Difference between revisions of "Journal Club"

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|style="padding:.4em;"|YL Jung
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[https://doi.org/10.1101/2024.04.04.24305313 Single-cell RNA sequencing of human tissue supports successful drug targets]
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|style="padding:.4em;"|EJ Sung
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[https://doi.org/10.1101/2023.11.21.568145 ANDES: a novel best-match approach for enhancing gene set analysis in embedding spaces]
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|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|IS Choi
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[https://doi.org/10.1101/2024.09.24.614685 Evaluating the Utilities of Foundation Models in Single-cell Data Analysis]
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|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|SB Baek
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[https://doi.org/10.1101/2024.09.24.614685 Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation]
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|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|JH Cha
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[https://doi.org/10.1101/2024.09.24.614685 scEMB: Learning context representation of genes based on large-scale single-cell transcriptomics]
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|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|HB Lee
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[https://doi.org/10.1101/2024.09.09.611960 Mouse-Geneformer: A Deep Learning Model for Mouse Single-Cell Transcriptome and Its Cross-Species Utility]
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|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|YL Jung
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[https://doi.org/10.1101/2024.08.16.608180 Quantized multi-task learning for context-specific representations of gene network dynamics]
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|style="padding:.4em;"|EJ Sung
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[https://doi.org/10.17632/wdxwy8gmrz.1 Systematic Functional Annotation and Visualization of Biological Networks]
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|style="padding:.4em;"|IS Choi
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[https://doi.org/10.1038/s41592-024-02303-9 CellRank 2: unified fate mapping in multiview single-cell data]
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|style="padding:.4em;"|24-27
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|style="padding:.4em;"|SB Baek
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[https://doi.org/10.1101/2024.07.29.605556 scPRINT: pre-training on 50 million cells allows robust gene network predictions]
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|style="padding:.4em;"|JH Cha
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[https://doi.org/10.1038/s41467-024-46440-3 Bidirectional generation of structure and properties through a single molecular foundation model]
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{|class=wikitable style="text-align:center;"
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|+style="text-align:left;font-size:12pt" | 2024-2 Microbiome
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!scope="col" style="padding:.4em" | Paper<br/>index
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|YR Kim
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[https://doi.org/10.1080/19490976.2024.2418984 Fecal microbial marker panel for aiding diagnosis of autism spectrum disorders]
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|style="padding:.4em;"|YR Kim
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[https://doi.org/10.1038/s41564-024-01739-1 Multikingdom and functional gut microbiota markers for autism spectrum disorder]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|JY Kim
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[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;"|WJ Kim
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[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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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|24-66
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|style="padding:.4em;"|G Koh
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[https://doi.org/10.1038/s41467-024-52561-6 Gut metagenomes of Asian octogenarians reveal metabolic potential expansion and distinct microbial species associated with aging phenotypes]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|24-65
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|style="padding:.4em;"|SH Ahn
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[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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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|HJ Kim
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[https://doi.org/10.1186/s13059-024-03320-9 VirRep: a hybrid language representation learning framework for identifying viruses from human gut metagenomes]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|24-63
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|style="padding:.4em;"|HJ Kim
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[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;" rowspan=1|2024/12/4
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|24-62
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|style="padding:.4em;"|JY Ma
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[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;" rowspan=1|2024/11/27
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|24-61
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|style="padding:.4em;"|JH Cha
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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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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|24-60
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|style="padding:.4em;"|NY Kim
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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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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|24-59
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|style="padding:.4em;"|YR Kim
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[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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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|24-58
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|style="padding:.4em;"|JY Kim
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|style="padding:.4em;text-align:left"|
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[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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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|24-57
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|style="padding:.4em;"|WJ Kim
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[https://doi.org/10.1186/s40168-024-01876-z Prokaryotic‑virus‑encoded auxiliary metabolic genes throughout the global oceans]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|24-56
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|style="padding:.4em;"|G Koh
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[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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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|24-55
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|style="padding:.4em;"|SH Ahn
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[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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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|24-54
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|style="padding:.4em;"|HJ Kim
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[https://doi.org/10.1101/2024.05.28.596318 vClassifier: a toolkit for species-level classification of prokaryotic viruses]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|24-53
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|style="padding:.4em;"|HJ Kim
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[https://doi.org/10.1101/2024.07.26.605250 GRAViTy-V2: a grounded viral taxonomy application]
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|style="padding:.4em;"|24-52
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|style="padding:.4em;"|JY Ma
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[https://doi.org/10.1038/s41467-024-52533-w Accurately predicting enzyme functions through geometric graph learning on ESMFold-predicted structures]
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|style="padding:.4em;"|24-51
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|style="padding:.4em;"|JH Cha
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|style="padding:.4em;text-align:left"|
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[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)]
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{|class=wikitable style="text-align:center;"
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|+style="text-align:left;font-size:12pt" | 2024-1 scOmics
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!scope="col" style="padding:.4em" | Paper<br/>index
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|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|HB Lee
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[https://doi.org/10.1126/science.adj4857 A blueprint for tumor-infiltrating B cells across human cancers]
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|style="padding:.4em;"|24-24
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|style="padding:.4em;"|YL Jung
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[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]
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|style="padding:.4em;"|24-23
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|style="padding:.4em;"|EJ Sung
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[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]
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|style="padding:.4em;"|IS Choi
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[https://doi.org/10.1038/s41591-024-02856-4 A visual-language foundation model for computational pathology]
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[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.ccell.2023.12.013 Clinical and molecular features of acquired resistance to immunotherapy in non-small cell lungcancer]
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|style="padding:.4em;"|JH Cha
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[https://doi.org/10.1101/2024.06.04.597354 Cell-Graph Compass: Modeling Single Cells with Graph Structure Foundation Model]
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|style="padding:.4em;"|YL Jung
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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]
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|style="padding:.4em;"|EJ Sung
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[https://doi.org/10.1038/s43588-024-00597-5 Population-level comparisons of gene regulatory networks modeled on highthroughput single-cell transcriptomics data]
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[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.2024.100553 Unified cross-modality integration and analysis of T cell receptors and T cell transcriptomes by low-resource-aware representation learning]
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|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|JH Cha
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[https://doi.org/10.1101/2023.07.18.549602 Contextual AI models for single-cell protein biology]
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|style="padding:.4em;"|EJ Sung
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[https://doi.org/10.1101/2024.04.15.589472 Nicheformer: a foundation model for single-cell and spatial omics]
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|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|IS Choi
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[https://doi.org/10.1101/2023.05.29.542705 Large Scale Foundation Model on Single-cell Transcriptomics]
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|style="padding:.4em;"|SB Baek
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[https://doi.org/10.1038/s41592-024-02201-0 scGPT: toward building a foundation modelfor single-cell multi-omics using generative AI]
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|style="padding:.4em;"|JH Cha
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[https://doi.org/10.1038/s41586-023-06139-9 Transfer learning enables predictions in network biology]
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[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]
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[https://doi.org/10.1038/s41592-023-02117-1 SEVtras delineates small extracellular vesicles at droplet resolution from single-cell transcriptomes]
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|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|SB Baek
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[https://doi.org/10.1038/s41587-023-01728-5 A relay velocity model infers cell-dependent RNA velocity]
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|style="padding:.4em;"|EJ Sung
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[https://doi.org/10.1038/s41467-023-44206-x Pathway centric analysis for single-cell RNA-seq and spatial transcriptomics data with GSDensity]
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|style="padding:.4em;"|JH Cha
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[https://doi.org/10.1038/s41587-023-01734-7 Dandelion uses the single-cell adaptive immune receptor repertoire to explore lymphocyte developmental origins]
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|style="padding:.4em;"|IS Choi
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[https://doi.org/10.1016/j.cell.2023.11.026 Automatic cell-type harmonization and integration across Human Cell Atlas datasets]
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[https://doi.org/10.1038/s41592-023-01994-w Deep generative modeling of transcriptional dynamics for RNA velocity analysis in single cells]
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[https://doi.org/10.1038/s41587-021-00896-6 Gene signature extraction and cell identity recognition at the single-cell level with Cell-ID]
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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]
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|style="padding:.4em;" rowspan=1|2024/10/02
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|style="padding:.4em;"|NY Kim
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[https://doi.org/10.1038/s41591-024-03067-7 Strain-specific gut microbial signatures in type 2 diabetes identified in a cross-cohort analysis of 8,117 metagenomes]
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[https://doi.org/10.1186/s40168-024-01832-x Gut virome-wide association analysis identifes cross-population viral signatures for infammatory bowel disease]
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|style="padding:.4em;"|JY Kim
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[https://doi.org/10.48550/arXiv.1806.00064 Efficient Low-rank Multimodal Fusion with Modality-Specific Factors]
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|style="padding:.4em;"|JY Kim
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[https://doi.org/10.48550/arXiv.1707.07250 Tensor Fusion Network for Multimodal Sentiment Analysis]
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|style="padding:.4em;"|24-49
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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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|style="padding:.4em;" rowspan=1|2024/09/11
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|style="padding:.4em;"|24-47
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|style="padding:.4em;"|G Koh
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[https://doi.org/10.1186/s13059-024-03325-4 Gut microbiota DPP4-like enzymes are increased in type-2 diabetes and contribute to incretin inactivation]
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|style="padding:.4em;"|SH Ahn
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[https://pubmed.ncbi.nlm.nih.gov/31510656 Deep learning with multimodal representation for pancancer prognosis prediction]
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|style="padding:.4em;"|HJ Kim
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[https://pubmed.ncbi.nlm.nih.gov/32881682 Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosis]
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|style="padding:.4em;"|HJ Kim
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[https://doi.org/10.1016/j.ccell.2022.07.004 Pan-cancer integrative histology-genomic analysis via multimodal deep learning]
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[https://doi.org/10.1016/j.chom.2024.03.005 A metagenomics pipeline reveals insertion sequence-driven evolution of the microbiota]
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|style="padding:.4em;"|JH Cha
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[https://doi.org/10.48550/arXiv.2303.00915 BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs]
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|style="padding:.4em;"|JH Cha
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[https://arxiv.org/abs/2103.00020 Learning Transferable Visual Models From Natural Language Supervision]
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|style="padding:.4em;"|24-42
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|style="padding:.4em;"|JY Ma
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[https://doi.org/10.1038/s41592-022-01616-x BIONIC: biological network integration using convolutions]
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[https://doi.org/10.1038/s41587-023-01917-2 Protein remote homology detection and structural alignment using deep learning]
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[https://doi.org/10.1038/s41586-024-07487-w Accurate structure prediction of biomolecular interactions with AlphaFold 3]
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|style="padding:.4em;"|24-39
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[https://doi.org/10.1186/s40168-023-01737-1 Gut microbiome-metabolome interactions predict host condition]
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[https://doi.org/10.1038/s41591-024-02963-2 Microbiome confounders and quantitative profiling challenge predicted microbial targets in colorectal cancer development]
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|style="padding:.4em;"|SH Ahn
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[https://doi.org/10.1038/s41564-024-01751-5 A multi-kingdom collection of 33,804 reference genomes for the human vaginal microbiome]
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[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]
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[https://doi.org/10.1101/2024.06.04.596112 Compositional Differential Abundance Testing: Defining and Finding a New Type of Health-Microbiome Associations]
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[https://doi.org/10.1016/j.cell.2024.05.013 Discovery of antimicrobial peptides in the global microbiome with machine learning]
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[https://doi.org/10.1016/j.cell.2024.05.029 Custom scoring based on ecological topology of gut microbiota associated with cancer immunotherapy outcome]
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[https://doi.org/10.1038/s41586-024-07336-w Paternal microbiome perturbations impact offspring fitness]
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[https://doi.org/10.1016/j.crmeth.2024.100775 Interactions-based classification of a single microbial sample]
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[https://doi.org/10.1101/2024.04.10.588779 Accurate estimation of intraspecificmicrobial gene content variation inmetagenomic data with MIDAS v3 andStrainPGC]
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[https://doi.org/10.1016/j.cell.2024.03.021  A pan-cancer analysis of the microbiome inmetastatic cancer]
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[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]
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[https://doi.org/10.1016/j.chom.2024.02.010 Stratification of Fusobacterium nucleatum by localhealth status in the oral cavity defines its subspeciesdisease association]
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[https://doi.org/10.1080/19490976.2024.2309684 A universe of human gut-derived bacterialprophages: unveiling the hidden viral players inintestinal microecology]
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[https://doi.org/10.1038/s41388-024-02974-w Robustness of cancer microbiome signals over a broad range of methodological variation]
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[https://doi.org/10.1038/s41586-024-07182-w A distinct Fusobacterium nucleatum clade dominates the colorectal cancer niche]
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[https://doi.org/10.1016/j.cell.2024.01.039 A cryptic plasmid is among the most numerous genetic elements in the human gut]
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[https://doi.org/10.1016/j.cell.2024.03.014 Gut microbiome and metabolome profiling in Framingham heart study reveals cholesterol-metabolizing bacteria]
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[https://doi.org/10.1101/2024.03.18.584290 Fecal microbial load is a major determinant of gut microbiome variation and aconfounder for disease associations]
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[https://doi.org/10.1038/s41586-024-07162-0 A host-microbiota interactome reveals extensive transkingdom connectivity]
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[https://doi.org/10.1101/2024.02.02.578701 Metagenomic estimation of dietary intake from human stool]
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[https://doi.org/10.1038/s41467-024-45793-z A metagenomic catalog of the early-life human gut virome]
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[https://doi.org/10.1101/2024.01.08.574624 Large-scale computational analyses of gut microbial CAZyme repertoires enabled by Cayman]
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[https://doi.org/10.1038/s41467-024-44720-6 Defining the biogeographical map and potential bacterial translocation of microbiome in human ‘surface organs’]
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[https://doi.org/10.1038/s41467-023-42997-7 Gut microbial structural variation associates with immune checkpoint inhibitor response]
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[https://doi.org/10.1080/19490976.2024.2307586 Fungal signature differentiates alcohol-associated liver disease from nonalcoholic fatty liver disease]
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|style="padding:.4em;"|24-12
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|style="padding:.4em;"|JY Kim
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[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]
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[https://doi.org/10.1038/s41467-023-42112-w Disease-specific loss of microbial cross feeding interactions in the human gut]
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|style="padding:.4em;"|JY Ma
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[https://doi.org/10.1038/s41592-023-02092-7 Multigroup analysis of compositions of microbiomes with covariate adjustments and repeated measures]
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[https://doi.org/10.1038/s41467-023-40719-7 Microdiversity of the vaginal microbiome is associated with preterm birth]
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|style="padding:.4em;"|HJ Kim
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[https://doi.org/10.1038/s41564-023-01584-8 Large language models improve annotation of prokaryotic viral proteins]
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|style="padding:.4em;"|24-10
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|style="padding:.4em;"|G Koh
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[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]
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|style="padding:.4em;"|JH Cha
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[https://academic.oup.com/nargab/article/2/2/lqaa023/5826153 Visualizing ’omic feature rankings and log-ratios using Qurro]
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|style="padding:.4em;"|JH Cha
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[https://doi.org/10.1038/s41467-019-10656-5 Establishing microbial composition measurement standards with reference frames]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|24-5
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|style="padding:.4em;"|NY Kim
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[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]
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|style="padding:.4em;" rowspan=1|2024/03/13
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|style="padding:.4em;"|YR Kim
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[https://doi.org/10.1038/s41467-023-44289-6 Differential responses of the gut microbiome and resistome to antibiotic exposures in infants and adults]
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|style="padding:.4em;" rowspan=1|2024/03/13
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|style="padding:.4em;"|JY Kim
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[https://doi.org/10.1038/s41467-023-44290-z Effective binning of metagenomic contigs using contrastive multi-view representation learning]
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|style="padding:.4em;"|WJ Kim
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[https://doi.org/10.1038/s41559-020-01353-4 Polarization of microbial communities between competitive and cooperative metabolism]
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|style="padding:.4em;"|G Koh
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[https://onlinelibrary.wiley.com/doi/full/10.1002/advs.202303925 Metagenomic Insight into The Global Dissemination of The Antibiotic Resistome]
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|style="padding:.4em;"|G Koh
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[https://doi.org/10.1073/pnas.2008731118 Conjugative plasmids interact with insertion sequences to shape the horizontal transfer of antimicrobial resistance genes]
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|+style="text-align:left;font-size:12pt" | 2024-1 Advanced scOmics Data Analysis
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|style="padding:.4em;"|EB Hong
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[https://doi.org/10.1038/s41586-023-07011-6 Spatial transcriptomics reveal neuron–astrocyte synergy in long-term memory]
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|style="padding:.4em;" rowspan=1|2024/06/18
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|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|JJ Heo
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[https://doi.org/10.1038/s41467-021-22197-x scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses]
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|style="padding:.4em;" rowspan=1|2024/06/18
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|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|SM Han
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[https://doi.org/10.1126/science.abi4882 Spatial transcriptomics of planktonic and sessile bacterial populations at single-cell resolution]
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|style="padding:.4em;"|HJ Choi
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[https://doi.org/10.1038/s41590-024-01792-2 Human lung cancer harbors spatially organized stem-immunity hubs associated with response to immunotherapy]
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|style="padding:.4em;" rowspan=1|2024/06/11
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|style="padding:.4em;" rowspan=1|Single-cell
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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;" rowspan=1|Single-cell
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|style="padding:.4em;"|24-27
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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;"|24-26
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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;"|24-24
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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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|style="padding:.4em;"|24-20
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|style="padding:.4em;"|JH Lee
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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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|style="padding:.4em;"|24-19
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|style="padding:.4em;"|YH Lee
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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;"|24-17
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|style="padding:.4em;"|DY Won
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[https://doi.org/10.1038/s41587-023-01979-2 Spatial metatranscriptomics resolves host–bacteria–fungi interactomes]
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|style="padding:.4em;"|SG Oh
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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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|style="padding:.4em;"|SY Park
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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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[https://doi.org/10.1038/s41593-023-01455-9 Spatial transcriptomics reveals the distinct organization of mouse prefrontal cortex and neuronal subtypes regulating chronic pain]
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|style="padding:.4em;"|JH Nam
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[https://doi.org/10.1038/s41467-023-39933-0 Spatial cellular architecture predicts prognosis in glioblastoma]
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|style="padding:.4em;"|HS Na
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[https://doi.org/10.1016/j.celrep.2024.113784 Single-cell spatial transcriptomic and translatomic profiling of dopaminergic neurons in health, aging, and disease]
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|style="padding:.4em;"|PK Kim
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[https://doi.org/10.1038/s41467-022-30511-4 Transcriptional adaptation of olfactory sensory neurons to GPCR identity and activity]
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|style="padding:.4em;"|SH Kwon
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[https://doi.org/10.1038/s41467-021-26271-2 Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions]
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|style="padding:.4em;"|Q Zhen
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|style="padding:.4em;"|CR Leenaars
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[https://doi.org/10.1038/s41556-023-01316-4 Single-cell spatial multi-omics and deep learning dissect enhancer-driven gene regulatory networks in liver zonation]
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|style="padding:.4em;"|JY Kim
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[https://doi.org/10.1038/s41467-022-35319-w Spatial transcriptomics landscape of lesions from non-communicable inflammatory skin diseases]
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[https://doi.org/10.1016/j.cmet.2022.07.010 Neuregulin 4 suppresses NASH-HCC development by restraining tumor-prone liver microenvironment]
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|style="padding:.4em;"|G Koh
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[https://doi.org/10.1038/s41593-023-01334-3 Single-nucleus multiregion transcriptomic analysis of brain vasculature in Alzheimer’s disease]
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|style="padding:.4em;"|SH Ahn
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[https://doi.org/10.1136/gutjnl-2023-330243 Single-cell transcriptomic analysis deciphers heterogenous cancer stem-like cells in colorectal cancer and their organ-specific metastasis]
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|style="padding:.4em;"|EJ Sung
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[https://doi.org/10.1038/s41467-022-31519-6 Single cell sequencing identifies clonally expanded synovial CD4+ TPH cells expressing GPR56 in rheumatoid arthritis]
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[https://doi.org/10.1016/j.ccell.2023.09.011 Progenitor-like exhausted SPRY1+CD8+ T cells potentiate responsiveness to neoadjuvant PD-1 blockade in esophageal squamous cell carcinoma]
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|+style="text-align:left;font-size:12pt" | 2023-2 scOmics
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|style="padding:.4em;"|23-40
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|style="padding:.4em;"|IS Choi
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|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|23-39
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|style="padding:.4em;"|SB Baek
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[https://doi.org/10.1038/s43587-023-00514-x scDiffCom: a tool for differential analysis of cell–cell interactions provides a mouse atlas of aging changes in intercellular communication]
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|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|23-38
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|style="padding:.4em;"|JH Cha
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|style="padding:.4em;"|23-37
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|style="padding:.4em;"|EJ Sung
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[https://doi.org/10.1038/s41588-023-01523-7 Precise identification of cell states altered in disease using healthy single-cell references]
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|style="padding:.4em;"|23-36
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|style="padding:.4em;"|IS Choi
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[https://aacrjournals.org/clincancerres/article/29/19/3924/729105/Learning-Individual-Survival-Models-from-PanCancer Learning Individual Survival Models from PanCancer Whole Transcriptome Data]
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[https://doi.org/10.1038/s41592-023-01971-3 Dictys: dynamic gene regulatory network dissects developmental continuum with single-cell multiomics]
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[https://www.science.org/doi/10.1126/sciimmunol.adf4968 Preexisting tumor-resident T cells with cytotoxic potential associate with response to neoadjuvant anti–PD-1 in head and neck cancer]
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|style="padding:.4em;"|EJ Sung
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.1038/s41588-022-01273-y MHC II immunogenicity shapes the neoepitope landscape in human tumors]
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|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|IS Choi
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[https://doi.org/10.1038/s41586-023-06130-4 Hallmarks of transcriptional intratumour heterogeneity across a thousand tumours]
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|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|JW Yu
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[https://doi.org/10.1038/s41467-023-37353-8 Pan-cancer classification of single cells in the tumour microenvironment]
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|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|23-30
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|style="padding:.4em;"|JH Cha
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[https://doi.org/10.1038/s41587-023-01686-y Single-cell mapping of combinatorial target antigens for CAR switches using logic gates]
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|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|23-29
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|style="padding:.4em;"|SB Baek
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[https://doi.org/10.1038/s41587-023-01782-z Comparative analysis of cell–cell communication at single-cell resolution]
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|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|23-28
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|style="padding:.4em;"|EJ Sung
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[https://doi.org/10.1038/s43018-023-00566-3 Phenotypic diversity of T cells in human primary and metastatic brain tumors revealed by multiomic interrogation]
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|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|23-27
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|style="padding:.4em;"|IS Choi
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.1038/s41591-023-02324-5 An integrated tumor, immune and microbiome atlas of colon cancer]
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|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|23-26
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|style="padding:.4em;"|SB Baek
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.1038/s41587-022-01476-y Multi-omic single-cell velocity models epigenome–transcriptome interactions and improves cell fate prediction]
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|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|23-25
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|style="padding:.4em;"|JH Cha
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.1016/j.immuni.2023.04.010 Recruitment of epitope-specific T cell clones with a low-avidity threshold supports efficacy against mutational escape upon re-infection]
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{|class=wikitable style="text-align:center;"
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|+style="text-align:left;font-size:12pt" | 2023-2 Microbiome
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|23-66
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|style="padding:.4em;"|HJ Kim
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.1186/s40168-023-01607-w Phages are unrecognized players in the ecology of the oral pathogen Porphyromonas gingivalis]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|23-65
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|style="padding:.4em;"|JH Cha
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.1038/s41564-023-01439-2 A predicted CRISPR-mediated symbiosis between uncultivated archaea]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|23-64
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|style="padding:.4em;"|SH Ahn
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|style="padding:.4em;text-align:left"|
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[https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-023-01692-x Integrating compositional and functional content to describe vaginal microbiomes in health and disease]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|23-63
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|style="padding:.4em;"|JY Ma
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.1038/s41587-023-01696-w Contamination source modeling with SCRuB improves cancer phenotype prediction from microbiome data]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|23-62
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|style="padding:.4em;"|NY Kim
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.1038/s41586-023-06431-8 Mapping the T cell repertoire to a complex gut bacterial community]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|23-61
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|style="padding:.4em;"|YR Kim
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.1101/2023.07.03.547607 Multi-view integration of microbiome data for identifying disease-associated modules]
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|style="padding:.4em;" rowspan=1|2024/01/24
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|23-60
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|style="padding:.4em;"|JY Kim
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.1101/2023.09.28.559994 Phage-bacteria dynamics during the first years of life revealed by trans-kingdom marker gene analysis]
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|style="padding:.4em;" rowspan=1|2024/01/24
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|23-59
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|style="padding:.4em;"|WJ Kim
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.1038/s41593-023-01361-0 Multi-level analysis of the gut–brain axis shows autism spectrum disorder-associated molecular and microbial profiles]
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|style="padding:.4em;" rowspan=1|2024/01/17
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|23-58
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|style="padding:.4em;"|G Koh
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.1101/2023.11.21.568153 Metagenomic Immunoglobulin Sequencing (MIG-Seq) Exposes Patterns of IgA Antibody Binding in the Healthy Human Gut Microbiome]
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|style="padding:.4em;" rowspan=1|2024/01/17
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|23-57
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|style="padding:.4em;"|SH Ahn
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.1038/s41467-023-41042-x Impact of dietary interventions on pre-diabetic oral and gut microbiome, metabolites and cytokines]
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|style="padding:.4em;" rowspan=1|2024/01/10
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|23-56
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|style="padding:.4em;"|HJ Kim
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.1038/s41592-023-02018-3 Fast and robust metagenomic sequence comparison through sparse chaining with skani]
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|style="padding:.4em;" rowspan=1|2024/01/10
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|23-55
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|style="padding:.4em;"|JY Ma
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.1038/s41591-023-02599-8 Bacterial SNPs in the human gut microbiome associate with host BMI]
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|style="padding:.4em;" rowspan=1|2023/01/03
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|23-54
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|style="padding:.4em;"|JH Cha
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.1080/19490976.2023.2245562 Multimodal metagenomic analysis reveals microbial single nucleotide variants as superior biomarkers for early detection of colorectal cancer]
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|style="padding:.4em;" rowspan=1|2023/01/03
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|23-53
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|style="padding:.4em;"|NY Kim
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.1016/j.chom.2023.10.005 Multi-kingdom gut microbiota analyses define bacterial-fungal interplay and microbial markers of pan-cancer immunotherapy across cohorts]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|23-52
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|style="padding:.4em;"|YR Kim
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.1016/j.xcrm.2023.101251 Prior antibiotic administration disrupts anti-PD-1 responses in advanced gastric cancer by altering the gut microbiome and systemic immune response]
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|style="padding:.4em;" rowspan=1|2023/12/27
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|23-51
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|style="padding:.4em;"|JY Kim
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.1016/j.cell.2023.05.046 Ultra-deep sequencing of Hadza hunter-gatherers recovers vanishing gut microbes]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|23-50
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|style="padding:.4em;"|WJ Kim
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.1186/s40168-023-01472-7 Altered infective competence of the human gut microbiome in COVID-19]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|23-49
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|style="padding:.4em;"|G Koh
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[https://onlinelibrary.wiley.com/doi/full/10.1002/aisy.202300342 Host-Variable-Embedding Augmented Microbiome-Based Simultaneous Detection of Multiple Diseases by Deep Learning]
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|style="padding:.4em;" rowspan=1|2023/12/06
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|23-48
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|style="padding:.4em;"|SH Ahn
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.1038/s41467-023-39264-0 A data-driven approach for predicting the impact of drugs on the human microbiome]
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|style="padding:.4em;"|23-47
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|style="padding:.4em;"|HJ Kim
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.1101/2023.04.06.535777 Activation of programmed cell death and counter-defense functions of phage accessory genes]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|23-46
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|style="padding:.4em;"|JH Cha
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.1038/s41467-023-39459-5 Top-down identification of keystone taxa in the microbiome]
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|style="padding:.4em;"|23-45
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|style="padding:.4em;"|JY Ma
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.1016/j.cels.2022.12.007 Pitfalls of genotyping microbial communities with rapidly growing genome collections]
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|style="padding:.4em;" rowspan=1|2023/11/22
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|23-44
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|style="padding:.4em;"|NY Kim
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.1186/s13059-023-03028-2 Reconstruction of the last bacterial common ancestor from 183 pangenomes reveals a versatile ancient core genome]
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|style="padding:.4em;" rowspan=1|2023/11/22
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|23-43
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|style="padding:.4em;"|SH Lee
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.1038/s41587-023-01868-8 Generation of accurate, expandable phylogenomic trees with uDance]
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|style="padding:.4em;" rowspan=1|2023/11/08
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|23-42
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|style="padding:.4em;"|WJ Kim
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.1016/j.immuni.2023.04.003 Phage display sequencing reveals that genetic, environmental, and intrinsic factors influence variation of human antibody epitope repertoire]
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|style="padding:.4em;" rowspan=1|2023/11/08
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|23-41
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|style="padding:.4em;"|JY Kim
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.1016/j.immuni.2023.04.017 Phage-display immunoprecipitation sequencing of the antibody epitope repertoire in inflammatory bowel disease reveals distinct antibody signatures]
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|style="padding:.4em;" rowspan=1|2023/11/01
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|23-40
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|style="padding:.4em;"|G Koh
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.15252/msb.202311525 Consistency across multi-omics layers in a drug-perturbed gut microbial community]
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|style="padding:.4em;" rowspan=1|2023/11/01
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|23-39
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|style="padding:.4em;"|HJ Kim
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.1038/s41587-023-01953-y Identification of mobile genetic elements with geNomad]
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|style="padding:.4em;" rowspan=1|2023/10/25
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|23-38
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|style="padding:.4em;"|SH Ahn
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.1038/s41591-023-02407-3 Microbiome-derived cobalamin and succinyl-CoA as biomarkers for improved screening of anal cancer]
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|style="padding:.4em;" rowspan=1|2023/10/11
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|23-37
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|style="padding:.4em;"|JH Cha
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.1038/s41591-023-02424-2 The airway microbiome mediates the interaction between environmental exposure and respiratory health in humans]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|23-36
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|style="padding:.4em;"|JY Ma
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.1080/19490976.2023.2224474 Ordering taxa in image convolution networks improves microbiome-based machine learning accuracy]
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|style="padding:.4em;" rowspan=1|2023/09/27
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|23-35
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|style="padding:.4em;"|NY Kim
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.1101/2023.08.12.553040 The defensome of complex bacterial communities]
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|style="padding:.4em;" rowspan=1|2023/09/27
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|23-34
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|style="padding:.4em;"|SH Lee
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.1016/j.cell.2023.03.011 Dietary tryptophan metabolite released by intratumoral Lactobacillus reuteri facilitates immune checkpoint inhibitor treatment]
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|style="padding:.4em;" rowspan=1|2023/09/20
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|23-33
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|style="padding:.4em;"|WJ Kim
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.1038/s43587-022-00306-9 Toward an improved definition of a healthy microbiome for healthy aging]
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|style="padding:.4em;" rowspan=1|2023/09/20
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|23-32
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|style="padding:.4em;"|JY Kim
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.1038/s43587-022-00287-9 Associations of the skin, oral and gut microbiome with aging, frailty and infection risk reservoirs in older adults]
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|style="padding:.4em;" rowspan=1|2023/09/13
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|23-31
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|style="padding:.4em;"|G Koh
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.1186/s40168-023-01614-x Statistical modeling of gut microbiota for personalized health status monitoring]
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|style="padding:.4em;" rowspan=1|2023/09/13
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|23-30
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|style="padding:.4em;"|SH Ahn
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.7554/eLife.50240 Adjusting for age improves identification of gut microbiome alterations in multiple diseases]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|23-29
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|style="padding:.4em;"|HJ Kim
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.1038/s41564-023-01370-6 Centenarians have a diverse gut virome with the potential to modulate metabolism and promote healthy lifespan]
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|style="padding:.4em;" rowspan=1|2023/09/06
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|23-28
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|style="padding:.4em;"|JH Cha
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.1016/j.chom.2023.01.003 Longitudinal comparison of the developing gut virome in infants and their mothers]
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{|class=wikitable style="text-align:center;"
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|+style="text-align:left;font-size:12pt" | 2023-1 ADVANCED MICROBIOME DATA ANALYSIS
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!scope="col" style="padding:.4em" | Paper title
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|-
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|style="padding:.4em;" rowspan=1|2023/06/13
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|23-24
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|style="padding:.4em;"|JY Kim
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.1016/j.chom.2020.03.005 Structure of the Mucosal and Stool Microbiome in Lynch Syndrome]
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|style="padding:.4em;" rowspan=1|2023/06/13
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|23-23
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|style="padding:.4em;"|WJ Kim
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.1038/s41586-019-1237-9 Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases]
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|style="padding:.4em;" rowspan=1|2023/06/13
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|23-22
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|style="padding:.4em;"|SH Ahn
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.1016/j.jare.2022.03.007 Roles of oral microbiota and oral-gut microbial transmission in hypertension]
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|style="padding:.4em;"|SY Lim
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[https://doi.org/10.1016/j.chom.2022.08.009 Human gut microbiota stimulate defined innate immune responses that vary from phylum to strain]
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|style="padding:.4em;"|BS Kim
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[https://doi.org/10.1038/s41591-023-02217-7 Gut microbial metabolism of 5-ASA diminishes its clinical efficacy in inflammatory bowel disease]
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|style="padding:.4em;"|JY Kim
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[https://doi.org/10.1016/j.chom.2023.01.004 Deficient butyrate-producing capacity in the gut microbiome is associated with bacterial network disturbances and fatigue symptoms in ME/CFS]
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|style="padding:.4em;"|EJ Sung
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[https://doi.org/10.1016/j.ccell.2022.11.013 Gut microbiota-mediated nucleotide synthesis attenuates the response to neoadjuvant chemoradiotherapy in rectal cancer]
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|style="padding:.4em;"|23-17
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|style="padding:.4em;"|G Koh
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[https://doi.org/10.1016/j.chom.2021.06.019 Multi-omics reveal microbial determinants impacting responses to biologic therapies in inflammatory bowel disease]
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|style="padding:.4em;"|SH Lee
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[https://doi.org/10.1038/s41586-022-05181-3 Identification of trypsin-degrading commensals in the large intestine]
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|style="padding:.4em;"|JP Hong
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[https://doi.org/10.1038/s41586-022-05546-8 Questioning the fetal microbiome illustrates pitfalls of low-biomass microbial studies]
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|style="padding:.4em;"|23-14
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|style="padding:.4em;"|MR Jang
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[https://doi.org/10.1016/j.chom.2023.01.013 Tissue-resident Lachnospiraceae family bacteria protect against colorectal carcinogenesis by promoting tumor immune surveillance]
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|style="padding:.4em;"|23-13
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|style="padding:.4em;"|JW Yu
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[https://doi.org/10.1016/j.cell.2022.09.005 Pan-cancer analyses reveal cancer-type-specific fungal ecologies and bacteriome interactions]
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|style="padding:.4em;"|23-12
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|style="padding:.4em;"|NY Kim
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[https://doi.org/10.1016/j.cell.2022.09.015 A pan-cancer mycobiome analysis reveals fungal involvement in gastrointestinal and lung tumors]
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|style="padding:.4em;"|HR Shin
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[https://doi.org/10.1038/s41564-021-01030-7 Multi-kingdom microbiota analyses identify bacterial–fungal interactions and biomarkers of colorectal cancer across cohorts]
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|style="padding:.4em;"|SG Oh
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[https://doi.org/10.1038/s42255-022-00716-4 The antitumour effects of caloric restriction are mediated by the gut microbiome]
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|style="padding:.4em;"|WJ Kim
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[https://doi.org/10.1038/s41591-022-01964-3 Variability of strain engraftment and predictability of microbiome composition after fecal microbiota transplantation across different diseases]
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|style="padding:.4em;"|SM Han
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[https://doi.org/10.1038/s41591-022-01913-0 Drivers and determinants of strain dynamics following fecal microbiota transplantation]
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|style="padding:.4em;"|YY Kim
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[https://doi.org/10.1016/j.cell.2022.11.023 Mobile genetic elements from the maternal microbiome shape infant gut microbial assembly and metabolism]
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|style="padding:.4em;"|SH Heo
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[https://doi.org/10.1016/j.chom.2023.01.018 Mother-to-infant microbiota transmission and infant microbiota development across multiple body sites]
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|style="padding:.4em;"|SY Yang
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[https://doi.org/10.1038/s41467-023-36633-7 Population-level impacts of antibiotic usage on the human gut microbiome]
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|style="padding:.4em;"|YH Yoon
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[https://doi.org/10.1038/s41586-022-05438-x Enterococci enhance Clostridioides difficile pathogenesis]
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|style="padding:.4em;"|DH Lee
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|style="padding:.4em;"|YJ Roh
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|style="padding:.4em;"|SH Ahn
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[https://doi.org/10.1016/j.cell.2022.08.021 Gut microbiome of multiple sclerosis patients and paired household healthy controls reveal associations with disease risk and course]
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|style="padding:.4em;"|JW Yu
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|style="padding:.4em;"|IS Choi
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|style="padding:.4em;"|EJ Sung
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|style="padding:.4em;"|G Koh
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|style="padding:.4em;"|JH Cha
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|style="padding:.4em;"|SB Baek
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[https://doi.org/10.1038/s41591-023-02371-y Pan-cancer T cell atlas links a cellular stress response state to immunotherapy resistance]
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|style="padding:.4em;"|EJ Sung
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[https://doi.org/10.1016/j.patter.2022.100651 Self-supervised graph representation learning integrates multiple molecular networks and decodes gene-disease relationships]
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|style="padding:.4em;"|IS Choi
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[https://doi.org/10.1016/j.ccell.2022.10.008 High-resolution single-cell atlas reveals diversity and plasticity of tissue-resident neutrophils in non-small cell lung cancer]
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|style="padding:.4em;"|G Koh
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|style="padding:.4em;"|JW Yu
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|style="padding:.4em;"|JH Cha
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|style="padding:.4em;"|SB Baek
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[https://doi.org/10.1016/j.cell.2022.11.028 Integrative single-cell analysis of cardiogenesis indentifies developmental trajectories and non-conding mutations in congenital heart disease]
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|style="padding:.4em;"|IS Choi
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|style="padding:.4em;"|G Koh
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[https://www.nature.com/articles/s41588-022-01141-9 Cancer cell states recur across tumor types and form specific interactions with the tumor microenvironment]
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|style="padding:.4em;"|JH Cha
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[https://www.nature.com/articles/s41590-022-01262-7 Pre-encoded responsiveness to type I interferon in the peripheral immune system defines outcome of PD1 blockade therapy]
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[https://www.biorxiv.org/content/10.1101/2022.03.16.484513v1 Integrated single-cell profiling dissects cell-state-specific enhancer landscapes of human tumor-infiltrating T cells]
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|style="padding:.4em;"|EJ Sung
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|style="padding:.4em;"|IS Choi
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[https://www.nature.com/articles/s41588-022-01134-8 Single-nucleus and spatial transcriptome profiling of pancreatic cancer identifies multicellular dynamics associated with neoadjuvant treatment]
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|style="padding:.4em;"|JW Yu
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|style="padding:.4em;"|JH Cha
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|style="padding:.4em;"|23-27
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|style="padding:.4em;"|JY Ma
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[https://doi.org/10.1016/j.chom.2023.05.024 Enterosignatures define common bacterial guilds in the human gut microbiome]
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|style="padding:.4em;"|SH Lee
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[https://doi.org/10.1016/j.chom.2023.05.027 The TaxUMAP atlas: Efficient display of large clinical microbiome data reveals ecological competition in protection against bacteremia]
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[http://dx.doi.org/10.1038/nbt.3704 Measurement of bacterial replication rates in microbial communities]
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|style="padding:.4em;"|JY Kim
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[https://doi.org/10.1186/s40168-023-01564-4 Skin microbiome diferentiates into distinct cutotypes with unique metabolic functions upon exposure to polycyclic aromatic hydrocarbons]
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|style="padding:.4em;" rowspan=1|2023/08/11
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|23-22
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|style="padding:.4em;"|SH Ahn
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.1016/j.xcrm.2023.100920 Enrichment of oral-derived bacteria in inflamed colorectal tumors and distinct associations of Fusobacterium in the mesenchymal subtype]
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|style="padding:.4em;" rowspan=1|2023/08/04
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|23-21
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|style="padding:.4em;"|HJ Kim
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.1038/s41586-023-05989-7 Profiling the human intestinal environment under physiological conditions]
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|style="padding:.4em;" rowspan=1|2023/07/28
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|23-20
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|style="padding:.4em;"|JH Cha
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.1186/s40168-023-01494-1 Genome-centric metagenomics reveals the host-driven dynamics and ecological role of CPR bacteria in an activated sludge system]
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|style="padding:.4em;" rowspan=1|2023/07/14
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|23-19
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|style="padding:.4em;"|JY Ma
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.1016/j.patter.2022.100658 Enhanced metagenomic deep learning for disease prediction and consistent signature recognition by restructured microbiome 2D representations]
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|style="padding:.4em;" rowspan=1|2023/07/07
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|23-18
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|style="padding:.4em;"|NY Kim
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.1186/s13059-022-02809-5 Gene fow and introgression are pervasive forces shaping the evolution of bacterial species]
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|style="padding:.4em;" rowspan=1|2023/06/30
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|23-17
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|style="padding:.4em;"|JH Cha
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.1186/s40168-022-01435-4 Alterations of oral microbiota and impact on the gut microbiome in type 1 diabetes mellitus revealed by integrated multi‑omic analyses]
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|style="padding:.4em;" rowspan=1|2023/06/23
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|23-16
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|style="padding:.4em;"|HJ Kim
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.1038/s41467-022-33397-4 Deciphering microbial gene function using natural language processing]
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|style="padding:.4em;" rowspan=1|2023/06/16
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|23-15
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|style="padding:.4em;"|SH Lee
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.1101/2022.11.28.518265 Rethinking bacterial relationships in light of their molecular abilities]
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|style="padding:.4em;" rowspan=1|2023/06/02
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|23-14
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|style="padding:.4em;"|JY Ma
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.1016/j.immuni.2022.08.016 The CD4+ T cell response to a commensal-derived epitope transitions from a tolerant to an inflammatory state in Crohn’s disease]
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|style="padding:.4em;" rowspan=1|2023/05/26
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|23-13
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|style="padding:.4em;"|NY Kim
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.1038/s41591-018-0203-7 Antigen discovery and specification of immunodominance hierarchies for MHCIIrestricted epitopes]
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|style="padding:.4em;" rowspan=1|2023/05/19
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|23-12
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|style="padding:.4em;"|SH Lee
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.1101/2022.10.11.511790 Single Cell Transcriptomics Reveals the Hidden Microbiomes of Human Tissues]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|23-11
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|style="padding:.4em;"|JY Kim
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.1038/s41564-017-0096-0 Stability of the human faecal microbiome in a cohort of adult men]
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|style="padding:.4em;" rowspan=1|2023/04/28
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|23-10
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|style="padding:.4em;"|WJ Kim
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.1038/s41564-017-0084-4 Metatranscriptome of human faecal microbial communities in a cohort of adult men]
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|style="padding:.4em;" rowspan=1|2023/03/24
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|23-9
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|style="padding:.4em;"|SH Ahn
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.1016/j.ccell.2022.09.009 Tumor microbiome links cellular programs and immunity in pancreatic cancer]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|23-8
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|style="padding:.4em;"|HJ Kim
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.1038/s41467-022-32832-w Extensive gut virome variation and its associations with host and environmental factors in a population-level cohort]
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|style="padding:.4em;" rowspan=1|2023/03/10
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|23-7
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|style="padding:.4em;"|JH Cha
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.1038/s41586-022-05620-1 The person-to-person transmission landscape of the gut and oral microbiomes]
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|style="padding:.4em;" rowspan=1|2023/02/21
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|23-6
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|style="padding:.4em;"|JY Ma
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.1101/2023.01.30.526328 BIRDMAn: A Bayesian differential abundance framework that enables robust inference of host-microbe associations]
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|style="padding:.4em;" rowspan=1|2023/02/14
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|style="padding:.4em;"|23-5
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|style="padding:.4em;"|NY Kim
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[https://www.nature.com/articles/s41564-022-01157-1 Phage–host coevolution in natural populations]
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|style="padding:.4em;" rowspan=1|2023/01/31
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|23-4
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|style="padding:.4em;"|SH Lee
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|style="padding:.4em;text-align:left"|
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[https://www.nature.com/articles/s41467-022-29968-0 A randomized controlled trial for response of microbiome network to exercise and diet intervention in patients with nonalcoholic fatty liver disease]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|23-3
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|style="padding:.4em;"|SH Ahn
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[https://www.biorxiv.org/content/10.1101/2022.05.19.492684v1 Scalable power analysis and effect size exploration of microbiome community differences with Evident]
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|style="padding:.4em;"|23-2
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|style="padding:.4em;"|HJ Kim
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[https://www.biorxiv.org/content/10.1101/2022.08.05.502982v1 Phanta: Phage-inclusive profiling of human gut metagenomes]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|23-1
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|style="padding:.4em;"|JH Cha
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|style="padding:.4em;text-align:left"|
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[https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1010373 Computational approach to modeling microbiome landscapes associated with chronic human disease progression]
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!scope="col" style="padding:.4em" | Paper title
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|style="padding:.4em;" rowspan=1|2022/12/28
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|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|22-32
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|style="padding:.4em;"|EJ Sung
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[https://www.biorxiv.org/content/10.1101/2022.08.19.504505v1 SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks]
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|style="padding:.4em;" rowspan=1|2022/11/29
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|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|22-31
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|style="padding:.4em;"|JH Cha, SB Baek, IS Choi
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|style="padding:.4em;text-align:left"|
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[https://www.pnas.org/doi/10.1073/pnas.2105859118 Representation learning of RNA velocity reveals robust cell transitions]
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[https://www.nature.com/articles/s41467-022-34188-7 UniTVelo: temporally unified RNA velocity reinforces single-cell trajectory inference]
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[https://www.sciencedirect.com/science/article/pii/S0092867421015774 Mapping transcriptomic vector fields of single cells]
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|style="padding:.4em;"|22-30
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|style="padding:.4em;"|IS Choi
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|style="padding:.4em;text-align:left"|
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[https://www.nature.com/articles/s41467-022-31535-6 Network-based machine learning approach to predict immunotherapy response in cancer patients]
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|style="padding:.4em;" rowspan=1|2022/11/08
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|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|22-29
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|style="padding:.4em;"|SB Baek
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[https://www.biorxiv.org/content/10.1101/2022.05.04.490536v1 Modeling fragment counts improves single-cell ATAC-seq analysis]
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|style="padding:.4em;" rowspan=1|2022/10/11
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|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|22-28
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|style="padding:.4em;"|G Koh
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[https://www.nature.com/articles/s41586-022-04718-w Extricating human tumour immune alterations from tissue inflammation]
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|style="padding:.4em;" rowspan=1|2022/09/13
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|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|22-25
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|style="padding:.4em;"|JW Yu
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[https://www.biorxiv.org/content/10.1101/2022.06.15.495325v1 T cell receptor convergence is an indicator of antigen-specific T cell response in cancer immunotherapies]
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|style="padding:.4em;" rowspan=1|2022/09/06
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|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|22-26
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|style="padding:.4em;"|JH Cha
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|style="padding:.4em;text-align:left"|
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[https://www.nature.com/articles/s41587-021-01091-3 Identifying phenotype-associated subpopulations by integrating bulk and single-cell sequencing data]
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|style="padding:.4em;" rowspan=1|2022/09/01
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|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|22-27
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|style="padding:.4em;"|SB Baek
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[https://www.biorxiv.org/content/10.1101/2021.12.06.471401v1 MIRA: Joint regulatory modeling of multimodal expression and chromatin accessibility in single cells]
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|style="padding:.4em;" rowspan=1|2022/08/25
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|style="padding:.4em;"|22-24
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|style="padding:.4em;"|EJ Sung
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[https://www.sciencedirect.com/science/article/pii/S1535610822000654 Immune phenotypic linkage between colorectal cancer and liver metastasis]
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|style="padding:.4em;"|22-23
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|style="padding:.4em;"|IS Choi
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|style="padding:.4em;text-align:left"|
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[https://doi.org/10.1101/2022.02.05.479217 Biologically informed deep learning to infer gene program activity in single cells]
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|style="padding:.4em;"|22-22
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|style="padding:.4em;"|SB Baek
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[https://www.nature.com/articles/s43018-022-00356-3 Cell type and gene expression deconvolution with BayesPrism enables Bayesian integrative analysis across bulk and single-cell RNA sequencing in oncology]
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|style="padding:.4em;" rowspan=1|2022/07/28
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|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|22-21
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|style="padding:.4em;"|JH Cha
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[https://pubmed.ncbi.nlm.nih.gov/34462589/ Mapping single-cell data to reference atlases by transfer learning]
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|style="padding:.4em;"|22-20
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|style="padding:.4em;"|JW Yu
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[https://doi.org/10.1101/2021.10.31.466532 Pan-cancer mapping of single T cell profiles reveals a TCF1:CXCR6-CXCL16 regulatory axis essential for effective anti-tumor immunity]
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|style="padding:.4em;"|22-19
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|style="padding:.4em;"|IS Choi
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[https://pubmed.ncbi.nlm.nih.gov/34845454/ Identifying disease-critical cell types and cellular processes across the human body by integration of single-cell profiles and human genetics]
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|style="padding:.4em;" rowspan=1|2022/07/07
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|style="padding:.4em;"|22-18
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|style="padding:.4em;"|EJ Sung
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[https://www.biorxiv.org/content/10.1101/2021.06.07.447430v2 Metacells untangle large and complex single-cell transcriptome networks]
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[https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1812-2 MetaCell: analysis of single-cell RNA-seq data using K-nn graph partitions]
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[https://genomebiology.biomedcentral.com/articles/10.1186/s13059-022-02667-1 Metacell‑2: a divide‑and‑conquer metacell algorithm for scalable scRNA‑seq analysis]
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|style="padding:.4em;"|SB Baek
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[https://pubmed.ncbi.nlm.nih.gov/34675423/ Co-varying neighborhood analysis identifies cell populations associated with phenotypes of interest from single-cell transcriptomics]
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|style="padding:.4em;"|22-16
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|style="padding:.4em;"|JH Cha
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[https://pubmed.ncbi.nlm.nih.gov/34426704/ Integrating T cell receptor sequences and transcriptional profiles by clonotype neighbor graph analysis (CoNGA)]
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|style="padding:.4em;"|JW Yu
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[https://pubmed.ncbi.nlm.nih.gov/34986867/ Hepatocellular carcinoma patients with high circulating cytotoxic T cells and intra-tumoral immune signature benefit from pembrolizumab: results from a single-arm phase 2 trial]
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|style="padding:.4em;"|EJ Sung
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[https://pubmed.ncbi.nlm.nih.gov/35199064/ Effect of imputation on gene network reconstruction from single-cell RNA-seq data]
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|style="padding:.4em;"|IS Choi
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[https://pubmed.ncbi.nlm.nih.gov/35105355/ Diagnostic Evidence GAuge of Single cells (DEGAS): a flexible deep transfer learning framework for prioritizing cells in relation to disease]
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|style="padding:.4em;" rowspan=1|2022/04/07
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|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|22-12
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|style="padding:.4em;"|SB Baek
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[https://pubmed.ncbi.nlm.nih.gov/35172892/ Integrating single-cell sequencing data with GWAS summary statistics reveals CD16+monocytes and memory CD8+T cells involved in severe COVID-19]
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|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|22-11
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|style="padding:.4em;"|JH Cha
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|style="padding:.4em;text-align:left"|
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[https://pubmed.ncbi.nlm.nih.gov/34663807/ Single cell T cell landscape and T cell receptor repertoire profiling of AML in context of PD-1 blockade therapy]
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|style="padding:.4em;"|22-10
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|style="padding:.4em;"|JW Yu
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[https://pubmed.ncbi.nlm.nih.gov/34594031/ Systematic investigation of cytokine signaling activity at the tissue and single-cell levels]
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|style="padding:.4em;"|22-9
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|style="padding:.4em;"|EJ Sung
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[https://pubmed.ncbi.nlm.nih.gov/34852236/ Neoantigen-driven B cell and CD4 T follicular helper cell collaboration promotes anti-tumor CD8 T cell responses]
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|style="padding:.4em;"|SB Baek
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[https://pubmed.ncbi.nlm.nih.gov/34930412/ MultiMAP: dimensionality reduction and integration of multimodal data]
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|style="padding:.4em;"|IS Choi
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[https://www.biorxiv.org/content/10.1101/2020.10.19.345983v2 CellRank for directed single-cell fate mapping]
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|style="padding:.4em;"|JH Cha
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[https://pubmed.ncbi.nlm.nih.gov/34653365/ Single-cell analyses reveal key immune cell subsets associated with response to PD-L1 blockade in triple-negative breast cancer]
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|style="padding:.4em;"|IS Choi
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[https://pubmed.ncbi.nlm.nih.gov/34390642/ Chromatin and gene-regulatory dynamics of the developing human cerebral cortex at single-cell resolution]
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[https://pubmed.ncbi.nlm.nih.gov/34767762/ Single-cell analysis of human non-small cell lung cancer lesions refines tumor classification and patient stratification]
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[https://www.biorxiv.org/content/10.1101/2022.08.02.502504v1 A novel in silico method employs chemical and protein similarity algorithms to accurately identify chemical transformations in the human gut microbiome]
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[https://www.biorxiv.org/content/10.1101/2021.09.13.460160v3 Inference of disease-associated microbial biomarkers based on metagenomic and metatranscriptomic data]
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|style="padding:.4em;"|SH Lee
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[https://www.sciencedirect.com/science/article/pii/S0092867422009199?via%3Dihub Personalized microbiome-driven effects of non-nutritive sweeteners on human glucose tolerance]
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[https://www.nature.com/articles/s41564-022-01121-z Identification of shared and disease-specific host gene–microbiome associations across human diseases using multi-omic integration]
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[https://www.sciencedirect.com/science/article/pii/S193131282200049X Caudovirales bacteriophages are associated with improved executive function and memory in flies, mice, and humans]
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[https://www.biorxiv.org/content/10.1101/2021.10.06.463341v2.full SynTracker: a synteny based tool for tracking microbial strains]
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[https://www.nature.com/articles/s41587-022-01226-0 Identification of antimicrobial peptides from the human gut microbiome using deep learning]
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[https://www.nature.com/articles/s41586-022-04648-7 Discovery of bioactive microbial gene products in inflammatory bowel disease]
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[https://www.nature.com/articles/s43588-022-00247-8 Large-scale microbiome data integration enables robust biomarker identification]
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[https://www.nature.com/articles/s41467-022-30512-3 Predicting cancer prognosis and drug response from the tumor microbiome]
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[https://www.cell.com/cell-reports/pdf/S2211-1247(22)00770-7.pdf Thousands of small, novel genes predicted in global phage genomes]
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[https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-022-01231-0 MetaPop: a pipeline for macro- and microdiversity analyses and visualization of microbial and viral metagenome-derived populations]
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[https://www.nature.com/articles/s41586-022-04862-3 Biosynthetic potential of the global ocean microbiome]
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[https://www.nature.com/articles/s41596-020-00480-3 Tutorial: assessing metagenomics software with the CAMI benchmarking toolkit disease]
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[https://www.sciencedirect.com/science/article/pii/S2666379121002561 Identification of Faecalibacterium prausnitzii strains for gut microbiome-based intervention in Alzheimer’s-type dementia]
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[https://www.biorxiv.org/content/10.1101/2022.02.21.480893v1 Integrating phylogenetic and functional data in microbiome studies]
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[https://journals.asm.org/doi/10.1128/mSystems.00252-19 Comprehensive Analysis Reveals the Evolution and Pathogenicity of Aeromonas, Viewed from Both Single Isolated Species and Microbial Communities]
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[https://genomemedicine.biomedcentral.com/articles/10.1186/s13073-022-01011-3 Microbiota of the prostate tumor environment investigated by whole-transcriptome profiling]
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[https://genomebiology.biomedcentral.com/articles/10.1186/s13059-021-02576-9 Species-resolved sequencing of low-biomass or degraded microbiomes using 2bRAD-M]
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[https://pubmed.ncbi.nlm.nih.gov/34517888/ Gut microbial determinants of clinically important improvement in patients with rheumatoid arthritis]
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[https://pubmed.ncbi.nlm.nih.gov/34880502/ Gut microbiota modulates weight gain in mice after discontinued smoke exposure]
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[https://pubmed.ncbi.nlm.nih.gov/34819672/ The human microbiome encodes resistance to the antidiabetic drug acarbose]
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[https://pubmed.ncbi.nlm.nih.gov/34618582/ Commensal bacteria promote endocrine resistance in prostate cancer through androgen biosynthesis]
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[https://www.biorxiv.org/content/10.1101/2022.07.06.499075v1 Maast: genotyping thousands of microbial strains efficiently]
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[https://www.biorxiv.org/content/10.1101/2022.02.01.478746v2 Scalable microbial strain inference in metagenomic data using StrainFacts]
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[https://www.biorxiv.org/content/10.1101/2022.02.15.480535v1 StrainPanDA: linked reconstruction of strain composition and gene content profiles via pangenome-based decomposition of metagenomic data]
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[https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02042-y Analysis of 1321 Eubacterium rectale genomes from metagenomes uncovers complex phylogeographic population structure and subspecies functional adaptations]
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[https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3000102 Evolutionary dynamics of bacteria in the gut microbiome within and across hosts]
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|style="padding:.4em;"|JH Cha
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[https://elifesciences.org/articles/42693 Extensive transmission of microbes along the gastrointestinal tract]
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|style="padding:.4em;"|NY Kim
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[https://www.cell.com/cell-host-microbe/fulltext/S1931-3128(19)30041-1?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS1931312819300411%3Fshowall%3Dtrue Distinct Genetic and Functional Traits of Human Intestinal Prevotella copri Strains Are Associated with Different Habitual Diets]
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|style="padding:.4em;"|SH Lee
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[https://www.nature.com/articles/nmeth.3802 Strain-level microbial epidemiology and population genomics from shotgun metagenomics]
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[https://www.cell.com/cell/fulltext/S0092-8674(21)00942-9#secsectitle0025 Chromatin and gene-regulatory dynamics of the developing human cerebral cortex at single-cell resolution]
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[https://www.biorxiv.org/content/10.1101/2021.02.09.430114v2 Single-cell ATAC and RNA sequencing reveal pre-existing and persistent subpopulations of cells associated with relapse of prostate cancer]
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[https://www.biorxiv.org/content/10.1101/2021.03.16.435578v1 Integrated single-cell transcriptomics and epigenomics reveals strong germinal center-associated etiology of autoimmune risk loci]
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[https://www.biorxiv.org/content/10.1101/2021.07.28.453784v1 Functional Inference of Gene Regulation using Single-Cell Multi-Omics]
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[https://www.biorxiv.org/content/10.1101/2021.03.24.436532v1 Single-cell analyses reveal a continuum of cell state and composition changes in the malignant transformation of polyps to colorectal cancer]
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[https://www.cell.com/cancer-cell/fulltext/S1535-6108(21)00165-3 Single-cell sequencing links multiregional immune landscapes and tissue-resident T cells in ccRCC to tumor topology and therapy efficacy]
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[https://www.sciencedirect.com/science/article/pii/S1535610821001173 Tumor and immune reprogramming during immunotherapy in advanced renal cell carcinoma]
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[https://www.sciencedirect.com/science/article/pii/S1074761321001199 Single-cell chromatin accessibility landscape identifies tissue repair program in human regulatory T cells]
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[https://www.nature.com/articles/s41591-021-01232-w Identification of resistance pathways and therapeutic targets in relapsed multiple myeloma patients through single-cell sequencing]
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[https://www.nature.com/articles/s41591-021-01323-8 A single-cell map of intratumoral changes during anti-PD1 treatment of patients with breast cancer]
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[https://www.sciencedirect.com/science/article/abs/pii/S0092867420316135 Single-cell landscape of the ecosystem in early-relapse hepatocellular carcinoma]
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[https://www.nature.com/articles/s41586-021-03552-w Interpreting type 1 diabetes risk with genetics and single-cell epigenomics]
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[https://www.sciencedirect.com/science/article/pii/S0092867421000726 Massive expansion of human gut bacteriophage diversity]
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[https://www.sciencedirect.com/science/article/pii/S1931312821001451 The infant gut resistome associates with E. coli, environmental exposures, gut microbiome maturity, and asthma-associated bacterial composition]
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[https://www.sciencedirect.com/science/article/pii/S1931312820306703?dgcid=rss_sd_all Methotrexate impacts conserved pathways in diverse human gut bacteria leading to decreased host immune activation]
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[https://science.sciencemag.org/content/366/6471/eaax9176 A metagenomic strategy for harnessing the chemical repertoire of the human microbiome]
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[https://www.nature.com/articles/s41467-019-10927-1 Predictive metabolomic profiling of microbial communities using amplicon or metagenomic sequences]
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[https://www.nature.com/articles/s41564-018-0306-4 Gut microbiome structure and metabolic activity in inflammatory bowel disease]
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[https://www.nature.com/articles/s41591-020-01183-8 Microbiome connections with host metabolism and habitual diet from 1,098 deeply phenotyped individuals]
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[https://www.nature.com/articles/s41467-020-18476-8 A predictive index for health status using species-level gut microbiome profiling]
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[https://www.nature.com/articles/s41586-019-1237-9 Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases]
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[https://www.nature.com/articles/s41467-021-21475-y Gastrointestinal microbiota composition predicts peripheral inflammatory state during treatment of human tuberculosis]
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[https://www.sciencedirect.com/science/article/pii/S1931312820301694 Structure of the Mucosal and Stool Microbiome in Lynch Syndrome]
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[https://science.sciencemag.org/content/369/6506/936 Cross-reactivity between tumor MHC class 1-restricted antigens and an enterococcal bacteriophage]
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[https://www.nature.com/articles/s41564-020-00831-6 Bifidobacterium bifidum strains synergize with immune checkpoint inhibitors to reduce tumour burden in mice]
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[https://www.nature.com/articles/s41591-020-01223-3 The gut microbiome modulates the protective association between a Mediterranean diet and cardiometabolic disease risk]
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[https://www.nature.com/articles/s41467-019-14177-z Impact of commonly used drugs on the composition and metabolic function of the gut microbiota]
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[https://www.sciencedirect.com/science/article/pii/S0092867420305638 Personalized Mapping of Drug Metabolism by the Human Gut Microbiome]
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[https://www.cell.com/fulltext/S0092-8674(17)30107-1 Mining the Human Gut Microbiota for Immunomodulatory Organisms]
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[https://www.nature.com/articles/s41586-020-2095-1 Microbiome analyses of blood and tissues suggest cancer diagnostic approach]
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[https://science.sciencemag.org/content/368/6494/973 The human tumor microbiome is composed of tumor type-specific intracellular bacteria]
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[https://www.nature.com/articles/s41590-020-0784-4 Functional CRISPR dissection of gene networks controlling human regulatory T cell identity]
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[https://www.sciencedirect.com/science/article/pii/S0092867420306887 Molecular Pathways of Colon Inflammation Induced by Cancer Immunotherapy]
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[https://www.nature.com/articles/s41588-020-00721-x Single-cell epigenomic analyses implicate candidate causal variants at inherited risk loci for Alzheimer’s and Parkinson’s diseases]
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[https://www.nature.com/articles/s41588-018-0156-2 Genetic determinants of co-accessible chromatin regions in activated T cells across humans]
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[https://www.sciencedirect.com/science/article/pii/S009286742030341X Single-Cell Analyses Inform Mechanisms of Myeloid-Targeted Therapies in Colon Cancer]
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[https://pubmed.ncbi.nlm.nih.gov/32393797/ Multi-omic single-cell snapshots reveal multiple independent trajectories to drug tolerance in a melanoma cell line]
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[https://pubmed.ncbi.nlm.nih.gov/31959990/ Targeted Therapy Guided by Single-Cell Transcriptomic Analysis in Drug-Induced Hypersensitivity Syndrome: A Case Report]
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|style="padding:.4em;" rowspan=2|2020/05/19
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|style="padding:.4em;"|20-6
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|style="padding:.4em;"|SN Lee
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[https://pubmed.ncbi.nlm.nih.gov/32066951/ Distinct Microbial and Immune Niches of the Human Colon]
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|style="padding:.4em;"|20-5
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|style="padding:.4em;"|DJ Park
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[https://pubmed.ncbi.nlm.nih.gov/31375813/ Massively Parallel Single-Cell Chromatin Landscapes of Human Immune Cell Development and Intratumoral T Cell Exhaustion]
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|style="padding:.4em;" rowspan=2|2020/05/12
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|style="padding:.4em;"|20-4
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|style="padding:.4em;"|SY Park
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[https://pubmed.ncbi.nlm.nih.gov/29434354/ Single-cell Gene Expression Reveals a Landscape of Regulatory T Cell Phenotypes Shaped by the TCR]
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|style="padding:.4em;"|20-3
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|style="padding:.4em;"|NY Kim
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[https://pubmed.ncbi.nlm.nih.gov/30078704/ A Single-Cell Atlas of In Vivo Mammalian Chromatin Accessibility]
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|style="padding:.4em;" rowspan=2|2020/04/28
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|style="padding:.4em;" rowspan=2|Single-cell
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|style="padding:.4em;"|20-2
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|style="padding:.4em;"|SB Baek
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[https://pubmed.ncbi.nlm.nih.gov/32014031/ scAI: An Unsupervised Approach for the Integrative Analysis of Parallel Single-Cell Transcriptomic and Epigenomic Profiles]
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|style="padding:.4em;"|20-1
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|style="padding:.4em;"|JH Cha
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[https://pubmed.ncbi.nlm.nih.gov/31792411/ Single-cell Multiomic Analysis Identifies Regulatory Programs in Mixed-Phenotype Acute Leukemia]
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{|class=wikitable style="text-align:center;"
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|style="padding:.4em;" rowspan=2|2019/10/15
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|style="padding:.4em;" rowspan=2|Microbiome
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|style="padding:.4em;"|19-51
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|style="padding:.4em;"|CY Kim
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[https://www.sciencedirect.com/science/article/pii/S1931312819303488 Obese Individuals with and without Type 2 Diabetes Show Different Gut Microbial Functional Capacity and Composition]
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|style="padding:.4em;"|19-50
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|style="padding:.4em;"|SH Lee
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[https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-019-0722-6 Clustering co-abundant genes identifies components of the gut microbiome that are reproducibly associated with colorectal cancer and inflammatory bowel disease]
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|style="padding:.4em;" rowspan=2|2019/10/08
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|style="padding:.4em;" rowspan=2|Single-cell
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|style="padding:.4em;"|19-49
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|style="padding:.4em;"|SB Baek
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|style="padding:.4em;text-align:left"|
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[https://www.nature.com/articles/s41587-019-0206-z Massively parallel single-cell chromatin landscapes of human immune cell development and intratumoral T cell exhaustion]
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|style="padding:.4em;"|19-48
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|style="padding:.4em;"|SB Baek
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|style="padding:.4em;text-align:left"|
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[https://www.biorxiv.org/content/10.1101/739011v1 Assessment of computational methods for the analysis of single-cell ATAC-seq data]
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|style="padding:.4em;" rowspan=2|2019/10/01
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|style="padding:.4em;" rowspan=2|Microbiome
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|style="padding:.4em;"|19-47
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|style="padding:.4em;"|MY Lee
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|style="padding:.4em;text-align:left"|
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[https://www.sciencedirect.com/science/article/pii/S1931312819303026 Meta-Analysis Reveals Reproducible Gut Microbiome Alterations in Response to a High-Fat Diet]
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|style="padding:.4em;"|19-46
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|style="padding:.4em;"|NY Kim
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|style="padding:.4em;text-align:left"|
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[https://www.sciencedirect.com/science/article/pii/S0092867419307731 Tumor Microbiome Diversity and Composition Influence Pancreatic Cancer Outcomes]
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|style="padding:.4em;" rowspan=2|2019/09/24
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|style="padding:.4em;" rowspan=2|Single-cell
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|style="padding:.4em;"|19-45
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|style="padding:.4em;"|JH Cha
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|style="padding:.4em;text-align:left"|
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[https://genome.cshlp.org/content/early/2019/04/01/gr.243725.118 The accessible chromatin landscape of the murine hippocampus at single-cell resolution]
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|style="padding:.4em;"|19-44
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|style="padding:.4em;"|SB Baek
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|style="padding:.4em;text-align:left"|
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[https://www.sciencedirect.com/science/article/pii/S009286741830446X Integrated Single-Cell Analysis Maps the Continuous Regulatory Landscape of Human Hematopoietic Differentiation]
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|style="padding:.4em;" rowspan=2|2019/09/17
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|style="padding:.4em;" rowspan=2|Microbiome
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|style="padding:.4em;"|19-43
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|style="padding:.4em;"|SH Lee
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|style="padding:.4em;text-align:left"|
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[https://science.sciencemag.org/content/365/6449/eaau4735 A sparse covarying unit that describes healthy and impaired human gut microbiota development]
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|style="padding:.4em;"|19-42
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|style="padding:.4em;"|CY Kim
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|style="padding:.4em;text-align:left"|
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[https://www.sciencedirect.com/science/article/pii/S0092867419307810 Large-Scale Analyses of Human Microbiomes Reveal Thousands of Small, Novel Genes]
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|style="padding:.4em;" rowspan=2|2019/09/10
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|style="padding:.4em;" rowspan=2|Single-cell
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|style="padding:.4em;"|19-41
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|style="padding:.4em;"|KS Kim
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|style="padding:.4em;text-align:left"|
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[https://www.sciencedirect.com/science/article/pii/S0092867419307329?via%3Dihub Intra- and Inter-cellular Rewiring of the Human Colon during Ulcerative Colitis]
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|style="padding:.4em;"|19-40
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|style="padding:.4em;"|IS Choi
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|style="padding:.4em;text-align:left"|
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[https://www.nature.com/articles/nbt.4042 Multiplexed droplet single-cell RNA-sequencing using natural genetic variation]
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|style="padding:.4em;" rowspan=2|2019/09/03
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|style="padding:.4em;" rowspan=2|Microbiome
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|style="padding:.4em;"|19-39
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|style="padding:.4em;"|NY Kim
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|style="padding:.4em;text-align:left"|
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[https://www.sciencedirect.com/science/article/pii/S193131281930352X?via%3Dihub The Landscape of Genetic Content in the Gut and Oral Human Microbiome]
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|style="padding:.4em;"|19-38
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|style="padding:.4em;"|CY Kim
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|style="padding:.4em;text-align:left"|
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[https://www.sciencedirect.com/science/article/pii/S0092867419307755?via%3Dihub Benchmarking Metagenomics Tools for Taxonomic Classification]
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|style="padding:.4em;" rowspan=2|2019/08/20
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|style="padding:.4em;"|19-37
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|style="padding:.4em;"|JH Cha
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[https://www.biorxiv.org/content/10.1101/719088v1 Coexpression uncovers a unified single-cell transcriptomic landscape]
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|style="padding:.4em;"|19-36
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|style="padding:.4em;"|MY Lee
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|style="padding:.4em;text-align:left"|
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[https://www.biorxiv.org/content/10.1101/586859v1 Single-cell interactomes of the human brain reveal cell-type specific convergence of brain disorders]
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|style="padding:.4em;" rowspan=3|2019/08/14
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|style="padding:.4em;"|19-35
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|style="padding:.4em;"|SH Lee
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|style="padding:.4em;text-align:left"|
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[https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004075 Proportionality: a valid alternative to correlation for relative data]
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|style="padding:.4em;"|19-34
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|style="padding:.4em;"|SH Lee
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|style="padding:.4em;text-align:left"|
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[https://www.nature.com/articles/s41598-017-16520-0 propr: An R-package for Identifying Proportionally Abundant Features Using Compositional Data Analysis]
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|style="padding:.4em;"|19-33
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|style="padding:.4em;"|HJ Han
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[https://www.nature.com/articles/s41591-018-0157-9 Robust prediction of response to immune checkpoint blockade therapy in metastatic melanoma]
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|style="padding:.4em;" rowspan=2|2019/08/13
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|style="padding:.4em;"|19-32
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|style="padding:.4em;"|KS Kim
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|style="padding:.4em;text-align:left"|
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[https://www.nature.com/articles/s41592-018-0254-1 A test metric for assessing single-cell RNA-seq batch correction]
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|style="padding:.4em;"|19-31
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|style="padding:.4em;"|KS Kim
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|style="padding:.4em;text-align:left"|
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[https://www.sciencedirect.com/science/article/pii/S0092867419305598 Comprehensive Integration of Single-Cell Data]
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|style="padding:.4em;" rowspan=2|2019/08/08
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|style="padding:.4em;"|19-30
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|style="padding:.4em;"|JH Cha
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|style="padding:.4em;text-align:left"|
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[https://www.sciencedirect.com/science/article/pii/S0092867418313941 Defining T Cell States Associated with Response to Checkpoint Immunotherapy in Melanoma]
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|style="padding:.4em;"|19-29
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|style="padding:.4em;"|KS Kim
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[https://www.sciencedirect.com/science/article/pii/S009286741831242X High-Dimensional Analysis Delineates Myeloid and Lymphoid Compartment Remodeling during Successful Immune-Checkpoint Cancer Therapy]
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|style="padding:.4em;" rowspan=2|2019/08/07
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|style="padding:.4em;"|19-28
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|style="padding:.4em;"|HJ Han
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[https://www.biorxiv.org/content/10.1101/555557v1 A single-cell reference map for human blood and tissue T cell activation reveals functional states in health and disease]
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|style="padding:.4em;"|19-27
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|style="padding:.4em;"|IS Choi
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[https://www.sciencedirect.com/science/article/pii/S009286741831568X Dysfunctional CD8 T Cells Form a Proliferative, Dynamically Regulated Compartment within Human Melanoma]
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|style="padding:.4em;" rowspan=2|2019/07/30
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|style="padding:.4em;"|19-26
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|style="padding:.4em;"|IS Choi
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|style="padding:.4em;text-align:left"|
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[https://www.nature.com/articles/nm.4466 High-dimensional single-cell analysis predicts response to anti-PD-1 immunotherapy]
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|style="padding:.4em;"|19-25
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|style="padding:.4em;"|JW Cho
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[https://www.sciencedirect.com/science/article/pii/S0092867418311784 A Cancer Cell Program Promotes T Cell Exclusion and Resistance to Checkpoint Blockade]
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|style="padding:.4em;" rowspan=2|2019/07/23
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|style="padding:.4em;"|19-24
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|style="padding:.4em;"|HJ Han
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|style="padding:.4em;text-align:left"|
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[https://www.ncbi.nlm.nih.gov/pubmed/26006008 COMPASS identifies T-cell subsets correlated with clinical outcomes.]
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|style="padding:.4em;"|19-23
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|style="padding:.4em;"|HJ Han
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|style="padding:.4em;text-align:left"|
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[https://www.nature.com/articles/ncomms14825 Sensitive detection of rare disease-associated cell subsets via representation learning]
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|style="padding:.4em;" rowspan=3|2019/05/30
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|style="padding:.4em;"|19-22
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[https://www.ncbi.nlm.nih.gov/pubmed/30936547 Meta-analysis of fecal metagenomes reveals global microbial signatures that are specific for colorectal cancer]
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|style="padding:.4em;"|19-21
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[https://www.ncbi.nlm.nih.gov/pubmed/30936548 Metagenomic analysis of colorectal cancer datasets identifies cross-cohort microbial diagnostic signatures and a link with choline degradation]
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|style="padding:.4em;"|19-20
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[https://www.ncbi.nlm.nih.gov/pubmed/30664783 Microbial network disturbances in relapsing refractory Crohn's disease.]
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|style="padding:.4em;" rowspan=3|2019/05/23
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|style="padding:.4em;"|19-19
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[https://www.ncbi.nlm.nih.gov/pubmed/30867587 New insights from uncultivated genomes of the global human gut microbiome]
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|style="padding:.4em;"|19-18
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[https://www.ncbi.nlm.nih.gov/pubmed/30745586 A new genomic blueprint of the human gut microbiota]
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|style="padding:.4em;"|19-17
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|style="padding:.4em;text-align:left"|
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[https://www.ncbi.nlm.nih.gov/pubmed/30661755 Extensive Unexplored Human Microbiome Diversity Revealed by Over 150,000 Genomes from Metagenomes Spanning Age, Geography, and Lifestyle.]
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|style="padding:.4em;" rowspan=2|2019/05/16
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|style="padding:.4em;"|19-16
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[https://www.ncbi.nlm.nih.gov/pubmed/29311644 Dynamics of metatranscription in the inflammatory bowel disease gut microbiome.]
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|style="padding:.4em;"|19-15
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[https://www.ncbi.nlm.nih.gov/pubmed/29335555 Metatranscriptome of human faecal microbial communities in a cohort of adult men.]
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|style="padding:.4em;" rowspan=2|2019/05/09
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|style="padding:.4em;"|19-14
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[https://www.ncbi.nlm.nih.gov/pubmed/30193113 Post-Antibiotic Gut Mucosal Microbiome Reconstitution Is Impaired by Probiotics and Improved by Autologous FMT.]
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|style="padding:.4em;"|19-13
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[https://www.ncbi.nlm.nih.gov/pubmed/30193112 Personalized Gut Mucosal Colonization Resistance to Empiric Probiotics Is Associated with Unique Host and Microbiome Features.]
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|style="padding:.4em;" rowspan=2|2019/05/02
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|style="padding:.4em;"|19-12
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[https://www.ncbi.nlm.nih.gov/pubmed/30753825 Distinct Immune Cell Populations Define Response to Anti-PD-1 Monotherapy and Anti-PD-1/Anti-CTLA-4 Combined Therapy.]
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|style="padding:.4em;"|19-11
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[https://www.ncbi.nlm.nih.gov/pubmed/30778252 Subsets of exhausted CD8+ T cells differentially mediate tumor control and respond to checkpoint blockade.]
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|style="padding:.4em;" rowspan=2|2019/4/11
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|style="padding:.4em;"|19-10
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[https://www.ncbi.nlm.nih.gov/pubmed/30479382 Lineage tracking reveals dynamic relationships of T cells in colorectal cancer.]
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[https://www.ncbi.nlm.nih.gov/pubmed/30523328 Single cell dissection of plasma cell heterogeneity in symptomatic and asymptomatic myeloma.]
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|style="padding:.4em;" rowspan=2|2019/4/4
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|style="padding:.4em;"|19-8
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[https://www.ncbi.nlm.nih.gov/pubmed/29942092 Single-cell profiling of breast cancer T cells reveals a tissue-resident memory subset associated with improved prognosis.]
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|style="padding:.4em;"|19-7
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[https://www.ncbi.nlm.nih.gov/pubmed/29942094 Global characterization of T cells in non-small-cell lung cancer by single-cell sequencing]
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|style="padding:.4em;" rowspan=2|2019/3/28
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[https://www.ncbi.nlm.nih.gov/pubmed/28319088 Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors.]
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|style="padding:.4em;"|19-5
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[https://www.ncbi.nlm.nih.gov/pubmed/28622514 Landscape of Infiltrating T Cells in Liver Cancer Revealed by Single-Cell Sequencing.]
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|style="padding:.4em;" rowspan=3|2019/3/21
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|style="padding:.4em;"|19-4
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[https://www.ncbi.nlm.nih.gov/pubmed/29988129 Phenotype molding of stromal cells in the lung tumor microenvironment.]
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[https://www.ncbi.nlm.nih.gov/pubmed/30787436 A single-cell molecular map of mouse gastrulation and early organogenesis]
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[https://www.ncbi.nlm.nih.gov/pubmed/30787437 The single-cell transcriptional landscape of mammalian organogenesis]
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[https://www.ncbi.nlm.nih.gov/pubmed/29961579 Single-Cell Map of Diverse Immune Phenotypes in the Breast Tumor Microenvironment]
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[https://www.ncbi.nlm.nih.gov/pubmed/29198524 Single-Cell Transcriptomic Analysis of Primary and Metastatic Tumor Ecosystems in Head and Neck Cancer]
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|style="padding:.4em;"|18-12
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=A+pan-cancer+analysis+of+enhancer+expression+in+nearly+9000+patient+samples A Pan-Cancer Analysis of Enhancer Expression in Nearly 9000 Patient Samples.]
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|style="padding:.4em;"|18-11
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=Machine+learning+identifies+stemness+features+associated+with+oncogenic+dedifferentiation Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation.]
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|style="padding:.4em;" rowspan=2|2018/06/07
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|style="padding:.4em;"|18-10
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=Chemoresistance+evolution+in+triple-negative+breast+cancer+delineated+by+single-cell+sequencing Chemoresistance Evolution in Triple-Negative Breast Cancer Delineated by Single-Cell Sequencing.]
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|-
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|style="padding:.4em;" rowspan=2|2018/05/31
 +
|style="padding:.4em;"|18-8
 +
|style="padding:.4em;"|
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|style="padding:.4em;text-align:left"|
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=Mapping+human+pluripotent+stem+cell+differentiation+pathways+using+high+throughput+single-cell+RNA-sequencing Mapping human pluripotent stem cell differentiation pathways using high throughput single-cell RNA-sequencing.]
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|-
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|style="padding:.4em;"|18-7
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|style="padding:.4em;"|
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|style="padding:.4em;text-align:left"|
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=A+single-cell+RNA-seq+survey+of+the+developmental+landscape+of+the+human+prefrontal+cortex A single-cell RNA-seq survey of the developmental landscape of the human prefrontal cortex.]
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|-
 +
|style="padding:.4em;" rowspan=2|2018/05/24
 +
|style="padding:.4em;"|18-6
 +
|style="padding:.4em;"|CY Kim
 +
|style="padding:.4em;text-align:left"|
 +
[https://www.ncbi.nlm.nih.gov/pubmed/?term=Single-cell+RNA+sequencing+identifies+celltype-specific+cis-eQTLs+and+co-expression+QTLs Single-cell RNA sequencing identifies celltype-specific cis-eQTLs and co-expression QTLs.]
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|-
 +
|style="padding:.4em;"|18-5
 +
|style="padding:.4em;"|HJ Han
 +
|style="padding:.4em;text-align:left"|
 +
[https://www.ncbi.nlm.nih.gov/pubmed/?term=FOCS%3A+a+novel+method+for+analyzing+enhancer+and+gene+activity+patterns+infers+an+extensive+enhancer-promoter+map FOCS: a novel method for analyzing enhancer and gene activity patterns infers an extensive enhancer-promoter map.]
 +
|-
 +
|style="padding:.4em;" rowspan=2|2018/05/17
 +
|style="padding:.4em;"|18-4
 +
|style="padding:.4em;"|KS Kim
 +
|style="padding:.4em;text-align:left"|
 +
[https://www.ncbi.nlm.nih.gov/pubmed/29149608 A Distinct Gene Module for Dysfunction Uncoupled from Activation in Tumor-Infiltrating T Cells.]
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|-
 +
|style="padding:.4em;"|18-3
 +
|style="padding:.4em;"|SH Lee
 +
|style="padding:.4em;text-align:left"|
 +
[https://www.ncbi.nlm.nih.gov/pubmed/29610481 A global transcriptional network connecting noncoding mutations to changes in tumor gene expression.]
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|-
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|style="padding:.4em;" rowspan=2|2018/05/10
 +
|style="padding:.4em;"|18-2
 +
|style="padding:.4em;"|JW Cho
 +
|style="padding:.4em;text-align:left"|
 +
[https://www.ncbi.nlm.nih.gov/pubmed/?term=Inferring+regulatory+element+landscapes+and+transcription+factor+networks+from+cancer+methylomes Inferring regulatory element landscapes and transcription factor networks from cancer methylomes.]
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|-
 +
|style="padding:.4em;"|18-1
 +
|style="padding:.4em;"|DS Bae
 +
|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.]
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|}
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{|class=wikitable style="text-align:center;"
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|+style="text-align:left;font-size:12pt" | 2017
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|-
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!scope="col" style="padding:.4em" |Date
 +
!scope="col" style="padding:.4em" | Paper<br/>index
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!scope="col" style="padding:.4em" | Presenter
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!scope="col" style="padding:.4em" | Paper title
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|-
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|style="padding:.4em;" rowspan=2|2017/06/28
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|style="padding:.4em;"|17-36
 +
|style="padding:.4em;"|HJ Han
 +
|style="padding:.4em;text-align:left"|
 +
[https://www.ncbi.nlm.nih.gov/pubmed/28104840 Tumor aneuploidy correlates with markers of immune evasion and with reduced response to immunotherapy.]
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|-
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|style="padding:.4em;"|17-35
 +
|style="padding:.4em;"|JW Cho
 +
|style="padding:.4em;text-align:left"|
 +
[https://www.ncbi.nlm.nih.gov/pubmed/27240091 Landscape of tumor-infiltrating T cell repertoire of human cancers.]
 +
|-
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|style="padding:.4em;" rowspan=2|2017/06/14
 +
|style="padding:.4em;"|17-34
 +
|style="padding:.4em;"|JW Cho
 +
|style="padding:.4em;text-align:left"|
 +
[http://biorxiv.org/content/early/2015/09/01/025908 The landscape of T cell infiltration in human cancer and its association with antigen presenting gene expression.]
 +
|-
 +
|style="padding:.4em;"|17-33
 +
|style="padding:.4em;"|MY Lee
 +
|style="padding:.4em;text-align:left"|
 +
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.08.052 A Distinct Gene Module for Dysfunction Uncoupled from Activation in Tumor-Infiltrating T Cells.]
 +
|-
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|style="padding:.4em;" rowspan=2|2017/06/07
 +
|style="padding:.4em;"|17-32
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|style="padding:.4em;"|
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|style="padding:.4em;text-align:left"|
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[http://www.tandfonline.com/doi/full/10.1080/2162402X.2016.1253654 Identification of genetic determinants of breast cancer immune phenotypes by integrative genome-scale analysis.]
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|-
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|style="padding:.4em;"|17-31
 +
|style="padding:.4em;"|MY Lee
 +
|style="padding:.4em;text-align:left"|
 +
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.celrep.2016.03.075 Genomic Correlates of Immune-Cell Infiltrates in Colorectal Carcinoma.]
 +
|-
 +
|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"|
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.celrep.2016.12.019 Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade.]
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|-
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|style="padding:.4em;" rowspan=2|2017/05/24
 +
|style="padding:.4em;"|17-28
 +
|style="padding:.4em;"|EB Kim
 +
|style="padding:.4em;text-align:left"|
 +
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.12.022 Systemic Immunity Is Required for Effective Cencer Immunotherapy.]
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|-
 +
|style="padding:.4em;"|17-27
 +
|style="padding:.4em;"|CY Kim
 +
|style="padding:.4em;text-align:left"|
 +
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.10.020 Linking the Human Gut Microbiome to Inflammatory Cytokine Production Capacity.]
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|style="padding:.4em;" rowspan=2|2017/05/17
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|style="padding:.4em;"|17-26
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|style="padding:.4em;"|
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|style="padding:.4em;text-align:left"|
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.10.018 Host and Environmental Factors Influencing Individual Human Cytokine Responses.]
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|style="padding:.4em;"|17-25
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|style="padding:.4em;"|
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|style="padding:.4em;text-align:left"|
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.10.017 A Functional Genomics Approach to Understand Variation in Cytokine Production in Humans.]
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|-
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|style="padding:.4em;" rowspan=2|2017/04/26
 +
|style="padding:.4em;"|17-24
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|style="padding:.4em;"|
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|style="padding:.4em;text-align:left"|
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1038%2FNMETH.4177 Pooled CRISPR screening with single-cell transcriptome readout.]
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|-
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|style="padding:.4em;"|17-23
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|style="padding:.4em;"|
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|style="padding:.4em;text-align:left"|
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.11.039 Dissecting Immune Circuits by Linking CRISPR-Pooled Screens with Single-Cell RNA-Seq.]
 +
|-
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|style="padding:.4em;" rowspan=2|2017/04/12
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|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.]
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|-
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|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
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|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
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|style="padding:.4em;"|17-18
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|style="padding:.4em;"|
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|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
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|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
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|style="padding:.4em;"|17-16
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|style="padding:.4em;"|
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|style="padding:.4em;text-align:left"|
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[https://www.ncbi.nlm.nih.gov/pubmed/27580035 Single-cell analysis of mixed-lineage states leading to a binary cell fate choice.]
 +
|-
 +
|style="padding:.4em;"|17-15
 +
|style="padding:.4em;"|EB Kim
 +
|style="padding:.4em;text-align:left"|
 +
[https://www.ncbi.nlm.nih.gov/pubmed/27281220 Dissecting direct reprogramming from fibroblast to neuron using single-cell RNA-seq.]
 +
|-
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|style="padding:.4em;" rowspan=2|2017/03/15
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|style="padding:.4em;"|17-14
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|style="padding:.4em;"|
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|style="padding:.4em;text-align:left"|
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1126%2Fscience.aad0501 Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq.]
 +
|-
 +
|style="padding:.4em;"|17-13
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|style="padding:.4em;"|
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|style="padding:.4em;text-align:left"|
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[https://www.ncbi.nlm.nih.gov/pubmed/26084335 Single-cell mRNA sequencing identifies subclonal heterogeneity in anti-cancer drug responses of lung adenocarcinoma cells.]
 +
|-
 +
|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.]
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|-
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|style="padding:.4em;" rowspan=1|2017/01/31
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|style="padding:.4em;"|17-06
 +
|style="padding:.4em;"|CY Kim
 +
|style="padding:.4em;text-align:left"|
 +
[//www.ncbi.nlm.nih.gov/pubmed/27912059 Microbiota Diurnal Rhythmicity Programs Host Transcriptome Oscillations.]
 +
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|style="padding:.4em;" rowspan=1|2017/01/24
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|style="padding:.4em;"|17-05
 +
|style="padding:.4em;"|JW Cho
 +
|style="padding:.4em;text-align:left"|
 +
[//www.ncbi.nlm.nih.gov/pubmed/27851914 Transcriptional Landscape of Human Tissue Lymphocytes Unveils Uniqueness of Tumor-Infiltrating T Regulatory Cells.]
 +
|-
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|style="padding:.4em;" rowspan=2|2017/01/17
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|style="padding:.4em;"|17-04
 +
|style="padding:.4em;"|HJ Han
 +
|style="padding:.4em;text-align:left"|
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[//www.ncbi.nlm.nih.gov/pubmed/25938943 Mapping long-range promoter contacts in human cells with high-resolution capture Hi-C]
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|-
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|style="padding:.4em;"|17-03
 +
|style="padding:.4em;"|EB Kim
 +
|style="padding:.4em;text-align:left"|
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[//www.ncbi.nlm.nih.gov/pubmed/27306882 CHiCAGO: robust detection of DNA looping interactions in Capture Hi-C data.]
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|-
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|style="padding:.4em;" rowspan=1|2017/01/10
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|style="padding:.4em;"|17-02
 +
|style="padding:.4em;"|JE Shim
 +
|style="padding:.4em;text-align:left"|
 +
[//www.ncbi.nlm.nih.gov/pubmed/26527291 ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis]
 +
|-
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|style="padding:.4em;" rowspan=1|2017/01/03
 +
|style="padding:.4em;"|17-01
 +
|style="padding:.4em;"|SH Lee
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|style="padding:.4em;text-align:left"|
 +
[//www.ncbi.nlm.nih.gov/pubmed/26287467 Single-cell messenger RNA sequencing reveals rare intestinal cell types]
 +
|-
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|}
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{|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.]
 +
|-
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|style="padding:.4em;" rowspan=1|2016/11/29
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|style="padding:.4em;"|2016-29
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|style="padding:.4em;"|DS Bae
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|style="padding:.4em;text-align:left"|
 +
[http://www.ncbi.nlm.nih.gov/pubmed/24658644 The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells.]
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|-
 +
|style="padding:.4em;" rowspan=1|2016/11/22
 +
|style="padding:.4em;"|2016-28
 +
|style="padding:.4em;"|CY Kim
 +
|style="padding:.4em;text-align:left"|
 +
[http://www.ncbi.nlm.nih.gov/pubmed/26887813 Classification of low quality cells from single-cell RNA-seq data.]
 +
|-
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|style="padding:.4em;" rowspan=1|2016/11/15
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|style="padding:.4em;"|2016-27
 +
|style="padding:.4em;"|MY Lee
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|style="padding:.4em;text-align:left"|
 +
[http://www.ncbi.nlm.nih.gov/pubmed/26000487 Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells.]
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|-
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|style="padding:.4em;" rowspan=1|2016/11/8
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|style="padding:.4em;"|2016-26
 +
|style="padding:.4em;"|JW Cho
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|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
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|style="padding:.4em;"|2016-25
 +
|style="padding:.4em;"|HJ Han
 +
|style="padding:.4em;text-align:left"|
 +
[http://www.ncbi.nlm.nih.gov/pubmed/27426982 Causal Mechanistic Regulatory Network for Glioblastoma Deciphered Using Systems Genetics Network Analysis.]
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|-
 +
|style="padding:.4em;" rowspan=1|2016/10/25
 +
|style="padding:.4em;"|2016-24
 +
|style="padding:.4em;"|MY Lee
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|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]
 +
|-
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|style="padding:.4em;" rowspan=1|2016/8/31
 +
|style="padding:.4em;"|2016-18
 +
|style="padding:.4em;"|DS Bae
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|style="padding:.4em;text-align:left"|
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[http://www.ncbi.nlm.nih.gov/pubmed/27264179 A Computational Drug Repositioning Approach for Targeting Oncogenic Transcription Factors]
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|-
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|style="padding:.4em;" rowspan=1|2016/8/16
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|style="padding:.4em;"|2016-17
 +
|style="padding:.4em;"|JW Cho
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|style="padding:.4em;text-align:left"|
 +
[http://www.ncbi.nlm.nih.gov/pubmed/27309802 The landscape of accessible chromatin in mammalian preimplantation embryos]
 +
|-
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|style="padding:.4em;" rowspan=1|2016/8/8
 +
|style="padding:.4em;"|2016-16
 +
|style="padding:.4em;"|EB Kim
 +
|style="padding:.4em;text-align:left"|
 +
[http://www.ncbi.nlm.nih.gov/pubmed/27064255 Enhancer-promoter interactions are encoded by complex genomic signatures on looping chromatin]
 +
|-
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|style="padding:.4em;" rowspan=1|2016/8/1
 +
|style="padding:.4em;"|2016-15
 +
|style="padding:.4em;"|MY Lee
 +
|style="padding:.4em;text-align:left"|
 +
[http://www.ncbi.nlm.nih.gov/pubmed/27040498 Personalized Immunomonitoring Uncovers Molecular Networks that Stratify Lupus Patients]
 +
|-
 +
|style="padding:.4em;" rowspan=1|2016/7/25
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|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
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|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
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|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
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|style="padding:.4em;"|2016-2
 +
|style="padding:.4em;"|CY Kim,SJ Kwon
 +
|style="padding:.4em;text-align:left"|
 +
[http://www.ncbi.nlm.nih.gov/pubmed/26780608 A predictive computational framework for direct reprogramming between human cell types]
 +
|-
 +
|style="padding:.4em;"| 2016-1
 +
|style="padding:.4em;"| CY Kim,SJ Kwon
 +
|style="padding:.4em;text-align:left"|
 +
[http://www.ncbi.nlm.nih.gov/pubmed/25126793 CellNet: Network biology applied to stem cell engineering]
 +
|}
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{|class=wikitable style="text-align:center;"
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|+style="text-align:left;font-size:12pt" | 2015
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|-
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!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" | Presenter
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!scope="col" style="padding:.4em" | Paper title
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|style="padding:.4em;" rowspan=2|2015/06/11
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|style="padding:.4em;"|2015-55
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|style="padding:.4em;"|
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|style="padding:.4em;text-align:left"|
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[http://genome.cshlp.org/content/24/2/340.long Improved exome prioritization of disease genes through cross-species phenotype comparison.]
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|-
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|style="padding:.4em;"|2015-54
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|style="padding:.4em;"|
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|style="padding:.4em;text-align:left"|
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[http://www.sciencedirect.com/science/article/pii/S0002929714001128 Phevor combines multiple biomedical ontologies for accurate identification of disease-causing alleles in single individuals and small nuclear families.]
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|-
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|style="padding:.4em;" rowspan=2|2015/06/04
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|style="padding:.4em;"|2015-53
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|style="padding:.4em;"|
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|style="padding:.4em;text-align:left"|
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[http://www.nature.com/nmeth/journal/v10/n11/full/nmeth.2656.html eXtasy: variant prioritization by genomic data fusion.]
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|-
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|style="padding:.4em;"|2015-52
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|style="padding:.4em;"|
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|style="padding:.4em;text-align:left"|
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[http://genome.cshlp.org/content/21/9/1529.long A probabilistic disease-gene finder for personal genomes.]
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|-
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|style="padding:.4em;" rowspan=2|2015/05/28
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|style="padding:.4em;"|2015-51
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|style="padding:.4em;"|
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|style="padding:.4em;text-align:left"|
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[http://www.nature.com/ng/journal/v46/n12/full/ng.3141.html Systematic analysis of noncoding somatic mutations and gene expression alterations across 14 tumor types.]
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|-
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|style="padding:.4em;"|2015-50
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|style="padding:.4em;"|
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|style="padding:.4em;text-align:left"|
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[http://www.nature.com/nbt/journal/v28/n5/full/nbt.1630.html GREAT improves functional interpretation of cis-regulatory regions.]
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|-
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|style="padding:.4em;" rowspan=2|2015/05/21
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|style="padding:.4em;"|2015-49
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|style="padding:.4em;"|
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|style="padding:.4em;text-align:left"|
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[http://www.nature.com/nmeth/journal/v11/n3/full/nmeth.2832.html Functional annotation of noncoding sequence variants.]
 +
|-
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|style="padding:.4em;"|2015-48
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|style="padding:.4em;"|
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|style="padding:.4em;text-align:left"|
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[http://genomebiology.com/content/15/10/480 FunSeq2: A framework for prioritizing noncoding regulatory variants in cancer.]
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|-
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|style="padding:.4em;" rowspan=2|2015/05/14
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|style="padding:.4em;"|2015-47
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|style="padding:.4em;"|
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|style="padding:.4em;text-align:left"|
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[http://www.nature.com/nmeth/journal/v12/n2/full/nmeth.3215.html Selecting causal genes from genome-wide association studies via functionally coherent subnetworks.]
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|-
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|style="padding:.4em;"|2015-46
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|style="padding:.4em;"|
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|style="padding:.4em;text-align:left"|
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[http://www.nature.com/ncomms/2015/150119/ncomms6890/full/ncomms6890.html Biological interpretation of genome-wide association studies using predicted gene functions.]
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|-
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|style="padding:.4em;" rowspan=3|2015/05/07
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|style="padding:.4em;"|2015-45
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|style="padding:.4em;"|
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|style="padding:.4em;text-align:left"|
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[http://www.nature.com/ncomms/2014/140626/ncomms5212/full/ncomms5212.html Human symptoms-disease network.]
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|-
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|style="padding:.4em;"|2015-44
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|style="padding:.4em;"|
 +
|style="padding:.4em;text-align:left"|
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[http://www.sciencedirect.com/science/article/pii/S0092867413010246 A nondegenerate code of deleterious variants in Mendelian loci contributes to complex disease risk.]
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|-
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|style="padding:.4em;"|2015-43
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|style="padding:.4em;"|
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|style="padding:.4em;text-align:left"|
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[http://www.sciencemag.org/content/347/6224/1257601.long Uncovering disease-disease relationships through the incomplete interactome.]
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|-
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|style="padding:.4em;" rowspan=2|2015/04/30
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|style="padding:.4em;"|2015-42
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|style="padding:.4em;"|
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|style="padding:.4em;text-align:left"|
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[http://genome.cshlp.org/content/25/1/142.long The discovery of integrated gene networks for autism and related disorders.]
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|-
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|style="padding:.4em;"|2015-41
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|style="padding:.4em;"|
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|style="padding:.4em;text-align:left"|
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[http://msb.embopress.org/content/10/12/774.long Integrated systems analysis reveals a molecular network underlying autism spectrum disorders.]
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|-
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|style="padding:.4em;" rowspan=3|2015/04/23
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|style="padding:.4em;"|2015-40
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|style="padding:.4em;"|
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|style="padding:.4em;text-align:left"|
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[http://www.nature.com/nature/journal/v518/n7539/full/nature13990.html Dissecting neural differentiation regulatory networks through epigenetic footprinting.]
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|-
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|style="padding:.4em;"|2015-39
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|style="padding:.4em;"|
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|style="padding:.4em;text-align:left"|
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[http://www.nature.com/nature/journal/v518/n7539/full/nature14221.html Cell-of-origin chromatin organization shapes the mutational landscape of cancer.]
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|-
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|style="padding:.4em;"|2015-38
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|style="padding:.4em;"|
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|style="padding:.4em;text-align:left"|
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[http://www.nature.com/nature/journal/v518/n7539/full/nature14248.html Integrative analysis of 111 reference human epigenomes.]
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|-
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|style="padding:.4em;" rowspan=2|2015/04/09
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|style="padding:.4em;"|2015-37
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|style="padding:.4em;"|
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|style="padding:.4em;text-align:left"|
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[http://genome.cshlp.org/content/25/2/246.long Genome-wide analysis of local chromatin packing in Arabidopsis thaliana.]
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|-
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|style="padding:.4em;"|2015-36
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|style="padding:.4em;"|
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|style="padding:.4em;text-align:left"|
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[http://www.sciencedirect.com/science/article/pii/S0092867414014974 A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping.]
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|-
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|style="padding:.4em;" rowspan=2|2015/04/02
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|style="padding:.4em;"|2015-35
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|style="padding:.4em;"|
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|style="padding:.4em;text-align:left"|
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[http://www.nature.com/nrg/journal/v14/n6/full/nrg3454.html Exploring the three-dimensional organization of genomes: interpreting chromatin interaction data.]
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|-
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|style="padding:.4em;"|2015-34
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|style="padding:.4em;"|
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|style="padding:.4em;text-align:left"|
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[http://www.sciencemag.org/content/347/6225/1010.long Transcribed enhancers lead waves of coordinated transcription in transitioning mammalian cells.]
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|-
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|style="padding:.4em;" rowspan=2|2015/03/26
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|style="padding:.4em;"|2015-33
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|style="padding:.4em;"|
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|style="padding:.4em;text-align:left"|
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[http://genome.cshlp.org/content/25/2/257.long Cupid: simultaneous reconstruction of microRNA-target and ceRNA networks.]
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|-
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|style="padding:.4em;"|2015-32
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|style="padding:.4em;"|
 +
|style="padding:.4em;text-align:left"|
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[http://genome.cshlp.org/content/25/1/41.long Characterization of the neural stem cell gene regulatory network identifies OLIG2 as a multifunctional regulator of self-renewal.]
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|-
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|style="padding:.4em;" rowspan=2|2015/03/19
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|style="padding:.4em;"|2015-31
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|style="padding:.4em;"|
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|style="padding:.4em;text-align:left"|
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[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
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|style="padding:.4em;"|
 +
|style="padding:.4em;text-align:left"|
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[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
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|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;"
 +
|+style="text-align:left;font-size:12pt" | 2014
 +
|-
 +
!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|2014/12/23
 +
|style="padding:.4em;"|2014-41
 +
|style="padding:.4em;"|CY Kim
 +
|style="padding:.4em;text-align:left"|
 +
[http://www.sciencedirect.com/science/article/pii/S0092867413012270 Super-enhancers in the control of cell identity and disease.]
 +
|-
 +
|style="padding:.4em;"|2014-40
 +
|style="padding:.4em;"|CY Kim
 +
|style="padding:.4em;text-align:left"|
 +
[http://www.sciencedirect.com/science/article/pii/S0092867413003929 Master transcription factors and mediator establish super-enhancers at key cell identity genes.]
 +
|-
 +
|style="padding:.4em;" rowspan=4|2014/12/09
 +
|style="padding:.4em;"|2014-39
 +
|style="padding:.4em;"|HH Kim
 +
|style="padding:.4em;text-align:left"|
 +
[http://www.sciencedirect.com/science/article/pii/S0092867414013713 Unraveling the biology of a fungal meningitis pathogen using chemical genetics.]
 +
|-
 +
|style="padding:.4em;"|2014-38
 +
|style="padding:.4em;"|JE Shim
 +
|style="padding:.4em;text-align:left"|
 +
[http://www.sciencedirect.com/science/article/pii/S0092867414014226 A proteome-scale map of the human interactome network.]
 +
|-
 +
|style="padding:.4em;"|2014-37
 +
|style="padding:.4em;"|JE Shim
 +
|style="padding:.4em;text-align:left"|
 +
[http://msb.embopress.org/content/10/9/752.long The role of the interactome in the maintenance of deleterious variability in human populations.]
 +
|-
 +
|style="padding:.4em;"|2014-36
 +
|style="padding:.4em;"|HS Shim
 +
|style="padding:.4em;text-align:left"|
 +
[http://www.sciencemag.org/content/342/6154/1235587.long Integrative annotation of variants from 1092 humans: application to cancer genomics.]
 +
|-
 +
|style="padding:.4em;" rowspan=2|2014/12/02
 +
|style="padding:.4em;"|2014-35
 +
|style="padding:.4em;"|HJ Han
 +
|style="padding:.4em;text-align:left"|
 +
[http://genome.cshlp.org/content/23/8/1319.long Mapping functional transcription factor networks from gene expression data.]
 +
|-
 +
|style="padding:.4em;"|2014-34
 +
|style="padding:.4em;"|KS Kim
 +
|style="padding:.4em;text-align:left"|
 +
[http://www.nature.com/nrg/journal/v15/n7/full/nrg3684.html In pursuit of design principles of regulatory sequences.]
 +
|-
 +
|style="padding:.4em;" rowspan=2|2014/11/25
 +
|style="padding:.4em;"|2014-33
 +
|style="padding:.4em;"|KS Kim
 +
|style="padding:.4em;text-align:left"|
 +
[http://genomemedicine.com/content/3/6/36 Epigenomics of human embryonic stem cells and induced pluripotent stem cells: insights into pluripotency and implications for disease.]
 +
|-
 +
|style="padding:.4em;"|2014-32
 +
|style="padding:.4em;"|HJ Han
 +
|style="padding:.4em;text-align:left"|
 +
[http://www.sciencedirect.com/science/article/pii/S009286741300891X Developmental fate and cellular maturity encoded in human regulatory DNA landscapes.]
 +
|-
 +
|style="padding:.4em;" rowspan=2|2014/11/18
 +
|style="padding:.4em;"|2014-31
 +
|style="padding:.4em;"|SM Yang
 +
|style="padding:.4em;text-align:left"|
 +
[http://genomemedicine.com/content/6/8/64 The 'dnet' approach promotes emerging research on cancer patient survival.]
 +
|-
 +
|style="padding:.4em;"|2014-30
 +
|style="padding:.4em;"|HJ Han
 +
|style="padding:.4em;text-align:left"|
 +
[http://www.sciencedirect.com/science/article/pii/S0092867414010368 Determination and inference of eukaryotic transcription factor sequence specificity.]
 +
|-
 +
|style="padding:.4em;" rowspan=3|2014/11/11
 +
|style="padding:.4em;"|2014-29
 +
|style="padding:.4em;"|DS Bae
 +
|style="padding:.4em;text-align:left"|
 +
[http://www.pnas.org/content/110/16/6412.long Transcription factors interfering with dedifferentiation induce cell type-specific transcriptional profiles.]
 +
|-
 +
|style="padding:.4em;"|2014-28
 +
|style="padding:.4em;"|HH Kim
 +
|style="padding:.4em;text-align:left"|
 +
[http://www.sciencedirect.com/science/article/pii/S0092867414009350 Dissecting engineered cell types and enhancing cell fate conversion via CellNet.]
 +
|-
 +
|style="padding:.4em;"|2014-27
 +
|style="padding:.4em;"|HH Kim
 +
|style="padding:.4em;text-align:left"|
 +
[http://www.sciencedirect.com/science/article/pii/S0092867414009349 CellNet: network biology applied to stem cell engineering.]
 +
|-
 +
|style="padding:.4em;" rowspan=2|2014/11/04
 +
|style="padding:.4em;"|2014-26
 +
|style="padding:.4em;"|HS Shim
 +
|style="padding:.4em;text-align:left"|
 +
[http://www.sciencedirect.com/science/article/pii/S0092867414009775 Predicting Cancer-Specific Vulnerability via Data-Driven Detection of Synthetic Lethality.]
 +
|-
 +
|style="padding:.4em;"|2014-25
 +
|style="padding:.4em;"|JH Shin
 +
|style="padding:.4em;text-align:left"|
 +
[http://www.nature.com/nmeth/journal/v11/n9/full/nmeth.3046.html Phen-Gen: combining phenotype and genotype to analyze rare disorders.]
 +
|-
 +
|style="padding:.4em;" rowspan=2|2014/10/28
 +
|style="padding:.4em;"|2014-24
 +
|style="padding:.4em;"|HJ Han
 +
|style="padding:.4em;text-align:left"|
 +
[http://www.nature.com/nbt/journal/vaop/ncurrent/full/nbt.2877.html A community effort to assess and improve drug sensitivity prediction algorithms.]
 +
|-
 +
|style="padding:.4em;"|2014-23
 +
|style="padding:.4em;"|HS Shim
 +
|style="padding:.4em;text-align:left"|
 +
[http://www.nature.com/ng/journal/v46/n9/full/ng.3051.html Multi-tiered genomic analysis of head and neck cancer ties TP53 mutation to 3p loss.]
 +
|-
 +
|style="padding:.4em;" rowspan=2|2014/09/30
 +
|style="padding:.4em;"|2014-22
 +
|style="padding:.4em;"|JE Shim
 +
|style="padding:.4em;text-align:left"|
 +
[http://www.nature.com/nmeth/journal/v10/n11/full/nmeth.2651.html Network-based stratification of tumor mutations.]
 +
|-
 +
|style="padding:.4em;"|2014-21
 +
|style="padding:.4em;"|KS Kim
 +
|style="padding:.4em;text-align:left"|
 +
[http://www.sciencedirect.com/science/article/pii/S0092867414001457 Synonymous mutations frequently act as driver mutations in human cancers.]
 +
|-
 +
|style="padding:.4em;" rowspan=2|2014/09/23
 +
|style="padding:.4em;"|2014-20
 +
|style="padding:.4em;"|CY Kim
 +
|style="padding:.4em;text-align:left"|
 +
[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003460 VarWalker: Personalized Mutation Network Analysis of Putative Cancer Genes from Next-Generation Sequencing Data.]
 +
|-
 +
|style="padding:.4em;"|2014-19
 +
|style="padding:.4em;"|HJ Han
 +
|style="padding:.4em;text-align:left"|
 +
[http://genomebiology.com/content/13/12/R124 DriverNet: uncovering the impact of somatic driver mutations on transcriptional networks in cancer.]
 +
|-
 +
|style="padding:.4em;" rowspan=2|2014/09/16
 +
|style="padding:.4em;"|2014-18
 +
|style="padding:.4em;"|JH Shin
 +
|style="padding:.4em;text-align:left"|
 +
[http://genomebiology.com/content/14/5/R52 Integrated analysis of recurrent properties of cancer genes to identify novel drivers.]
 +
|-
 +
|style="padding:.4em;"|2014-17
 +
|style="padding:.4em;"|AR Cho
 +
|style="padding:.4em;text-align:left"|
 +
[http://genomemedicine.com/content/4/11/89 Improving the prediction of the functional impact of cancer mutations by baseline tolerance transformation.]
 +
|-
 +
|style="padding:.4em;" rowspan=2|2014/09/02
 +
|style="padding:.4em;"|2014-16
 +
|style="padding:.4em;"|JE Shim
 +
|style="padding:.4em;text-align:left"|
 +
[http://www.ncbi.nlm.nih.gov/pubmed/?term=23228031 A network module-based method for identifying cancer prognostic signatures.]
 +
|-
 +
|style="padding:.4em;"|2014-15
 +
|style="padding:.4em;"|AR Cho
 +
|style="padding:.4em;text-align:left"|
 +
[http://www.ncbi.nlm.nih.gov/pubmed/?term=24429628 Realizing the promise of cancer predisposition genes.]
 +
|-
 +
|style="padding:.4em;" rowspan=2|2014/08/19
 +
|style="padding:.4em;"|2014-14
 +
|style="padding:.4em;"|JE Shim
 +
|style="padding:.4em;text-align:left"|
 +
[http://www.ncbi.nlm.nih.gov/pubmed/?term=24952901 Assessing the clinical utility of cancer genomic and proteomic data across tumor types.]
 +
|-
 +
|style="padding:.4em;"|2014-13
 +
|style="padding:.4em;"|HS Shim
 +
|style="padding:.4em;text-align:left"|
 +
[http://www.ncbi.nlm.nih.gov/pubmed/?term=24132290 Mutational landscape and significance across 12 major cancer types.]
 +
|-
 +
|style="padding:.4em;" rowspan=2|2014/08/12
 +
|style="padding:.4em;"|2014-12
 +
|style="padding:.4em;"|HS Shim
 +
|style="padding:.4em;text-align:left"|
 +
[http://www.ncbi.nlm.nih.gov/pubmed/23945592 Signatures of mutational processes in human cancer.]
 +
|-
 +
|style="padding:.4em;"|2014-11
 +
|style="padding:.4em;"|HJ Kim
 +
|style="padding:.4em;text-align:left"|
 +
[http://www.ncbi.nlm.nih.gov/pubmed/24390350 Discovery and saturation analysis of cancer genes across 21 tumour types.]
 +
|-
 +
|style="padding:.4em;" rowspan=2|2014/08/05
 +
|style="padding:.4em;"|2014-10
 +
|style="padding:.4em;"|CY Kim
 +
|style="padding:.4em;text-align:left"|
 +
[http://www.ncbi.nlm.nih.gov/pubmed/?term=24084849 Comprehensive identification of mutational cancer driver genes across 12 tumor types.]
 +
|-
 +
|style="padding:.4em;"|2014-9
 +
|style="padding:.4em;"|JE Shim
 +
|style="padding:.4em;text-align:left"|
 +
[http://www.ncbi.nlm.nih.gov/pubmed/?term=24037244 IntOGen-mutations identifies cancer drivers across tumor types.]
 +
|-
 +
|style="padding:.4em;" rowspan=3|2014/07/29
 +
|style="padding:.4em;"|2014-8
 +
|style="padding:.4em;"|CY Kim
 +
|style="padding:.4em;text-align:left"|
 +
[http://www.ncbi.nlm.nih.gov/pubmed/23900255 Computational approaches to identify functional genetic variants in cancer genomes.]
 +
|-
 +
|style="padding:.4em;"|2014-7
 +
|style="padding:.4em;"|AR Cho
 +
|style="padding:.4em;text-align:left"|
 +
[http://www.ncbi.nlm.nih.gov/pubmed/?term=23770567 Mutational heterogeneity in cancer and the search for new cancer-associated genes.]
 +
|-
 +
|style="padding:.4em;"|2014-6
 +
|style="padding:.4em;"|AR Cho
 +
|style="padding:.4em;text-align:left"|
 +
[http://www.nature.com/nmeth/journal/v11/n4/abs/nmeth.2891.html Cancer genomes: discerning drivers from passengers]
 +
|-
 +
|style="padding:.4em;" rowspan=3|2014/07/22
 +
|style="padding:.4em;"|2014-5
 +
|style="padding:.4em;"|AR Cho
 +
|style="padding:.4em;text-align:left"|
 +
[http://www.ncbi.nlm.nih.gov/pubmed/24071849 The Cancer Genome Atlas Pan-Cancer analysis project.]
 +
|-
 +
|style="padding:.4em;"|2014-4
 +
|style="padding:.4em;"|AR Cho
 +
|style="padding:.4em;text-align:left"|
 +
[http://www.ncbi.nlm.nih.gov/pubmed/23539594 Cancer genome landscapes.]
 +
|-
 +
|style="padding:.4em;"|2014-3
 +
|style="padding:.4em;"|AR Cho
 +
|style="padding:.4em;text-align:left"|
 +
[http://www.ncbi.nlm.nih.gov/pubmed/?term=21900272 Distinguishing driver and passenger mutations in an evolutionary history categorized by interference.]
 +
|-
 +
|style="padding:.4em;" rowspan=2|2014/07/15
 +
|style="padding:.4em;"|2014-2
 +
|style="padding:.4em;"|HJ Han
 +
|style="padding:.4em;text-align:left"|
 +
[http://www.ncbi.nlm.nih.gov/pubmed/?term=24670764 A promoter-level mammalian expression atlas.]
 +
|-
 +
|style="padding:.4em;"|2014-1
 +
|style="padding:.4em;"|HJ Han
 +
|style="padding:.4em;text-align:left"|
 +
[http://www.ncbi.nlm.nih.gov/pubmed/?term=24670763 An atlas of active enhancers across human cell types and tissues.]
 +
|}
 +
{|class=wikitable style="text-align:center;"
 +
|+style="text-align:left;font-size:12pt" | 2013
 +
|-
 +
!scope="col" style="padding:.4em" | Date
 +
!scope="col" style="padding:.4em" | Paper<br/>index
 +
!scope="col" style="padding:.4em" | Presenter
 +
!scope="col" style="padding:.4em" | Paper title
 +
|-
 +
|style="padding:.4em;" rowspan=2|2013/06/26
 +
|style="padding:.4em;"|2013-31
 +
|style="padding:.4em;"|HJ Han
 +
|style="padding:.4em;text-align:left"|[http://nar.oxfordjournals.org/content/40/16/7690.long Integration of Hi-C and ChIP-seq data reveals distinct types of chromatin linkages]
 +
|-
 +
|style="padding:.4em;"|2013-30
 +
|style="padding:.4em;"|YH Ko
 +
|style="padding:.4em;text-align:left"|[http://www.nature.com/nbt/journal/v31/n4/full/nbt.2519.html Deciphering molecular circuits from genetic variation underlying transcriptional responsiveness to stimuli]
 +
|-
 +
|style="padding:.4em;" rowspan=2|2013/06/04
 +
|style="padding:.4em;"|2013-29
 +
|style="padding:.4em;"|KS Kim
 +
|style="padding:.4em;text-align:left"|[https://www.cell.com/abstract/S0092-8674(13)00439-X Mapping the Human miRNA Interactome by CLASH Reveals Frequent Noncanonical Binding]
 +
|-
 +
|style="padding:.4em;"|2013-28
 +
|style="padding:.4em;"|ER Kim
 +
|style="padding:.4em;text-align:left"|[http://www.nature.com/nbt/journal/v31/n3/full/nbt.2514.html Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples]
 +
|-
 +
|style="padding:.4em;" rowspan=2|2013/05/28
 +
|style="padding:.4em;"|2013-27
 +
|style="padding:.4em;"|YH Ko
 +
|style="padding:.4em;text-align:left"|[http://www.pnas.org/content/102/38/13544.long Discovering statistically significant pathways in expression profiling studies]
 +
|-
 +
|style="padding:.4em;"|2013-26
 +
|style="padding:.4em;"|JE Shim
 +
|style="padding:.4em;text-align:left"|[http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0058977 Prediction and Validation of Gene-Disease Associations Using Methods Inspired by Social Network Analyses]
 +
|-
 +
|style="padding:.4em;" rowspan=2|2013/05/21
 +
|style="padding:.4em;"|2013-25
 +
|style="padding:.4em;"|HJ Han
 +
|style="padding:.4em;text-align:left"|[http://www.nature.com/nature/journal/v496/n7446/full/nature11981.html Dynamic regulatory network controlling TH17 cell differentiation]
 +
|-
 +
|style="padding:.4em;"|2013-24
 +
|style="padding:.4em;"|T Lee
 +
|style="padding:.4em;text-align:left"|[http://www.sciencedirect.com/science/article/pii/S0092867412013529 Deciphering and Prediction of Transcriptome Dynamics under Fluctuating Field Conditions]
 +
 +
|-
 +
|style="padding:.4em;" rowspan=2|2013/05/14
 +
|style="padding:.4em;"|2013-23
 +
|style="padding:.4em;"|JE Shim
 +
|style="padding:.4em;text-align:left"|[http://www.sciencedirect.com/science/article/pii/S0092867412015565 Integrative eQTL-Based Analyses Reveal the Biology of Breast Cancer Risk Loci]
 +
 +
|-
 +
|style="padding:.4em;"|2013-22
 +
|style="padding:.4em;"|HJ Kim
 +
|style="padding:.4em;text-align:left"|[http://www.nature.com/ng/journal/v45/n2/full/ng.2504.html Chromatin marks identify critical cell types for fine mapping complex trait variants]
 +
|-
 +
|style="padding:.4em;" rowspan=2|'''2013/05/07'''
 +
|style="padding:.4em;"|2013-21
 +
|style="padding:.4em;"|ER Kim
 +
|style="padding:.4em;text-align:left"|[http://www.cell.com/abstract/S0092-8674(12)01555-3 Genome-wide Chromatin State Transitions Associated with Developmental and Environmental Cues]
 +
|-
 +
|style="padding:.4em;"|2013-20
 +
|style="padding:.4em;"|HS Shim
 +
|style="padding:.4em;text-align:left"|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1003201 Human Disease-Associated Genetic Variation Impacts Large Intergenic Non-Coding RNA Expression]
 +
|-
 +
|style="padding:.4em;"|2013/04/09
 +
|style="padding:.4em;"|2013-19
 +
|style="padding:.4em;"|HJ Kim
 +
|style="padding:.4em;text-align:left"|[http://genome.cshlp.org/content/22/9/1790.long Annotation of functional variation in personal genomes using RegulomeDB]
 +
|-
 +
|style="padding:.4em;"|2013/04/02
 +
|style="padding:.4em;"|2013-18
 +
|style="padding:.4em;"|HJ Kim
 +
|style="padding:.4em;text-align:left"|[http://genome.cshlp.org/content/22/9/1775.long The GENCODE v7 catalog of human long noncoding RNAs: Analysis of their gene structure, evolution, and expression]
 +
|-
 +
|style="padding:.4em;" |2013/03/12
 +
|style="padding:.4em;"|2013-17
 +
|style="padding:.4em;"| ER Kim
 +
|style="padding:.4em;text-align:left"|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002311 Global Mapping of Cell Type–Specific Open Chromatin by FAIRE-seq Reveals the Regulatory Role of the NFI Family in Adipocyte Differentiation]
 +
|-
 +
|style="padding:.4em;" rowspan=1|2013/03/05
 +
|style="padding:.4em;"|2013-16
 +
|style="padding:.4em;"| ER Kim
 +
|style="padding:.4em;text-align:left"|[http://www.nature.com/nrg/journal/v10/n10/full/nrg2641.html ChIP–seq: advantages and challenges of a maturing technology]
 +
|-
 +
|style="padding:.4em;" rowspan=3|2013/02/21
 +
|style="padding:.4em;"|2013-15
 +
|style="padding:.4em;"| KS Kim
 +
|style="padding:.4em;text-align:left"|[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002830 Novel Modeling of Combinatorial miRNA Targeting Identifies SNP with Potential Role in Bone Density]
 +
|-
 +
|style="padding:.4em;"|2013-14
 +
|style="padding:.4em;"| KS Kim
 +
|style="padding:.4em;text-align:left"|[http://www.cell.com/abstract/S0092-8674(12)01424-9 A Molecular Roadmap of Reprogramming Somatic Cells into iPS Cells]
 +
|-
 +
|style="padding:.4em;"|2013-13
 +
|style="padding:.4em;"| HJ Han
 +
|style="padding:.4em;text-align:left"|[http://genome.cshlp.org/content/22/6/1015.long Differential DNase I hypersensitivity reveals factor-dependent chromatin dynamics.]
 +
|-
 +
|style="padding:.4em;" rowspan=3|2013/02/15
 +
|style="padding:.4em;"|2013-12
 +
|style="padding:.4em;"| HJ Han
 +
|style="padding:.4em;text-align:left"|[http://www.nature.com/ng/journal/v43/n3/full/ng.759.html Chromatin accessibility pre-determines glucocorticoid receptor binding patterns]
 +
|-
 +
|style="padding:.4em;"|2013-11
 +
|style="padding:.4em;"| JE Shim, CY Kim
 +
|style="padding:.4em;text-align:left"|[http://www.nature.com/nbt/journal/v30/n11/full/nbt.2422.html Interpreting noncoding genetic variation in complex traits and human disease]
 +
|-
 +
|style="padding:.4em;"|2013-10
 +
|style="padding:.4em;"|HJ Han, JH Kim
 +
|style="padding:.4em;text-align:left"|[http://genome.cshlp.org/content/21/3/447.full.pdf+html  Accurate inference of transcription factor binding from DNA sequence and chromatin accessibility data]
 +
|-
 +
|style="padding:.4em;" rowspan=3|2013/02/08
 +
|style="padding:.4em;"|2013-09
 +
|style="padding:.4em;"|HJ Kim
 +
|style="padding:.4em;text-align:left"|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002599 Widespread Site-Dependent Buffering of Human Regulatory Polymorphism]
 +
|-
 +
|style="padding:.4em;"|2013-08
 +
|style="padding:.4em;"|JE Shim, CY Kim
 +
|style="padding:.4em;text-align:left;"|[http://genome.cshlp.org/content/22/9/1748.long Linking disease associations with regulatory information in the human genome]
 +
|-
 +
|style="padding:.4em;"|2013-07
 +
|style="padding:.4em;"|HJ Han, JH Kim
 +
|style="padding:.4em;text-align:left"|[http://genome.cshlp.org/content/22/9/1658.long Understanding transcriptional regulation by integrative analysis of transcription factor binding data]
 +
|-
 +
|style="padding:.4em;" rowspan=3|2013/01/25
 +
|style="padding:.4em;"|2013-06
 +
|style="padding:.4em;"|HJ Han, '''JH Kim'''
 +
|style="padding:.4em;text-align:left;"|[http://www.nature.com/nature/journal/v489/n7414/full/nature11279.html The long-range interaction landscape of gene promoters]
 +
|-
 +
|style="padding:.4em;"|2013-05
 +
|style="padding:.4em;"|'''ER Kim''', HS Shim
 +
|style="padding:.4em;text-align:left;"|[http://www.nature.com/nature/journal/v489/n7414/full/nature11233.html Landscape of transcription in human cells]
 +
|-
 +
|style="padding:.4em;"|2013-04
 +
|style="padding:.4em;"|'''HJ Han''', JH Kim
 +
|style="padding:.4em;text-align:left;"|[http://www.nature.com/nature/journal/v489/n7414/full/nature11245.html Architecture of the human regulatory network derived from ENCODE data]
 +
 +
|-
 +
|style="padding:.4em;" rowspan=2|2013/01/18
 +
|style="padding:.4em;"|2013-03
 +
|style="padding:.4em;"|KS Kim, '''TH Kim'''
 +
|style="padding:.4em;text-align:left;"|[http://www.nature.com/nature/journal/v489/n7414/full/nature11212.html An expansive human regulatory lexicon encoded in transcription factor footprints]
 +
|-
 +
|style="padding:.4em;"|2013-02
 +
|style="padding:.4em;"|'''HJ Han''', JH Kim
 +
|style="padding:.4em;text-align:left;"|[http://www.nature.com/nature/journal/v489/n7414/full/nature11232.html The accessible chromatin landscape of the human genome]
 +
|-
 +
|style="padding:.4em;" rowspan=1|2013/01/11
 +
|style="padding:.4em;"|2013-01
 +
|style="padding:.4em;"| '''JE Shim''', CY Kim
 +
|style="padding:.4em;text-align:left"|[http://www.nature.com/nature/journal/v489/n7414/full/nature11247.html An integrated encyclopedia of DNA elements in the human genome]
 +
|}
 +
 +
2012
 
{|border ="1"
 
{|border ="1"
−
|style="color:black; background-color:#dcdcdc;" align="center" |Date
+
|style="color:black; background-color:#dcdcdc; padding:5px;" align="center" |Date
−
|style="color:black; background-color:#dcdcdc;" align="center"|Paper_index
+
|style="color:black; background-color:#dcdcdc;" align="center"|Paper index
−
|style="color:black; background-color:#dcdcdc;" align="center"|Paper_title
+
|style="color:black; background-color:#dcdcdc;" align="center"|Paper title
 
|-
 
|-
−
|rowspan = "39" |2012/07/02
+
|rowspan = "1"|2013/01/11
−
~2012/07/19
+
|align ="center"|2012-81
−
| align="center"|2012-73
+
|[http://genome.cshlp.org/content/22/8/1589.full (TH Kim) MuSiC: identifying mutational significance in cancer genomes.]
−
|[http://compbio.mit.edu/publications/68_Marbach_GenomeResearch_12.pdf (HJ Han & YH Ko) Predictive regulatory models in Drosophila melanogaster by integrative inference of transcriptional networks]
+
|-
 +
|rowspan = "1"|2012/12/04
 +
|align ="center"|2012-80
 +
|[http://genome.cshlp.org/content/22/8/1383 (CY KIM) Human genomic disease variants: A neutral evolutionary explanation]
 +
|-
 +
|rowspan = "2"|2012/11/20
 +
| align="center"|2012-79
 +
|[http://www.cell.com/abstract/S0092-8674(12)00639-3 (HS Shim) Circuitry and Dynamics of Human Transcription Factor Regulatory Networks]
 +
|-
 +
| align="center"|2012-78
 +
|[http://www.nature.com/nature/journal/v487/n7408/full/nature11288.html (HJ Kim) Interpreting cancer genomes using systematic host network perturbations by tumour virus proteins]
 +
|-
 +
|rowspan = "2"|2012/11/06
 +
| align="center"|2012-77
 +
|[http://www.sciencemag.org/content/337/6099/1190.short (HJ Han) Systematic Localization of Common Disease-Associated Variation in Regulatory DNA]
 +
|-
 +
| align="center"|2012-76
 +
|[http://www.pnas.org/cgi/doi/10.1073/pnas.1201904109 (KS Kim) A public resource facilitating clinical use of genomes]
 +
|-
 +
|rowspan = "6" |2012/07/19
 +
| align="center"|2012-75
 +
|[http://genome.cshlp.org/content/22/7/1334 (HJ Han & YH Ko) Predictive regulatory models in Drosophila melanogaster by integrative inference of transcriptional networks]
 
|-
 
|-
−
| align="center"|2012-72
+
| align="center"|2012-74
 
|[http://www.sciencemag.org/content/337/6090/100.full (JE Shim) An Abundance of Rare Functional Variants in 202 Drug Target Genes Sequenced in 14,002 People]  
 
|[http://www.sciencemag.org/content/337/6090/100.full (JE Shim) An Abundance of Rare Functional Variants in 202 Drug Target Genes Sequenced in 14,002 People]  
 
|-
 
|-
−
| align="center"|2012-71
+
| align="center"|2012-73
 
|[http://www.sciencemag.org/content/337/6090/64.abstract (JE Shim) Evolution and Functional Impact of Rare Coding Variation from Deep Sequencing of Human Exomes]
 
|[http://www.sciencemag.org/content/337/6090/64.abstract (JE Shim) Evolution and Functional Impact of Rare Coding Variation from Deep Sequencing of Human Exomes]
 
|-
 
|-
−
| align="center"|2012-70
+
| align="center"|2012-72
 
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2063581/ (SH Hwang) Network-based classification of breast cancer metastasis]
 
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2063581/ (SH Hwang) Network-based classification of breast cancer metastasis]
 
|-
 
|-
−
| align="center"|2012-69
+
| align="center"|2012-71
 +
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002707 (T Lee&CY Kim)Brain Expression Genome-Wide Association Study (eGWAS) Identifies Human Disease-Associated Variants]
 +
|-
 +
| align="center"|2012-70
 
|[http://genome.cshlp.org/content/20/9/1297 (ER Kim&TH Kim)The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data]
 
|[http://genome.cshlp.org/content/20/9/1297 (ER Kim&TH Kim)The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data]
 
|-
 
|-
−
| align="center"|2012-68
+
|rowspan = "8" |2012/07/16
 +
| align="center"|2012-69
 
|[http://www.nature.com/ng/journal/v43/n5/full/ng.806.html (ER Kim&TH Kim)A framework for variation discovery and genotyping using next-generation DNA sequencing data]
 
|[http://www.nature.com/ng/journal/v43/n5/full/ng.806.html (ER Kim&TH Kim)A framework for variation discovery and genotyping using next-generation DNA sequencing data]
 
|-
 
|-
−
| align="center"|2012-67
+
| align="center"|2012-66
 
|[http://bioinformatics.oxfordjournals.org/content/25/16/2078.long (ER Kim&TH Kim)The Sequence Alignment/Map format and SAMtools]
 
|[http://bioinformatics.oxfordjournals.org/content/25/16/2078.long (ER Kim&TH Kim)The Sequence Alignment/Map format and SAMtools]
 
|-
 
|-
−
| align="center"|2012-66
+
| align="center"|2012-65
 
|[http://bioinformatics.oxfordjournals.org/content/early/2011/06/07/bioinformatics.btr330 (ER Kim&TH Kim)The Variant Call Format and VCFtools]
 
|[http://bioinformatics.oxfordjournals.org/content/early/2011/06/07/bioinformatics.btr330 (ER Kim&TH Kim)The Variant Call Format and VCFtools]
 
|-
 
|-
−
| align="center"|2012-65
+
| align="center"|2012-64
−
|[http://www.cell.com/abstract/S0092-8674(12)00104-3 (YH Go&HS Han)The Impact of the Gut Microbiota on Human Health: An Integrative View]
+
|[http://www.cell.com/abstract/S0092-8674(12)00104-3 (YH Go&HJ Han)The Impact of the Gut Microbiota on Human Health: An Integrative View]
 
|-
 
|-
−
|align="center"|2012-64
+
|align="center"|2012-63
 
|[http://www.sciencemag.org/content/336/6086/1262.abstract (T Lee&CY Kim)Host-Gut Microbiota Metabolic Interactions]
 
|[http://www.sciencemag.org/content/336/6086/1262.abstract (T Lee&CY Kim)Host-Gut Microbiota Metabolic Interactions]
−
|-
 
−
|align="center"|2012-63
 
−
|[http://stm.sciencemag.org/content/4/137/137rv6 (T Lee&CY Kim)Therapeutic Modulation of Microbiota-Host Metabolic Interactions]
 
 
|-
 
|-
 
|align="center"|2012-62
 
|align="center"|2012-62
Line 48: Line 4,557:
 
|[http://stm.sciencemag.org/content/4/137/137rv5 (SH Hwang&HJ Cho)Microbiota-Targeted Therapies: An Ecological Perspective]
 
|[http://stm.sciencemag.org/content/4/137/137rv5 (SH Hwang&HJ Cho)Microbiota-Targeted Therapies: An Ecological Perspective]
 
|-
 
|-
 +
|rowspan = "3" |2012/07/13
 
|align="center"|2012-59
 
|align="center"|2012-59
 
|[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002358 (JH Shin&HJ Kim)Metabolic Reconstruction for Metagenomic Data and Its Application to the Human Microbiome]
 
|[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002358 (JH Shin&HJ Kim)Metabolic Reconstruction for Metagenomic Data and Its Application to the Human Microbiome]
Line 57: Line 4,567:
 
|[http://www.nature.com/nature/journal/v486/n7402/full/nature11234.html (JH Shin&HJ Kim)Structure, function and diversity of the healthy human microbiome]
 
|[http://www.nature.com/nature/journal/v486/n7402/full/nature11234.html (JH Shin&HJ Kim)Structure, function and diversity of the healthy human microbiome]
 
|-
 
|-
 +
|rowspan = "4" |2012/07/12
 
|align="center"|2012-56
 
|align="center"|2012-56
 
|[http://nar.oxfordjournals.org/content/40/D1/D957.long (AR Cho&JH Ju)COLT-Cancer: functional genetic screening resource for essential genes in human cancer cell lines]
 
|[http://nar.oxfordjournals.org/content/40/D1/D957.long (AR Cho&JH Ju)COLT-Cancer: functional genetic screening resource for essential genes in human cancer cell lines]
Line 69: Line 4,580:
 
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature10983.html (JH Soh)The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups]
 
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature10983.html (JH Soh)The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups]
 
|-
 
|-
 +
|rowspan = "6" |2012/07/09
 
|align="center"|2012-52
 
|align="center"|2012-52
 
|[http://www.nature.com/nature/journal/v483/n7391/full/nature11003.html (ER Kim&TH Kim)The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity]
 
|[http://www.nature.com/nature/journal/v483/n7391/full/nature11003.html (ER Kim&TH Kim)The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity]
Line 87: Line 4,599:
 
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature10989.html (JE Shim&KS Kim)Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations]
 
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature10989.html (JE Shim&KS Kim)Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations]
 
|-
 
|-
 +
|rowspan = "6" |2012/07/06
 
| align="center"|2012-46
 
| align="center"|2012-46
 
|[http://www.g3journal.org/content/1/3/233.full (T Lee&CY Kim)Integrating Rare-Variant Testing, Function Prediction, and Gene Network in Composite Resequencing-Based Genome-Wide Association Studies (CR-GWAS)]
 
|[http://www.g3journal.org/content/1/3/233.full (T Lee&CY Kim)Integrating Rare-Variant Testing, Function Prediction, and Gene Network in Composite Resequencing-Based Genome-Wide Association Studies (CR-GWAS)]
Line 105: Line 4,618:
 
|[http://www.nature.com/ng/journal/v44/n6/full/ng.2303.html (JE Shim&KS Kim)Exome sequencing and the genetic basis of complex traits]
 
|[http://www.nature.com/ng/journal/v44/n6/full/ng.2303.html (JE Shim&KS Kim)Exome sequencing and the genetic basis of complex traits]
 
|-
 
|-
 +
|rowspan = "6" |2012/07/02
 
|align="center"|2012-40
 
|align="center"|2012-40
 
|[http://www.cell.com/abstract/S0092-8674(12)00573-9 (JH Soh)Functional Repurposing Revealed by Comparing S. pombe and S. cerevisiae Genetic Interactions]
 
|[http://www.cell.com/abstract/S0092-8674(12)00573-9 (JH Soh)Functional Repurposing Revealed by Comparing S. pombe and S. cerevisiae Genetic Interactions]
Line 232: Line 4,746:
 
|align="center"|2012-01
 
|align="center"|2012-01
 
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1852724/ Most Rare Missense Alleles Are Deleterious in Humans: Implications for Complex Disease and Association Studies]
 
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1852724/ Most Rare Missense Alleles Are Deleterious in Humans: Implications for Complex Disease and Association Studies]
 +
|}
 +
2011
 +
{|border ="1"
 +
|style="color:black; background-color:#dcdcdc;" align="center" |Date
 +
|style="color:black; background-color:#dcdcdc;" align="center"|Paper_index
 +
|style="color:black; background-color:#dcdcdc;" align="center"|Paper_title
 
|-
 
|-
 
|rowspan = "6" |2011/11/28
 
|rowspan = "6" |2011/11/28
Line 400: Line 4,920:
 
|align="center"|2011-01
 
|align="center"|2011-01
 
|[http://physiolgenomics.physiology.org/content/33/1/18.long Data analysis and bioinformatics tools for tandem mass spectrometry in proteomics]
 
|[http://physiolgenomics.physiology.org/content/33/1/18.long Data analysis and bioinformatics tools for tandem mass spectrometry in proteomics]
 +
|}
 +
2010
 +
{|border ="1"
 +
|style="color:black; background-color:#dcdcdc;" align="center" |Date
 +
|style="color:black; background-color:#dcdcdc;" align="center"|Paper_index
 +
|style="color:black; background-color:#dcdcdc;" align="center"|Paper_title
 
|-
 
|-
 
|rowspan = "2"|2010/12/27
 
|rowspan = "2"|2010/12/27

Latest revision as of 11:29, 14 November 2024

2024-2 scOmics
Date Team Paper
index 		Presenter Paper title
2025/1/21 Single-cell 25-2 YL Jung

Single-cell RNA sequencing of human tissue supports successful drug targets

2025/1/14 Single-cell 25-1 EJ Sung

ANDES: a novel best-match approach for enhancing gene set analysis in embedding spaces

2025/1/7 Single-cell 24-34 IS Choi

Evaluating the Utilities of Foundation Models in Single-cell Data Analysis

2024/12/31 Single-cell 24-33 SB Baek

Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation

2024/12/24 Single-cell 24-32 JH Cha

scEMB: Learning context representation of genes based on large-scale single-cell transcriptomics

2024/12/17 Single-cell 24-31 HB Lee

Mouse-Geneformer: A Deep Learning Model for Mouse Single-Cell Transcriptome and Its Cross-Species Utility

2024/12/10 Single-cell 24-30 YL Jung

Quantized multi-task learning for context-specific representations of gene network dynamics

2024/12/3 Single-cell 24-29 EJ Sung

Systematic Functional Annotation and Visualization of Biological Networks

2024/11/26 Single-cell 24-28 IS Choi

CellRank 2: unified fate mapping in multiview single-cell data

2024/11/19 Single-cell 24-27 SB Baek

scPRINT: pre-training on 50 million cells allows robust gene network predictions

2024/11/12 Single-cell 24-26 JH Cha

Bidirectional generation of structure and properties through a single molecular foundation model

2024-2 Microbiome
Date Team Paper
index 		Presenter Paper title
2025/1/8 Microbiome 24-70 YR Kim

Fecal microbial marker panel for aiding diagnosis of autism spectrum disorders

2025/1/8 Microbiome 24-69 YR Kim

Multikingdom and functional gut microbiota markers for autism spectrum disorder

2024/12/18 Microbiome 24-68 JY Kim

A realistic benchmark for differential abundance testing and confounder adjustment in human microbiome studies

2024/12/18 Microbiome 24-67 WJ Kim

Microbial community-scale metabolic modelling predicts personalized short-chain fatty acid production profiles in the human gut

2024/12/11 Microbiome 24-66 G Koh

Gut metagenomes of Asian octogenarians reveal metabolic potential expansion and distinct microbial species associated with aging phenotypes

2024/12/11 Microbiome 24-65 SH Ahn

Gut microbiota wellbeing index predicts overall health in a cohort of 1000 infants

2024/12/4 Microbiome 24-64 HJ Kim

VirRep: a hybrid language representation learning framework for identifying viruses from human gut metagenomes

2024/12/4 Microbiome 24-63 HJ Kim

Ultrafast and accurate sequence alignment and clustering of viral genomes

2024/12/4 Microbiome 24-62 JY Ma

The OMG dataset: An Open MetaGenomic corpus for mixed-modality genomic language modeling

2024/11/27 Microbiome 24-61 JH Cha

Genomic language model predicts protein co-regulation and function

2024/11/27 Microbiome 24-60 NY Kim

Protein Set 1 Transformer: A protein-based genome language model to power high diversity viromics

2024/11/20 Microbiome 24-59 YR Kim

Prophage-DB: A comprehensive database to explore diversity,distribution, and ecology of prophages

2024/11/20 Microbiome 24-58 JY Kim

Strain‑resolved de‑novo metagenomic assembly of viral genomes and microbial 16S rRNAs

2024/11/13 Microbiome 24-57 WJ Kim

Prokaryotic‑virus‑encoded auxiliary metabolic genes throughout the global oceans

2024/11/13 Microbiome 24-56 G Koh

Unexplored microbial diversity from 2,500 food metagenomes and links with the human microbiome

2024/11/6 Microbiome 24-55 SH Ahn

Pangenomes of Human Gut Microbiota Uncover Links Between Genetic Diversity and Stress Response

2024/11/6 Microbiome 24-54 HJ Kim

vClassifier: a toolkit for species-level classification of prokaryotic viruses

2024/11/6 Microbiome 24-53 HJ Kim

GRAViTy-V2: a grounded viral taxonomy application

2024/10/16 Microbiome 24-52 JY Ma

Accurately predicting enzyme functions through geometric graph learning on ESMFold-predicted structures

2024/10/16 Microbiome 24-51 JH Cha

Improved detection of microbiome-disease associations via population structure-aware generalized linear mixed effects models (microSLAM)

2024-1 scOmics
Date Team Paper
index 		Presenter Paper title
2024/11/12 Single-cell 24-25 HB Lee

A blueprint for tumor-infiltrating B cells across human cancers

2024/10/29 Single-cell 24-24 YL Jung

Systematic dissection of tumor-normal single-cell ecosystems across a thousand tumors of 30 cancer types

2024/10/08 Single-cell 24-23 EJ Sung

scDrugPrio: a framework for the analysis of single‑cell transcriptomics to address multiple problems in precision medicine in immune‑mediated inflammatory diseases

2024/09/24 Single-cell 24-22 IS Choi

A visual-language foundation model for computational pathology

2024/09/10 Single-cell 24-21 SB Baek

SLIDE: Significant Latent Factor Interaction Discovery and Exploration across biological domains

2024/09/03 Single-cell 24-20 HB Lee

Clinical and molecular features of acquired resistance to immunotherapy in non-small cell lungcancer

2024/08/30 Single-cell 24-19 JH Cha

Cell-Graph Compass: Modeling Single Cells with Graph Structure Foundation Model

2024/08/16 Single-cell 24-18 YL Jung

Single-cell transcriptome landscape of circulating CD4+ T cell populations in autoimmune diseases

2024/08/09 Single-cell 24-17 EJ Sung

Population-level comparisons of gene regulatory networks modeled on highthroughput single-cell transcriptomics data

2024/08/02 Single-cell 24-16 IS Choi

node2vec2rank: Large Scale and Stable Graph Differential Analysis via Multi-Layer Node Embeddings and Ranking

2024/07/26 Single-cell 24-15 SB Baek

Unified cross-modality integration and analysis of T cell receptors and T cell transcriptomes by low-resource-aware representation learning

2024/07/19 Single-cell 24-14 JH Cha

Contextual AI models for single-cell protein biology

2024/07/12 Single-cell 24-13 EJ Sung

Nicheformer: a foundation model for single-cell and spatial omics

2024/07/05 Single-cell 24-12 IS Choi

Large Scale Foundation Model on Single-cell Transcriptomics

2024/06/28 Single-cell 24-11 SB Baek

scGPT: toward building a foundation modelfor single-cell multi-omics using generative AI

2024/06/21 Single-cell 24-10 JH Cha

Transfer learning enables predictions in network biology

2024/06/07 Single-cell 24-9 EJ Sung

The Web-Based Portal SpatialTME Integrates Histological Images with Single-Cell and Spatial Transcriptomics to Explore the Tumor Microenvironment

2024/05/17 Single-cell 24-8 IS Choi

SEVtras delineates small extracellular vesicles at droplet resolution from single-cell transcriptomes

2024/05/10 Single-cell 24-7 SB Baek

A relay velocity model infers cell-dependent RNA velocity

2024/05/03 Single-cell 24-5 EJ Sung

Pathway centric analysis for single-cell RNA-seq and spatial transcriptomics data with GSDensity

2024/04/26 Single-cell 24-6 JH Cha

Dandelion uses the single-cell adaptive immune receptor repertoire to explore lymphocyte developmental origins

2024/04/05 Single-cell 24-4 IS Choi

Automatic cell-type harmonization and integration across Human Cell Atlas datasets

2024/03/22 Single-cell 24-3 SB Baek

Deep generative modeling of transcriptional dynamics for RNA velocity analysis in single cells

2024/03/15 Single-cell 24-2 JH Cha

Gene signature extraction and cell identity recognition at the single-cell level with Cell-ID

2024/03/08 Single-cell 24-1 EJ Sung

Polygenic regression uncovers trait-relevant cellular contexts through pathway activation transformation of single-cell RNA sequencing data


2024-1 Microbiome
Date Team Paper
index 		Presenter Paper title
2024/10/02 Microbiome 24-51 NY Kim

Strain-specific gut microbial signatures in type 2 diabetes identified in a cross-cohort analysis of 8,117 metagenomes

2024/10/02 Microbiome 24-50 YR Kim

Gut virome-wide association analysis identifes cross-population viral signatures for infammatory bowel disease

2024/09/25 Microbiome 24-48-2 JY Kim

Efficient Low-rank Multimodal Fusion with Modality-Specific Factors

2024/09/25 Microbiome 24-48-1 JY Kim

Tensor Fusion Network for Multimodal Sentiment Analysis

2024/09/25 Microbiome 24-49 WJ Kim

Gut symbionts alleviate MASH through a secondary bile acid biosynthetic pathway

2024/09/11 Microbiome 24-47 G Koh

Gut microbiota DPP4-like enzymes are increased in type-2 diabetes and contribute to incretin inactivation

2024/09/11 Microbiome 24-46 SH Ahn

Deep learning with multimodal representation for pancancer prognosis prediction

2024/09/04 Microbiome 24-45-2 HJ Kim

Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosis

2024/09/04 Microbiome 24-45-1 HJ Kim

Pan-cancer integrative histology-genomic analysis via multimodal deep learning

2024/09/04 Microbiome 24-44 NY Kim

A metagenomics pipeline reveals insertion sequence-driven evolution of the microbiota

2024/08/21 Microbiome 24-43-2 JH Cha

BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs

2024/08/21 Microbiome 24-43-1 JH Cha

Learning Transferable Visual Models From Natural Language Supervision

2024/08/21 Microbiome 24-42 JY Ma

BIONIC: biological network integration using convolutions

2024/08/14 Microbiome 24-41 G Koh

Protein remote homology detection and structural alignment using deep learning

2024/08/14 Microbiome 24-41 YR Kim

Accurate structure prediction of biomolecular interactions with AlphaFold 3

2024/08/07 Microbiome 24-39 WJ Kim

Gut microbiome-metabolome interactions predict host condition

2024/08/07 Microbiome 24-38 JY kim

Microbiome confounders and quantitative profiling challenge predicted microbial targets in colorectal cancer development

2024/07/31 Microbiome 24-37 SH Ahn

A multi-kingdom collection of 33,804 reference genomes for the human vaginal microbiome

2024/07/31 Microbiome 24-36 HJ Kim

Efficient and accurate detection of viral sequences at single-cell resolution reveals putative novel viruses perturbing host gene expression

2024/07/24 Microbiome 24-35 JY Ma

Compositional Differential Abundance Testing: Defining and Finding a New Type of Health-Microbiome Associations

2024/07/24 Microbiome 24-34 JH Cha

Discovery of antimicrobial peptides in the global microbiome with machine learning

2024/07/17 Microbiome 24-33 NY Kim

Custom scoring based on ecological topology of gut microbiota associated with cancer immunotherapy outcome

2024/07/17 Microbiome 24-32 YR Kim

Paternal microbiome perturbations impact offspring fitness

2024/07/10 Microbiome 24-31 JY Kim

Interactions-based classification of a single microbial sample

2024/07/10 Microbiome 24-30 NY Kim

Accurate estimation of intraspecificmicrobial gene content variation inmetagenomic data with MIDAS v3 andStrainPGC

2024/07/03 Microbiome 24-29 WJ Kim

A pan-cancer analysis of the microbiome inmetastatic cancer

2024/07/03 Microbiome 24-28 G Koh

A specific enterotype derived from gut microbiomeof older individuals enables favorable responses toimmune checkpoint blockade therapy

2024/06/26 Microbiome 24-27 SH Ahn

Stratification of Fusobacterium nucleatum by localhealth status in the oral cavity defines its subspeciesdisease association

2024/06/26 Microbiome 24-26 HJ Kim

A universe of human gut-derived bacterialprophages: unveiling the hidden viral players inintestinal microecology

2024/06/19 Microbiome 24-25 JY Ma

Robustness of cancer microbiome signals over a broad range of methodological variation

2024/06/19 Microbiome 24-24 JY Cha

A distinct Fusobacterium nucleatum clade dominates the colorectal cancer niche

2024/06/05 Microbiome 24-22 YR Kim

A cryptic plasmid is among the most numerous genetic elements in the human gut

2024/06/05 Microbiome 24-21 JY Kim

Gut microbiome and metabolome profiling in Framingham heart study reveals cholesterol-metabolizing bacteria

2024/05/29 Microbiome 24-20 WJ Kim

Fecal microbial load is a major determinant of gut microbiome variation and aconfounder for disease associations

2024/05/29 Microbiome 24-19 G Koh

A host-microbiota interactome reveals extensive transkingdom connectivity

2024/05/22 Microbiome 24-18 SH Ahn

Metagenomic estimation of dietary intake from human stool

2024/05/22 Microbiome 24-17 HJ Kim

A metagenomic catalog of the early-life human gut virome

2024/05/08 Microbiome 24-16 JY Ma

Large-scale computational analyses of gut microbial CAZyme repertoires enabled by Cayman

2024/05/08 Microbiome 24-15 JH Cha

Defining the biogeographical map and potential bacterial translocation of microbiome in human ‘surface organs’

2024/05/01 Microbiome 24-14 NY Kim

Gut microbial structural variation associates with immune checkpoint inhibitor response

2024/05/01 Microbiome 24-13 YR Kim

Fungal signature differentiates alcohol-associated liver disease from nonalcoholic fatty liver disease

2024/04/24 Microbiome 24-12 JY Kim

Incorporating metabolic activity, taxonomy and community structure to improve microbiome based predictive models for host phenotype prediction

2024/04/24 Microbiome 24-11 WJ Kim

Disease-specific loss of microbial cross feeding interactions in the human gut

2024/04/03 Microbiome 24-7 JY Ma

Multigroup analysis of compositions of microbiomes with covariate adjustments and repeated measures

2024/04/03 Microbiome 24-9 SH Ahn

Microdiversity of the vaginal microbiome is associated with preterm birth

2024/03/27 Microbiome 24-8 HJ Kim

Large language models improve annotation of prokaryotic viral proteins

2024/03/27 Microbiome 24-10 G Koh

Clinically relevant antibiotic resistance genes are linked to a limited set of taxa within gut microbiome worldwide

2024/03/20 Microbiome 24-6-2 JH Cha

Visualizing ’omic feature rankings and log-ratios using Qurro

2024/03/20 Microbiome 24-6-1 JH Cha

Establishing microbial composition measurement standards with reference frames

2024/03/20 Microbiome 24-5 NY Kim

AnnoPRO: a strategy for protein function annotation based on multi-scale protein representation and a hybrid deep learning of dual-path encoding

2024/03/13 Microbiome 24-4 YR Kim

Differential responses of the gut microbiome and resistome to antibiotic exposures in infants and adults

2024/03/13 Microbiome 24-3 JY Kim

Effective binning of metagenomic contigs using contrastive multi-view representation learning

2024/03/06 Microbiome 24-2 WJ Kim

Polarization of microbial communities between competitive and cooperative metabolism

2024/03/06 Microbiome 24-1-2 G Koh

Metagenomic Insight into The Global Dissemination of The Antibiotic Resistome

2024/03/06 Microbiome 24-1-1 G Koh

Conjugative plasmids interact with insertion sequences to shape the horizontal transfer of antimicrobial resistance genes


2024-1 Advanced scOmics Data Analysis
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

Human lung cancer harbors spatially organized stem-immunity hubs associated with response to immunotherapy

2024/06/11 Single-cell 24-28 SA Choi

Single-cell transcriptomics captures features of human midbrain development and dopamine neuron diversity in brain organoids

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

The single-cell stereo-seq reveals region-specific cell subtypes and transcriptome profiling in Arabidopsis leaves

2024/06/11 Single-cell 24-25 HJ Lee

Single-cell and bulk transcriptome sequencing identifies two epithelial tumor cell states and refines the consensus molecular classification of colorectal cancer

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

Immune microniches shape intestinal Treg function

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

Single-cell massively-parallel multiplexed microbial sequencing (M3-seq) identifies rare bacterial populations and profiles phage infection

2024/05/28 Single-cell 24-18 EB Yu

Spatial transcriptomics demonstrates the role of CD4 T cells in effector CD8 T cell differentiation during chronic viral infection

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

Dissecting the immune suppressive human prostate tumor microenvironment via integrated single-cell and spatial transcriptomic analyses

2024/05/21 Single-cell 24-15 SY Park

Single-nucleus genomics in outbred rats with divergent cocaine addiction-like behaviors reveals changes in amygdala GABAergic inhibition

2024/05/21 Single-cell 24-14 HS Moon

Spatial transcriptomics reveals the distinct organization of mouse prefrontal cortex and neuronal subtypes regulating chronic pain

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

Single-cell spatial transcriptomic and translatomic profiling of dopaminergic neurons in health, aging, and disease

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

Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions

2024/05/14 Single-cell 24-9 Q Zhen

Single-Cell Analysis Reveals Cxcl14+ Fibroblast Accumulation in Regenerating Diabetic Wounds Treated by Hydrogel-Delivering Carbon Monoxide

2024/05/07 Single-cell 24-8 CR Leenaars

Single-cell RNA sequencing provides a high-resolution roadmap for understanding the multicellular compartmentation of specialized metabolism

2024/05/07 Single-cell 24-7 YR Kim

Single-cell spatial multi-omics and deep learning dissect enhancer-driven gene regulatory networks in liver zonation

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

Single-cell transcriptomic analysis deciphers heterogenous cancer stem-like cells in colorectal cancer and their organ-specific metastasis

2024/04/23 Single-cell 24-2 EJ Sung

Single cell sequencing identifies clonally expanded synovial CD4+ TPH cells expressing GPR56 in rheumatoid arthritis

2024/04/23 Single-cell 24-1 HJ Kim

Progenitor-like exhausted SPRY1+CD8+ T cells potentiate responsiveness to neoadjuvant PD-1 blockade in esophageal squamous cell carcinoma


2023-2 scOmics
Date Team Paper
index 		Presenter Paper title
2024/02/20 Single-cell 23-40 IS Choi

Population-level integration of single-cell datasets enables multi-scale analysis across samples

2024/02/06 Single-cell 23-39 SB Baek

scDiffCom: a tool for differential analysis of cell–cell interactions provides a mouse atlas of aging changes in intercellular communication

2024/01/30 Single-cell 23-38 JH Cha

Modeling intercellular communication in tissues using spatial graphs of cells

2024/01/16 Single-cell 23-37 EJ Sung

Precise identification of cell states altered in disease using healthy single-cell references

2024/01/09 Single-cell 23-36 IS Choi

Learning Individual Survival Models from PanCancer Whole Transcriptome Data

2024/01/02 Single-cell 23-35 SB Baek

Dictys: dynamic gene regulatory network dissects developmental continuum with single-cell multiomics

2023/12/12 Single-cell 23-34 JH Cha

Preexisting tumor-resident T cells with cytotoxic potential associate with response to neoadjuvant anti–PD-1 in head and neck cancer

2023/12/05 Single-cell 23-33 EJ Sung

MHC II immunogenicity shapes the neoepitope landscape in human tumors

2023/11/28 Single-cell 23-32 IS Choi

Hallmarks of transcriptional intratumour heterogeneity across a thousand tumours

2023/11/21 Single-cell 23-31 JW Yu

Pan-cancer classification of single cells in the tumour microenvironment

2023/10/31 Single-cell 23-30 JH Cha

Single-cell mapping of combinatorial target antigens for CAR switches using logic gates

2023/10/24 Single-cell 23-29 SB Baek

Comparative analysis of cell–cell communication at single-cell resolution

2023/09/26 Single-cell 23-28 EJ Sung

Phenotypic diversity of T cells in human primary and metastatic brain tumors revealed by multiomic interrogation

2023/09/19 Single-cell 23-27 IS Choi

An integrated tumor, immune and microbiome atlas of colon cancer

2023/09/12 Single-cell 23-26 SB Baek

Multi-omic single-cell velocity models epigenome–transcriptome interactions and improves cell fate prediction

2023/09/05 Single-cell 23-25 JH Cha

Recruitment of epitope-specific T cell clones with a low-avidity threshold supports efficacy against mutational escape upon re-infection



2023-2 Microbiome
Date Team Paper
index 		Presenter Paper title
2024/02/21 Microbiome 23-66 HJ Kim

Phages are unrecognized players in the ecology of the oral pathogen Porphyromonas gingivalis

2024/02/21 Microbiome 23-65 JH Cha

A predicted CRISPR-mediated symbiosis between uncultivated archaea

2024/02/14 Microbiome 23-64 SH Ahn

Integrating compositional and functional content to describe vaginal microbiomes in health and disease

2024/02/14 Microbiome 23-63 JY Ma

Contamination source modeling with SCRuB improves cancer phenotype prediction from microbiome data

2024/02/07 Microbiome 23-62 NY Kim

Mapping the T cell repertoire to a complex gut bacterial community

2024/02/07 Microbiome 23-61 YR Kim

Multi-view integration of microbiome data for identifying disease-associated modules

2024/01/24 Microbiome 23-60 JY Kim

Phage-bacteria dynamics during the first years of life revealed by trans-kingdom marker gene analysis

2024/01/24 Microbiome 23-59 WJ Kim

Multi-level analysis of the gut–brain axis shows autism spectrum disorder-associated molecular and microbial profiles

2024/01/17 Microbiome 23-58 G Koh

Metagenomic Immunoglobulin Sequencing (MIG-Seq) Exposes Patterns of IgA Antibody Binding in the Healthy Human Gut Microbiome

2024/01/17 Microbiome 23-57 SH Ahn

Impact of dietary interventions on pre-diabetic oral and gut microbiome, metabolites and cytokines

2024/01/10 Microbiome 23-56 HJ Kim

Fast and robust metagenomic sequence comparison through sparse chaining with skani

2024/01/10 Microbiome 23-55 JY Ma

Bacterial SNPs in the human gut microbiome associate with host BMI

2023/01/03 Microbiome 23-54 JH Cha

Multimodal metagenomic analysis reveals microbial single nucleotide variants as superior biomarkers for early detection of colorectal cancer

2023/01/03 Microbiome 23-53 NY Kim

Multi-kingdom gut microbiota analyses define bacterial-fungal interplay and microbial markers of pan-cancer immunotherapy across cohorts

2023/12/27 Microbiome 23-52 YR Kim

Prior antibiotic administration disrupts anti-PD-1 responses in advanced gastric cancer by altering the gut microbiome and systemic immune response

2023/12/27 Microbiome 23-51 JY Kim

Ultra-deep sequencing of Hadza hunter-gatherers recovers vanishing gut microbes

2023/12/13 Microbiome 23-50 WJ Kim

Altered infective competence of the human gut microbiome in COVID-19

2023/12/13 Microbiome 23-49 G Koh

Host-Variable-Embedding Augmented Microbiome-Based Simultaneous Detection of Multiple Diseases by Deep Learning

2023/12/06 Microbiome 23-48 SH Ahn

A data-driven approach for predicting the impact of drugs on the human microbiome

2023/12/06 Microbiome 23-47 HJ Kim

Activation of programmed cell death and counter-defense functions of phage accessory genes

2023/11/29 Microbiome 23-46 JH Cha

Top-down identification of keystone taxa in the microbiome

2023/11/29 Microbiome 23-45 JY Ma

Pitfalls of genotyping microbial communities with rapidly growing genome collections

2023/11/22 Microbiome 23-44 NY Kim

Reconstruction of the last bacterial common ancestor from 183 pangenomes reveals a versatile ancient core genome

2023/11/22 Microbiome 23-43 SH Lee

Generation of accurate, expandable phylogenomic trees with uDance

2023/11/08 Microbiome 23-42 WJ Kim

Phage display sequencing reveals that genetic, environmental, and intrinsic factors influence variation of human antibody epitope repertoire

2023/11/08 Microbiome 23-41 JY Kim

Phage-display immunoprecipitation sequencing of the antibody epitope repertoire in inflammatory bowel disease reveals distinct antibody signatures

2023/11/01 Microbiome 23-40 G Koh

Consistency across multi-omics layers in a drug-perturbed gut microbial community

2023/11/01 Microbiome 23-39 HJ Kim

Identification of mobile genetic elements with geNomad

2023/10/25 Microbiome 23-38 SH Ahn

Microbiome-derived cobalamin and succinyl-CoA as biomarkers for improved screening of anal cancer

2023/10/11 Microbiome 23-37 JH Cha

The airway microbiome mediates the interaction between environmental exposure and respiratory health in humans

2023/10/11 Microbiome 23-36 JY Ma

Ordering taxa in image convolution networks improves microbiome-based machine learning accuracy

2023/09/27 Microbiome 23-35 NY Kim

The defensome of complex bacterial communities

2023/09/27 Microbiome 23-34 SH Lee

Dietary tryptophan metabolite released by intratumoral Lactobacillus reuteri facilitates immune checkpoint inhibitor treatment

2023/09/20 Microbiome 23-33 WJ Kim

Toward an improved definition of a healthy microbiome for healthy aging

2023/09/20 Microbiome 23-32 JY Kim

Associations of the skin, oral and gut microbiome with aging, frailty and infection risk reservoirs in older adults

2023/09/13 Microbiome 23-31 G Koh

Statistical modeling of gut microbiota for personalized health status monitoring

2023/09/13 Microbiome 23-30 SH Ahn

Adjusting for age improves identification of gut microbiome alterations in multiple diseases

2023/09/06 Microbiome 23-29 HJ Kim

Centenarians have a diverse gut virome with the potential to modulate metabolism and promote healthy lifespan

2023/09/06 Microbiome 23-28 JH Cha

Longitudinal comparison of the developing gut virome in infants and their mothers



2023-1 ADVANCED MICROBIOME DATA ANALYSIS
Date Team Paper
index 		Presenter Paper title
2023/06/13 Microbiome 23-24 JY Kim

Structure of the Mucosal and Stool Microbiome in Lynch Syndrome

2023/06/13 Microbiome 23-23 WJ Kim

Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases

2023/06/13 Microbiome 23-22 SH Ahn

Roles of oral microbiota and oral-gut microbial transmission in hypertension

2023/05/30 Microbiome 23-21 SY Lim

Human gut microbiota stimulate defined innate immune responses that vary from phylum to strain

2023/05/30 Microbiome 23-20 BS Kim

Gut microbial metabolism of 5-ASA diminishes its clinical efficacy in inflammatory bowel disease

2023/05/30 Microbiome 23-19 JY Kim

Deficient butyrate-producing capacity in the gut microbiome is associated with bacterial network disturbances and fatigue symptoms in ME/CFS

2023/05/23 Microbiome 23-18 EJ Sung

Gut microbiota-mediated nucleotide synthesis attenuates the response to neoadjuvant chemoradiotherapy in rectal cancer

2023/05/23 Microbiome 23-17 G Koh

Multi-omics reveal microbial determinants impacting responses to biologic therapies in inflammatory bowel disease

2023/05/23 Microbiome 23-16 SH Lee

Identification of trypsin-degrading commensals in the large intestine

2023/05/16 Microbiome 23-15 JP Hong

Questioning the fetal microbiome illustrates pitfalls of low-biomass microbial studies

2023/05/16 Microbiome 23-14 MR Jang

Tissue-resident Lachnospiraceae family bacteria protect against colorectal carcinogenesis by promoting tumor immune surveillance

2023/05/16 Microbiome 23-13 JW Yu

Pan-cancer analyses reveal cancer-type-specific fungal ecologies and bacteriome interactions

2023/05/09 Microbiome 23-12 NY Kim

A pan-cancer mycobiome analysis reveals fungal involvement in gastrointestinal and lung tumors

2023/05/09 Microbiome 23-11 HR Shin

Multi-kingdom microbiota analyses identify bacterial–fungal interactions and biomarkers of colorectal cancer across cohorts

2023/05/09 Microbiome 23-10 SG Oh

The antitumour effects of caloric restriction are mediated by the gut microbiome

2023/05/02 Microbiome 23-9 WJ Kim

Variability of strain engraftment and predictability of microbiome composition after fecal microbiota transplantation across different diseases

2023/05/02 Microbiome 23-8 SM Han

Drivers and determinants of strain dynamics following fecal microbiota transplantation

2023/05/02 Microbiome 23-7 YY Kim

Mobile genetic elements from the maternal microbiome shape infant gut microbial assembly and metabolism

2023/04/18 Microbiome 23-6 SH Heo

Mother-to-infant microbiota transmission and infant microbiota development across multiple body sites

2023/04/18 Microbiome 23-5 SY Yang

Population-level impacts of antibiotic usage on the human gut microbiome

2023/04/18 Microbiome 23-4 YH Yoon

Enterococci enhance Clostridioides difficile pathogenesis

2023/04/11 Microbiome 23-3 DH Lee

Targeting keystone species helps restore the dysbiosis of butyrate‐producing bacteria in nonalcoholic fatty liver disease

2023/04/11 Microbiome 23-2 YJ Roh

Differential Oral Microbial Input Determines Two Microbiota Pneumo-Types Associated with Health Status

2023/04/11 Microbiome 23-1 SH Ahn

Gut microbiome of multiple sclerosis patients and paired household healthy controls reveal associations with disease risk and course



2023-1 scOmics
Date Team Paper
index 		Presenter Paper title
2023/08/30 Single-cell 23-24 JW Yu

Decoupling the correlation between cytotoxic and exhausted T lymphocyte transcriptomic signatures enhances melanoma immunotherapy response prediction from tumor expression

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

A single-cell atlas of glioblastoma evolution under therapy reveals cell-intrinsic and cell-extrinsic therapeutic targets

2023/07/26 Single-cell 23-21 G Koh

Single-cell meta-analyses reveal responses of tumor-reactive CXCL13+ T cells to immune-checkpoint blockade

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

Self-supervised graph representation learning integrates multiple molecular networks and decodes gene-disease relationships

2023/06/21 Single-cell 23-16 IS Choi

High-resolution single-cell atlas reveals diversity and plasticity of tissue-resident neutrophils in non-small cell lung cancer

2023/06/14 Single-cell 23-15 G Koh

Parallel single-cell and bulk transcriptome analyses reveal key features of the gastric tumor microenvironment

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

Temporal single-cell tracing reveals clonal revival and expansion of precursor exhausted T cells during anti-PD-1 therapy in lung cancer

2023/05/17 Single-cell 23-12 SB Baek

Integrative single-cell analysis of cardiogenesis indentifies developmental trajectories and non-conding mutations in congenital heart disease

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

Cancer cell states recur across tumor types and form specific interactions with the tumor microenvironment

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

Pre-encoded responsiveness to type I interferon in the peripheral immune system defines outcome of PD1 blockade therapy

2023/02/21 Single-cell 23-6 SB Baek

Integrated single-cell profiling dissects cell-state-specific enhancer landscapes of human tumor-infiltrating T cells

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

Single-nucleus and spatial transcriptome profiling of pancreatic cancer identifies multicellular dynamics associated with neoadjuvant treatment

2023/01/25 Single-cell 23-3 G Koh

Cross-tissue, single-cell stromal atlas identifies shared pathological fibroblast phenotypes in four chronic inflammatory diseases

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

Tissue-resident memory and circulating T cells are early responders to pre-surgical cancer immunotherapy


2023-1 Microbiome
Date Team Paper
index 		Presenter Paper title
2023/08/25 Microbiome 23-27 JY Ma

Enterosignatures define common bacterial guilds in the human gut microbiome

2023/08/25 Microbiome 23-26 NY Kim

PhyloMed: a phylogeny-based test of mediation effect in microbiome

2023/08/18 Microbiome 23-25 SH Lee

The TaxUMAP atlas: Efficient display of large clinical microbiome data reveals ecological competition in protection against bacteremia

2023/08/18 Microbiome 23-24 WJ Kim

Measurement of bacterial replication rates in microbial communities

2023/08/11 Microbiome 23-23 JY Kim

Skin microbiome diferentiates into distinct cutotypes with unique metabolic functions upon exposure to polycyclic aromatic hydrocarbons

2023/08/11 Microbiome 23-22 SH Ahn

Enrichment of oral-derived bacteria in inflamed colorectal tumors and distinct associations of Fusobacterium in the mesenchymal subtype

2023/08/04 Microbiome 23-21 HJ Kim

Profiling the human intestinal environment under physiological conditions

2023/07/28 Microbiome 23-20 JH Cha

Genome-centric metagenomics reveals the host-driven dynamics and ecological role of CPR bacteria in an activated sludge system

2023/07/14 Microbiome 23-19 JY Ma

Enhanced metagenomic deep learning for disease prediction and consistent signature recognition by restructured microbiome 2D representations

2023/07/07 Microbiome 23-18 NY Kim

Gene fow and introgression are pervasive forces shaping the evolution of bacterial species

2023/06/30 Microbiome 23-17 JH Cha

Alterations of oral microbiota and impact on the gut microbiome in type 1 diabetes mellitus revealed by integrated multi‑omic analyses

2023/06/23 Microbiome 23-16 HJ Kim

Deciphering microbial gene function using natural language processing

2023/06/16 Microbiome 23-15 SH Lee

Rethinking bacterial relationships in light of their molecular abilities

2023/06/02 Microbiome 23-14 JY Ma

The CD4+ T cell response to a commensal-derived epitope transitions from a tolerant to an inflammatory state in Crohn’s disease

2023/05/26 Microbiome 23-13 NY Kim

Antigen discovery and specification of immunodominance hierarchies for MHCIIrestricted epitopes

2023/05/19 Microbiome 23-12 SH Lee

Single Cell Transcriptomics Reveals the Hidden Microbiomes of Human Tissues

2023/05/12 Microbiome 23-11 JY Kim

Stability of the human faecal microbiome in a cohort of adult men

2023/04/28 Microbiome 23-10 WJ Kim

Metatranscriptome of human faecal microbial communities in a cohort of adult men

2023/03/24 Microbiome 23-9 SH Ahn

Tumor microbiome links cellular programs and immunity in pancreatic cancer

2023/03/17 Microbiome 23-8 HJ Kim

Extensive gut virome variation and its associations with host and environmental factors in a population-level cohort

2023/03/10 Microbiome 23-7 JH Cha

The person-to-person transmission landscape of the gut and oral microbiomes

2023/02/21 Microbiome 23-6 JY Ma

BIRDMAn: A Bayesian differential abundance framework that enables robust inference of host-microbe associations

2023/02/14 Microbiome 23-5 NY Kim

Phage–host coevolution in natural populations

2023/01/31 Microbiome 23-4 SH Lee

A randomized controlled trial for response of microbiome network to exercise and diet intervention in patients with nonalcoholic fatty liver disease

2023/01/25 Microbiome 23-3 SH Ahn

Scalable power analysis and effect size exploration of microbiome community differences with Evident

2023/01/17 Microbiome 23-2 HJ Kim

Phanta: Phage-inclusive profiling of human gut metagenomes

2023/01/11 Microbiome 23-1 JH Cha

Computational approach to modeling microbiome landscapes associated with chronic human disease progression


2022 scOmics
Date Team Paper
index 		Presenter Paper title
2022/12/28 Single-cell 22-32 EJ Sung

SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks

2022/11/29 Single-cell 22-31 JH Cha, SB Baek, IS Choi

Representation learning of RNA velocity reveals robust cell transitions UniTVelo: temporally unified RNA velocity reinforces single-cell trajectory inference Mapping transcriptomic vector fields of single cells

2022/11/22 Single-cell 22-30 IS Choi

Network-based machine learning approach to predict immunotherapy response in cancer patients

2022/11/08 Single-cell 22-29 SB Baek

Modeling fragment counts improves single-cell ATAC-seq analysis

2022/10/11 Single-cell 22-28 G Koh

Extricating human tumour immune alterations from tissue inflammation

2022/09/13 Single-cell 22-25 JW Yu

T cell receptor convergence is an indicator of antigen-specific T cell response in cancer immunotherapies

2022/09/06 Single-cell 22-26 JH Cha

Identifying phenotype-associated subpopulations by integrating bulk and single-cell sequencing data

2022/09/01 Single-cell 22-27 SB Baek

MIRA: Joint regulatory modeling of multimodal expression and chromatin accessibility in single cells

2022/08/25 Single-cell 22-24 EJ Sung

Immune phenotypic linkage between colorectal cancer and liver metastasis

2022/08/18 Single-cell 22-23 IS Choi

Biologically informed deep learning to infer gene program activity in single cells

2022/08/11 Single-cell 22-22 SB Baek

Cell type and gene expression deconvolution with BayesPrism enables Bayesian integrative analysis across bulk and single-cell RNA sequencing in oncology

2022/07/28 Single-cell 22-21 JH Cha

Mapping single-cell data to reference atlases by transfer learning

2022/07/21 Single-cell 22-20 JW Yu

Pan-cancer mapping of single T cell profiles reveals a TCF1:CXCR6-CXCL16 regulatory axis essential for effective anti-tumor immunity

2022/07/14 Single-cell 22-19 IS Choi

Identifying disease-critical cell types and cellular processes across the human body by integration of single-cell profiles and human genetics

2022/07/07 Single-cell 22-18 EJ Sung

Metacells untangle large and complex single-cell transcriptome networks MetaCell: analysis of single-cell RNA-seq data using K-nn graph partitions Metacell‑2: a divide‑and‑conquer metacell algorithm for scalable scRNA‑seq analysis

2022/06/23 Single-cell 22-17 SB Baek

Co-varying neighborhood analysis identifies cell populations associated with phenotypes of interest from single-cell transcriptomics

2022/06/09 Single-cell 22-16 JH Cha

Integrating T cell receptor sequences and transcriptional profiles by clonotype neighbor graph analysis (CoNGA)

2022/06/02 Single-cell 22-15 JW Yu

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

2022/05/19 Single-cell 22-14 EJ Sung

Effect of imputation on gene network reconstruction from single-cell RNA-seq data

2022/05/12 Single-cell 22-13 IS Choi

Diagnostic Evidence GAuge of Single cells (DEGAS): a flexible deep transfer learning framework for prioritizing cells in relation to disease

2022/04/07 Single-cell 22-12 SB Baek

Integrating single-cell sequencing data with GWAS summary statistics reveals CD16+monocytes and memory CD8+T cells involved in severe COVID-19

2022/03/25 Single-cell 22-11 JH Cha

Single cell T cell landscape and T cell receptor repertoire profiling of AML in context of PD-1 blockade therapy

2022/03/18 Single-cell 22-10 JW Yu

Systematic investigation of cytokine signaling activity at the tissue and single-cell levels

2022/03/04 Single-cell 22-9 EJ Sung

Neoantigen-driven B cell and CD4 T follicular helper cell collaboration promotes anti-tumor CD8 T cell responses

2022/02/25 Single-cell 22-8 SB Baek

MultiMAP: dimensionality reduction and integration of multimodal data

2022/02/18 Single-cell 22-7 IS Choi

CellRank for directed single-cell fate mapping

2022/02/11 Single-cell 22-6 JH Cha

Single-cell analyses reveal key immune cell subsets associated with response to PD-L1 blockade in triple-negative breast cancer

2022/02/04 Single-cell 22-5 IS Choi

Chromatin and gene-regulatory dynamics of the developing human cerebral cortex at single-cell resolution

2022/01/28 Single-cell 22-4 EJ Sung

Single-cell analysis of human non-small cell lung cancer lesions refines tumor classification and patient stratification

2022/01/28 Single-cell 22-3 JH Cha

Pan-cancer single-cell landscape of tumor-infiltrating T cells

2022/01/14 Single-cell 22-2 JW Yu

Atlas of clinically distinct cell states and ecosystems across human solid tumors

2022/01/07 Single-cell 22-1 SB Baek

Single-cell ATAC and RNA sequencing reveal pre-existing and persistent cells associated with prostate cancer relapse


2022 Microbiome
Date Team Paper
index 		Presenter Paper title
2022/12/28 Microbiome 22-34 JY Ma

A novel in silico method employs chemical and protein similarity algorithms to accurately identify chemical transformations in the human gut microbiome

2022/11/29 Microbiome 22-33 NY Kim

Inference of disease-associated microbial biomarkers based on metagenomic and metatranscriptomic data

2022/11/22 Microbiome 22-32 SH Lee

Personalized microbiome-driven effects of non-nutritive sweeteners on human glucose tolerance

2022/10/11 Microbiome 22-31 SH Ahn

Identification of shared and disease-specific host gene–microbiome associations across human diseases using multi-omic integration

2022/09/27 Microbiome 22-30 HJ Kim

Caudovirales bacteriophages are associated with improved executive function and memory in flies, mice, and humans

2022/09/13 Microbiome 22-29 JH Cha

SynTracker: a synteny based tool for tracking microbial strains

2022/09/06 Microbiome 22-28 JY Ma

Identification of antimicrobial peptides from the human gut microbiome using deep learning

2022/09/01 Microbiome 22-27 NY Kim

Discovery of bioactive microbial gene products in inflammatory bowel disease

2022/08/25 Microbiome 22-26 SH Lee

Large-scale microbiome data integration enables robust biomarker identification

2022/08/18 Microbiome 22-25 SH Ahn

Predicting cancer prognosis and drug response from the tumor microbiome

2022/08/11 Microbiome 22-24 HJ Kim

Thousands of small, novel genes predicted in global phage genomes

2022/08/04 Microbiome 22-23 JH Cha

MetaPop: a pipeline for macro- and microdiversity analyses and visualization of microbial and viral metagenome-derived populations

2022/07/28 Microbiome 22-22 NY Kim

Biosynthetic potential of the global ocean microbiome

2022/07/14 Microbiome 22-21 JY Ma

Compositionally Aware Phylogenetic Beta-Diversity Measures Better Resolve Microbiomes Associated with Phenotype

2022/07/07 Microbiome 22-20 SH Lee

Tutorial: assessing metagenomics software with the CAMI benchmarking toolkit disease Critical Assessment of Metagenome Interpretation: the second round of challenges

2022/06/24 Microbiome 22-19 SH Ann

Identification of Faecalibacterium prausnitzii strains for gut microbiome-based intervention in Alzheimer’s-type dementia

2022/06/10 Microbiome 22-18 HJ Kim

Microbiome and metabolome features of the cardiometabolic disease spectrum

2022/06/03 Microbiome 22-17 JH Cha

Functional and genetic markers of niche partitioning among enigmatic members of the human oral microbiome

2022/05/27 Microbiome 22-16 NY Kim

Integrating phylogenetic and functional data in microbiome studies

2022/05/20 Microbiome 22-15 MY Ma

Pandora: nucleotide-resolution bacterial pan-genomics with reference graphs

2022/05/13 Microbiome 22-14 SH Lee

Multivariable association discovery in population-scale meta-omics studies

2022/04/08 Microbiome 22-13 SH Ahn

Comprehensive Analysis Reveals the Evolution and Pathogenicity of Aeromonas, Viewed from Both Single Isolated Species and Microbial Communities

2022/04/01 Microbiome 22-12 HJ Kim

AGAMEMNON: an Accurate metaGenomics And MEtatranscriptoMics quaNtificatiON analysis suite

2022/03/18 Microbiome 22-11 JH Cha

Metapangenomics of the oral microbiome provides insights into habitat adaptation and cultivar diversity

2022/03/04 Microbiome 22-10 JY Ma

Microbiota of the prostate tumor environment investigated by whole-transcriptome profiling

2022/02/25 Microbiome 22-9 NY Kim

Species-resolved sequencing of low-biomass or degraded microbiomes using 2bRAD-M

2022/02/18 Microbiome 22-8 SH Lee

Microbial co-occurrence complicates associations of gut microbiome with US immigration, dietary intake and obesity

2022/02/11 Microbiome 22-7 SH Ahn

Gut microbial determinants of clinically important improvement in patients with rheumatoid arthritis

2022/02/04 Microbiome 22-6 JH Cha

Gut microbiota modulates weight gain in mice after discontinued smoke exposure

2022/01/28 Microbiome 22-5 JY Ma

The human microbiome encodes resistance to the antidiabetic drug acarbose

2022/01/28 Microbiome 22-4 SH Lee

Commensal bacteria promote endocrine resistance in prostate cancer through androgen biosynthesis

2022/01/14 Microbiome 22-3 HJ Kim

The influence of the gut microbiome on BCG-induced trained immunity

2022/01/07 Microbiome 22-2 JY Ma

Towards the biogeography of prokaryotic genes

2022/01/07 Microbiome 22-1 NY Kim

ReprDB and panDB: minimalist databases with maximal microbial representation


2022 Microbiome Special JC
Date Team Paper
index 		Presenter Paper title
2022/08/30 Microbiome 22-15 HY Kang

Maast: genotyping thousands of microbial strains efficiently

2022/08/30 Microbiome 22-14 YJ Roh

MIDAS2: Metagenomic Intra-species Diversity Analysis System

2022/08/30 Microbiome 22-13 SC Yang

Scalable microbial strain inference in metagenomic data using StrainFacts

2022/08/26 Microbiome 22-12 SH Ahn

StrainPanDA: linked reconstruction of strain composition and gene content profiles via pangenome-based decomposition of metagenomic data

2022/08/26 Microbiome 22-11 HJ Kim

Metagenomic strain detection with SameStr: identification of a persisting core gut microbiota transferable by fecal transplantation

2022/08/26 Microbiome 22-10 JY Ma

Fast and accurate metagenotyping of the human gut microbiome with GT-Pro

2022/08/19 Microbiome 22-9 JH Cha

inStrain profiles population microdiversity from metagenomic data and sensitively detects shared microbial strains

2022/08/19 Microbiome 22-8 NY Kim

Longitudinal linked-read sequencing reveals ecological and evolutionary responses of a human gut microbiome during antibiotic treatment

2022/08/19 Microbiome 22-7 SH Lee

Dispersal strategies shape persistence and evolution of human gut bacteria

2022/08/09 Microbiome 22-6 SH Ahn

The long-term genetic stability and individual specificity of the human gut microbiome

2022/08/09 Microbiome 22-5 HJ Kim

Analysis of 1321 Eubacterium rectale genomes from metagenomes uncovers complex phylogeographic population structure and subspecies functional adaptations

2022/08/09 Microbiome 22-4 JY Ma

Evolutionary dynamics of bacteria in the gut microbiome within and across hosts

2022/07/29 Microbiome 22-3 JH Cha

Extensive transmission of microbes along the gastrointestinal tract

2022/07/29 Microbiome 22-2 NY Kim

Distinct Genetic and Functional Traits of Human Intestinal Prevotella copri Strains Are Associated with Different Habitual Diets

2022/07/29 Microbiome 22-1 SH Lee

Strain-level microbial epidemiology and population genomics from shotgun metagenomics


2021-2nd semester
Date Team Paper
index 		Presenter Paper title
2021/11/23 Single-cell 21-39 IS Choi

Chromatin and gene-regulatory dynamics of the developing human cerebral cortex at single-cell resolution

2021/11/16 Single-cell 21-38 SB Back

Single-cell ATAC and RNA sequencing reveal pre-existing and persistent subpopulations of cells associated with relapse of prostate cancer

2021/11/09 Single-cell 21-37 JH Cha

Integrated single-cell transcriptomics and epigenomics reveals strong germinal center-associated etiology of autoimmune risk loci

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

Single-cell analyses reveal a continuum of cell state and composition changes in the malignant transformation of polyps to colorectal cancer

2021/10/19 Single-cell 21-34 JH Cha

Single-cell sequencing links multiregional immune landscapes and tissue-resident T cells in ccRCC to tumor topology and therapy efficacy

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

Single-cell chromatin accessibility landscape identifies tissue repair program in human regulatory T cells

2021/09/14 Single-cell 21-31 IS Choi

Identification of resistance pathways and therapeutic targets in relapsed multiple myeloma patients through single-cell sequencing

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

2021-1st semester
Date Team Paper
index 		Presenter Paper title
2021/06/03 - 21-27 HJ Kim

Massive expansion of human gut bacteriophage diversity

21-26 JY Ma

The infant gut resistome associates with E. coli, environmental exposures, gut microbiome maturity, and asthma-associated bacterial composition

2021/05/27 - 21-25 JK Yoon

Methotrexate impacts conserved pathways in diverse human gut bacteria leading to decreased host immune activation

2021/05/20 - 21-24 NY Kim

A metagenomic strategy for harnessing the chemical repertoire of the human microbiome

21-23 IS Choi

Predictive metabolomic profiling of microbial communities using amplicon or metagenomic sequences

2021/05/13 - 21-22 SA Kim

Gut microbiome structure and metabolic activity in inflammatory bowel disease

21-21 HJ Kim

Microbiome connections with host metabolism and habitual diet from 1,098 deeply phenotyped individuals

2021/05/06 - 21-20 JY Ma

A predictive index for health status using species-level gut microbiome profiling

21-19 JH Cha

Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases

2021/04/29 - 21-18 SB Baek

Gastrointestinal microbiota composition predicts peripheral inflammatory state during treatment of human tuberculosis

21-17 SR You

Structure of the Mucosal and Stool Microbiome in Lynch Syndrome

2021/04/22 - 21-16 HH Eom

Cross-reactivity between tumor MHC class 1-restricted antigens and an enterococcal bacteriophage

21-15 JH Park

Bifidobacterium bifidum strains synergize with immune checkpoint inhibitors to reduce tumour burden in mice

2021/04/15 - 21-14 MH Lee

The gut microbiome modulates the protective association between a Mediterranean diet and cardiometabolic disease risk

2021/04/08 - 21-13 YY Jang

Impact of commonly used drugs on the composition and metabolic function of the gut microbiota

2021/04/01 - 21-12 NY Kim

Personalized Mapping of Drug Metabolism by the Human Gut Microbiome

2021/03/25 - 21-11 JM Lee

Mining the Human Gut Microbiota for Immunomodulatory Organisms

2021/03/18 - 21-10 JH Cha

Microbiome analyses of blood and tissues suggest cancer diagnostic approach

2021/03/11 - 21-9 JH Cha

The human tumor microbiome is composed of tumor type-specific intracellular bacteria

2021
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

Single-cell epigenomic analyses implicate candidate causal variants at inherited risk loci for Alzheimer’s and Parkinson’s diseases

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

Single-Cell Analysis of Crohn's Disease Lesions Identifies a Pathogenic Cellular Module Associated with Resistance to Anti-TNF Therapy

21-1 JW Cho

Multi-omic single-cell snapshots reveal multiple independent trajectories to drug tolerance in a melanoma cell line

2020-1st semester
Date Team Paper
index 		Presenter Paper title
2021/02/01 - 20-15 JW Cho

Multi-omic single-cell snapshots reveal multiple independent trajectories to drug tolerance in a melanoma cell line

20-14 JW Choi

Dysfunctional CD8 T Cells Form a Proliferative, Dynamically Regulated Compartment Within Human Melanoma

20-13 JW Seo

A Cancer Cell Program Promotes T Cell Exclusion and Resistance to Checkpoint Blockade

2020/06/09 - 20-12 JY Lee

Single-cell Transcriptional Diversity Is a Hallmark of Developmental Potential

20-11 JH Kim

Landscape and Dynamics of Single Immune Cells in Hepatocellular Carcinoma

2020/06/02 - 20-10 HY Seo

Peripheral T Cell Expansion Predicts Tumour Infiltration and Clinical Response

20-9 KH Hong

Defining T Cell States Associated With Response to Checkpoint Immunotherapy in Melanoma

2020/05/26 - 20-8 JY Seong

Rapid Non-Uniform Adaptation to Conformation-Specific KRAS(G12C) Inhibition

20-7 OY Min

Targeted Therapy Guided by Single-Cell Transcriptomic Analysis in Drug-Induced Hypersensitivity Syndrome: A Case Report

2020/05/19 - 20-6 SN Lee

Distinct Microbial and Immune Niches of the Human Colon

20-5 DJ Park

Massively Parallel Single-Cell Chromatin Landscapes of Human Immune Cell Development and Intratumoral T Cell Exhaustion

2020/05/12 - 20-4 SY Park

Single-cell Gene Expression Reveals a Landscape of Regulatory T Cell Phenotypes Shaped by the TCR

20-3 NY Kim

A Single-Cell Atlas of In Vivo Mammalian Chromatin Accessibility

2020/04/28 Single-cell 20-2 SB Baek

scAI: An Unsupervised Approach for the Integrative Analysis of Parallel Single-Cell Transcriptomic and Epigenomic Profiles

20-1 JH Cha

Single-cell Multiomic Analysis Identifies Regulatory Programs in Mixed-Phenotype Acute Leukemia

2019-2nd semester
Date Team Paper
index 		Presenter Paper title
2019/10/15 Microbiome 19-51 CY Kim

Obese Individuals with and without Type 2 Diabetes Show Different Gut Microbial Functional Capacity and Composition

19-50 SH Lee

Clustering co-abundant genes identifies components of the gut microbiome that are reproducibly associated with colorectal cancer and inflammatory bowel disease

2019/10/08 Single-cell 19-49 SB Baek

Massively parallel single-cell chromatin landscapes of human immune cell development and intratumoral T cell exhaustion

19-48 SB Baek

Assessment of computational methods for the analysis of single-cell ATAC-seq data

2019/10/01 Microbiome 19-47 MY Lee

Meta-Analysis Reveals Reproducible Gut Microbiome Alterations in Response to a High-Fat Diet

19-46 NY Kim

Tumor Microbiome Diversity and Composition Influence Pancreatic Cancer Outcomes

2019/09/24 Single-cell 19-45 JH Cha

The accessible chromatin landscape of the murine hippocampus at single-cell resolution

19-44 SB Baek

Integrated Single-Cell Analysis Maps the Continuous Regulatory Landscape of Human Hematopoietic Differentiation

2019/09/17 Microbiome 19-43 SH Lee

A sparse covarying unit that describes healthy and impaired human gut microbiota development

19-42 CY Kim

Large-Scale Analyses of Human Microbiomes Reveal Thousands of Small, Novel Genes

2019/09/10 Single-cell 19-41 KS Kim

Intra- and Inter-cellular Rewiring of the Human Colon during Ulcerative Colitis

19-40 IS Choi

Multiplexed droplet single-cell RNA-sequencing using natural genetic variation

2019/09/03 Microbiome 19-39 NY Kim

The Landscape of Genetic Content in the Gut and Oral Human Microbiome

19-38 CY Kim

Benchmarking Metagenomics Tools for Taxonomic Classification

2019
Date Paper
index 		Presenter Paper title
2019/08/20 19-37 JH Cha

Coexpression uncovers a unified single-cell transcriptomic landscape

19-36 MY Lee

Single-cell interactomes of the human brain reveal cell-type specific convergence of brain disorders

2019/08/14 19-35 SH Lee

Proportionality: a valid alternative to correlation for relative data

19-34 SH Lee

propr: An R-package for Identifying Proportionally Abundant Features Using Compositional Data Analysis

19-33 HJ Han

Robust prediction of response to immune checkpoint blockade therapy in metastatic melanoma

2019/08/13 19-32 KS Kim

A test metric for assessing single-cell RNA-seq batch correction

19-31 KS Kim

Comprehensive Integration of Single-Cell Data

2019/08/08 19-30 JH Cha

Defining T Cell States Associated with Response to Checkpoint Immunotherapy in Melanoma

19-29 KS Kim

High-Dimensional Analysis Delineates Myeloid and Lymphoid Compartment Remodeling during Successful Immune-Checkpoint Cancer Therapy

2019/08/07 19-28 HJ Han

A single-cell reference map for human blood and tissue T cell activation reveals functional states in health and disease

19-27 IS Choi

Dysfunctional CD8 T Cells Form a Proliferative, Dynamically Regulated Compartment within Human Melanoma

2019/07/30 19-26 IS Choi

High-dimensional single-cell analysis predicts response to anti-PD-1 immunotherapy

19-25 JW Cho

A Cancer Cell Program Promotes T Cell Exclusion and Resistance to Checkpoint Blockade

2019/07/23 19-24 HJ Han

COMPASS identifies T-cell subsets correlated with clinical outcomes.

19-23 HJ Han

Sensitive detection of rare disease-associated cell subsets via representation learning

2019/05/30 19-22

Meta-analysis of fecal metagenomes reveals global microbial signatures that are specific for colorectal cancer

19-21

Metagenomic analysis of colorectal cancer datasets identifies cross-cohort microbial diagnostic signatures and a link with choline degradation

19-20

Microbial network disturbances in relapsing refractory Crohn's disease.

2019/05/23 19-19

New insights from uncultivated genomes of the global human gut microbiome

19-18

A new genomic blueprint of the human gut microbiota

19-17

Extensive Unexplored Human Microbiome Diversity Revealed by Over 150,000 Genomes from Metagenomes Spanning Age, Geography, and Lifestyle.

2019/05/16 19-16

Dynamics of metatranscription in the inflammatory bowel disease gut microbiome.

19-15

Metatranscriptome of human faecal microbial communities in a cohort of adult men.

2019/05/09 19-14

Post-Antibiotic Gut Mucosal Microbiome Reconstitution Is Impaired by Probiotics and Improved by Autologous FMT.

19-13

Personalized Gut Mucosal Colonization Resistance to Empiric Probiotics Is Associated with Unique Host and Microbiome Features.

2019/05/02 19-12

Distinct Immune Cell Populations Define Response to Anti-PD-1 Monotherapy and Anti-PD-1/Anti-CTLA-4 Combined Therapy.

19-11

Subsets of exhausted CD8+ T cells differentially mediate tumor control and respond to checkpoint blockade.

2019/4/11 19-10

Lineage tracking reveals dynamic relationships of T cells in colorectal cancer.

19-9

Single cell dissection of plasma cell heterogeneity in symptomatic and asymptomatic myeloma.

2019/4/4 19-8

Single-cell profiling of breast cancer T cells reveals a tissue-resident memory subset associated with improved prognosis.

19-7

Global characterization of T cells in non-small-cell lung cancer by single-cell sequencing

2019/3/28 19-6

Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors.

19-5

Landscape of Infiltrating T Cells in Liver Cancer Revealed by Single-Cell Sequencing.

2019/3/21 19-4

Phenotype molding of stromal cells in the lung tumor microenvironment.

19-3-1

A single-cell molecular map of mouse gastrulation and early organogenesis

19-3

The single-cell transcriptional landscape of mammalian organogenesis

2019/3/14 19-2

Single-Cell Map of Diverse Immune Phenotypes in the Breast Tumor Microenvironment

19-1

Single-Cell Transcriptomic Analysis of Primary and Metastatic Tumor Ecosystems in Head and Neck Cancer

2018
Date Paper
index 		Presenter Paper title
2018/06/14 18-12

A Pan-Cancer Analysis of Enhancer Expression in Nearly 9000 Patient Samples.

18-11

Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation.

2018/06/07 18-10

Developmental and oncogenic programs in H3K27M gliomas dissected by single-cell RNA-seq.

18-9

Chemoresistance Evolution in Triple-Negative Breast Cancer Delineated by Single-Cell Sequencing.

2018/05/31 18-8

Mapping human pluripotent stem cell differentiation pathways using high throughput single-cell RNA-sequencing.

18-7

A single-cell RNA-seq survey of the developmental landscape of the human prefrontal cortex.

2018/05/24 18-6 CY Kim

Single-cell RNA sequencing identifies celltype-specific cis-eQTLs and co-expression QTLs.

18-5 HJ Han

FOCS: a novel method for analyzing enhancer and gene activity patterns infers an extensive enhancer-promoter map.

2018/05/17 18-4 KS Kim

A Distinct Gene Module for Dysfunction Uncoupled from Activation in Tumor-Infiltrating T Cells.

18-3 SH Lee

A global transcriptional network connecting noncoding mutations to changes in tumor gene expression.

2018/05/10 18-2 JW Cho

Inferring regulatory element landscapes and transcription factor networks from cancer methylomes.

18-1 DS Bae

Genomic and Transcriptomic Features of Response to Anti-PD-1 Therapy in Metastatic Melanoma.

2017
Date Paper
index 		Presenter Paper title
2017/06/28 17-36 HJ Han

Tumor aneuploidy correlates with markers of immune evasion and with reduced response to immunotherapy.

17-35 JW Cho

Landscape of tumor-infiltrating T cell repertoire of human cancers.

2017/06/14 17-34 JW Cho

The landscape of T cell infiltration in human cancer and its association with antigen presenting gene expression.

17-33 MY Lee

A Distinct Gene Module for Dysfunction Uncoupled from Activation in Tumor-Infiltrating T Cells.

2017/06/07 17-32

Identification of genetic determinants of breast cancer immune phenotypes by integrative genome-scale analysis.

17-31 MY Lee

Genomic Correlates of Immune-Cell Infiltrates in Colorectal Carcinoma.

2017/05/31 17-30 DS Bae

Genomic and Transcriptomic Features of Response to Anti-PD-1 Therapy in Metastatic Melanoma.

17-29 JW Cho

Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade.

2017/05/24 17-28 EB Kim

Systemic Immunity Is Required for Effective Cencer Immunotherapy.

17-27 CY Kim

Linking the Human Gut Microbiome to Inflammatory Cytokine Production Capacity.

2017/05/17 17-26

Host and Environmental Factors Influencing Individual Human Cytokine Responses.

17-25

A Functional Genomics Approach to Understand Variation in Cytokine Production in Humans.

2017/04/26 17-24

Pooled CRISPR screening with single-cell transcriptome readout.

17-23

Dissecting Immune Circuits by Linking CRISPR-Pooled Screens with Single-Cell RNA-Seq.

2017/04/12 17-22 SH Lee

Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens.

17-21 CY Kim

Wishbone identifies bifurcating developmental trajectories from single-cell data.

2017/04/05 17-20 KS Kim

Reversed graph embedding resolves complex single-cell developmental trajectories.

17-19 SH Lee

Single-cell mRNA quantification and differential analysis with Census.

2017/03/29 17-18

Single-Cell Transcriptomic Analysis Defines Heterogeneity and Transcriptional Dynamics in the Adult Neural Stem Cell Lineage.

17-17 DS Bae

Single-Cell Network Analysis Identifies DDIT3 as a Nodal Lineage Regulator in Hematopoiesis.

2017/03/22 17-16

Single-cell analysis of mixed-lineage states leading to a binary cell fate choice.

17-15 EB Kim

Dissecting direct reprogramming from fibroblast to neuron using single-cell RNA-seq.

2017/03/15 17-14

Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq.

17-13

Single-cell mRNA sequencing identifies subclonal heterogeneity in anti-cancer drug responses of lung adenocarcinoma cells.

2017/02/28 17-12 KS Kim

A Comprehensive Characterization of the Function of LincRNAs in Transcriptional Regulation Through Long-Range Chromatin Interactions.

17-11 JE Shim

Epigenomic Deconvolution of Breast Tumors Reveals Metabolic Coupling between Constituent Cell Types.

2017/02/21 17-10 EB Kim

Convergence of dispersed regulatory mutations predicts driver genes in prostate cancer.

17-09 EB Kim

Chromatin structure-based prediction of recurrent noncoding mutations in cancer.

2017/02/07 17-08 DS Bae

Somatic Mutations and Neoepitope Homology in Melanomas Treated with CTLA-4 Blockade.

17-07 MY Lee

Chemotherapy weakly contributes to predicted neoantigen expression in ovarian cancer.

2017/01/31 17-06 CY Kim

Microbiota Diurnal Rhythmicity Programs Host Transcriptome Oscillations.

2017/01/24 17-05 JW Cho

Transcriptional Landscape of Human Tissue Lymphocytes Unveils Uniqueness of Tumor-Infiltrating T Regulatory Cells.

2017/01/17 17-04 HJ Han

Mapping long-range promoter contacts in human cells with high-resolution capture Hi-C

17-03 EB Kim

CHiCAGO: robust detection of DNA looping interactions in Capture Hi-C data.

2017/01/10 17-02 JE Shim

ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis

2017/01/03 17-01 SH Lee

Single-cell messenger RNA sequencing reveals rare intestinal cell types

2016
Date Paper
index 		Presenter Paper title
2016/12/27 2016-31 EB Kim

Decoding the regulatory network of early blood development from single-cell gene expression measurements.

2016/12/6 2016-30 KS Kim

Single-Cell RNA-Seq with Waterfall Reveals Molecular Cascades underlying Adult Neurogenesis.

2016/11/29 2016-29 DS Bae

The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells.

2016/11/22 2016-28 CY Kim

Classification of low quality cells from single-cell RNA-seq data.

2016/11/15 2016-27 MY Lee

Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells.

2016/11/8 2016-26 JW Cho

Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets.

2016/11/1 2016-25 HJ Han

Causal Mechanistic Regulatory Network for Glioblastoma Deciphered Using Systems Genetics Network Analysis.

2016/10/25 2016-24 MY Lee

Comprehensive analyses of tumor immunity: implications for cancer immunotherapy.

2016/10/11 2016-23 JE Shim

Widespread parainflammation in human cancer

2016/9/27 2016-22 ER Kim

Analysis of protein-coding genetic variation in 60,706 humans

2016/9/20 2016-21 SM Yang

Functional characterization of somatic mutations in cancer using network-based inference of protein activity pubmed fulltext

2016/9/13 2016-20 KS Kim

Exploiting single-cell expression to characterize co-expression replicability.

2016/9/6 2016-19 T Lee

Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade

2016/8/31 2016-18 DS Bae

A Computational Drug Repositioning Approach for Targeting Oncogenic Transcription Factors

2016/8/16 2016-17 JW Cho

The landscape of accessible chromatin in mammalian preimplantation embryos

2016/8/8 2016-16 EB Kim

Enhancer-promoter interactions are encoded by complex genomic signatures on looping chromatin

2016/8/1 2016-15 MY Lee

Personalized Immunomonitoring Uncovers Molecular Networks that Stratify Lupus Patients

2016/7/25 2016-14 CY Kim

Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets

2016/7/18 2016-13 HJ Han

Identification of transcriptional regulators in the mouse immune system

2016/6/8 2016-12 DS Bae, CY Kim

Mapping the effects of drugs on the immune system

2016-11 DS Bae, CY Kim

Elucidating compound mechanism of action by network perturbation analysis

2016/6/1 2016-10 MY Lee,SM Cho

Integrative approaches for large-scale transcriptome-wide association studies

2016-9 MY Lee,SM Cho

Tissue-specific regulatory circuits reveal variable modular perturbations across complex diseases

2016/5/18 2016-8 CY Kim,SJ Kwon

Survey of variation in human transcription factors reveals prevalent DNA binding changes

2016-7 CY Kim,SJ Kwon

Large-scale identification of sequence variants influencing human transcription factor occupancy in vivo

2016/5/11 2016-6 DS Bae, CY Kim

Predicting Cancer-specific vulnerability via data-driven detection of synthetic lethality

2016-5 DS Bae, CY Kim

Dynamic regulatory network controlling Th17 cell differentiation

2016/5/4 2016-4 MY Lee,SM Cho

Characterization of the immunophenotypes and antigenomes of colorectal cancers reveals distinct tumor escape mechanisms and novel targets for immunotherapy

2016-3 MY Lee,SM Cho

Regulators of genetic risk of breast cancer identified by integrative network analysis

2016/4/27 2016-2 CY Kim,SJ Kwon

A predictive computational framework for direct reprogramming between human cell types

2016-1 CY Kim,SJ Kwon

CellNet: Network biology applied to stem cell engineering

2015
Date Paper
index 		Presenter Paper title
2015/06/11 2015-55

Improved exome prioritization of disease genes through cross-species phenotype comparison.

2015-54

Phevor combines multiple biomedical ontologies for accurate identification of disease-causing alleles in single individuals and small nuclear families.

2015/06/04 2015-53

eXtasy: variant prioritization by genomic data fusion.

2015-52

A probabilistic disease-gene finder for personal genomes.

2015/05/28 2015-51

Systematic analysis of noncoding somatic mutations and gene expression alterations across 14 tumor types.

2015-50

GREAT improves functional interpretation of cis-regulatory regions.

2015/05/21 2015-49

Functional annotation of noncoding sequence variants.

2015-48

FunSeq2: A framework for prioritizing noncoding regulatory variants in cancer.

2015/05/14 2015-47

Selecting causal genes from genome-wide association studies via functionally coherent subnetworks.

2015-46

Biological interpretation of genome-wide association studies using predicted gene functions.

2015/05/07 2015-45

Human symptoms-disease network.

2015-44

A nondegenerate code of deleterious variants in Mendelian loci contributes to complex disease risk.

2015-43

Uncovering disease-disease relationships through the incomplete interactome.

2015/04/30 2015-42

The discovery of integrated gene networks for autism and related disorders.

2015-41

Integrated systems analysis reveals a molecular network underlying autism spectrum disorders.

2015/04/23 2015-40

Dissecting neural differentiation regulatory networks through epigenetic footprinting.

2015-39

Cell-of-origin chromatin organization shapes the mutational landscape of cancer.

2015-38

Integrative analysis of 111 reference human epigenomes.

2015/04/09 2015-37

Genome-wide analysis of local chromatin packing in Arabidopsis thaliana.

2015-36

A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping.

2015/04/02 2015-35

Exploring the three-dimensional organization of genomes: interpreting chromatin interaction data.

2015-34

Transcribed enhancers lead waves of coordinated transcription in transitioning mammalian cells.

2015/03/26 2015-33

Cupid: simultaneous reconstruction of microRNA-target and ceRNA networks.

2015-32

Characterization of the neural stem cell gene regulatory network identifies OLIG2 as a multifunctional regulator of self-renewal.

2015/03/19 2015-31

Decoding the regulatory network of early blood development from single-cell gene expression measurements.

2015-30

Computational and analytical challenges in single-cell transcriptomics.

2015/03/12 2015-29

Extensive Strain-Level Copy-Number Variation across Human Gut Microbiome Species.

2015-28

Richness of human gut microbiome correlates with metabolic markers.

2015-27

A metagenome-wide association study of gut microbiota in type 2 diabetes.

2015/03/09 2015-26 HJ Han

Practical guidelines for the comprehensive analysis of ChIP-seq data.

2015-25 T Lee

Patterns of regulatory activity across diverse human cell types predict tissue identity, transcription factor binding, and long-range interactions.

2015-24 ER Kim

Rapid neurogenesis through transcriptional activation in human stem cells.

2015/03/02 2015-23 DS Bae

Genome-Scale CRISPR-Mediated Control of Gene Repression and Activation.

2015-22 CY Kim

Integrating multiple genomic data to predict disease-causing nonsynonymous single nucleotide variants in exome sequencing studies.

2015-21 ER Kim

The Genotype-Tissue Expression (GTEx) project.

2015/02/24 2015-20 ER Kim

Mouse regulatory DNA landscapes reveal global principles of cis-regulatory evolution.

2015-19 KS Kim

Principles of regulatory information conservation between mouse and human.

2015-18 BH Kang

Conservation of trans-acting circuitry during mammalian regulatory evolution.

2015/02/16 2015-17 T Lee

Genetic and epigenetic fine mapping of causal autoimmune disease variants.

2015-16 CY Kim

A general framework for estimating the relative pathogenicity of human genetic variants.

2015-15 CY Kim

A probabilistic model to predict clinical phenotypic traits from genome sequencing.

2015/02/02 2015-14 BH Kang

Relating the metatranscriptome and metagenome of the human gut.

2015-13 BH Kang

Alterations of the human gut microbiome in liver cirrhosis.

2015-12 CY Kim

An integrated catalog of reference genes in the human gut microbiome.

2015-11 CY Kim

Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes.

2015/01/26 2015-10 HH Kim

Computational meta'omics for microbial community studies.

2015-09 HH Kim

Functional profiling of the gut microbiome in disease-associated inflammation.

2015-08 BH Kang

Biodiversity and functional genomics in the human microbiome.

2015-07 BH Kang

Chapter 12: Human Microbiome Analysis.

2015-06 BH Kang

Conducting a Microbiome Study.

2015/01/12 2015-05 KS Kim

Small RNA changes en route to distinct cellular states of induced pluripotency.

2015-04 DS Bae

Genome-wide characterization of the routes to pluripotency.

2015-03 DS Bae

Divergent reprogramming routes lead to alternative stem-cell states.

2015/01/05 2015-02 HJ Han

Global view of enhancer-promoter interactome in human cells.

2015-01 HJ Han

Combining Hi-C data with phylogenetic correlation to predict the target genes of distal regulatory elements in human genome.

2014
Date Paper
index 		Presenter Paper title
2014/12/23 2014-41 CY Kim

Super-enhancers in the control of cell identity and disease.

2014-40 CY Kim

Master transcription factors and mediator establish super-enhancers at key cell identity genes.

2014/12/09 2014-39 HH Kim

Unraveling the biology of a fungal meningitis pathogen using chemical genetics.

2014-38 JE Shim

A proteome-scale map of the human interactome network.

2014-37 JE Shim

The role of the interactome in the maintenance of deleterious variability in human populations.

2014-36 HS Shim

Integrative annotation of variants from 1092 humans: application to cancer genomics.

2014/12/02 2014-35 HJ Han

Mapping functional transcription factor networks from gene expression data.

2014-34 KS Kim

In pursuit of design principles of regulatory sequences.

2014/11/25 2014-33 KS Kim

Epigenomics of human embryonic stem cells and induced pluripotent stem cells: insights into pluripotency and implications for disease.

2014-32 HJ Han

Developmental fate and cellular maturity encoded in human regulatory DNA landscapes.

2014/11/18 2014-31 SM Yang

The 'dnet' approach promotes emerging research on cancer patient survival.

2014-30 HJ Han

Determination and inference of eukaryotic transcription factor sequence specificity.

2014/11/11 2014-29 DS Bae

Transcription factors interfering with dedifferentiation induce cell type-specific transcriptional profiles.

2014-28 HH Kim

Dissecting engineered cell types and enhancing cell fate conversion via CellNet.

2014-27 HH Kim

CellNet: network biology applied to stem cell engineering.

2014/11/04 2014-26 HS Shim

Predicting Cancer-Specific Vulnerability via Data-Driven Detection of Synthetic Lethality.

2014-25 JH Shin

Phen-Gen: combining phenotype and genotype to analyze rare disorders.

2014/10/28 2014-24 HJ Han

A community effort to assess and improve drug sensitivity prediction algorithms.

2014-23 HS Shim

Multi-tiered genomic analysis of head and neck cancer ties TP53 mutation to 3p loss.

2014/09/30 2014-22 JE Shim

Network-based stratification of tumor mutations.

2014-21 KS Kim

Synonymous mutations frequently act as driver mutations in human cancers.

2014/09/23 2014-20 CY Kim

VarWalker: Personalized Mutation Network Analysis of Putative Cancer Genes from Next-Generation Sequencing Data.

2014-19 HJ Han

DriverNet: uncovering the impact of somatic driver mutations on transcriptional networks in cancer.

2014/09/16 2014-18 JH Shin

Integrated analysis of recurrent properties of cancer genes to identify novel drivers.

2014-17 AR Cho

Improving the prediction of the functional impact of cancer mutations by baseline tolerance transformation.

2014/09/02 2014-16 JE Shim

A network module-based method for identifying cancer prognostic signatures.

2014-15 AR Cho

Realizing the promise of cancer predisposition genes.

2014/08/19 2014-14 JE Shim

Assessing the clinical utility of cancer genomic and proteomic data across tumor types.

2014-13 HS Shim

Mutational landscape and significance across 12 major cancer types.

2014/08/12 2014-12 HS Shim

Signatures of mutational processes in human cancer.

2014-11 HJ Kim

Discovery and saturation analysis of cancer genes across 21 tumour types.

2014/08/05 2014-10 CY Kim

Comprehensive identification of mutational cancer driver genes across 12 tumor types.

2014-9 JE Shim

IntOGen-mutations identifies cancer drivers across tumor types.

2014/07/29 2014-8 CY Kim

Computational approaches to identify functional genetic variants in cancer genomes.

2014-7 AR Cho

Mutational heterogeneity in cancer and the search for new cancer-associated genes.

2014-6 AR Cho

Cancer genomes: discerning drivers from passengers

2014/07/22 2014-5 AR Cho

The Cancer Genome Atlas Pan-Cancer analysis project.

2014-4 AR Cho

Cancer genome landscapes.

2014-3 AR Cho

Distinguishing driver and passenger mutations in an evolutionary history categorized by interference.

2014/07/15 2014-2 HJ Han

A promoter-level mammalian expression atlas.

2014-1 HJ Han

An atlas of active enhancers across human cell types and tissues.

2013
Date Paper
index 		Presenter Paper title
2013/06/26 2013-31 HJ Han Integration of Hi-C and ChIP-seq data reveals distinct types of chromatin linkages
2013-30 YH Ko Deciphering molecular circuits from genetic variation underlying transcriptional responsiveness to stimuli
2013/06/04 2013-29 KS Kim Mapping the Human miRNA Interactome by CLASH Reveals Frequent Noncanonical Binding
2013-28 ER Kim Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples
2013/05/28 2013-27 YH Ko Discovering statistically significant pathways in expression profiling studies
2013-26 JE Shim Prediction and Validation of Gene-Disease Associations Using Methods Inspired by Social Network Analyses
2013/05/21 2013-25 HJ Han Dynamic regulatory network controlling TH17 cell differentiation
2013-24 T Lee Deciphering and Prediction of Transcriptome Dynamics under Fluctuating Field Conditions
2013/05/14 2013-23 JE Shim Integrative eQTL-Based Analyses Reveal the Biology of Breast Cancer Risk Loci
2013-22 HJ Kim Chromatin marks identify critical cell types for fine mapping complex trait variants
2013/05/07 2013-21 ER Kim Genome-wide Chromatin State Transitions Associated with Developmental and Environmental Cues
2013-20 HS Shim Human Disease-Associated Genetic Variation Impacts Large Intergenic Non-Coding RNA Expression
2013/04/09 2013-19 HJ Kim Annotation of functional variation in personal genomes using RegulomeDB
2013/04/02 2013-18 HJ Kim The GENCODE v7 catalog of human long noncoding RNAs: Analysis of their gene structure, evolution, and expression
2013/03/12 2013-17 ER Kim Global Mapping of Cell Type–Specific Open Chromatin by FAIRE-seq Reveals the Regulatory Role of the NFI Family in Adipocyte Differentiation
2013/03/05 2013-16 ER Kim ChIP–seq: advantages and challenges of a maturing technology
2013/02/21 2013-15 KS Kim Novel Modeling of Combinatorial miRNA Targeting Identifies SNP with Potential Role in Bone Density
2013-14 KS Kim A Molecular Roadmap of Reprogramming Somatic Cells into iPS Cells
2013-13 HJ Han Differential DNase I hypersensitivity reveals factor-dependent chromatin dynamics.
2013/02/15 2013-12 HJ Han Chromatin accessibility pre-determines glucocorticoid receptor binding patterns
2013-11 JE Shim, CY Kim Interpreting noncoding genetic variation in complex traits and human disease
2013-10 HJ Han, JH Kim Accurate inference of transcription factor binding from DNA sequence and chromatin accessibility data
2013/02/08 2013-09 HJ Kim Widespread Site-Dependent Buffering of Human Regulatory Polymorphism
2013-08 JE Shim, CY Kim Linking disease associations with regulatory information in the human genome
2013-07 HJ Han, JH Kim Understanding transcriptional regulation by integrative analysis of transcription factor binding data
2013/01/25 2013-06 HJ Han, JH Kim The long-range interaction landscape of gene promoters
2013-05 ER Kim, HS Shim Landscape of transcription in human cells
2013-04 HJ Han, JH Kim Architecture of the human regulatory network derived from ENCODE data
2013/01/18 2013-03 KS Kim, TH Kim An expansive human regulatory lexicon encoded in transcription factor footprints
2013-02 HJ Han, JH Kim The accessible chromatin landscape of the human genome
2013/01/11 2013-01 JE Shim, CY Kim An integrated encyclopedia of DNA elements in the human genome

2012

Date Paper index Paper title
2013/01/11 2012-81 (TH Kim) MuSiC: identifying mutational significance in cancer genomes.
2012/12/04 2012-80 (CY KIM) Human genomic disease variants: A neutral evolutionary explanation
2012/11/20 2012-79 (HS Shim) Circuitry and Dynamics of Human Transcription Factor Regulatory Networks
2012-78 (HJ Kim) Interpreting cancer genomes using systematic host network perturbations by tumour virus proteins
2012/11/06 2012-77 (HJ Han) Systematic Localization of Common Disease-Associated Variation in Regulatory DNA
2012-76 (KS Kim) A public resource facilitating clinical use of genomes
2012/07/19 2012-75 (HJ Han & YH Ko) Predictive regulatory models in Drosophila melanogaster by integrative inference of transcriptional networks
2012-74 (JE Shim) An Abundance of Rare Functional Variants in 202 Drug Target Genes Sequenced in 14,002 People
2012-73 (JE Shim) Evolution and Functional Impact of Rare Coding Variation from Deep Sequencing of Human Exomes
2012-72 (SH Hwang) Network-based classification of breast cancer metastasis
2012-71 (T Lee&CY Kim)Brain Expression Genome-Wide Association Study (eGWAS) Identifies Human Disease-Associated Variants
2012-70 (ER Kim&TH Kim)The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data
2012/07/16 2012-69 (ER Kim&TH Kim)A framework for variation discovery and genotyping using next-generation DNA sequencing data
2012-66 (ER Kim&TH Kim)The Sequence Alignment/Map format and SAMtools
2012-65 (ER Kim&TH Kim)The Variant Call Format and VCFtools
2012-64 (YH Go&HJ Han)The Impact of the Gut Microbiota on Human Health: An Integrative View
2012-63 (T Lee&CY Kim)Host-Gut Microbiota Metabolic Interactions
2012-62 (AR Cho,JH Ju)Interactions Between the Microbiota and the Immune System
2012-61 (SH Hwang&HJ Cho)The Application of Ecological Theory Toward an Understanding of the Human Microbiome
2012-60 (SH Hwang&HJ Cho)Microbiota-Targeted Therapies: An Ecological Perspective
2012/07/13 2012-59 (JH Shin&HJ Kim)Metabolic Reconstruction for Metagenomic Data and Its Application to the Human Microbiome
2012-58 (JH Shin&HJ Kim)A framework for human microbiome research
2012-57 (JH Shin&HJ Kim)Structure, function and diversity of the healthy human microbiome
2012/07/12 2012-56 (AR Cho&JH Ju)COLT-Cancer: functional genetic screening resource for essential genes in human cancer cell lines
2012-55 (YH Go&HJ Han)Google Goes Cancer: Improving Outcome Prediction for Cancer Patients by Network-Based Ranking of Marker Genes
2012-54 (YH Go&HJ Han)A pharmacogenomic method for individualized prediction of drug sensitivity
2012-53 (JH Soh)The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups
2012/07/09 2012-52 (ER Kim&TH Kim)The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity
2012-51 (ER Kim&TH Kim)Systematic identification of genomic markers of drug sensitivity in cancer cells
2012-50 (ER Kim&TH Kim)Subtype and pathway specific responses to anticancer compounds in breast cancer
2012-49 (JE Shim&KS Kim)De novo mutations revealed by whole-exome sequencing are strongly associated with autism
2012-48 (JE Shim&KS Kim)Patterns and rates of exonic de novo mutations in autism spectrum disorders
2012-47 (JE Shim&KS Kim)Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations
2012/07/06 2012-46 (T Lee&CY Kim)Integrating Rare-Variant Testing, Function Prediction, and Gene Network in Composite Resequencing-Based Genome-Wide Association Studies (CR-GWAS)
2012-45 (T Lee&CY Kim)Type 2 Diabetes Risk Alleles Demonstrate Extreme Directional Differentiation among Human Populations, Compared to Other Diseases
2012-44 (AR Cho&JH Ju)Predicting mutation outcome from early stochastic variation in genetic interaction partners
2012-43 (AR Cho&JH Ju)Fitness Trade-Offs and Environmentally Induced Mutation Buffering in Isogenic C. elegans
2012-42 (JE Shim&KS Kim)Identification of microRNA-regulated gene networks by expression analysis of target genes
2012-41 (JE Shim&KS Kim)Exome sequencing and the genetic basis of complex traits
2012/07/02 2012-40 (JH Soh)Functional Repurposing Revealed by Comparing S. pombe and S. cerevisiae Genetic Interactions
2012-39 (ER Kim&TH Kim)De novo discovery of mutated driver pathways in cancer
2012-38 (YH Go&HJ Han)A systems biology approach to prediction of oncogenes and molecular perturbation targets in B-cell lymphomas
2012-37 (SH Hwang&HJ Cho)PREDICT: a method for inferring novel drug indications with application to personalized medicine
2012-36 (SH Hwang&HJ Cho)Synergistic response to oncogenic mutations defines gene class critical to cancer phenotype
2012-35 (JH Shin&HJ Kim)Detecting Novel Associations in Large Data Sets
2012/03/05 2012-34 (8,HH Kim)Mapping and quantifying mammalian transcriptomes by RNA-Seq.
2012-33 (11,Go&Ju)Differential expression analysis for sequence count data
2012-32 (12,Go&Ju)edgeR: a Bioconductor package for differential expression analysis of digital gene expression data
2012/02/27
2012/02/28 		2012-31 (1,JW Song)RNA-Seq: a revolutionary tool for transcriptomics
2012-30 (2,JW Song)Computational methods for transcriptome annotation and quantification using RNA-seq
2012-29 (3,HJ Han)From RNA-seq reads to differential expression results
2012-28 (4,AR Cho)Comprehensive comparative analysis of strand-specific RNA sequencing methods
2012-27 (5,T Lee)A Low-Cost Library Construction Protocol and Data Analysis Pipeline for Illumina-Based Strand-Specific Multiplex RNA-Seq
2012-26 (6,So&Shin)Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation
2012-25 (7,So&Shin)TopHat: discovering splice junctions with RNA-Seq
2012/02/06 2012-24 mirConnX: condition-specific mRNA-microRNA network integrator
2012-23 Construction and Analysis of an Integrated Regulatory Network Derived from High-Throughput Sequencing Data
2012-22 A Densely Interconnected Genome-Wide Network of MicroRNAs and Oncogenic Pathways Revealed Using Gene Expression Signatures
2012-21 Reprogramming of miRNA networks in cancer and leukemia
2012-20 An Extensive MicroRNA-Mediated Network of RNA-RNA Interactions Regulates Established Oncogenic Pathways in Glioblastoma
2012/01/30 2012-19 Principles and Strategies for Developing Network Models in Cancer
2012-18 Reverse engineering of regulatory networks in human B cells
2012-17 Variations in DNA elucidate molecular networks that cause disease
2012-16 Harnessing gene expression to identify the genetic basis of drug resistance
2012-15 An Integrated Approach to Uncover Drivers of Cancer
2012/01/09 2012-14 Genetic variation in an individual human exome.
2012-13 Predicting phenotypic variation in yeast from individual genome sequences.
2012-12 Clinical assessment incorporating a personal genome.
2012/01/09
2012/01/16 		2012-11 Human allelic variation: perspective from protein function, structure, and evolution.
2012-10 Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm.
2012-09 Prediction of deleterious human alleles.
2012-08 Human non-synonymous SNPs: server and survey.
2012-07 A method and server for predicting damaging missense mutations.
2012-06 SNAP: predict effect of non-synonymous polymorphisms on function
2012/01/09
2012/01/16 		2012-05 Computational and statistical approaches to analyzing variants identified by exome sequencing.
2012-04 Shifting paradigm of association studies: value of rare single-nucleotide polymorphisms.
2012-03 Targeted capture and massively parallel sequencing of 12 human exomes.
2012/01/09
2012/01/16 		2012-02 The distribution of fitness effects of new mutations.
2012-01 Most Rare Missense Alleles Are Deleterious in Humans: Implications for Complex Disease and Association Studies

2011

Date Paper_index Paper_title
2011/11/28 2011-49 (Shin)Data-Driven Methods to Discover Molecular Determinants of Serious Adverse Drug Events
2011-48 (Shin)Network pharmacology: the next paradigm in drug discovery
2011-47 (Oh)Systematic exploration of synergistic drug pairs
2011-46 (Shim)Chemogenomic profiling predicts antifungal synergies
2011-45 (Beck)A strategy for predicting the chemosensitivity of human cancers and its application to drug discovery
2011-44 (Hwang)Analysis of drug-induced effect patterns to link structure and side effects of medicines
2011/11/14 2011-43 Network-Based Elucidation of Human Disease Similarities Reveals Common Functional Modules Enriched for Pluripotent Drug Targets
2011-42 Cross-species discovery of syncretic drug combinations that potentiate the antifungal fluconazole
2011-41 Analysis of multiple compound–protein interactions reveals novel bioactive molecules
2011/11/07 2011-40 Drug—target network
2011-39 A side effect resource to capture phenotypic effects of drugs
2011-38 Quantitative systems-level determinants of human genes targeted by successful drugs
2011-37 Drug Target Identification Using Side-Effect Similarity
2011/11/07 2011-36 Discovery and Preclinical Validation of Drug Indications Using Compendia of Public Gene Expression Data
2011-35 Exploiting drug–disease relationships for computational drug repositioning
2011-34 Drug Discovery in a Multidimensional World: Systems, Patterns, and Networks
2011/10/05 2011-33 Analysis of membrane proteins in metagenomics: Networks of correlated environmental features and protein families
2011-32 Quantifying environmental adaptation of metabolic pathways in metagenomics
2011-31 Quantitative assessment of protein function prediction from metagenomics shotgun sequences
2011/10/04 2011-30 A human gut microbial gene catalogue established by metagenomic sequencing
2011-29 Molecular eco-systems biology: towards an understanding of community function
2011-28 Microbial community profiling for human microbiome projects: Tools, techniques, and challenges
2011-27 Unravelling the effects of the environment and host genotype on the gut microbiome
2011/09/19 2011-26 independently evolved virulence effectors converge onto hubs in a plant immune system Network
2011-25 Evidence for network evolution in an arabidopsis interactome Map
2011/09/05 2011-24 Exome Sequencing of Ion Channel Genes Reveals Complex Profiles Confounding Personal Risk Assessment in Epilepsy
2011-23 Pluripotency factors in Embryonic stem cells Regulate Differentiation into Germ Layers
2011/09/22 2011-22 Integrated Genome-scale predition of Detrimental Mutations in Transcription Networks
2011-21 From expression QTLs to personalized transcriptomics
2011/06/20 2011-20 a user's guide to the encyclopedia of DNA elements(ENCODE)
2011/03/30 2011-19 enterotypes of the human gut microbiome
2011-18 toward molecular trait-based ecology, through intergration of biogeochemical, geographical and metagenomic data
2011/03/16 2011-17 variable pathogenicity determines individual lifespan in caenorhabditis elegans
2011-16 a high-resolution c.elegans essential gene network based on phenotypic profiling of a complex tissue
2011/04/25 2011-15 Hallmarks of Cancer : The next generation
2011-14 Mapping Cancer Origins
2011-13 Genetic Interactions in Cancer Progression and Treatment
2011/04/11 2011-12 Proteins encoded in genomic regions associated with immune-mediated disease physically interact and suggest underlying biology
2011-11 ***Changed!***

Identifying relationships among genomic disease regions: predicting genes at pathogenic SNP associations and rare deletions

2011/03/28 2011-10 profiling translatomes of discrete cell populations resolves altered cellular priorities during hypoxia in Arabidopsis
2011-09 cell-type specific analysis of translating RNAs in developing flowers reveals new levels of control
2011/03/14 2011-08 phenotypic landscape of a bacterial cell
2011-07 cross-species chemogenomic profiling reveals evolutionarily conserved drug mode of action
2011/02/28 2011-06 genomic patterns of pleiotropy and the evolution of complexity
2011-05 simultaneous genome-wide inference of physical, genetic, regulatory, and functional pathway
2011/02/21 2011-04 dynamic interaction networks in a hierarchically organized tissue
2011/02/14 2011-03 rewiring of genetic networks in response to DNA damage
2011/01/31 2011-02 Applying mass spectrometry-based proteomics to genetics, genomics and network biology
2011-01 Data analysis and bioinformatics tools for tandem mass spectrometry in proteomics

2010

Date Paper_index Paper_title
2010/12/27 2010-29 Functional_roles_fornoise_in_genetic_circuits
2010-28 Estimation_of_effect_size_distribution_from_genome-wide_association_studies_and_implications_for_future_discoveries
2010/11/15 2010-27 Liver_and_Adipose_Expression_associated_SNPs_are_enriched_for_association_to_type_2_diabetes
2010-26 It's_the_machine_that_matters:_predicting_gene_function_and_phenotype_from_protein_networks
2010/11/01 2010-25 A_genome-wide_map_of_human_genetic_interactions_inferred_from_radiation_hybrid_genotypes
2010-24 A_Genome-Wide_Gene_Function_Prediction_Resource_for_Drosophila_melanogaster
2010/10/11 2010-23 Dissecting_spatio-temporal_protein_networks_driving_human_heart_development_and_related_disorders
2010/09/13 2010-22 Transposable_elements_have_rewired_the_core_regulatory_network_of_human_embryonic_stem_cells
2010-21 Limits_of_sequence_and_functional_conservation
2010/05/26 2010-20 network_based_elucidation_of_human_disease_similarities_reveals_common_functional_modules_enriched_for_pluripotent_drug_targets.pdf
2010/05/19 2010-19 interpreting_metabolomic_profiles_using_unbiased_pathway_models.pdf
2010/04/21 2010-18 identification_of_networks_of_co_occurring_tumor_related_dna_copy_number_changes_using_a_genome_wide_scoringapproach.pdf
2010/04/14 2010-17 an_atlas_of_combinatorial_transcriptional_regulation_in_mouse_and_man
2010/04/07 2010-16 systematic_discovery_of_nonobvious_human_disease_models_through_orthologous_phenotypes
2010-15 genome_side_association_study_of_107_phenotypes_in_Arabidopsis_thaliana_inbred_lines
2010/03/31 2010-14 toward_stem_cell_systems_biology_from_molecules_to_networks_and_landscapes.pdf
2010-13 systems_biology_of_stem_cell_fate_and_cellular_reprogramming
2010/03/24 2010-12 dynamic_modularity_in_protein_interaction_networks_predicts_breast_cancer_outcome
2010/01/18 2010-11 JournalClub_100118_a_tutorial_on_statistical_methods_for_population_association_studies
2010/01/16 2010-10 systems-level_dinamic_analyses_of_fate_change_in_murine_embryonic_stem_cells
2010/01/15 2010-09 distinguishing_direct_versus_indirect_transcription_factor-DNA-interactions
2010/01/14 2010-08 chemogenomic_profiling_predicts_antifungal_synergies
2010/01/13 2010-07 edgetic_perturbation_models_of_human_inherited_disorders
2010/01/12 2010-06 analysis_of_cell_fate_from_single-cell_gene_expression_profiles_in_C.elegans
2010/01/11 2010-05 predicting_new_molecular_targets_for_known_drugs.pdf

Reference:SEA(Similarity Ensemble Approach)

2010/01/09 2010-04 harnessing_gene_expression_to_identify_the_genetic_basis_of_drug_resistance
2010/01/08 2010-03 an_integrative_approach_to_reveal_driver_gene_fusions_from_paired_end_sequencing_data_in_cancer
2010/01/07 2010-02 a_phenotypic_profile_of_the_candida_albicans_regulatory_network
2010/01/06 2010-01 a_global_view_of_protein_expression_in_human_cells_tissues_and_organs



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