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[https:// | [https://doi.org/10.64898/2026.08.05.743110 Move BeTween modAlities (MBTA) employs flow matching to predict single cell data modalities] | ||
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[https:// | [https://doi.org/10.1038/s41467-026-72588-1 Network topology of the gut microbiome associates with metabolic health in obesity] | ||
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[https:// | [https://doi.org/10.1038/s41564-026-02339-x Strain-level transmission inference across multi-kingdom metagenomic data using TRACS] | ||
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|style="padding:.4em;"| | |style="padding:.4em;"|JH Hwang | ||
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[https:// | [https://doi.org/10.1038/s41592-026-03028-7 Quantifying uncertainty in protein representations across models and tasks] | ||
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|style="padding:.4em;"| | |style="padding:.4em;"|YY Jang | ||
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[https:// | [https://doi.org/10.1038/s42256-026-01286-w A knowledge-driven framework for predicting single-cell responses for unprofiled drugs] | ||
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|style="padding:.4em;"| | |style="padding:.4em;"|SB Lim | ||
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[https:// | [https://doi.org/10.1016/j.chom.2026.02.002 Strain-level genetic heterogeneity and colonization dynamics drive microbiome therapeutic efficacy] | ||
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[https:// | [https://doi.org/10.1038/s41592-026-03086-x eSIG-Net: an interaction language model that decodes the protein code of single mutations] | ||
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|style="padding:.4em;"|YR Jung | |||
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[https://www.nature.com/articles/s41592-025-02636-z xTrimoPGLM: unified 100-billion-parameter pretrained transformer for deciphering the language of proteins] | [https://doi.org/10.1038/s41586-026-11001-9 An operational perturbation proteomics-based virtual cell model] | ||
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|style="padding:.4em;"|25- | |style="padding:.4em;"|26-44 | ||
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[https://doi.org/10.1016/j.chom.2025.11.001 Exploring functional insights into the human gut microbiome via the structural proteome] | |||
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|style="padding:.4em;"|26-43 | |||
|style="padding:.4em;"|JY Kim | |||
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[https://doi.org/10.1016/j.chom.2026.01.013 Meta-analysis of the uncultured gut microbiome across 11,115 global metagenomes reveals a candidate signature of health] | |||
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|style="padding:.4em;"|26-42 | |||
|style="padding:.4em;"|SH Ahn | |||
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[https://doi.org/10.1038/s43588-026-00983-1 DeepSeMS: revealing the hidden biosynthetic potential of the global ocean microbiome with a large language model] | |||
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|style="padding:.4em;"|26-41 | |||
|style="padding:.4em;"|EJ Sung | |||
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[https://doi.org/10.1038/s41592-025-02980-0 Benchmarking algorithms for generalizable single-cell perturbation response prediction] | |||
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|style="padding:.4em;"|26-40 | |||
|style="padding:.4em;"|H-J Kim | |||
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[https://doi.org/10.1038/s41467-025-64177-5 Empathi: embedding-based phage protein annotation tool by hierarchical assignment] | |||
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|style="padding:.4em;"|26-39 | |||
|style="padding:.4em;"|JY Ma | |||
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[https://doi.org/10.1016/j.cell.2026.02.003 Immune-microbiome coordination defines interferon setpoints in healthy humans] | |||
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[https://doi.org/10.1016/j.cell.2026.08.035 Tahoe-100M: Mapping drug-induced molecular phenotypes at single-cell resolution] | |||
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|style="padding:.4em;"|HJ Kim | |||
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[https://doi.org/10.64898/2026.02.23.707420 Beyond alignment: synergistic integration is required for multimodal cell foundation models] | |||
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[https://doi.org/10.1038/s41467-026-78063-1 A structure-informed deep learning framework for modeling TCR-peptide-HLA interactions] | |||
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|style="padding:.4em;"|SW Jeon | |||
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[https://doi.org/10.1038/s41586-026-10476-w Genome-wide sweeps create ecological units in the human gut microbiome] | |||
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[https://doi.org/10.1038/s41586-025-09798-y Gene-specific selective sweeps are pervasive across human gut microbiomes] | |||
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|style="padding:.4em;"|26-34 | |||
|style="padding:.4em;"|JH Hwang | |||
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[https://doi.org/10.64898/2026.04.10.717844 EVEE: Interpretable variant effect prediction from genomic foundation model representations] | |||
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|style="padding:.4em;"|26-33 | |||
|style="padding:.4em;"|YY Jang | |||
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[https://doi.org/10.1038/s42256-026-01226-8 Pretraining a foundation model for small-molecule natural products] | |||
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|style="padding:.4em;"|26-32 | |||
|style="padding:.4em;"|SB Lim | |||
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[https://doi.org/10.1016/j.cell.2025.08.020 Culture-independent meta-pangenomics enabled by long-read metagenomics reveals associations with pediatric undernutrition] | |||
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|style="padding:.4em;"|HB Lee | |||
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[https://doi.org/10.1038/s41587-024-02428-4 A structurally informed human protein–protein interactome reveals proteome-wide perturbations caused by disease mutations] | |||
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|style="padding:.4em;"|EJ Sung | |||
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[https://doi.org/10.1016/j.cell.2026.04.028 D-SPIN constructs regulatory network models from scRNA-seq that reveal organizing principles of perturbation response] | |||
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|style="padding:.4em;"|H-J Kim | |||
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[https://doi.org/10.1038/s41467-026-72028-0 V- and VL-scores unveil viral signatures and origins of protein families] | |||
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|style="padding:.4em;"|26-28 | |||
|style="padding:.4em;"|JY Kim | |||
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[https://doi.org/10.1016/j.chom.2026.05.030 Meta-analysis reveals microbiome signatures for colorectal cancer that are universal across age groups and sequencing methods] | |||
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|style="padding:.4em;"|26-27 | |||
|style="padding:.4em;"|YR Jung | |||
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[https://doi.org/10.1038/s41467-026-70071-5 Illuminating cell states by a comprehensive and interpretable single cell foundation model] | |||
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|style="padding:.4em;"|26-26 | |||
|style="padding:.4em;"|SH Ahn | |||
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[https://doi.org/10.1038/s41586-025-09854-7 Gut micro-organisms associated with health, nutrition and dietary interventions] | |||
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|style="padding:.4em;"|YR Kim | |||
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[https://doi.org/10.1016/j.xcrm.2025.102516 Integrative multi-omics reveals microbial genomic variants driving altered host-microbe interactions in autism spectrum disorder] | |||
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|style="padding:.4em;"|26-24 | |||
|style="padding:.4em;"|JY Ma | |||
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[https://doi.org/10.1038/s41591-026-04312-x Diet–microbiome associations in 10,068 individuals from the Human Phenotype Project to guide personalized nutrition] | |||
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[https://doi.org/10.1038/s43588-026-01016-7 SpatialFormer: universal spatial representation learning from subcellular molecular to multicellular landscapes] | |||
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|style="padding:.4em;"|26-16 | |||
|style="padding:.4em;"|JH Hwang | |||
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[https://doi.org/10.1038/s41564-025-02114-4 A generative artificial intelligence approach for the discovery of antimicrobial peptides against multidrug-resistant bacteria] | |||
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|style="padding:.4em;"|26-15 | |||
|style="padding:.4em;"|Yy Jang | |||
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[https://doi.org/10.1016/j.cell.2025.06.035 Cell-type-directed network-correcting combination therapy for Alzheimer’s disease] | |||
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|style="padding:.4em;"|JU An | |||
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[https://doi.org/10.1016/j.cell.2025.08.018 AI mirrors experimental science to uncover a mechanism of gene transfer crucial to bacterial evolution] | |||
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[https://doi.org/10.1186/s13059-025-03721-4 Melody: meta-analysis of microbiome association studies for discovering generalizable microbial signatures] | |||
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|style="padding:.4em;"|26-12 | |||
|style="padding:.4em;"|HB Lee | |||
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[https://doi.org/10.1038/s41587-025-02761-2 Target sequence-conditioned design of peptide binders using masked language modeling] | |||
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[https://doi.org/10.1093/nar/gkaf1319 De novo origin of numerous microproteins in enterobacteria] | |||
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|style="padding:.4em;"|26-10 | |||
|style="padding:.4em;"|JY Kim | |||
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[https://doi.org/10.1038/s41592-025-02901-1 Bin Chicken: targeted metagenomic coassembly for the efficient recovery of novel genomes] | |||
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[https://doi.org/10.64898/2025.12.04.692287 Metax: A Coverage-Informed Probabilistic Framework for Accurate Cross-Domain Taxon Profiling] | |||
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|style="padding:.4em;"|EJ Sung | |||
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[https://doi.org/10.1016/j.ccell.2025.06.005 Gene context drift identifies drug targets to mitigate cancer treatment resistance] | |||
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|style="padding:.4em;"|JY Ma | |||
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[https://doi.org/10.1101/2025.09.09.675184 Disease-specific variant pathogenicity prediction using 1 multimodal biomedical language models] | |||
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|style="padding:.4em;"|JH Cha | |||
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[https://doi.org/10.1038/s43588-025-00855-0 MetaKSSD: boosting the scalability of the reference taxonomic marker database and the performance of metagenomic profiling using sketch operations] | |||
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[https://doi.org/10.1101/2025.11.24.685470 PULSAR: a Foundation Model for Multi-scale and Multi-cellular Biology] | |||
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|style="padding:.4em;"|26-04-02 | |||
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[https://www.biorxiv.org/content/10.64898/2025.12.12.693901v3 vir2vec: A Viral Genome-Wide Viral Embedding] | |||
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[https://www.biorxiv.org/content/10.64898/2025.12.28.696777v1 MicroGenomer: A Foundation Model for Transferable Microbial Genome Representations Enabling Multi-scale Genomic Understanding and Ecophysiological Trait Prediction] | |||
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[https://doi.org/10.1038/s41467-025-66311-9 Semi-inductive dataset construction and framework optimization for practical drug target interaction prediction with ScopeDTI] | |||
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[https://doi.org/10.1038/s41586-025-09603-w Efficient and accurate search in petabase-scale sequence repositories] | |||
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[https://pubmed.ncbi.nlm.nih.gov/40950083/ Logan: Planetary-Scale Genome Assembly Surveys Life’s Diversity] | |||
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[https://doi.org/10.1038/s41591-025-03788-3 AI-driven multi-omics modeling of myalgic encephalomyelitis/chronic fatigue syndrome] | |||
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|style="padding:.4em;"|YR Jung | |||
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[https://genome.cshlp.org/content/35/2/340 Diffusion-based generation of gene regulatory networks from scRNA-seq data with DigNet] | |||
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[https://doi.org/10.1101/2025.08.01.668221 ProteomeLM: A proteome-scale language model allowing fast prediction of protein-protein interactions and gene essentiality across taxa] | |||
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[https://doi.org/10.1038/s41591-025-03790-9 Deep phenotyping of health–disease continuum in the Human Phenotype Project] | |||
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[https://doi.org/10.1038/s41467-025-59926-5 CellFM: a large-scale foundation model pretrained on transcriptomics of 100 million human cells] | |||
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|style="padding:.4em;"|JY Kim | |||
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[https://doi.org/10.1016/j.chom.2025.07.015 Subspecies of the human gut microbiota carry implicit information for in-depth microbiome research] | |||
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[https://doi.org/10.1038/s41591-025-03693-9 Pooled analysis of 3,741 stool metagenomes from 18 cohorts for cross-stage and strain-level reproducible microbial biomarkers of colorectal cancer] | |||
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|style="padding:.4em;"|JY Ma | |||
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[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] | |||
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|style="padding:.4em;"|25-79 | |||
|style="padding:.4em;"|JH Cha | |||
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[https://doi.org/10.21203/rs.3.rs-5063726/v1 Microbiome-wide PheWAS links gut microbial SNVs to human health and exposures] | |||
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[https://doi.org/10.1016/j.patter.2025.101326 BioLLM: A standardized framework for integrating and benchmarking single-cell foundation models] | |||
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[https://doi.org/10.1038/s41587-025-02813-7 Predicting functions of uncharacterized gene products from microbial communities] | |||
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[https://www.biorxiv.org/content/10.1101/2025.07.31.667797v1.full OmniCellAgent: Towards AI Co-Scientists for Scientific Discovery in Precision Medicine] | |||
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|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;"|JY Ma | ||
|style="padding:.4em;text-align:left"| | |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] | [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/ | |style="padding:.4em;" rowspan=1|2025/9/2 | ||
|style="padding:.4em;"|25-66 | |style="padding:.4em;"|25-66 | ||
|style="padding:.4em;"|NY Kim | |style="padding:.4em;"|NY Kim | ||
|style="padding:.4em;text-align:left"| | |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] | [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] | |||
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|style="padding:.4em;"|HB Lee | |style="padding:.4em;"|HB Lee | ||
|style="padding:.4em;text-align:left"| | |style="padding:.4em;text-align:left"| | ||
[https://www. | [https://www.nature.com/articles/s41592-025-02723-1 Sliding Window Interaction Grammar (SWING): a generalized interaction language model for peptide and protein interactions] | ||
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|style="padding:.4em;" rowspan=1|2025/8/29 | |style="padding:.4em;" rowspan=1|2025/8/29 | ||
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[https://www.biorxiv.org/content/10.1101/2025.07.03.662911v1 SSAlign: Ultrafast and Sensitive Protein Structure Search at Scale] | [https://www.biorxiv.org/content/10.1101/2025.07.03.662911v1 SSAlign: Ultrafast and Sensitive Protein Structure Search at Scale] | ||
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|style="padding:.4em;" rowspan=1|2025/8/ | |style="padding:.4em;" rowspan=1|2025/8/29 | ||
|style="padding:.4em;"|25-62 | |style="padding:.4em;"|25-62 | ||
|style="padding:.4em;"|YR Jung | |style="padding:.4em;"|YR Jung | ||
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|2010/05/26 | |2010/05/26 | ||
|align="center"|2010-20 | |align="center"|2010-20 | ||
|[[ | |[[Media:100428 network based elucidation of human disease similarities reveals common functional modules enriched for pluripotent drug targets.pdf|network_based_elucidation_of_human_disease_similarities_reveals_common_functional_modules_enriched_for_pluripotent_drug_targets.pdf]] | ||
|- | |- | ||
|2010/05/19 | |2010/05/19 | ||
|align="center"|2010-19 | |align="center"|2010-19 | ||
|[[ | |[[Media:100421 interpreting metabolomic profiles using unbiased pathway models.pdf|interpreting_metabolomic_profiles_using_unbiased_pathway_models.pdf]] | ||
|- | |- | ||
|2010/04/21 | |2010/04/21 | ||
|align="center"|2010-18 | |align="center"|2010-18 | ||
|[[ | |[[Media:100414 identification of networks of co occurring tumor related dna copy number changes using a genome wide scoringapproach.pdf|identification_of_networks_of_co_occurring_tumor_related_dna_copy_number_changes_using_a_genome_wide_scoringapproach.pdf]] | ||
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|2010/04/14 | |2010/04/14 | ||
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|align="center"|2010-14 | |align="center"|2010-14 | ||
|[[ | |[[Media:100331 toward stem cell systems biology from molecules to networks and landscapes.pdf|toward_stem_cell_systems_biology_from_molecules_to_networks_and_landscapes.pdf]] | ||
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|align="center"|2010-13 | |align="center"|2010-13 | ||
Latest revision as of 18:39, 15 October 2026
| 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 |
2012
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