.org/10.1101/2024.06.16.599201 node2vec2rank: Large Scale and Stable Graph Differential Analysis via Multi-Layer Node Embeddings and Ranking]  | 
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[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]  | [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]  | ||
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| − | [https://doi.org/10.  | + | [https://doi.org/10.1038/s41591-024-03067-7 Strain-specific gut microbial signatures in type 2 diabetes identified in a cross-cohort analysis of 8,117 metagenomes]  | 
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| + | [https://doi.org/10.1016/j.cell.2024.03.034 Gut symbionts alleviate MASH through a secondary bile acid biosynthetic pathway]  | ||
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| − | [https://doi.org/10.  | + | [https://doi.org/10.1186/s13059-024-03325-4 Gut microbiota DPP4-like enzymes are increased in type-2 diabetes and contribute to incretin inactivation]  | 
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| + | |style="padding:.4em;" rowspan=1|Microbiome  | ||
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| + | |style="padding:.4em;"|SH Ahn  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
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| − | |style="padding:.4em;"|24-  | + | |style="padding:.4em;"|24-45-2  | 
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|style="padding:.4em;text-align:left"|  | |style="padding:.4em;text-align:left"|  | ||
| − | [https://  | + | [https://pubmed.ncbi.nlm.nih.gov/32881682 Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosis]  | 
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| + | |style="padding:.4em;"|HJ Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
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| + | |style="padding:.4em;"|NY Kim  | ||
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| + | |style="padding:.4em;"|JH Cha  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
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| + | |style="padding:.4em;"|JH Cha  | ||
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| + | |style="padding:.4em;"|JY Ma  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
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[https://doi.org/10.1038/s41587-023-01917-2 Protein remote homology detection and structural alignment using deep learning]  | [https://doi.org/10.1038/s41587-023-01917-2 Protein remote homology detection and structural alignment using deep learning]  | ||
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| + | |style="padding:.4em;"|YR Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
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| + | |style="padding:.4em;"|WJ Kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
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| + | |style="padding:.4em;"|JY kim  | ||
| + | |style="padding:.4em;text-align:left"|  | ||
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| − | |style="padding:.4em;"|24-  | + | |style="padding:.4em;"|24-35  | 
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[https://doi.org/10.1073/pnas.2008731118 Conjugative plasmids interact with insertion sequences to shape the horizontal transfer of antimicrobial resistance genes]  | [https://doi.org/10.1073/pnas.2008731118 Conjugative plasmids interact with insertion sequences to shape the horizontal transfer of antimicrobial resistance genes]  | ||
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| + | |style="padding:.4em;"|EB Hong  | ||
| + | |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]  | ||
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| + | |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]  | ||
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| + | |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]  | ||
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| + | |style="padding:.4em;"|HJ Choi  | ||
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| + | [https://doi.org/10.1038/s41590-024-01792-2 Human lung cancer harbors spatially organized stem-immunity hubs associated with response to immunotherapy]  | ||
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| + | |style="padding:.4em;"|SA Choi  | ||
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| + | [https://doi.org/10.1038/s41467-021-27464-5 Single-cell transcriptomics captures features of human midbrain development and dopamine neuron diversity in brain organoids]  | ||
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| + | |style="padding:.4em;"|HJ Cha  | ||
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| + | [https://doi.org/10.1016/j.chom.2023.08.019 Cell-type-specific responses to fungal infection in plants revealed by single-cell transcriptomics]  | ||
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| + | |style="padding:.4em;"|YK Jung  | ||
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| + | [https://www.sciencedirect.com/science/article/pii/S1534580722002519?via%3Dihub The single-cell stereo-seq reveals region-specific cell subtypes and transcriptome profiling in Arabidopsis leaves]  | ||
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| + | |style="padding:.4em;"|HJ Lee  | ||
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| + | [https://doi.org/10.1038/s41588-022-01100-4 Single-cell and bulk transcriptome sequencing identifies two epithelial tumor cell states and refines the consensus molecular classification of colorectal cancer]  | ||
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| + | |style="padding:.4em;"|HK Lee  | ||
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| + | [https://doi.org/10.1038/s42255-023-00876-x Delineating mouse β-cell identity during lifetime and in diabetes with a single cell atlas]  | ||
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| + | |style="padding:.4em;"|JI Lee  | ||
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| + | [https://doi.org/10.1038/s41587-023-01747-2 Multimodal spatiotemporal phenotyping of human retinal organoid development]  | ||
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| + | |style="padding:.4em;"|JH Lee  | ||
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| + | [https://doi.org/10.1038/s41586-024-07251-0 Immune microniches shape intestinal Treg function]  | ||
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| + | [https://doi.org/10.1016/j.devcel.2021.02.021 A single-cell analysis of the Arabidopsis vegetative shoot apex]  | ||
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| + | [https://doi.org/10.1038/s41467-023-40137-9 Droplet-based high-throughput single microbe RNA sequencing by smRandom-seq]  | ||
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| + | [https://doi.org/10.1038/s41564-023-01462-3 Single-cell massively-parallel multiplexed microbial sequencing (M3-seq) identifies rare bacterial populations and profiles phage infection]  | ||
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| + | |style="padding:.4em;"|EB Yu  | ||
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| + | [https://doi.org/10.1016/j.celrep.2022.111736 Spatial transcriptomics demonstrates the role of CD4 T cells in effector CD8 T cell differentiation during chronic viral infection]  | ||
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| + | |style="padding:.4em;"|DY Won  | ||
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| + | [https://doi.org/10.1038/s41467-023-36325-2 Dissecting the immune suppressive human prostate tumor microenvironment via integrated single-cell and spatial transcriptomic analyses]  | ||
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| + | [https://doi.org/10.1038/s41593-023-01452-y Single-nucleus genomics in outbred rats with divergent cocaine addiction-like behaviors reveals changes in amygdala GABAergic inhibition]  | ||
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| + | |style="padding:.4em;"|HS Moon  | ||
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| + | |style="padding:.4em;"|JH Nam  | ||
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| + | |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]  | ||
| + | |}  | ||
| + | |||
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 |