Network Biology Lab - User contributions [en]

Journal Club

2025-02-24T05:36:48Z

Sej4926:

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[https://doi.org/10.1101/2025.01.07.631644 A Single-Graph Visualization to Reveal Hidden Explainability Patterns of SHAP Feature Interactions in Machine Learning for Biomedical Issues]
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[https://doi.org/10.1101/2024.11.18.624166 Learning multi-cellular representations of single-cell transcriptomics data enables characterization of patient-level disease states]
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[https://doi.org/10.1016/j.cell.2024.08.037 Emergence of community behaviors in the gut microbiota upon drug treatment]
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[https://doi.org/10.1038/s41564-024-01832-5 Prediction of strain level phage–host interactions across the Escherichia genus using only genomic information]
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[https://doi.org/10.1101/2025.01.07.631807 Metagenomic estimation of absolute bacterial biomass in the mammalian gut through host-derived read normalization]
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|style="padding:.4em;"|SH Ahn
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[https://doi.org/10.1101/2024.10.02.616292 De novo discovery of conserved gene clusters in microbial genomes with Spacedust]
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|style="padding:.4em;"|SH Ahn
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[https://doi.org/10.1101/2024.04.14.589414 Rapid and Sensitive Protein Complex Alignment with Foldseek-Multimer]
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[https://www.medrxiv.org/content/10.1101/2024.04.04.24305313v1 Single-cell RNA sequencing of human tissue supports successful drug targets]
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[https://doi.org/10.1101/2024.12.30.630825 MGM as a large-scale pretrained foundation model for microbiome analyses in diverse contexts ]
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[https://doi.org/10.1126/science.ado9336 Sequence modeling and design from molecular to genome scale with Evo]
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[https://doi.org/10.1101/2024.12.18.629142 Human gut microbiome gene co-expression network reveals a loss in taxonomic and functional diversity in Parkinson’s disease]
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[https://doi.org/10.1101/2025.01.06.631550 Quantifying Metagenomic Strain Associations from Microbiomes with Anpan]
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[https://www.biorxiv.org/content/10.1101/2023.09.08.555192v7 Evaluating the Utilities of Foundation Models in Single-cell Data Analysis]
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[https://www.nature.com/articles/s41586-024-08328-6 Accurate predictions on small data with a tabular foundation model]
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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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[https://doi.org/10.1093/bioinformatics/btad663 TT3D: Leveraging precomputed protein 3D sequence models to predict protein–protein interactions]
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[https://doi.org/10.1093/bioinformatics/btac258 Topsy-Turvy: integrating a global view into sequence-based PPI prediction]
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[https://doi.org/10.1016/j.cels.2021.08.010 D-SCRIPT translates genome to phenome with sequence-based, structure-aware, genome-scale predictions of protein-protein interactions]
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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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[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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[https://doi.org/10.1038/s41592-024-02303-9 CellRank 2: unified fate mapping in multiview single-cell data]
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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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[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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[https://doi.org/10.1101/2024.12.13.628459 MaAsLin 3: Refining and extending generalized multivariable linear models for meta-omic association discovery]
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[https://doi.org/10.1038/s41564-024-01881-w Profiling lateral gene transfer events in the human microbiome using WAAFLE]
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[https://doi.org/10.1101/2024.05.30.596740 ProTrek: Navigating the Protein Universe through Tri-Modal Contrastive Learning]
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[https://doi.org/10.1101/2024.12.30.630844 The genetic diversity and populational specificity of the human gut virome at single nucleotide resolution]
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[https://doi.org/10.1186/s13059-024-03425-1 pan-Draft: automated reconstruction of species-representative metabolic models from multiple genomes]
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[https://doi.org/10.1016/j.cell.2024.09.019 A core microbiome signature as an indicator of health]
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[https://doi.org/10.1016/j.cell.2024.09.027 Using artificial intelligence to document the hidden RNA virosphere]
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[https://doi.org/10.1101/2023.07.17.549267 Learning a deep language model for microbiomes: the power of large scale unlabeled microbiome data]
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[https://doi.org/10.1126/science.adg7492 Accurate proteome-wide missense variant effect prediction with AlphaMissense]
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[https://doi.org/10.1038/s41591-024-02823-z A gut microbial signature for combination immune checkpoint blockade across cancer types]
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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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[https://doi.org/10.1038/s41564-024-01739-1 Multikingdom and functional gut microbiota markers for autism spectrum disorder]
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[https://doi.org/10.1186/s13059-024-03390-9 A realistic benchmark for differential abundance testing and confounder adjustment in human microbiome studies]
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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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[https://doi.org/10.1038/s41467-024-52561-6 Gut microbiota wellbeing index predicts overall health in a cohort of 1000 infants]
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[https://doi.org/10.1186/s13059-024-03320-9 VirRep: a hybrid language representation learning framework for identifying viruses from human gut metagenomes]
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[https://doi.org/10.1101/2024.06.27.601020 Ultrafast and accurate sequence alignment and clustering of viral genomes]
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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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[https://doi.org/10.1038/s41467-024-46947-9 Genomic language model predicts protein co-regulation and function]
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[https://doi.org/10.1101/2024.07.26.605391 Protein Set 1 Transformer: A protein-based genome language model to power high diversity viromics]
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[https://doi.org/10.1101/2024.07.11.603044 Prophage-DB: A comprehensive database to explore diversity,distribution, and ecology of prophages]
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[https://doi.org/10.1186/s40168-024-01904-y Strain‑resolved de‑novo metagenomic assembly of viral genomes and microbial 16S rRNAs]
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[https://doi.org/10.1186/s40168-024-01876-z Prokaryotic‑virus‑encoded auxiliary metabolic genes throughout the global oceans]
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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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[https://doi.org/10.1101/2024.04.17.589959 Pangenomes of Human Gut Microbiota Uncover Links Between Genetic Diversity and Stress Response]
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[https://doi.org/10.1101/2024.05.28.596318 vClassifier: a toolkit for species-level classification of prokaryotic viruses]
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[https://doi.org/10.1101/2024.07.26.605250 GRAViTy-V2: a grounded viral taxonomy application]
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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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[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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[https://doi.org/10.1126/science.adj4857 A blueprint for tumor-infiltrating B cells across human cancers]
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[https://doi.org/10.1038/s41467-024-48310-4 Systematic dissection of tumor-normal single-cell ecosystems across a thousand tumors of 30 cancer types]
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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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[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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[https://doi.org/10.1101/2024.06.04.597354 Cell-Graph Compass: Modeling Single Cells with Graph Structure Foundation Model]
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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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[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/10.1038/s41591-024-03067-7 Strain-specific gut microbial signatures in type 2 diabetes identified in a cross-cohort analysis of 8,117 metagenomes]
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[https://doi.org/10.1016/j.cell.2024.03.034 Gut symbionts alleviate MASH through a secondary bile acid biosynthetic pathway]
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[https://pubmed.ncbi.nlm.nih.gov/31510656 Deep learning with multimodal representation for pancancer prognosis prediction]
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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/10.1038/s41467-019-10656-5 Establishing microbial composition measurement standards with reference frames]
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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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[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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[https://doi.org/10.1038/s41467-023-44290-z Effective binning of metagenomic contigs using contrastive multi-view representation learning]
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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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[https://onlinelibrary.wiley.com/doi/full/10.1002/advs.202303925 Metagenomic Insight into The Global Dissemination of The Antibiotic Resistome]
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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="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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[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|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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[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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[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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[https://doi.org/10.1016/j.chom.2023.08.019 Cell-type-specific responses to fungal infection in plants revealed by single-cell transcriptomics]
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|style="padding:.4em;"|YK Jung
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[https://www.sciencedirect.com/science/article/pii/S1534580722002519?via%3Dihub The single-cell stereo-seq reveals region-specific cell subtypes and transcriptome profiling in Arabidopsis leaves]
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[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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[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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[https://doi.org/10.1038/s41586-024-07251-0 Immune microniches shape intestinal Treg function]
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[https://doi.org/10.1016/j.devcel.2021.02.021 A single-cell analysis of the Arabidopsis vegetative shoot apex]
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[https://doi.org/10.1038/s41467-023-40137-9 Droplet-based high-throughput single microbe RNA sequencing by smRandom-seq]
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[https://doi.org/10.1038/s41564-023-01462-3 Single-cell massively-parallel multiplexed microbial sequencing (M3-seq) identifies rare bacterial populations and profiles phage infection]
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[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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[https://doi.org/10.1038/s41587-023-01979-2 Spatial metatranscriptomics resolves host–bacteria–fungi interactomes]
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[https://doi.org/10.1038/s41467-023-36325-2 Dissecting the immune suppressive human prostate tumor microenvironment via integrated single-cell and spatial transcriptomic analyses]
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[https://doi.org/10.1038/s41593-023-01452-y Single-nucleus genomics in outbred rats with divergent cocaine addiction-like behaviors reveals changes in amygdala GABAergic inhibition]
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[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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[https://doi.org/10.1038/s41467-023-39933-0 Spatial cellular architecture predicts prognosis in glioblastoma]
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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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[https://doi.org/10.1038/s41467-022-30511-4 Transcriptional adaptation of olfactory sensory neurons to GPCR identity and activity]
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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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[https://doi.org/10.1021/acscentsci.3c01169 Single-Cell Analysis Reveals Cxcl14+ Fibroblast Accumulation in Regenerating Diabetic Wounds Treated by Hydrogel-Delivering Carbon Monoxide]
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[https://doi.org/10.1038/s41477-022-01291-y Single-cell RNA sequencing provides a high-resolution roadmap for understanding the multicellular compartmentation of specialized metabolism]
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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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[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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[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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[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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[https://doi.org/10.1038/s41592-023-02035-2 Population-level integration of single-cell datasets enables multi-scale analysis across samples]
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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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[https://doi.org/10.1038/s41587-022-01467-z Modeling intercellular communication in tissues using spatial graphs of cells]
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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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[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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[https://doi.org/10.1038/s41588-022-01273-y MHC II immunogenicity shapes the neoepitope landscape in human tumors]
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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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[https://doi.org/10.1038/s41467-023-37353-8 Pan-cancer classification of single cells in the tumour microenvironment]
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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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[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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[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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[https://doi.org/10.1038/s41591-023-02324-5 An integrated tumor, immune and microbiome atlas of colon cancer]
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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|2023/09/05
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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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[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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[https://doi.org/10.1038/s41564-023-01439-2 A predicted CRISPR-mediated symbiosis between uncultivated archaea]
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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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[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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[https://doi.org/10.1038/s41586-023-06431-8 Mapping the T cell repertoire to a complex gut bacterial community]
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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|Microbiome
|style="padding:.4em;"|23-60
|style="padding:.4em;"|JY Kim
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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;"|23-59
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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|Microbiome
|style="padding:.4em;"|23-58
|style="padding:.4em;"|G Koh
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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|Microbiome
|style="padding:.4em;"|23-57
|style="padding:.4em;"|SH Ahn
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41467-023-41042-x Impact of dietary interventions on pre-diabetic oral and gut microbiome, metabolites and cytokines]
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|style="padding:.4em;" rowspan=1|2024/01/10
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-56
|style="padding:.4em;"|HJ Kim
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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|Microbiome
|style="padding:.4em;"|23-55
|style="padding:.4em;"|JY Ma
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41591-023-02599-8 Bacterial SNPs in the human gut microbiome associate with host BMI]
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|style="padding:.4em;" rowspan=1|2023/01/03
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-54
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1080/19490976.2023.2245562 Multimodal metagenomic analysis reveals microbial single nucleotide variants as superior biomarkers for early detection of colorectal cancer]
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|style="padding:.4em;" rowspan=1|2023/01/03
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-53
|style="padding:.4em;"|NY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.chom.2023.10.005 Multi-kingdom gut microbiota analyses define bacterial-fungal interplay and microbial markers of pan-cancer immunotherapy across cohorts]
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|style="padding:.4em;" rowspan=1|2023/12/27
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-52
|style="padding:.4em;"|YR Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.xcrm.2023.101251 Prior antibiotic administration disrupts anti-PD-1 responses in advanced gastric cancer by altering the gut microbiome and systemic immune response]
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|style="padding:.4em;" rowspan=1|2023/12/27
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-51
|style="padding:.4em;"|JY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.cell.2023.05.046 Ultra-deep sequencing of Hadza hunter-gatherers recovers vanishing gut microbes]
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|style="padding:.4em;" rowspan=1|2023/12/13
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-50
|style="padding:.4em;"|WJ Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1186/s40168-023-01472-7 Altered infective competence of the human gut microbiome in COVID-19]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-49
|style="padding:.4em;"|G Koh
|style="padding:.4em;text-align:left"|
[https://onlinelibrary.wiley.com/doi/full/10.1002/aisy.202300342 Host-Variable-Embedding Augmented Microbiome-Based Simultaneous Detection of Multiple Diseases by Deep Learning]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-48
|style="padding:.4em;"|SH Ahn
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41467-023-39264-0 A data-driven approach for predicting the impact of drugs on the human microbiome]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-47
|style="padding:.4em;"|HJ Kim
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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
|style="padding:.4em;"|23-46
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41467-023-39459-5 Top-down identification of keystone taxa in the microbiome]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-45
|style="padding:.4em;"|JY Ma
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.cels.2022.12.007 Pitfalls of genotyping microbial communities with rapidly growing genome collections]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-44
|style="padding:.4em;"|NY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1186/s13059-023-03028-2 Reconstruction of the last bacterial common ancestor from 183 pangenomes reveals a versatile ancient core genome]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-43
|style="padding:.4em;"|SH Lee
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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|Microbiome
|style="padding:.4em;"|23-42
|style="padding:.4em;"|WJ Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.immuni.2023.04.003 Phage display sequencing reveals that genetic, environmental, and intrinsic factors influence variation of human antibody epitope repertoire]
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|style="padding:.4em;" rowspan=1|2023/11/08
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-41
|style="padding:.4em;"|JY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.immuni.2023.04.017 Phage-display immunoprecipitation sequencing of the antibody epitope repertoire in inflammatory bowel disease reveals distinct antibody signatures]
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|style="padding:.4em;" rowspan=1|2023/11/01
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-40
|style="padding:.4em;"|G Koh
|style="padding:.4em;text-align:left"|
[https://doi.org/10.15252/msb.202311525 Consistency across multi-omics layers in a drug-perturbed gut microbial community]
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|style="padding:.4em;" rowspan=1|2023/11/01
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-39
|style="padding:.4em;"|HJ Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41587-023-01953-y Identification of mobile genetic elements with geNomad]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-38
|style="padding:.4em;"|SH Ahn
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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
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-37
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41591-023-02424-2 The airway microbiome mediates the interaction between environmental exposure and respiratory health in humans]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-36
|style="padding:.4em;"|JY Ma
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1080/19490976.2023.2224474 Ordering taxa in image convolution networks improves microbiome-based machine learning accuracy]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-35
|style="padding:.4em;"|NY Kim
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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|Microbiome
|style="padding:.4em;"|23-34
|style="padding:.4em;"|SH Lee
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.cell.2023.03.011 Dietary tryptophan metabolite released by intratumoral Lactobacillus reuteri facilitates immune checkpoint inhibitor treatment]
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|style="padding:.4em;" rowspan=1|2023/09/20
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-33
|style="padding:.4em;"|WJ Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s43587-022-00306-9 Toward an improved definition of a healthy microbiome for healthy aging]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-32
|style="padding:.4em;"|JY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s43587-022-00287-9 Associations of the skin, oral and gut microbiome with aging, frailty and infection risk reservoirs in older adults]
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|style="padding:.4em;" rowspan=1|2023/09/13
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-31
|style="padding:.4em;"|G Koh
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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|Microbiome
|style="padding:.4em;"|23-30
|style="padding:.4em;"|SH Ahn
|style="padding:.4em;text-align:left"|
[https://doi.org/10.7554/eLife.50240 Adjusting for age improves identification of gut microbiome alterations in multiple diseases]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-29
|style="padding:.4em;"|HJ Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41564-023-01370-6 Centenarians have a diverse gut virome with the potential to modulate metabolism and promote healthy lifespan]
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|style="padding:.4em;" rowspan=1|2023/09/06
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-28
|style="padding:.4em;"|JH Cha
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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;"
|+style="text-align:left;font-size:12pt" | 2023-1 ADVANCED MICROBIOME DATA ANALYSIS
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|style="padding:.4em;"|23-24
|style="padding:.4em;"|JY Kim
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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|Microbiome
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|style="padding:.4em;"|WJ Kim
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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|Microbiome
|style="padding:.4em;"|23-22
|style="padding:.4em;"|SH Ahn
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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;"|23-21
|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;" rowspan=1|Microbiome
|style="padding:.4em;"|23-20
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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;" rowspan=1|Microbiome
|style="padding:.4em;"|23-19
|style="padding:.4em;"|JY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.chom.2023.01.004 Deficient butyrate-producing capacity in the gut microbiome is associated with bacterial network disturbances and fatigue symptoms in ME/CFS]
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|style="padding:.4em;" rowspan=1|2023/05/23
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-18
|style="padding:.4em;"|EJ Sung
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.ccell.2022.11.013 Gut microbiota-mediated nucleotide synthesis attenuates the response to neoadjuvant chemoradiotherapy in rectal cancer]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-17
|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;" rowspan=1|2023/05/23
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-16
|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;" rowspan=1|Microbiome
|style="padding:.4em;"|23-15
|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;" rowspan=1|2023/05/16
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-14
|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;" rowspan=1|2023/05/16
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-13
|style="padding:.4em;"|JW Yu
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.cell.2022.09.005 Pan-cancer analyses reveal cancer-type-specific fungal ecologies and bacteriome interactions]
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|style="padding:.4em;" rowspan=1|2023/05/09
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-12
|style="padding:.4em;"|NY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.cell.2022.09.015 A pan-cancer mycobiome analysis reveals fungal involvement in gastrointestinal and lung tumors]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-11
|style="padding:.4em;"|HR Shin
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41564-021-01030-7 Multi-kingdom microbiota analyses identify bacterial–fungal interactions and biomarkers of colorectal cancer across cohorts]
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|style="padding:.4em;" rowspan=1|2023/05/09
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-10
|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;" rowspan=1|2023/05/02
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-9
|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;" rowspan=1|2023/05/02
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-8
|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;" rowspan=1|2023/05/02
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-7
|style="padding:.4em;"|YY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.cell.2022.11.023 Mobile genetic elements from the maternal microbiome shape infant gut microbial assembly and metabolism]
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|style="padding:.4em;" rowspan=1|2023/04/18
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-6
|style="padding:.4em;"|SH Heo
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.chom.2023.01.018 Mother-to-infant microbiota transmission and infant microbiota development across multiple body sites]
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|style="padding:.4em;" rowspan=1|2023/04/18
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-5
|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;" rowspan=1|Microbiome
|style="padding:.4em;"|23-4
|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;" rowspan=1|Microbiome
|style="padding:.4em;"|23-3
|style="padding:.4em;"|DH Lee
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[https://doi.org/10.1002/imt2.61 Targeting keystone species helps restore the dysbiosis of butyrate‐producing bacteria in nonalcoholic fatty liver disease]
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[https://doi.org/10.1002/advs.202203115 Differential Oral Microbial Input Determines Two Microbiota Pneumo-Types Associated with Health Status]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-1
|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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{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2023-1 scOmics
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|style="padding:.4em;"|JW Yu
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[https://doi.org/10.1101/2023.01.17.524482 Decoupling the correlation between cytotoxic and exhausted T lymphocyte transcriptomic signatures enhances melanoma immunotherapy response prediction from tumor expression]
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|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-23
|style="padding:.4em;"|IS Choi
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[https://doi.org/10.1101/2023.07.28.550993 Major data analysis errors invalidate cancer microbiome findings]
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|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|EJ Sung
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[https://doi.org/10.1038/s43018-022-00475-x A single-cell atlas of glioblastoma evolution under therapy reveals cell-intrinsic and cell-extrinsic therapeutic targets]
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|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|G Koh
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[https://doi.org/10.1038/s43018-022-00433-7 Single-cell meta-analyses reveal responses of tumor-reactive CXCL13+ T cells to immune-checkpoint blockade]
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|style="padding:.4em;" rowspan=1|Single-cell
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!scope="col" style="padding:.4em" | Date
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!scope="col" style="padding:.4em" | Paper title
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|style="padding:.4em;" rowspan=1|2023/08/25
|style="padding:.4em;" rowspan=1|Microbiome
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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;" rowspan=1|Microbiome
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|style="padding:.4em;"|NY Kim
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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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[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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[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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[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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[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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[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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[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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[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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[https://doi.org/10.1016/j.ccell.2022.09.009 Tumor microbiome links cellular programs and immunity in pancreatic cancer]
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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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[https://www.nature.com/articles/s41564-022-01157-1 Phage–host coevolution in natural populations]
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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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[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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[https://www.pnas.org/doi/10.1073/pnas.2105859118 Representation learning of RNA velocity reveals robust cell transitions]
[https://www.nature.com/articles/s41467-022-34188-7 UniTVelo: temporally unified RNA velocity reinforces single-cell trajectory inference]
[https://www.sciencedirect.com/science/article/pii/S0092867421015774 Mapping transcriptomic vector fields of single cells]
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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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[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.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.nature.com/articles/s41596-020-00480-3 Tutorial: assessing metagenomics software with the CAMI benchmarking toolkit disease]
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[https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02195-w Functional and genetic markers of niche partitioning among enigmatic members of the human oral microbiome]
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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://genomebiology.biomedcentral.com/articles/10.1186/s13059-021-02473-1 Pandora: nucleotide-resolution bacterial pan-genomics with reference graphs]
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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://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.sciencedirect.com/science/article/pii/S1931312821002365 Dispersal strategies shape persistence and evolution of human gut bacteria]
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[https://www.sciencedirect.com/science/article/pii/S0092867421003524 The long-term genetic stability and individual specificity of the human gut microbiome]
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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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[https://elifesciences.org/articles/42693 Extensive transmission of microbes along the gastrointestinal tract]
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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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[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/s41467-020-14766-3 Trajectory-based differential expression analysis for single-cell sequencing data]
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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://www.sciencedirect.com/science/article/pii/S0092867419308967?via%3Dihub Single-Cell Analysis of Crohn's Disease Lesions Identifies a Pathogenic Cellular Module Associated with Resistance to Anti-TNF Therapy]
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[https://www.nature.com/articles/s41467-020-15956-9 Multi-omic single-cell snapshots reveal multiple independent trajectories to drug tolerance in a melanoma cell line]
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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/31974247/ Single-cell Transcriptional Diversity Is a Hallmark of Developmental Potential]
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[https://pubmed.ncbi.nlm.nih.gov/30388456/ Defining T Cell States Associated With Response to Checkpoint Immunotherapy in Melanoma]
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[https://pubmed.ncbi.nlm.nih.gov/31915379/ Rapid Non-Uniform Adaptation to Conformation-Specific KRAS(G12C) Inhibition]
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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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[https://pubmed.ncbi.nlm.nih.gov/32066951/ Distinct Microbial and Immune Niches of the Human Colon]
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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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[https://pubmed.ncbi.nlm.nih.gov/30078704/ A Single-Cell Atlas of In Vivo Mammalian Chromatin Accessibility]
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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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[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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[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;"|SB Baek
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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
|style="padding:.4em;" rowspan=2|Microbiome
|style="padding:.4em;"|19-47
|style="padding:.4em;"|MY Lee
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S1931312819303026 Meta-Analysis Reveals Reproducible Gut Microbiome Alterations in Response to a High-Fat Diet]
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|style="padding:.4em;"|19-46
|style="padding:.4em;"|NY Kim
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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
|style="padding:.4em;" rowspan=2|Single-cell
|style="padding:.4em;"|19-45
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://genome.cshlp.org/content/early/2019/04/01/gr.243725.118 The accessible chromatin landscape of the murine hippocampus at single-cell resolution]
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|style="padding:.4em;"|19-44
|style="padding:.4em;"|SB Baek
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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
|style="padding:.4em;" rowspan=2|Microbiome
|style="padding:.4em;"|19-43
|style="padding:.4em;"|SH Lee
|style="padding:.4em;text-align:left"|
[https://science.sciencemag.org/content/365/6449/eaau4735 A sparse covarying unit that describes healthy and impaired human gut microbiota development]
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|style="padding:.4em;"|19-42
|style="padding:.4em;"|CY Kim
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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
|style="padding:.4em;" rowspan=2|Single-cell
|style="padding:.4em;"|19-41
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S0092867419307329?via%3Dihub Intra- and Inter-cellular Rewiring of the Human Colon during Ulcerative Colitis]
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|style="padding:.4em;"|19-40
|style="padding:.4em;"|IS Choi
|style="padding:.4em;text-align:left"|
[https://www.nature.com/articles/nbt.4042 Multiplexed droplet single-cell RNA-sequencing using natural genetic variation]
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|style="padding:.4em;" rowspan=2|2019/09/03
|style="padding:.4em;" rowspan=2|Microbiome
|style="padding:.4em;"|19-39
|style="padding:.4em;"|NY Kim
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S193131281930352X?via%3Dihub The Landscape of Genetic Content in the Gut and Oral Human Microbiome]
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|style="padding:.4em;"|19-38
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S0092867419307755?via%3Dihub Benchmarking Metagenomics Tools for Taxonomic Classification]
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{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2019
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|style="padding:.4em;" rowspan=2|2019/08/20
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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
|style="padding:.4em;"|MY Lee
|style="padding:.4em;text-align:left"|
[https://www.biorxiv.org/content/10.1101/586859v1 Single-cell interactomes of the human brain reveal cell-type specific convergence of brain disorders]
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|style="padding:.4em;" rowspan=3|2019/08/14
|style="padding:.4em;"|19-35
|style="padding:.4em;"|SH Lee
|style="padding:.4em;text-align:left"|
[https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004075 Proportionality: a valid alternative to correlation for relative data]
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|style="padding:.4em;"|19-34
|style="padding:.4em;"|SH Lee
|style="padding:.4em;text-align:left"|
[https://www.nature.com/articles/s41598-017-16520-0 propr: An R-package for Identifying Proportionally Abundant Features Using Compositional Data Analysis]
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|style="padding:.4em;"|19-33
|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
|style="padding:.4em;"|19-32
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[https://www.nature.com/articles/s41592-018-0254-1 A test metric for assessing single-cell RNA-seq batch correction]
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|style="padding:.4em;"|19-31
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S0092867419305598 Comprehensive Integration of Single-Cell Data]
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|style="padding:.4em;" rowspan=2|2019/08/08
|style="padding:.4em;"|19-30
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S0092867418313941 Defining T Cell States Associated with Response to Checkpoint Immunotherapy in Melanoma]
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|style="padding:.4em;"|19-29
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S009286741831242X High-Dimensional Analysis Delineates Myeloid and Lymphoid Compartment Remodeling during Successful Immune-Checkpoint Cancer Therapy]
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|style="padding:.4em;" rowspan=2|2019/08/07
|style="padding:.4em;"|19-28
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[https://www.biorxiv.org/content/10.1101/555557v1 A single-cell reference map for human blood and tissue T cell activation reveals functional states in health and disease]
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|style="padding:.4em;"|19-27
|style="padding:.4em;"|IS Choi
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S009286741831568X Dysfunctional CD8 T Cells Form a Proliferative, Dynamically Regulated Compartment within Human Melanoma]
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|style="padding:.4em;" rowspan=2|2019/07/30
|style="padding:.4em;"|19-26
|style="padding:.4em;"|IS Choi
|style="padding:.4em;text-align:left"|
[https://www.nature.com/articles/nm.4466 High-dimensional single-cell analysis predicts response to anti-PD-1 immunotherapy]
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|style="padding:.4em;"|19-25
|style="padding:.4em;"|JW Cho
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S0092867418311784 A Cancer Cell Program Promotes T Cell Exclusion and Resistance to Checkpoint Blockade]
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|style="padding:.4em;" rowspan=2|2019/07/23
|style="padding:.4em;"|19-24
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/26006008 COMPASS identifies T-cell subsets correlated with clinical outcomes.]
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|style="padding:.4em;"|19-23
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[https://www.nature.com/articles/ncomms14825 Sensitive detection of rare disease-associated cell subsets via representation learning]
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|style="padding:.4em;" rowspan=3|2019/05/30
|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
|style="padding:.4em;"|
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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
|style="padding:.4em;"|
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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
|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
|style="padding:.4em;"|
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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
|style="padding:.4em;"|
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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
|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
|style="padding:.4em;"|
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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
|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
|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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[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
|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
|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
|style="padding:.4em;"|19-6
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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
|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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|style="padding:.4em;"|19-3-1
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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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|style="padding:.4em;"|19-3
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[https://www.ncbi.nlm.nih.gov/pubmed/30787437 The single-cell transcriptional landscape of mammalian organogenesis]
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|style="padding:.4em;" rowspan=2|2019/3/14
|style="padding:.4em;"|19-2
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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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|style="padding:.4em;"|19-1
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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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{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2018
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|style="padding:.4em;" rowspan=2|2018/06/14
|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
|style="padding:.4em;"|18-10
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=Developmental+and+oncogenic+programs+in+H3K27M+gliomas+dissected+by+single-cell+RNA-seq Developmental and oncogenic programs in H3K27M gliomas dissected by single-cell RNA-seq.]
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|style="padding:.4em;"|18-9
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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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|style="padding:.4em;" rowspan=2|2018/05/31
|style="padding:.4em;"|18-8
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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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[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.]
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|style="padding:.4em;" rowspan=2|2018/05/17
|style="padding:.4em;"|18-4
|style="padding:.4em;"|KS Kim
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[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
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[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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|style="padding:.4em;" rowspan=2|2018/05/10
|style="padding:.4em;"|18-2
|style="padding:.4em;"|JW Cho
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[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
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[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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{|class=wikitable style="text-align:center;"
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|style="padding:.4em;" rowspan=2|2017/06/28
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|style="padding:.4em;"|HJ Han
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[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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|style="padding:.4em;"|17-35
|style="padding:.4em;"|JW Cho
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[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
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[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.]
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|style="padding:.4em;"|17-33
|style="padding:.4em;"|MY Lee
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[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
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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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|style="padding:.4em;"|17-31
|style="padding:.4em;"|MY Lee
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.celrep.2016.03.075 Genomic Correlates of Immune-Cell Infiltrates in Colorectal Carcinoma.]
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|style="padding:.4em;" rowspan=2|2017/05/31
|style="padding:.4em;"|17-30
|style="padding:.4em;"|DS Bae
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[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.]
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|style="padding:.4em;"|17-29
|style="padding:.4em;"|JW Cho
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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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|style="padding:.4em;" rowspan=2|2017/05/24
|style="padding:.4em;"|17-28
|style="padding:.4em;"|EB Kim
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[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
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[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
|style="padding:.4em;"|17-26
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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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[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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|style="padding:.4em;" rowspan=2|2017/04/26
|style="padding:.4em;"|17-24
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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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|style="padding:.4em;"|17-23
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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
|style="padding:.4em;"|17-22
|style="padding:.4em;"|SH Lee
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[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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|style="padding:.4em;"|17-21
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1038%2Fnbt.3569 Wishbone identifies bifurcating developmental trajectories from single-cell data.]
|-
|style="padding:.4em;" rowspan=2|2017/04/05
|style="padding:.4em;"|17-20
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[http://biorxiv.org/content/early/2017/02/21/110668 Reversed graph embedding resolves complex single-cell developmental trajectories.]
|-
|style="padding:.4em;"|17-19
|style="padding:.4em;"|SH Lee
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1038%2Fnmeth.4150 Single-cell mRNA quantification and differential analysis with Census.]
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|style="padding:.4em;" rowspan=2|2017/03/29
|style="padding:.4em;"|17-18
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.celrep.2016.12.060 Single-Cell Transcriptomic Analysis Defines Heterogeneity and Transcriptional Dynamics in the Adult Neural Stem Cell Lineage.]
|-
|style="padding:.4em;"|17-17
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/26051941 Single-Cell Network Analysis Identifies DDIT3 as a Nodal Lineage Regulator in Hematopoiesis.]
|-
|style="padding:.4em;" rowspan=2|2017/03/22
|style="padding:.4em;"|17-16
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/27580035 Single-cell analysis of mixed-lineage states leading to a binary cell fate choice.]
|-
|style="padding:.4em;"|17-15
|style="padding:.4em;"|EB Kim
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/27281220 Dissecting direct reprogramming from fibroblast to neuron using single-cell RNA-seq.]
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|style="padding:.4em;" rowspan=2|2017/03/15
|style="padding:.4em;"|17-14
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1126%2Fscience.aad0501 Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq.]
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|style="padding:.4em;"|17-13
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/26084335 Single-cell mRNA sequencing identifies subclonal heterogeneity in anti-cancer drug responses of lung adenocarcinoma cells.]
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|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.]
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|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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|style="padding:.4em;" rowspan=1|2017/01/31
|style="padding:.4em;"|17-06
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[//www.ncbi.nlm.nih.gov/pubmed/27912059 Microbiota Diurnal Rhythmicity Programs Host Transcriptome Oscillations.]
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|style="padding:.4em;" rowspan=1|2017/01/24
|style="padding:.4em;"|17-05
|style="padding:.4em;"|JW Cho
|style="padding:.4em;text-align:left"|
[//www.ncbi.nlm.nih.gov/pubmed/27851914 Transcriptional Landscape of Human Tissue Lymphocytes Unveils Uniqueness of Tumor-Infiltrating T Regulatory Cells.]
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|style="padding:.4em;" rowspan=2|2017/01/17
|style="padding:.4em;"|17-04
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[//www.ncbi.nlm.nih.gov/pubmed/25938943 Mapping long-range promoter contacts in human cells with high-resolution capture Hi-C]
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|style="padding:.4em;"|17-03
|style="padding:.4em;"|EB Kim
|style="padding:.4em;text-align:left"|
[//www.ncbi.nlm.nih.gov/pubmed/27306882 CHiCAGO: robust detection of DNA looping interactions in Capture Hi-C data.]
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|style="padding:.4em;" rowspan=1|2017/01/10
|style="padding:.4em;"|17-02
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[//www.ncbi.nlm.nih.gov/pubmed/26527291 ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis]
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|style="padding:.4em;" rowspan=1|2017/01/03
|style="padding:.4em;"|17-01
|style="padding:.4em;"|SH Lee
|style="padding:.4em;text-align:left"|
[//www.ncbi.nlm.nih.gov/pubmed/26287467 Single-cell messenger RNA sequencing reveals rare intestinal cell types]
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|}
{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2016
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!scope="col" style="padding:.4em" |Date
!scope="col" style="padding:.4em" | Paper index
!scope="col" style="padding:.4em" | Presenter
!scope="col" style="padding:.4em" | Paper title
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|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.]
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|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
|style="padding:.4em;"|2016-29
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/24658644 The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells.]
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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
|style="padding:.4em;"|2016-27
|style="padding:.4em;"|MY Lee
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26000487 Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells.]
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|style="padding:.4em;" rowspan=1|2016/11/8
|style="padding:.4em;"|2016-26
|style="padding:.4em;"|JW Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26000488 Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets.]
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|style="padding:.4em;" rowspan=1|2016/11/1
|style="padding:.4em;"|2016-25
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27426982 Causal Mechanistic Regulatory Network for Glioblastoma Deciphered Using Systems Genetics Network Analysis.]
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|style="padding:.4em;" rowspan=1|2016/10/25
|style="padding:.4em;"|2016-24
|style="padding:.4em;"|MY Lee
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27549193 Comprehensive analyses of tumor immunity: implications for cancer immunotherapy.]
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|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]
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|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]
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|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]
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|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.]
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|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
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27264179 A Computational Drug Repositioning Approach for Targeting Oncogenic Transcription Factors]
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|style="padding:.4em;" rowspan=1|2016/8/16
|style="padding:.4em;"|2016-17
|style="padding:.4em;"|JW Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27309802 The landscape of accessible chromatin in mammalian preimplantation embryos]
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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]
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|style="padding:.4em;" rowspan=1|2016/7/25
|style="padding:.4em;"|2016-14
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=integration+of+summary+data+from+gwas+and+eqtl+studies Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets]
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|style="padding:.4em;" rowspan=1|2016/7/18
|style="padding:.4em;"|2016-13
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/23624555 Identification of transcriptional regulators in the mouse immune system]
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|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]
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|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]
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|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]
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|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]
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|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]
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|style="padding:.4em;" rowspan=2|2016/5/11
|style="padding:.4em;"|2016-6
|style="padding:.4em;"|DS Bae, CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/25171417 Predicting Cancer-specific vulnerability via data-driven detection of synthetic lethality]
|-
|style="padding:.4em;"| 2016-5
|style="padding:.4em;"|DS Bae, CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/23467089 Dynamic regulatory network controlling Th17 cell differentiation]
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|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]
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|style="padding:.4em;" rowspan=2|2016/4/27
|style="padding:.4em;"|2016-2
|style="padding:.4em;"|CY Kim,SJ Kwon
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26780608 A predictive computational framework for direct reprogramming between human cell types]
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|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;"
|+style="text-align:left;font-size:12pt" | 2015
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!scope="col" style="padding:.4em" | Date
!scope="col" style="padding:.4em" | Paper index
!scope="col" style="padding:.4em" | Presenter
!scope="col" style="padding:.4em" | Paper title
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|style="padding:.4em;" rowspan=2|2015/06/11
|style="padding:.4em;"|2015-55
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://genome.cshlp.org/content/24/2/340.long Improved exome prioritization of disease genes through cross-species phenotype comparison.]
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|style="padding:.4em;"|2015-54
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0002929714001128 Phevor combines multiple biomedical ontologies for accurate identification of disease-causing alleles in single individuals and small nuclear families.]
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|style="padding:.4em;" rowspan=2|2015/06/04
|style="padding:.4em;"|2015-53
|style="padding:.4em;"|
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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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|style="padding:.4em;"|2015-52
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://genome.cshlp.org/content/21/9/1529.long A probabilistic disease-gene finder for personal genomes.]
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|style="padding:.4em;" rowspan=2|2015/05/28
|style="padding:.4em;"|2015-51
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/ng/journal/v46/n12/full/ng.3141.html Systematic analysis of noncoding somatic mutations and gene expression alterations across 14 tumor types.]
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|style="padding:.4em;"|2015-50
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nbt/journal/v28/n5/full/nbt.1630.html GREAT improves functional interpretation of cis-regulatory regions.]
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|style="padding:.4em;" rowspan=2|2015/05/21
|style="padding:.4em;"|2015-49
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nmeth/journal/v11/n3/full/nmeth.2832.html Functional annotation of noncoding sequence variants.]
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|style="padding:.4em;"|2015-48
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://genomebiology.com/content/15/10/480 FunSeq2: A framework for prioritizing noncoding regulatory variants in cancer.]
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|style="padding:.4em;" rowspan=2|2015/05/14
|style="padding:.4em;"|2015-47
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nmeth/journal/v12/n2/full/nmeth.3215.html Selecting causal genes from genome-wide association studies via functionally coherent subnetworks.]
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|style="padding:.4em;"|2015-46
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/ncomms/2015/150119/ncomms6890/full/ncomms6890.html Biological interpretation of genome-wide association studies using predicted gene functions.]
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|style="padding:.4em;" rowspan=3|2015/05/07
|style="padding:.4em;"|2015-45
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/ncomms/2014/140626/ncomms5212/full/ncomms5212.html Human symptoms-disease network.]
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|style="padding:.4em;"|2015-44
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867413010246 A nondegenerate code of deleterious variants in Mendelian loci contributes to complex disease risk.]
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|style="padding:.4em;"|2015-43
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.sciencemag.org/content/347/6224/1257601.long Uncovering disease-disease relationships through the incomplete interactome.]

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|style="padding:.4em;" rowspan=2|2015/04/30
|style="padding:.4em;"|2015-42
|style="padding:.4em;"|
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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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|style="padding:.4em;"|2015-41
|style="padding:.4em;"|
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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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|style="padding:.4em;" rowspan=3|2015/04/23
|style="padding:.4em;"|2015-40
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v518/n7539/full/nature13990.html Dissecting neural differentiation regulatory networks through epigenetic footprinting.]
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|style="padding:.4em;"|2015-39
|style="padding:.4em;"|
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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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|style="padding:.4em;"|2015-38
|style="padding:.4em;"|
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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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|style="padding:.4em;" rowspan=2|2015/04/09
|style="padding:.4em;"|2015-37
|style="padding:.4em;"|
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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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|style="padding:.4em;"|2015-36
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414014974 A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping.]
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|style="padding:.4em;" rowspan=2|2015/04/02
|style="padding:.4em;"|2015-35
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nrg/journal/v14/n6/full/nrg3454.html Exploring the three-dimensional organization of genomes: interpreting chromatin interaction data.]
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|style="padding:.4em;"|2015-34
|style="padding:.4em;"|
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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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|style="padding:.4em;" rowspan=2|2015/03/26
|style="padding:.4em;"|2015-33
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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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|style="padding:.4em;"|2015-32
|style="padding:.4em;"|
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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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|style="padding:.4em;" rowspan=2|2015/03/19
|style="padding:.4em;"|2015-31
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nbt/journal/vaop/ncurrent/full/nbt.3154.html Decoding the regulatory network of early blood development from single-cell gene expression measurements.]
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|style="padding:.4em;"|2015-30
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nrg/journal/v16/n3/full/nrg3833.html Computational and analytical challenges in single-cell transcriptomics.]
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|style="padding:.4em;" rowspan=3|2015/03/12
|style="padding:.4em;"|2015-29
|style="padding:.4em;"|
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[http://www.sciencedirect.com/science/article/pii/S0092867415000136 Extensive Strain-Level Copy-Number Variation across Human Gut Microbiome Species.]
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|style="padding:.4em;"|2015-28
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|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 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.]
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|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 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"
|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 title
|-
|rowspan = "1"|2013/01/11
|align ="center"|2012-81
|[http://genome.cshlp.org/content/22/8/1589.full (TH Kim) MuSiC: identifying mutational significance in cancer genomes.]
|-
|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"|2012-74
|[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]
|-
| 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]
|-
| align="center"|2012-72
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2063581/ (SH Hwang) Network-based classification of breast cancer metastasis]
|-
| 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]
|-
|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]
|-
| align="center"|2012-66
|[http://bioinformatics.oxfordjournals.org/content/25/16/2078.long (ER Kim&TH Kim)The Sequence Alignment/Map format and SAMtools]
|-
| 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]
|-
| align="center"|2012-64
|[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"|2012-63
|[http://www.sciencemag.org/content/336/6086/1262.abstract (T Lee&CY Kim)Host-Gut Microbiota Metabolic Interactions]
|-
|align="center"|2012-62
|[http://www.sciencemag.org/content/336/6086/1268.full (AR Cho,JH Ju)Interactions Between the Microbiota and the Immune System]
|-
|align="center"|2012-61
|[http://www.sciencemag.org/content/336/6086/1255.abstract (SH Hwang&HJ Cho)The Application of Ecological Theory Toward an Understanding of the Human Microbiome]
|-
|align="center"|2012-60
|[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
|[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]
|-
| align="center"|2012-58
|[http://www.nature.com/nature/journal/v486/n7402/full/nature11209.html (JH Shin&HJ Kim)A framework for human microbiome research]
|-
|align="center"|2012-57
|[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
|[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]
|-
|align="center"|2012-55
|[http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002511 (YH Go&HJ Han)Google Goes Cancer: Improving Outcome Prediction for Cancer Patients by Network-Based Ranking of Marker Genes]
|-
| align="center"|2012-54
|[http://www.nature.com/msb/journal/v7/n1/full/msb201147.html (YH Go&HJ Han)A pharmacogenomic method for individualized prediction of drug sensitivity]
|-
|align="center"|2012-53
|[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
|[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]
|-
|align="center"|2012-51
|[http://www.nature.com/nature/journal/v483/n7391/full/nature11005.html (ER Kim&TH Kim)Systematic identification of genomic markers of drug sensitivity in cancer cells]
|-
| align="center"|2012-50
|[http://www.pnas.org/content/early/2011/10/13/1018854108.abstract (ER Kim&TH Kim)Subtype and pathway specific responses to anticancer compounds in breast cancer]
|-
|align="center"|2012-49
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature10945.html (JE Shim&KS Kim)De novo mutations revealed by whole-exome sequencing are strongly associated with autism]
|-
|align="center"|2012-48
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature11011.html (JE Shim&KS Kim)Patterns and rates of exonic de novo mutations in autism spectrum disorders]
|-
|align="center"|2012-47
|[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
|[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)]
|-
|align="center"|2012-45
|[http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002621 (T Lee&CY Kim)Type 2 Diabetes Risk Alleles Demonstrate Extreme Directional Differentiation among Human Populations, Compared to Other Diseases]
|-
|align="center"|2012-44
|[http://www.nature.com/nature/journal/v480/n7376/full/nature10665.html (AR Cho&JH Ju)Predicting mutation outcome from early stochastic variation in genetic interaction partners]
|-
|align="center"|2012-43
|[http://www.sciencemag.org/content/335/6064/82 (AR Cho&JH Ju)Fitness Trade-Offs and Environmentally Induced Mutation Buffering in Isogenic C. elegans]
|-
| align="center"|2012-42
|[http://genome.cshlp.org/content/22/6/1163 (JE Shim&KS Kim)Identification of microRNA-regulated gene networks by expression analysis of target genes]
|-
|align="center"|2012-41
|[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
|[http://www.cell.com/abstract/S0092-8674(12)00573-9 (JH Soh)Functional Repurposing Revealed by Comparing S. pombe and S. cerevisiae Genetic Interactions]
|-
|align="center"|2012-39
|[http://genome.cshlp.org/content/22/2/375.long (ER Kim&TH Kim)De novo discovery of mutated driver pathways in cancer]
|-
| align="center"|2012-38
|[http://www.nature.com/msb/journal/v4/n1/full/msb20082.html (YH Go&HJ Han)A systems biology approach to prediction of oncogenes and molecular perturbation targets in B-cell lymphomas]
|-
|align="center"|2012-37
|[http://www.nature.com/msb/journal/v7/n1/full/msb201126.html (SH Hwang&HJ Cho)PREDICT: a method for inferring novel drug indications with application to personalized medicine]
|-
|align="center"|2012-36
|[http://www.nature.com/nature/journal/v453/n7198/full/nature06973.html (SH Hwang&HJ Cho)Synergistic response to oncogenic mutations defines gene class critical to cancer phenotype]
|-
|align="center"|2012-35
|[http://www.sciencemag.org/content/334/6062/1518.full (JH Shin&HJ Kim)Detecting Novel Associations in Large Data Sets]
|-
|rowspan = "3" |2012/03/05
| align="center"|2012-34
|[http://www.ncbi.nlm.nih.gov/pubmed/18516045 (8,HH Kim)Mapping and quantifying mammalian transcriptomes by RNA-Seq.]
|-
|align="center"|2012-33
|[http://genomebiology.com/2010/11/10/R106 (11,Go&Ju)Differential expression analysis for sequence count data]
|-
|align="center"|2012-32
|[http://bioinformatics.oxfordjournals.org/content/26/1/139.short (12,Go&Ju)edgeR: a Bioconductor package for differential expression analysis of digital gene expression data ]
|-
|rowspan = "7" |2012/02/27 2012/02/28
| align="center"|2012-31
|[http://www.nature.com/nrg/journal/v10/n1/full/nrg2484.html (1,JW Song)RNA-Seq: a revolutionary tool for transcriptomics]
|-
|align="center"|2012-30
|[http://www.nature.com/nmeth/journal/v8/n6/full/nmeth.1613.html (2,JW Song)Computational methods for transcriptome annotation and quantification using RNA-seq]
|-
|align="center"|2012-29
|[http://genomebiology.com/2010/11/12/220 (3,HJ Han)From RNA-seq reads to differential expression results]
|-
|align="center"|2012-28
|[http://www.nature.com/nmeth/journal/v7/n9/full/nmeth.1491.html (4,AR Cho)Comprehensive comparative analysis of strand-specific RNA sequencing methods]
|-
|align="center"|2012-27
|[http://www.plosone.org/article/info:doi%2F10.1371%2Fjournal.pone.0026426 (5,T Lee)A Low-Cost Library Construction Protocol and Data Analysis Pipeline for Illumina-Based Strand-Specific Multiplex RNA-Seq]
|-
|align="center"|2012-26
|[http://www.nature.com/nbt/journal/v28/n5/abs/nbt.1621.html (6,So&Shin)Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation]
|-
|align="center"|2012-25
|[http://bioinformatics.oxfordjournals.org/content/25/9/1105.abstract (7,So&Shin)TopHat: discovering splice junctions with RNA-Seq]
|-
|rowspan = "5" |2012/02/06
| align="center"|2012-24
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3125733/ mirConnX: condition-specific mRNA-microRNA network integrator]
|-
|align="center"|2012-23
|[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002190 Construction and Analysis of an Integrated Regulatory Network Derived from High-Throughput Sequencing Data]
|-
|align="center"|2012-22
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002415 A Densely Interconnected Genome-Wide Network of MicroRNAs and Oncogenic Pathways Revealed Using Gene Expression Signatures]
|-
|align="center"|2012-21
|[http://genome.cshlp.org/content/20/5/589.short Reprogramming of miRNA networks in cancer and leukemia]
|-
|align="center"|2012-20
|[http://www.cell.com/abstract/S0092-8674(11)01152-4 An Extensive MicroRNA-Mediated Network of RNA-RNA Interactions Regulates Established Oncogenic Pathways in Glioblastoma]
|-
|rowspan = "5" |2012/01/30
|align="center"|2012-19
|[http://www.cell.com/abstract/S0092-8674(11)00237-6 Principles and Strategies for Developing Network Models in Cancer]
|-
|align="center"|2012-18
|[http://www.nature.com/ng/journal/v37/n4/full/ng1532.html Reverse engineering of regulatory networks in human B cells]
|-
|align="center"|2012-17
|[http://www.nature.com/nature/journal/v452/n7186/abs/nature06757.html Variations in DNA elucidate molecular networks that cause disease]
|-
|align="center"|2012-16
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2779083/ Harnessing gene expression to identify the genetic basis of drug resistance]
|-
|align="center"|2012-15
|[http://www.sciencedirect.com/science/article/pii/S0092867410012936 An Integrated Approach to Uncover Drivers of Cancer]
|-
|rowspan = "3" |2012/01/09
|align="center"|2012-14
|[http://www.ncbi.nlm.nih.gov/pubmed/18704161 Genetic variation in an individual human exome.]
|-
|align="center"|2012-13
|[http://www.ncbi.nlm.nih.gov/pubmed/22081227 Predicting phenotypic variation in yeast from individual genome sequences.]
|-
|align="center"|2012-12
|[http://www.ncbi.nlm.nih.gov/pubmed/20435227 Clinical assessment incorporating a personal genome.]
|-
|rowspan = "6" |2012/01/09 2012/01/16
| align="center"|2012-11
|[http://www.ncbi.nlm.nih.gov/pubmed/20399638 Human allelic variation: perspective from protein function, structure, and evolution.]
|-
|align="center"|2012-10
|[http://www.ncbi.nlm.nih.gov/pubmed/19561590 Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm.]
|-
|align="center"|2012-09
|[http://www.ncbi.nlm.nih.gov/pubmed/11230178 Prediction of deleterious human alleles.]
|-
|align="center"|2012-08
|[http://www.ncbi.nlm.nih.gov/pubmed/12202775 Human non-synonymous SNPs: server and survey.]
|-
|align="center"|2012-07
|[http://www.ncbi.nlm.nih.gov/pubmed/20354512 A method and server for predicting damaging missense mutations.]
|-
|align="center"|2012-06
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1920242/ SNAP: predict effect of non-synonymous polymorphisms on function]
|-
|rowspan = "3" |2012/01/09 2012/01/16
|align="center"|2012-05
|[http://www.ncbi.nlm.nih.gov/pubmed/21920052 Computational and statistical approaches to analyzing variants identified by exome sequencing.]
|-
|align="center"|2012-04
|[http://www.ncbi.nlm.nih.gov/pubmed/18179889 Shifting paradigm of association studies: value of rare single-nucleotide polymorphisms.]
|-
|align="center"|2012-03
|[http://www.ncbi.nlm.nih.gov/pubmed/19684571 Targeted capture and massively parallel sequencing of 12 human exomes.]
|-
|rowspan = "2" |2012/01/09 2012/01/16
|align="center"|2012-02
|[http://www.ncbi.nlm.nih.gov/pubmed/17637733 The distribution of fitness effects of new mutations.]
|-
|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]
|}
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
|align="center"|2011-49
|[http://bmir.stanford.edu/file_asset/index.php/1407/BMIR-2009-1355.pdf (Shin)Data-Driven Methods to Discover Molecular Determinants of Serious Adverse Drug Events]
|-
|align="center"|2011-48
|[http://www.nature.com/nchembio/journal/v4/n11/abs/nchembio.118.html (Shin)Network pharmacology: the next paradigm in drug discovery]
|-
|align="center"|2011-47
|[http://www.nature.com/msb/journal/v7/n1/full/msb201171.html (Oh)Systematic exploration of synergistic drug pairs]
|-
|align="center"|2011-46
|[http://www.nature.com/msb/journal/v5/n1/full/msb200995.html (Shim)Chemogenomic profiling predicts antifungal synergies]
|-
|align="center"|2011-45
|[http://www.pnas.org/content/104/32/13086 (Beck)A strategy for predicting the chemosensitivity of human cancers and its application to drug discovery]
|-
|align="center"|2011-44
|[http://www.nature.com/nchembio/journal/v1/n7/full/nchembio747.html (Hwang)Analysis of drug-induced effect patterns to link structure and side effects of medicines]
|-
|rowspan = "3" |2011/11/14
|align="center"|2011-43
|[http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000662 Network-Based Elucidation of Human Disease Similarities Reveals Common Functional Modules Enriched for Pluripotent Drug Targets]
|-
|align="center"|2011-42
|[http://www.nature.com/msb/journal/v7/n1/full/msb201131.html Cross-species discovery of syncretic drug combinations that potentiate the antifungal fluconazole]
|-
|align="center"|2011-41
|[http://www.nature.com/msb/journal/v7/n1/full/msb20115.html Analysis of multiple compound–protein interactions reveals novel bioactive molecules]
|-
|rowspan = "4" |2011/11/07
|align="center"|2011-40
|[http://www.nature.com/nbt/journal/v25/n10/abs/nbt1338.html Drug—target network]
|-
|align="center"|2011-39
|[http://www.nature.com/msb/journal/v6/n1/full/msb200998.html A side effect resource to capture phenotypic effects of drugs]
|-
|align="center"|2011-38
|[http://genome.cshlp.org/content/18/2/206.long Quantitative systems-level determinants of human genes targeted by successful drugs]
|-
|align="center"|2011-37
|[http://www.sciencemag.org/content/321/5886/263.short Drug Target Identification Using Side-Effect Similarity]
|-
|rowspan = "3" |2011/11/07
|align="center"|2011-36
|[http://stm.sciencemag.org/content/3/96/96ra77.short Discovery and Preclinical Validation of Drug Indications Using Compendia of Public Gene Expression Data]
|-
|align="center"|2011-35
|[http://bib.oxfordjournals.org/content/12/4/303.short Exploiting drug–disease relationships for computational drug repositioning]
|-
|align="center"|2011-34
|[http://www.springerlink.com/content/4489r051nu2t0ul1/ Drug Discovery in a Multidimensional World: Systems, Patterns, and Networks]
|-
|rowspan = "3" |2011/10/05
|align="center"|2011-33
|[http://genome.cshlp.org/content/20/7/960.full Analysis of membrane proteins in metagenomics: Networks of correlated environmental features and protein families]
|-
|align="center"|2011-32
|[http://www.pnas.org/content/106/5/1374.full Quantifying environmental adaptation of metabolic pathways in metagenomics]
|-
|align="center"|2011-31
|[http://www.pnas.org/content/104/35/13913.abstract Quantitative assessment of protein function prediction from metagenomics shotgun sequences]
|-
|rowspan = "4" |2011/10/04
|align="center"|2011-30
|[http://www.nature.com/nature/journal/v464/n7285/full/nature08821.html A human gut microbial gene catalogue established by metagenomic sequencing]
|-
|align="center"|2011-29
|[http://www.nature.com/nrmicro/journal/v6/n9/abs/nrmicro1935.html Molecular eco-systems biology: towards an understanding of community function]
|-
|align="center"|2011-28
|[http://genome.cshlp.org/content/early/2009/04/20/gr.085464.108 Microbial community profiling for human microbiome projects: Tools, techniques, and challenges]
|-
|align="center"|2011-27
|[http://www.nature.com/nrmicro/journal/v9/n4/full/nrmicro2540.html Unravelling the effects of the environment and host genotype on the gut microbiome]
|-
|rowspan = "2" |2011/09/19
|align="center"|2011-26
|[http://www.sciencemag.org/content/333/6042/596.full independently evolved virulence effectors converge onto hubs in a plant immune system Network]
|-
|align="center"|2011-25
|[http://www.sciencemag.org/content/333/6042/601.full Evidence for network evolution in an arabidopsis interactome Map]
|-
|rowspan = "2" |2011/09/05
|align="center"|2011-24
|[http://www.sciencedirect.com/science/article/pii/S0092867411005861 Exome Sequencing of Ion Channel Genes Reveals Complex Profiles Confounding Personal Risk Assessment in Epilepsy]
|-
|align="center"|2011-23
|[http://www.cell.com/abstract/S0092-8674(11)00543-5 Pluripotency factors in Embryonic stem cells Regulate Differentiation into Germ Layers]
|-
|rowspan = "2" |2011/09/22
|align="center"|2011-22
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002077 Integrated Genome-scale predition of Detrimental Mutations in Transcription Networks]
|-
|align="center"|2011-21
|[http://www.nature.com/nrg/journal/v12/n4/abs/nrg2969.html From expression QTLs to personalized transcriptomics]
|-
|2011/06/20
|align="center"|2011-20
|[http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001046 a user's guide to the encyclopedia of DNA elements(ENCODE)]
|-
|rowspan = "2" |2011/03/30
|align="center"|2011-19
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature09944.html enterotypes of the human gut microbiome]
|-
|align="center"|2011-18
|[http://www.nature.com/msb/journal/v7/n1/full/msb20116.html toward molecular trait-based ecology, through intergration of biogeochemical, geographical and metagenomic data]
|-
|rowspan = "2" |2011/03/16
|align="center"|2011-17
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002047 variable pathogenicity determines individual lifespan in ''caenorhabditis elegans'']
|-
|align="center"|2011-16
|[http://www.cell.com/abstract/S0092-8674(11)00371-0 a high-resolution ''c.elegans'' essential gene network based on phenotypic profiling of a complex tissue]
|-
|rowspan = "3" |2011/04/25
|align="center"|2011-15
|[http://www.ncbi.nlm.nih.gov/pubmed/21376230 Hallmarks of Cancer : The next generation]
|-
|align="center"|2011-14
|[http://www.cell.com/abstract/S0092-8674(11)00296-0 Mapping Cancer Origins]
|-
|align="center"|2011-13
|[http://www.cell.com/abstract/S0092-8674(11)00297-2 Genetic Interactions in Cancer Progression and Treatment]
|-
|rowspan = "2" |2011/04/11
|align="center"|2011-12
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1001273 Proteins encoded in genomic regions associated with immune-mediated disease physically interact and suggest underlying biology]
|-
|align="center"|2011-11
|***Changed!***
[http://www.ncbi.nlm.nih.gov/pubmed/19557189 Identifying relationships among genomic disease regions: predicting genes at pathogenic SNP associations and rare deletions]
|-
|rowspan = "2"|2011/03/28
|align="center"|2011-10
|[http://www.pnas.org/content/106/44/18843.long profiling translatomes of discrete cell populations resolves altered cellular priorities during hypoxia in Arabidopsis]
|-
|align="center"|2011-09
|[http://www.nature.com/msb/journal/v6/n1/full/msb201076.html cell-type specific analysis of translating RNAs in developing flowers reveals new levels of control]
|-
|rowspan = "2"|2011/03/14
|align="center"|2011-08
|[http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6WSN-51SFHJD-1&_user=44062&_coverDate=01%2F07%2F2011&_rdoc=1&_fmt=high&_orig=search&_origin=search&_sort=d&_docanchor=&view=c&_acct=C000004738&_version=1&_urlVersion=0&_userid=44062&md5=2f6017aab4c794ed7e78fbbd8447077a&searchtype=a phenotypic landscape of a bacterial cell]
|-
|align="center"|2011-07
|[http://www.nature.com/msb/journal/v6/n1/full/msb2010107.html cross-species chemogenomic profiling reveals evolutionarily conserved drug mode of action]
|-
|rowspan = "2"|2011/02/28
|align="center"|2011-06
|[http://www.pnas.org/content/early/2010/09/23/1004666107.abstract genomic patterns of pleiotropy and the evolution of complexity]
|-
|align="center"|2011-05
|[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1001009 simultaneous genome-wide inference of physical, genetic, regulatory, and functional pathway]
|-
|2011/02/21
|align="center"|2011-04
|[http://www.nature.com/msb/journal/v6/n1/full/msb201071.html dynamic interaction networks in a hierarchically organized tissue]
|-
|2011/02/14
|align="center"|2011-03
|[http://www.sciencemag.org/content/330/6009/1385.abstract rewiring of genetic networks in response to DNA damage]
|-
|rowspan = "2"|2011/01/31
|align="center"|2011-02
|[http://www.nature.com/nrg/journal/v10/n9/abs/nrg2633.html Applying mass spectrometry-based proteomics to genetics, genomics and network biology]
|-
|align="center"|2011-01
|[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
|align="center"|2010-29
|[http://www.nature.com/nature/journal/v467/n7312/full/nature09326.html Functional_roles_fornoise_in_genetic_circuits]
|-
|align="center"|2010-28
|[http://www.nature.com/ng/journal/v42/n7/full/ng.610.html Estimation_of_effect_size_distribution_from_genome-wide_association_studies_and_implications_for_future_discoveries]
|-
|rowspan = "2"|2010/11/15
|align="center"|2010-27
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1000932 Liver_and_Adipose_Expression_associated_SNPs_are_enriched_for_association_to_type_2_diabetes]
|-
|align="center"|2010-26
|[http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B8JDC-50J4MRJ-2&_user=44062&_coverDate=10%2F10%2F2010&_rdoc=1&_fmt=high&_orig=search&_origin=search&_sort=d&_docanchor=&view=c&_acct=C000004738&_version=1&_urlVersion=0&_userid=44062&md5=7667a67cf8ba3f68117b8d0ff85a8887&searchtype=a It's_the_machine_that_matters:_predicting_gene_function_and_phenotype_from_protein_networks]
|-
|rowspan = "2"|2010/11/01
|align="center"|2010-25
|[http://genome.cshlp.org/content/early/2010/06/15/gr.104216.109 A_genome-wide_map_of_human_genetic_interactions_inferred_from_radiation_hybrid_genotypes]
|-
|align="center"|2010-24
|[http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0012139 A_Genome-Wide_Gene_Function_Prediction_Resource_for_Drosophila_melanogaster]
|-
|2010/10/11
|align="center"|2010-23
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2913399/?tool=pubmed Dissecting_spatio-temporal_protein_networks_driving_human_heart_development_and_related_disorders]
|-
|rowspan = "2"|2010/09/13
|align="center"|2010-22
|[http://www.nature.com/ng/journal/v42/n7/full/ng.600.html Transposable_elements_have_rewired_the_core_regulatory_network_of_human_embryonic_stem_cells]
|-
|align="center"|2010-21
|[http://www.nature.com/ng/journal/v42/n7/full/ng0710-557.html Limits_of_sequence_and_functional_conservation]
|-
|2010/05/26
|align="center"|2010-20
|[[media:100428_network_based_elucidation_of_human_disease_similarities_reveals_common_functional_modules_enriched_for_pluripotent_drug_targets.pdf|network_based_elucidation_of_human_disease_similarities_reveals_common_functional_modules_enriched_for_pluripotent_drug_targets.pdf]]
|-
|2010/05/19
|align="center"|2010-19
|[[media:100421_interpreting_metabolomic_profiles_using_unbiased_pathway_models.pdf|interpreting_metabolomic_profiles_using_unbiased_pathway_models.pdf]]
|-
|2010/04/21
|align="center"|2010-18
|[[media:100414_identification_of_networks_of_co_occurring_tumor_related_dna_copy_number_changes_using_a_genome_wide_scoringapproach.pdf|identification_of_networks_of_co_occurring_tumor_related_dna_copy_number_changes_using_a_genome_wide_scoringapproach.pdf]]
|-
|2010/04/14
|align="center"|2010-17
|[http://www.cell.com/retrieve/pii/S0092867410000796 an_atlas_of_combinatorial_transcriptional_regulation_in_mouse_and_man]
|-
|rowspan = "2" |2010/04/07
|align="center"|2010-16
|[http://www.pnas.org/content/early/2010/03/11/0910200107.long systematic_discovery_of_nonobvious_human_disease_models_through_orthologous_phenotypes]
|-
|align="center"|2010-15
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature08800.html genome_side_association_study_of_107_phenotypes_in_Arabidopsis_thaliana_inbred_lines]
|-
|rowspan="2"|2010/03/31
|align="center"|2010-14
|[[media:100331_toward_stem_cell_systems_biology_from_molecules_to_networks_and_landscapes.pdf|toward_stem_cell_systems_biology_from_molecules_to_networks_and_landscapes.pdf]]
|-
|align="center"|2010-13
|[http://www.nature.com/nrm/journal/v10/n10/abs/nrm2766.html systems_biology_of_stem_cell_fate_and_cellular_reprogramming]
|-
|2010/03/24
|align="center"|2010-12
|[http://www.nature.com/nbt/journal/v27/n2/abs/nbt.1522.html dynamic_modularity_in_protein_interaction_networks_predicts_breast_cancer_outcome]
|-
|2010/01/18
|align="center"|2010-11
|JournalClub_100118_a_tutorial_on_statistical_methods_for_population_association_studies
|-
|2010/01/16
|align="center"|2010-10
|systems-level_dinamic_analyses_of_fate_change_in_murine_embryonic_stem_cells
|-
|2010/01/15
|align="center"|2010-09
|distinguishing_direct_versus_indirect_transcription_factor-DNA-interactions
|-
|2010/01/14
|align="center"|2010-08
|chemogenomic_profiling_predicts_antifungal_synergies
|-
|2010/01/13
|align="center"|2010-07
|edgetic_perturbation_models_of_human_inherited_disorders
|-
|2010/01/12
|align="center"|2010-06
|analysis_of_cell_fate_from_single-cell_gene_expression_profiles_in_C.elegans
|-
|2010/01/11
|align="center"|2010-05
|predicting_new_molecular_targets_for_known_drugs.pdf
Reference:SEA(Similarity Ensemble Approach)
|-
|2010/01/09
|align="center"|2010-04
|harnessing_gene_expression_to_identify_the_genetic_basis_of_drug_resistance
|-
|2010/01/08
|align="center"|2010-03
|an_integrative_approach_to_reveal_driver_gene_fusions_from_paired_end_sequencing_data_in_cancer
|-
|2010/01/07
|align="center"|2010-02
|a_phenotypic_profile_of_the_candida_albicans_regulatory_network
|-
|2010/01/06
|align="center"|2010-01
|a_global_view_of_protein_expression_in_human_cells_tissues_and_organs
|}

Journal Club

2025-02-21T08:50:43Z

Sej4926:

{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2025-1 Journal Club
|-
!scope="col" style="padding:.4em" | Date
!scope="col" style="padding:.4em" | Paper index
!scope="col" style="padding:.4em" | Presenter
!scope="col" style="padding:.4em" | Paper title
|-
|style="padding:.4em;" rowspan=1|2025/4/14
|style="padding:.4em;"|25-32
|style="padding:.4em;"|SB Lim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2025.01.07.631644 A Single-Graph Visualization to Reveal Hidden Explainability Patterns of SHAP Feature Interactions in Machine Learning for Biomedical Issues]
|-
|style="padding:.4em;" rowspan=1|2025/4/7
|style="padding:.4em;"|25-31
|style="padding:.4em;"|YR Jung
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2024.11.18.624166 Learning multi-cellular representations of single-cell transcriptomics data enables characterization of patient-level disease states]
|-
|style="padding:.4em;" rowspan=1|2025/4/7
|style="padding:.4em;"|25-30
|style="padding:.4em;"|YR Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.cell.2024.08.037 Emergence of community behaviors in the gut microbiota upon drug treatment]
|-
|style="padding:.4em;" rowspan=1|2025/3/31
|style="padding:.4em;"|25-29
|style="padding:.4em;"|JY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41564-024-01832-5 Prediction of strain level phage–host interactions across the Escherichia genus using only genomic information]
|-
|style="padding:.4em;" rowspan=1|2025/3/31
|style="padding:.4em;"|25-28
|style="padding:.4em;"|WJ Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2025.01.07.631807 Metagenomic estimation of absolute bacterial biomass in the mammalian gut through host-derived read normalization]
|-
|style="padding:.4em;" rowspan=1|2025/3/24
|style="padding:.4em;"|25-27
|style="padding:.4em;"|SH Ahn
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2024.10.02.616292 De novo discovery of conserved gene clusters in microbial genomes with Spacedust]
|-
|style="padding:.4em;" rowspan=1|2025/3/24
|style="padding:.4em;"|25-26
|style="padding:.4em;"|SH Ahn
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2024.04.14.589414 Rapid and Sensitive Protein Complex Alignment with Foldseek-Multimer]
|-
|style="padding:.4em;" rowspan=1|2025/3/24
|style="padding:.4em;"|25-25
|style="padding:.4em;"|EJ Sung
|style="padding:.4em;text-align:left"|
[https://www.medrxiv.org/content/10.1101/2024.04.04.24305313v1 Single-cell RNA sequencing of human tissue supports successful drug targets]
|-
|style="padding:.4em;" rowspan=1|2025/3/17
|style="padding:.4em;"|25-24
|style="padding:.4em;"|HJ Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2024.12.30.630825 MGM as a large-scale pretrained foundation model for microbiome analyses in diverse contexts ]
|-
|style="padding:.4em;" rowspan=1|2025/3/17
|style="padding:.4em;"|25-23
|style="padding:.4em;"|JY Ma
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1126/science.ado9336 Sequence modeling and design from molecular to genome scale with Evo]
|-
|style="padding:.4em;" rowspan=1|2025/3/10
|style="padding:.4em;"|25-22
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2024.12.18.629142 Human gut microbiome gene co-expression network reveals a loss in taxonomic and functional diversity in Parkinson’s disease]
|-
|style="padding:.4em;" rowspan=1|2025/3/10
|style="padding:.4em;"|25-21
|style="padding:.4em;"|NY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2025.01.06.631550 Quantifying Metagenomic Strain Associations from Microbiomes with Anpan]
|}

{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2024-2 scOmics
|-
!scope="col" style="padding:.4em" | Date
!scope="col" style="padding:.4em" | Team
!scope="col" style="padding:.4em" | Paper index
!scope="col" style="padding:.4em" | Presenter
!scope="col" style="padding:.4em" | Paper title
|-
|style="padding:.4em;" rowspan=1|2025/2/24
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|25-6
|style="padding:.4em;"|IS Choi
|style="padding:.4em;text-align:left"|
[https://www.biorxiv.org/content/10.1101/2023.09.08.555192v7 Evaluating the Utilities of Foundation Models in Single-cell Data Analysis]
|-
|style="padding:.4em;" rowspan=1|2025/2/19
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|25-5
|style="padding:.4em;"|SB Baek
|style="padding:.4em;text-align:left"|
[https://www.nature.com/articles/s41586-024-08328-6 Accurate predictions on small data with a tabular foundation model]
|-
|style="padding:.4em;" rowspan=1|2025/1/15
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|25-4
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2024.09.24.614685 scEMB: Learning context representation of genes based on large-scale single-cell transcriptomics]
|-
|style="padding:.4em;" rowspan=1|2025/1/8
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|25-3
|style="padding:.4em;"|HB Lee
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1093/bioinformatics/btad663 TT3D: Leveraging precomputed protein 3D sequence models to predict protein–protein interactions]
|-
|style="padding:.4em;" rowspan=1|2025/1/8
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|25-2
|style="padding:.4em;"|HB Lee
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1093/bioinformatics/btac258 Topsy-Turvy: integrating a global view into sequence-based PPI prediction]
|-
|style="padding:.4em;" rowspan=1|2025/1/8
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|25-1
|style="padding:.4em;"|HB Lee
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.cels.2021.08.010 D-SCRIPT translates genome to phenome with sequence-based, structure-aware, genome-scale predictions of protein-protein interactions]
|-
|style="padding:.4em;" rowspan=1|2024/12/17
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|24-30
|style="padding:.4em;"|YR Jung
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2024.08.16.608180 Quantized multi-task learning for context-specific representations of gene network dynamics]
|-
|style="padding:.4em;" rowspan=1|2024/12/3
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|24-29
|style="padding:.4em;"|EJ Sung
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2023.11.21.568145 ANDES: a novel best-match approach for enhancing gene set analysis in embedding spaces]
|-
|style="padding:.4em;" rowspan=1|2024/11/26
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|24-28
|style="padding:.4em;"|IS Choi
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41592-024-02303-9 CellRank 2: unified fate mapping in multiview single-cell data]
|-
|style="padding:.4em;" rowspan=1|2024/11/19
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|24-27
|style="padding:.4em;"|SB Baek
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2024.07.29.605556 scPRINT: pre-training on 50 million cells allows robust gene network predictions]
|-
|style="padding:.4em;" rowspan=1|2024/11/12
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|24-26
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41467-024-46440-3 Bidirectional generation of structure and properties through a single molecular foundation model]
|}

{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2024-2 Microbiome
|-
!scope="col" style="padding:.4em" | Date
!scope="col" style="padding:.4em" | Team
!scope="col" style="padding:.4em" | Paper index
!scope="col" style="padding:.4em" | Presenter
!scope="col" style="padding:.4em" | Paper title
|-
|style="padding:.4em;" rowspan=1|2025/2/24
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|25-14
|style="padding:.4em;"|SB Lim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2024.12.13.628459 MaAsLin 3: Refining and extending generalized multivariable linear models for meta-omic association discovery]
|-
|style="padding:.4em;" rowspan=1|2025/2/24
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|25-13
|style="padding:.4em;"|HB Lee
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41564-024-01881-w Profiling lateral gene transfer events in the human microbiome using WAAFLE]
|-
|style="padding:.4em;" rowspan=1|2025/2/17
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|25-12
|style="padding:.4em;"|JY Ma
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2024.05.30.596740 ProTrek: Navigating the Protein Universe through Tri-Modal Contrastive Learning]
|-
|style="padding:.4em;" rowspan=1|2025/2/17
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|25-11
|style="padding:.4em;"|JY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2024.12.30.630844 The genetic diversity and populational specificity of the human gut virome at single nucleotide resolution]
|-
|style="padding:.4em;" rowspan=1|2025/2/3
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|25-10
|style="padding:.4em;"|WJ Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1186/s13059-024-03425-1 pan-Draft: automated reconstruction of species-representative metabolic models from multiple genomes]
|-
|style="padding:.4em;" rowspan=1|2025/2/3
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|25-9
|style="padding:.4em;"|SH Ahn
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.cell.2024.09.019 A core microbiome signature as an indicator of health]
|-
|style="padding:.4em;" rowspan=1|2025/1/20
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|25-8
|style="padding:.4em;"|HJ Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.cell.2024.09.027 Using artificial intelligence to document the hidden RNA virosphere]
|-
|style="padding:.4em;" rowspan=1|2025/1/20
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|25-7
|style="padding:.4em;"|YR Kim
|style="padding:.4em;text-align:left"|
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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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[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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|style="padding:.4em;"|JY Ma
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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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|style="padding:.4em;"|G Koh
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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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|style="padding:.4em;"|SH Ahn
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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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[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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[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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|style="padding:.4em;"|SH Ahn
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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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[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;"|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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[https://academic.oup.com/nargab/article/2/2/lqaa023/5826153 Visualizing ’omic feature rankings and log-ratios using Qurro]
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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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[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;"|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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[https://doi.org/10.1038/s41559-020-01353-4 Polarization of microbial communities between competitive and cooperative metabolism]
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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="text-align:left;font-size:12pt" | 2024-1 Advanced scOmics Data Analysis
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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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[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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[https://doi.org/10.1126/science.abi4882 Spatial transcriptomics of planktonic and sessile bacterial populations at single-cell resolution]
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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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[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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[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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[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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[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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[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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[https://doi.org/10.1038/s41587-023-01747-2 Multimodal spatiotemporal phenotyping of human retinal organoid development]
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[https://doi.org/10.1038/s41586-024-07251-0 Immune microniches shape intestinal Treg function]
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[https://doi.org/10.1016/j.devcel.2021.02.021 A single-cell analysis of the Arabidopsis vegetative shoot apex]
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[https://doi.org/10.1038/s41467-023-40137-9 Droplet-based high-throughput single microbe RNA sequencing by smRandom-seq]
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[https://doi.org/10.1038/s41564-023-01462-3 Single-cell massively-parallel multiplexed microbial sequencing (M3-seq) identifies rare bacterial populations and profiles phage infection]
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[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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[https://doi.org/10.1038/s41587-023-01979-2 Spatial metatranscriptomics resolves host–bacteria–fungi interactomes]
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[https://doi.org/10.1038/s41467-023-36325-2 Dissecting the immune suppressive human prostate tumor microenvironment via integrated single-cell and spatial transcriptomic analyses]
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[https://doi.org/10.1038/s41593-023-01452-y Single-nucleus genomics in outbred rats with divergent cocaine addiction-like behaviors reveals changes in amygdala GABAergic inhibition]
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[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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[https://doi.org/10.1038/s41467-023-39933-0 Spatial cellular architecture predicts prognosis in glioblastoma]
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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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[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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[https://doi.org/10.1038/s41477-022-01291-y Single-cell RNA sequencing provides a high-resolution roadmap for understanding the multicellular compartmentation of specialized metabolism]
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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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[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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[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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[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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[https://doi.org/10.1038/s41592-023-02035-2 Population-level integration of single-cell datasets enables multi-scale analysis across samples]
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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/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;"|JH Cha
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[https://doi.org/10.1038/s41587-022-01467-z Modeling intercellular communication in tissues using spatial graphs of cells]
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|style="padding:.4em;" rowspan=1|Single-cell
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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;" rowspan=1|Single-cell
|style="padding:.4em;"|23-36
|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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|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/s41592-023-01971-3 Dictys: dynamic gene regulatory network dissects developmental continuum with single-cell multiomics]
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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://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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[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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[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;"|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;"|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;"|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;"|IS Choi
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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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[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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[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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[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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[https://doi.org/10.1038/s41564-023-01439-2 A predicted CRISPR-mediated symbiosis between uncultivated archaea]
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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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[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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[https://doi.org/10.1038/s41586-023-06431-8 Mapping the T cell repertoire to a complex gut bacterial community]
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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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[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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[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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[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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[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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[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;"|23-55
|style="padding:.4em;"|JY Ma
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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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[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;"|NY Kim
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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;"|YR Kim
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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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[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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[https://doi.org/10.1186/s40168-023-01472-7 Altered infective competence of the human gut microbiome in COVID-19]
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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;"|SH Ahn
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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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[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;"|JH Cha
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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;"|JY Ma
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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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[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;"|23-43
|style="padding:.4em;"|SH Lee
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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|Microbiome
|style="padding:.4em;"|23-42
|style="padding:.4em;"|WJ Kim
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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;"|23-41
|style="padding:.4em;"|JY Kim
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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|Microbiome
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|style="padding:.4em;"|G Koh
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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|Microbiome
|style="padding:.4em;"|23-39
|style="padding:.4em;"|HJ Kim
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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|Microbiome
|style="padding:.4em;"|23-38
|style="padding:.4em;"|SH Ahn
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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
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-37
|style="padding:.4em;"|JH Cha
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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
|style="padding:.4em;"|23-36
|style="padding:.4em;"|JY Ma
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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
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-35
|style="padding:.4em;"|NY Kim
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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|Microbiome
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|style="padding:.4em;"|SH Lee
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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
|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|WJ Kim
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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|Microbiome
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|style="padding:.4em;"|JY Kim
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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|Microbiome
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|style="padding:.4em;"|G Koh
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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|Microbiome
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|style="padding:.4em;"|SH Ahn
|style="padding:.4em;text-align:left"|
[https://doi.org/10.7554/eLife.50240 Adjusting for age improves identification of gut microbiome alterations in multiple diseases]
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|style="padding:.4em;"|HJ Kim
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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|Microbiome
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|style="padding:.4em;"|JH Cha
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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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[https://doi.org/10.1016/j.chom.2020.03.005 Structure of the Mucosal and Stool Microbiome in Lynch Syndrome]
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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|Microbiome
|style="padding:.4em;"|23-22
|style="padding:.4em;"|SH Ahn
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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;" rowspan=1|Microbiome
|style="padding:.4em;"|23-21
|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;" rowspan=1|Microbiome
|style="padding:.4em;"|23-20
|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;" rowspan=1|Microbiome
|style="padding:.4em;"|23-19
|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;" rowspan=1|Microbiome
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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;" rowspan=1|Microbiome
|style="padding:.4em;"|23-17
|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;" rowspan=1|2023/05/23
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-16
|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;" rowspan=1|Microbiome
|style="padding:.4em;"|23-15
|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;" rowspan=1|Microbiome
|style="padding:.4em;"|23-14
|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;" rowspan=1|2023/05/16
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-13
|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;" rowspan=1|2023/05/09
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-12
|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;" rowspan=1|Microbiome
|style="padding:.4em;"|23-11
|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;" rowspan=1|Microbiome
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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;" rowspan=1|Microbiome
|style="padding:.4em;"|23-9
|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;" rowspan=1|Microbiome
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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;" rowspan=1|Microbiome
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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;" rowspan=1|Microbiome
|style="padding:.4em;"|23-6
|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;" rowspan=1|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;" rowspan=1|Microbiome
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|style="padding:.4em;"|DH Lee
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[https://doi.org/10.1002/imt2.61 Targeting keystone species helps restore the dysbiosis of butyrate‐producing bacteria in nonalcoholic fatty liver disease]
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|style="padding:.4em;"|YJ Roh
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[https://doi.org/10.1002/advs.202203115 Differential Oral Microbial Input Determines Two Microbiota Pneumo-Types Associated with Health Status]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-1
|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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{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2023-1 scOmics
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|style="padding:.4em;"|JW Yu
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[https://doi.org/10.1101/2023.01.17.524482 Decoupling the correlation between cytotoxic and exhausted T lymphocyte transcriptomic signatures enhances melanoma immunotherapy response prediction from tumor expression]
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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/10.1101/2023.07.28.550993 Major data analysis errors invalidate cancer microbiome findings]
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|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|EJ Sung
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[https://doi.org/10.1038/s43018-022-00475-x A single-cell atlas of glioblastoma evolution under therapy reveals cell-intrinsic and cell-extrinsic therapeutic targets]
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|style="padding:.4em;"|G Koh
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[https://doi.org/10.1038/s43018-022-00433-7 Single-cell meta-analyses reveal responses of tumor-reactive CXCL13+ T cells to immune-checkpoint blockade]
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|style="padding:.4em;"|JW Yu
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[https://doi.org/10.1038/s41587-022-01342-x Estimation of tumor cell total mRNA expression in 15 cancer types predicts disease progression]
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|style="padding:.4em;"|JH Cha
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[https://doi.org/10.1038/s41587-022-01288-0 DIALOGUE maps multicellular programs in tissue from single-cell or spatial transcriptomics data]
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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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[https://doi.org/10.1186/s13059-022-02828-2 Parallel single-cell and bulk transcriptome analyses reveal key features of the gastric tumor microenvironment]
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|style="padding:.4em;"|JW Yu
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[https://doi.org/10.1038/s41467-022-32838-4 Mutated processes predict immune checkpoint inhibitor therapy benefit in metastatic melanoma]
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[https://doi.org/10.1038/s43018-021-00292-8 Temporal single-cell tracing reveals clonal revival and expansion of precursor exhausted T cells during anti-PD-1 therapy in lung cancer]
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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;"|EJ Sung
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[https://doi.org/10.1101/2022.12.20.521311 Supervised discovery of interpretable gene programs from single-cell data]
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|style="padding:.4em;"|IS Choi
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[https://doi.org/10.1038/s41586-022-05435-0 Effect of the intratumoral microbiota on spatial and cellular heterogeneity in cancer]
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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;"|JW Yu
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[https://www.biorxiv.org/content/10.1101/2022.08.05.502989v1 MetaTiME: Meta-components of the Tumor Immune 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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|style="padding:.4em;"|SB Baek
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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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[https://www.nature.com/articles/s41591-022-01799-y A T cell resilience model associated with response to immunotherapy in multiple tumor types]
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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;"|G Koh
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[https://pubmed.ncbi.nlm.nih.gov/35649411/ Cross-tissue, single-cell stromal atlas identifies shared pathological fibroblast phenotypes in four chronic inflammatory diseases]
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[https://www.sciencedirect.com/science/article/pii/S1535610822003178 Pan-cancer integrative histology-genomic analysis via multimodal deep learning]
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[https://pubmed.ncbi.nlm.nih.gov/35803260/ Tissue-resident memory and circulating T cells are early responders to pre-surgical cancer immunotherapy]
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[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;" rowspan=1|Microbiome
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|style="padding:.4em;"|NY Kim
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[https://doi.org/10.1186/s13059-023-02902-3 PhyloMed: a phylogeny-based test of mediation effect in microbiome]
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|style="padding:.4em;" rowspan=1|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;" rowspan=1|Microbiome
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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;"|SH Ahn
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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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[https://doi.org/10.1038/s41586-023-05989-7 Profiling the human intestinal environment under physiological conditions]
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|style="padding:.4em;"|JH Cha
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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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[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;"|NY Kim
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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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[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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[https://doi.org/10.1038/s41467-022-33397-4 Deciphering microbial gene function using natural language processing]
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|style="padding:.4em;"|SH Lee
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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;"|JY Ma
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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;"|NY Kim
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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|Microbiome
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|style="padding:.4em;"|SH Lee
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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;"|JY Kim
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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|Microbiome
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|style="padding:.4em;"|WJ Kim
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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
|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|SH Ahn
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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;"|HJ Kim
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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
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-7
|style="padding:.4em;"|JH Cha
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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
|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|JY Ma
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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|Microbiome
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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
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-4
|style="padding:.4em;"|SH Lee
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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;"|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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[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;"|23-1
|style="padding:.4em;"|JH Cha
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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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[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|Single-cell
|style="padding:.4em;"|22-31
|style="padding:.4em;"|JH Cha, SB Baek, IS Choi
|style="padding:.4em;text-align:left"|
[https://www.pnas.org/doi/10.1073/pnas.2105859118 Representation learning of RNA velocity reveals robust cell transitions]
[https://www.nature.com/articles/s41467-022-34188-7 UniTVelo: temporally unified RNA velocity reinforces single-cell trajectory inference]
[https://www.sciencedirect.com/science/article/pii/S0092867421015774 Mapping transcriptomic vector fields of single cells]
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|style="padding:.4em;"|IS Choi
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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|Single-cell
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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;"|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
|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|JW Yu
|style="padding:.4em;text-align:left"|
[https://www.biorxiv.org/content/10.1101/2022.06.15.495325v1 T cell receptor convergence is an indicator of antigen-specific T cell response in cancer immunotherapies]
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|style="padding:.4em;" rowspan=1|2022/09/06
|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://www.nature.com/articles/s41587-021-01091-3 Identifying phenotype-associated subpopulations by integrating bulk and single-cell sequencing data]
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|style="padding:.4em;" rowspan=1|2022/09/01
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|22-27
|style="padding:.4em;"|SB Baek
|style="padding:.4em;text-align:left"|
[https://www.biorxiv.org/content/10.1101/2021.12.06.471401v1 MIRA: Joint regulatory modeling of multimodal expression and chromatin accessibility in single cells]
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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;" rowspan=1|Single-cell
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|style="padding:.4em;"|IS Choi
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2022.02.05.479217 Biologically informed deep learning to infer gene program activity in single cells]
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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|Single-cell
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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;" rowspan=1|Single-cell
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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;"|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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[https://www.biorxiv.org/content/10.1101/2021.06.07.447430v2 Metacells untangle large and complex single-cell transcriptome networks]
[https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1812-2 MetaCell: analysis of single-cell RNA-seq data using K-nn graph partitions]
[https://genomebiology.biomedcentral.com/articles/10.1186/s13059-022-02667-1 Metacell‑2: a divide‑and‑conquer metacell algorithm for scalable scRNA‑seq analysis]
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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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[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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[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;"|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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[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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[https://pubmed.ncbi.nlm.nih.gov/34594031/ Systematic investigation of cytokine signaling activity at the tissue and single-cell levels]
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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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[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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[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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|style="padding:.4em;"|JH Cha
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[https://pubmed.ncbi.nlm.nih.gov/34914499/ Pan-cancer single-cell landscape of tumor-infiltrating T cells]
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[https://pubmed.ncbi.nlm.nih.gov/34597583/ Atlas of clinically distinct cell states and ecosystems across human solid tumors]
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[https://pubmed.ncbi.nlm.nih.gov/34489465/ Single-cell ATAC and RNA sequencing reveal pre-existing and persistent cells associated with prostate cancer relapse]
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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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[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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|style="padding:.4em;" rowspan=1|Microbiome
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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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|style="padding:.4em;" rowspan=1|Microbiome
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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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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|SH Lee
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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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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|SH Ahn
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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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|style="padding:.4em;" rowspan=1|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://journals.asm.org/doi/10.1128/msystems.00050-22 Compositionally Aware Phylogenetic Beta-Diversity Measures Better Resolve Microbiomes Associated with Phenotype]
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[https://www.nature.com/articles/s41596-020-00480-3 Tutorial: assessing metagenomics software with the CAMI benchmarking toolkit disease]
[https://www.nature.com/articles/s41592-022-01431-4 Critical Assessment of Metagenome Interpretation: the second round of challenges]
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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.nature.com/articles/s41591-022-01688-4 Microbiome and metabolome features of the cardiometabolic disease spectrum]
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[https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02195-w Functional and genetic markers of niche partitioning among enigmatic members of the human oral microbiome]
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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://genomebiology.biomedcentral.com/articles/10.1186/s13059-021-02473-1 Pandora: nucleotide-resolution bacterial pan-genomics with reference graphs]
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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://genomebiology.biomedcentral.com/articles/10.1186/s13059-022-02610-4 AGAMEMNON: an Accurate metaGenomics And MEtatranscriptoMics quaNtificatiON analysis suite]
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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/34893089/ Microbial co-occurrence complicates associations of gut microbiome with US immigration, dietary intake and obesity]
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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://pubmed.ncbi.nlm.nih.gov/34551799/ The influence of the gut microbiome on BCG-induced trained immunity]
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[https://pubmed.ncbi.nlm.nih.gov/34912116/ Towards the biogeography of prokaryotic genes]
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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://www.nature.com/articles/s41587-021-01102-3 Fast and accurate metagenotyping of the human gut microbiome with GT-Pro]
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[https://www.nature.com/articles/s41587-020-00797-0 inStrain profiles population microdiversity from metagenomic data and sensitively detects shared microbial strains]
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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://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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[https://www.nature.com/articles/nmeth.3802 Strain-level microbial epidemiology and population genomics from shotgun metagenomics]
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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.nature.com/articles/s41586-021-03552-w Interpreting type 1 diabetes risk with genetics and single-cell epigenomics]
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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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{|class=wikitable style="text-align:center;"
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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/s41467-020-14766-3 Trajectory-based differential expression analysis for single-cell sequencing data]
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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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|style="padding:.4em;"|JH Cha
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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://www.sciencedirect.com/science/article/pii/S0092867419308967?via%3Dihub Single-Cell Analysis of Crohn's Disease Lesions Identifies a Pathogenic Cellular Module Associated with Resistance to Anti-TNF Therapy]
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|style="padding:.4em;"|21-1
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[https://www.nature.com/articles/s41467-020-15956-9 Multi-omic single-cell snapshots reveal multiple independent trajectories to drug tolerance in a melanoma cell line]
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{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2020-1st semester
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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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|style="padding:.4em;"|20-14
|style="padding:.4em;"|JW Choi
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[https://pubmed.ncbi.nlm.nih.gov/30595452/ Dysfunctional CD8 T Cells Form a Proliferative, Dynamically Regulated Compartment Within Human Melanoma]
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|style="padding:.4em;"|JW Seo
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[https://pubmed.ncbi.nlm.nih.gov/30388455/ A Cancer Cell Program Promotes T Cell Exclusion and Resistance to Checkpoint Blockade]
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|style="padding:.4em;"|JY Lee
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[https://pubmed.ncbi.nlm.nih.gov/31974247/ Single-cell Transcriptional Diversity Is a Hallmark of Developmental Potential]
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|style="padding:.4em;"|JH Kim
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[https://pubmed.ncbi.nlm.nih.gov/31675496/ Landscape and Dynamics of Single Immune Cells in Hepatocellular Carcinoma]
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[https://pubmed.ncbi.nlm.nih.gov/32103181/ Peripheral T Cell Expansion Predicts Tumour Infiltration and Clinical Response]
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|style="padding:.4em;"|KH Hong
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[https://pubmed.ncbi.nlm.nih.gov/30388456/ Defining T Cell States Associated With Response to Checkpoint Immunotherapy in Melanoma]
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[https://pubmed.ncbi.nlm.nih.gov/31915379/ Rapid Non-Uniform Adaptation to Conformation-Specific KRAS(G12C) Inhibition]
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|style="padding:.4em;"|OY Min
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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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[https://pubmed.ncbi.nlm.nih.gov/32066951/ Distinct Microbial and Immune Niches of the Human Colon]
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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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[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
|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
|style="padding:.4em;" rowspan=2|Single-cell
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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
|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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|+style="text-align:left;font-size:12pt" | 2019-2nd semester
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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;"|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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[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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[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|Microbiome
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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
|style="padding:.4em;"|NY Kim
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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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[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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[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|Microbiome
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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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[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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[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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[https://www.nature.com/articles/nbt.4042 Multiplexed droplet single-cell RNA-sequencing using natural genetic variation]
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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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[https://www.sciencedirect.com/science/article/pii/S0092867419307755?via%3Dihub Benchmarking Metagenomics Tools for Taxonomic Classification]
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[https://www.biorxiv.org/content/10.1101/719088v1 Coexpression uncovers a unified single-cell transcriptomic landscape]
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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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[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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[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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[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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[https://www.nature.com/articles/s41592-018-0254-1 A test metric for assessing single-cell RNA-seq batch correction]
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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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[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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[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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[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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[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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[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;"|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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[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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[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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[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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[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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[https://www.ncbi.nlm.nih.gov/pubmed/30664783 Microbial network disturbances in relapsing refractory Crohn's disease.]
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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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[https://www.ncbi.nlm.nih.gov/pubmed/30745586 A new genomic blueprint of the human gut microbiota]
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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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[https://www.ncbi.nlm.nih.gov/pubmed/29311644 Dynamics of metatranscription in the inflammatory bowel disease gut microbiome.]
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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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[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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[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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[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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[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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[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
|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
|style="padding:.4em;"|19-6
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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
|style="padding:.4em;"|
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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
|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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|style="padding:.4em;"|19-3-1
|style="padding:.4em;"|
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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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|style="padding:.4em;"|19-3
|style="padding:.4em;"|
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[https://www.ncbi.nlm.nih.gov/pubmed/30787437 The single-cell transcriptional landscape of mammalian organogenesis]
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|style="padding:.4em;" rowspan=2|2019/3/14
|style="padding:.4em;"|19-2
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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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|style="padding:.4em;"|19-1
|style="padding:.4em;"|
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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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{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2018
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!scope="col" style="padding:.4em" |Date
!scope="col" style="padding:.4em" | Paper index
!scope="col" style="padding:.4em" | Presenter
!scope="col" style="padding:.4em" | Paper title
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|style="padding:.4em;" rowspan=2|2018/06/14
|style="padding:.4em;"|18-12
|style="padding:.4em;"|
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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
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=Machine+learning+identifies+stemness+features+associated+with+oncogenic+dedifferentiation Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation.]
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|style="padding:.4em;" rowspan=2|2018/06/07
|style="padding:.4em;"|18-10
|style="padding:.4em;"|
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=Developmental+and+oncogenic+programs+in+H3K27M+gliomas+dissected+by+single-cell+RNA-seq Developmental and oncogenic programs in H3K27M gliomas dissected by single-cell RNA-seq.]
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|style="padding:.4em;"|18-9
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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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|style="padding:.4em;" rowspan=2|2018/05/31
|style="padding:.4em;"|18-8
|style="padding:.4em;"|
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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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|style="padding:.4em;"|18-7
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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.]
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|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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|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
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[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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{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2017
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!scope="col" style="padding:.4em" |Date
!scope="col" style="padding:.4em" | Paper index
!scope="col" style="padding:.4em" | Presenter
!scope="col" style="padding:.4em" | Paper title
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|style="padding:.4em;" rowspan=2|2017/06/28
|style="padding:.4em;"|17-36
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/28104840 Tumor aneuploidy correlates with markers of immune evasion and with reduced response to immunotherapy.]
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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.]
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|style="padding:.4em;"|17-33
|style="padding:.4em;"|MY Lee
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[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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[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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|style="padding:.4em;"|17-31
|style="padding:.4em;"|MY Lee
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.celrep.2016.03.075 Genomic Correlates of Immune-Cell Infiltrates in Colorectal Carcinoma.]
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|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.]
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|style="padding:.4em;"|17-29
|style="padding:.4em;"|JW Cho
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.celrep.2016.12.019 Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade.]
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|style="padding:.4em;" rowspan=2|2017/05/24
|style="padding:.4em;"|17-28
|style="padding:.4em;"|EB Kim
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[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
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[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
|style="padding:.4em;"|17-26
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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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[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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|style="padding:.4em;" rowspan=2|2017/04/26
|style="padding:.4em;"|17-24
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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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|style="padding:.4em;"|17-23
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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
|style="padding:.4em;"|17-22
|style="padding:.4em;"|SH Lee
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[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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|style="padding:.4em;"|17-21
|style="padding:.4em;"|CY Kim
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1038%2Fnbt.3569 Wishbone identifies bifurcating developmental trajectories from single-cell data.]
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|style="padding:.4em;" rowspan=2|2017/04/05
|style="padding:.4em;"|17-20
|style="padding:.4em;"|KS Kim
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[http://biorxiv.org/content/early/2017/02/21/110668 Reversed graph embedding resolves complex single-cell developmental trajectories.]
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|style="padding:.4em;"|17-19
|style="padding:.4em;"|SH Lee
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1038%2Fnmeth.4150 Single-cell mRNA quantification and differential analysis with Census.]
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|style="padding:.4em;" rowspan=2|2017/03/29
|style="padding:.4em;"|17-18
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[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.]
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|style="padding:.4em;"|17-17
|style="padding:.4em;"|DS Bae
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[https://www.ncbi.nlm.nih.gov/pubmed/26051941 Single-Cell Network Analysis Identifies DDIT3 as a Nodal Lineage Regulator in Hematopoiesis.]
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|style="padding:.4em;" rowspan=2|2017/03/22
|style="padding:.4em;"|17-16
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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.]
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|style="padding:.4em;"|17-15
|style="padding:.4em;"|EB Kim
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[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
|style="padding:.4em;"|17-14
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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.]
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|style="padding:.4em;"|17-13
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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.]
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|style="padding:.4em;" rowspan=2|2017/02/28
|style="padding:.4em;"|17-12
|style="padding:.4em;"|KS Kim
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[https://www.ncbi.nlm.nih.gov/pubmed/27824113 A Comprehensive Characterization of the Function of LincRNAs in Transcriptional Regulation Through Long-Range Chromatin Interactions.]
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|style="padding:.4em;"|17-11
|style="padding:.4em;"|JE Shim
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[//pubmed.gov/27851969 Epigenomic Deconvolution of Breast Tumors Reveals Metabolic Coupling between Constituent Cell Types.]
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|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.]
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|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.]
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|style="padding:.4em;" rowspan=2|2017/02/07
|style="padding:.4em;"|17-08
|style="padding:.4em;"|DS Bae
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[http://biorxiv.org/content/early/2016/11/17/088286 Somatic Mutations and Neoepitope Homology in Melanomas Treated with CTLA-4 Blockade.]
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|style="padding:.4em;"|17-07
|style="padding:.4em;"|MY Lee
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[http://biorxiv.org/content/early/2016/11/28/090134 Chemotherapy weakly contributes to predicted neoantigen expression in ovarian cancer.]
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|style="padding:.4em;" rowspan=1|2017/01/31
|style="padding:.4em;"|17-06
|style="padding:.4em;"|CY Kim
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[//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
|style="padding:.4em;"|17-05
|style="padding:.4em;"|JW Cho
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[//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
|style="padding:.4em;"|17-04
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[//www.ncbi.nlm.nih.gov/pubmed/25938943 Mapping long-range promoter contacts in human cells with high-resolution capture Hi-C]
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|style="padding:.4em;"|17-03
|style="padding:.4em;"|EB Kim
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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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|style="padding:.4em;" rowspan=1|2017/01/10
|style="padding:.4em;"|17-02
|style="padding:.4em;"|JE Shim
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[//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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[//www.ncbi.nlm.nih.gov/pubmed/26287467 Single-cell messenger RNA sequencing reveals rare intestinal cell types]
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{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2016
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|style="padding:.4em;" rowspan=1|2016/12/27
|style="padding:.4em;"|2016-31
|style="padding:.4em;"|EB Kim
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[http://www.ncbi.nlm.nih.gov/pubmed/25664528 Decoding the regulatory network of early blood development from single-cell gene expression measurements.]
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|style="padding:.4em;" rowspan=1|2016/12/6
|style="padding:.4em;"|2016-30
|style="padding:.4em;"|KS Kim
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[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
|style="padding:.4em;"|2016-29
|style="padding:.4em;"|DS Bae
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[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
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[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
|style="padding:.4em;"|2016-27
|style="padding:.4em;"|MY Lee
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[http://www.ncbi.nlm.nih.gov/pubmed/26000487 Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells.]
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|style="padding:.4em;" rowspan=1|2016/11/8
|style="padding:.4em;"|2016-26
|style="padding:.4em;"|JW Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26000488 Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets.]
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|style="padding:.4em;" rowspan=1|2016/11/1
|style="padding:.4em;"|2016-25
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27426982 Causal Mechanistic Regulatory Network for Glioblastoma Deciphered Using Systems Genetics Network Analysis.]
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|style="padding:.4em;" rowspan=1|2016/10/25
|style="padding:.4em;"|2016-24
|style="padding:.4em;"|MY Lee
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27549193 Comprehensive analyses of tumor immunity: implications for cancer immunotherapy.]
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|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]
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|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]
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|style="padding:.4em;" rowspan=1|2016/9/20
|style="padding:.4em;"|2016-21
|style="padding:.4em;"|SM Yang
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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]
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|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.]
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|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
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27264179 A Computational Drug Repositioning Approach for Targeting Oncogenic Transcription Factors]
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|style="padding:.4em;" rowspan=1|2016/8/16
|style="padding:.4em;"|2016-17
|style="padding:.4em;"|JW Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27309802 The landscape of accessible chromatin in mammalian preimplantation embryos]
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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
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[http://www.ncbi.nlm.nih.gov/pubmed/27040498 Personalized Immunomonitoring Uncovers Molecular Networks that Stratify Lupus Patients]
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|style="padding:.4em;" rowspan=1|2016/7/25
|style="padding:.4em;"|2016-14
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=integration+of+summary+data+from+gwas+and+eqtl+studies Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets]
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|style="padding:.4em;" rowspan=1|2016/7/18
|style="padding:.4em;"|2016-13
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/23624555 Identification of transcriptional regulators in the mouse immune system]
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|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]
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|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]
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|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]
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|style="padding:.4em;" rowspan=2|2016/5/11
|style="padding:.4em;"|2016-6
|style="padding:.4em;"|DS Bae, CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/25171417 Predicting Cancer-specific vulnerability via data-driven detection of synthetic lethality]
|-
|style="padding:.4em;"| 2016-5
|style="padding:.4em;"|DS Bae, CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/23467089 Dynamic regulatory network controlling Th17 cell differentiation]
|-
|style="padding:.4em;" rowspan=2|2016/5/4
|style="padding:.4em;"|2016-4
|style="padding:.4em;"|MY Lee,SM Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/25853550 Characterization of the immunophenotypes and antigenomes of colorectal cancers reveals distinct tumor escape mechanisms and novel targets for immunotherapy]
|-
|style="padding:.4em;"| 2016-3
|style="padding:.4em;"|MY Lee,SM Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26618344 Regulators of genetic risk of breast cancer identified by integrative network analysis]
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|style="padding:.4em;" rowspan=2|2016/4/27
|style="padding:.4em;"|2016-2
|style="padding:.4em;"|CY Kim,SJ Kwon
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26780608 A predictive computational framework for direct reprogramming between human cell types]
|-
|style="padding:.4em;"| 2016-1
|style="padding:.4em;"| CY Kim,SJ Kwon
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/25126793 CellNet: Network biology applied to stem cell engineering]
|}

{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2015
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!scope="col" style="padding:.4em" | Date
!scope="col" style="padding:.4em" | Paper index
!scope="col" style="padding:.4em" | Presenter
!scope="col" style="padding:.4em" | Paper title
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|style="padding:.4em;" rowspan=2|2015/06/11
|style="padding:.4em;"|2015-55
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://genome.cshlp.org/content/24/2/340.long Improved exome prioritization of disease genes through cross-species phenotype comparison.]
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|style="padding:.4em;"|2015-54
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0002929714001128 Phevor combines multiple biomedical ontologies for accurate identification of disease-causing alleles in single individuals and small nuclear families.]
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|style="padding:.4em;" rowspan=2|2015/06/04
|style="padding:.4em;"|2015-53
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nmeth/journal/v10/n11/full/nmeth.2656.html eXtasy: variant prioritization by genomic data fusion.]
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|style="padding:.4em;"|2015-52
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://genome.cshlp.org/content/21/9/1529.long A probabilistic disease-gene finder for personal genomes.]
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|style="padding:.4em;" rowspan=2|2015/05/28
|style="padding:.4em;"|2015-51
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/ng/journal/v46/n12/full/ng.3141.html Systematic analysis of noncoding somatic mutations and gene expression alterations across 14 tumor types.]
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|style="padding:.4em;"|2015-50
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nbt/journal/v28/n5/full/nbt.1630.html GREAT improves functional interpretation of cis-regulatory regions.]
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|style="padding:.4em;" rowspan=2|2015/05/21
|style="padding:.4em;"|2015-49
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nmeth/journal/v11/n3/full/nmeth.2832.html Functional annotation of noncoding sequence variants.]
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|style="padding:.4em;"|2015-48
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://genomebiology.com/content/15/10/480 FunSeq2: A framework for prioritizing noncoding regulatory variants in cancer.]
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|style="padding:.4em;" rowspan=2|2015/05/14
|style="padding:.4em;"|2015-47
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nmeth/journal/v12/n2/full/nmeth.3215.html Selecting causal genes from genome-wide association studies via functionally coherent subnetworks.]
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|style="padding:.4em;"|2015-46
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/ncomms/2015/150119/ncomms6890/full/ncomms6890.html Biological interpretation of genome-wide association studies using predicted gene functions.]
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|style="padding:.4em;" rowspan=3|2015/05/07
|style="padding:.4em;"|2015-45
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/ncomms/2014/140626/ncomms5212/full/ncomms5212.html Human symptoms-disease network.]
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|style="padding:.4em;"|2015-44
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867413010246 A nondegenerate code of deleterious variants in Mendelian loci contributes to complex disease risk.]
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|style="padding:.4em;"|2015-43
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.sciencemag.org/content/347/6224/1257601.long Uncovering disease-disease relationships through the incomplete interactome.]

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|style="padding:.4em;" rowspan=2|2015/04/30
|style="padding:.4em;"|2015-42
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://genome.cshlp.org/content/25/1/142.long The discovery of integrated gene networks for autism and related disorders.]
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|style="padding:.4em;"|2015-41
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://msb.embopress.org/content/10/12/774.long Integrated systems analysis reveals a molecular network underlying autism spectrum disorders.]
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|style="padding:.4em;" rowspan=3|2015/04/23
|style="padding:.4em;"|2015-40
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v518/n7539/full/nature13990.html Dissecting neural differentiation regulatory networks through epigenetic footprinting.]
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|style="padding:.4em;"|2015-39
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v518/n7539/full/nature14221.html Cell-of-origin chromatin organization shapes the mutational landscape of cancer.]
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|style="padding:.4em;"|2015-38
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v518/n7539/full/nature14248.html Integrative analysis of 111 reference human epigenomes.]
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|style="padding:.4em;" rowspan=2|2015/04/09
|style="padding:.4em;"|2015-37
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://genome.cshlp.org/content/25/2/246.long Genome-wide analysis of local chromatin packing in Arabidopsis thaliana.]
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|style="padding:.4em;"|2015-36
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414014974 A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping.]
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|style="padding:.4em;" rowspan=2|2015/04/02
|style="padding:.4em;"|2015-35
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nrg/journal/v14/n6/full/nrg3454.html Exploring the three-dimensional organization of genomes: interpreting chromatin interaction data.]
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|style="padding:.4em;"|2015-34
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.sciencemag.org/content/347/6225/1010.long Transcribed enhancers lead waves of coordinated transcription in transitioning mammalian cells.]
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|style="padding:.4em;" rowspan=2|2015/03/26
|style="padding:.4em;"|2015-33
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://genome.cshlp.org/content/25/2/257.long Cupid: simultaneous reconstruction of microRNA-target and ceRNA networks.]
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|style="padding:.4em;"|2015-32
|style="padding:.4em;"|
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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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|style="padding:.4em;" rowspan=2|2015/03/19
|style="padding:.4em;"|2015-31
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nbt/journal/vaop/ncurrent/full/nbt.3154.html Decoding the regulatory network of early blood development from single-cell gene expression measurements.]
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|style="padding:.4em;"|2015-30
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nrg/journal/v16/n3/full/nrg3833.html Computational and analytical challenges in single-cell transcriptomics.]
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|style="padding:.4em;" rowspan=3|2015/03/12
|style="padding:.4em;"|2015-29
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867415000136 Extensive Strain-Level Copy-Number Variation across Human Gut Microbiome Species.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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 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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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 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"
|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 title
|-
|rowspan = "1"|2013/01/11
|align ="center"|2012-81
|[http://genome.cshlp.org/content/22/8/1589.full (TH Kim) MuSiC: identifying mutational significance in cancer genomes.]
|-
|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"|2012-74
|[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]
|-
| 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]
|-
| align="center"|2012-72
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2063581/ (SH Hwang) Network-based classification of breast cancer metastasis]
|-
| 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]
|-
|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]
|-
| align="center"|2012-66
|[http://bioinformatics.oxfordjournals.org/content/25/16/2078.long (ER Kim&TH Kim)The Sequence Alignment/Map format and SAMtools]
|-
| 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]
|-
| align="center"|2012-64
|[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"|2012-63
|[http://www.sciencemag.org/content/336/6086/1262.abstract (T Lee&CY Kim)Host-Gut Microbiota Metabolic Interactions]
|-
|align="center"|2012-62
|[http://www.sciencemag.org/content/336/6086/1268.full (AR Cho,JH Ju)Interactions Between the Microbiota and the Immune System]
|-
|align="center"|2012-61
|[http://www.sciencemag.org/content/336/6086/1255.abstract (SH Hwang&HJ Cho)The Application of Ecological Theory Toward an Understanding of the Human Microbiome]
|-
|align="center"|2012-60
|[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
|[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]
|-
| align="center"|2012-58
|[http://www.nature.com/nature/journal/v486/n7402/full/nature11209.html (JH Shin&HJ Kim)A framework for human microbiome research]
|-
|align="center"|2012-57
|[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
|[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]
|-
|align="center"|2012-55
|[http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002511 (YH Go&HJ Han)Google Goes Cancer: Improving Outcome Prediction for Cancer Patients by Network-Based Ranking of Marker Genes]
|-
| align="center"|2012-54
|[http://www.nature.com/msb/journal/v7/n1/full/msb201147.html (YH Go&HJ Han)A pharmacogenomic method for individualized prediction of drug sensitivity]
|-
|align="center"|2012-53
|[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
|[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]
|-
|align="center"|2012-51
|[http://www.nature.com/nature/journal/v483/n7391/full/nature11005.html (ER Kim&TH Kim)Systematic identification of genomic markers of drug sensitivity in cancer cells]
|-
| align="center"|2012-50
|[http://www.pnas.org/content/early/2011/10/13/1018854108.abstract (ER Kim&TH Kim)Subtype and pathway specific responses to anticancer compounds in breast cancer]
|-
|align="center"|2012-49
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature10945.html (JE Shim&KS Kim)De novo mutations revealed by whole-exome sequencing are strongly associated with autism]
|-
|align="center"|2012-48
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature11011.html (JE Shim&KS Kim)Patterns and rates of exonic de novo mutations in autism spectrum disorders]
|-
|align="center"|2012-47
|[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
|[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)]
|-
|align="center"|2012-45
|[http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002621 (T Lee&CY Kim)Type 2 Diabetes Risk Alleles Demonstrate Extreme Directional Differentiation among Human Populations, Compared to Other Diseases]
|-
|align="center"|2012-44
|[http://www.nature.com/nature/journal/v480/n7376/full/nature10665.html (AR Cho&JH Ju)Predicting mutation outcome from early stochastic variation in genetic interaction partners]
|-
|align="center"|2012-43
|[http://www.sciencemag.org/content/335/6064/82 (AR Cho&JH Ju)Fitness Trade-Offs and Environmentally Induced Mutation Buffering in Isogenic C. elegans]
|-
| align="center"|2012-42
|[http://genome.cshlp.org/content/22/6/1163 (JE Shim&KS Kim)Identification of microRNA-regulated gene networks by expression analysis of target genes]
|-
|align="center"|2012-41
|[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
|[http://www.cell.com/abstract/S0092-8674(12)00573-9 (JH Soh)Functional Repurposing Revealed by Comparing S. pombe and S. cerevisiae Genetic Interactions]
|-
|align="center"|2012-39
|[http://genome.cshlp.org/content/22/2/375.long (ER Kim&TH Kim)De novo discovery of mutated driver pathways in cancer]
|-
| align="center"|2012-38
|[http://www.nature.com/msb/journal/v4/n1/full/msb20082.html (YH Go&HJ Han)A systems biology approach to prediction of oncogenes and molecular perturbation targets in B-cell lymphomas]
|-
|align="center"|2012-37
|[http://www.nature.com/msb/journal/v7/n1/full/msb201126.html (SH Hwang&HJ Cho)PREDICT: a method for inferring novel drug indications with application to personalized medicine]
|-
|align="center"|2012-36
|[http://www.nature.com/nature/journal/v453/n7198/full/nature06973.html (SH Hwang&HJ Cho)Synergistic response to oncogenic mutations defines gene class critical to cancer phenotype]
|-
|align="center"|2012-35
|[http://www.sciencemag.org/content/334/6062/1518.full (JH Shin&HJ Kim)Detecting Novel Associations in Large Data Sets]
|-
|rowspan = "3" |2012/03/05
| align="center"|2012-34
|[http://www.ncbi.nlm.nih.gov/pubmed/18516045 (8,HH Kim)Mapping and quantifying mammalian transcriptomes by RNA-Seq.]
|-
|align="center"|2012-33
|[http://genomebiology.com/2010/11/10/R106 (11,Go&Ju)Differential expression analysis for sequence count data]
|-
|align="center"|2012-32
|[http://bioinformatics.oxfordjournals.org/content/26/1/139.short (12,Go&Ju)edgeR: a Bioconductor package for differential expression analysis of digital gene expression data ]
|-
|rowspan = "7" |2012/02/27 2012/02/28
| align="center"|2012-31
|[http://www.nature.com/nrg/journal/v10/n1/full/nrg2484.html (1,JW Song)RNA-Seq: a revolutionary tool for transcriptomics]
|-
|align="center"|2012-30
|[http://www.nature.com/nmeth/journal/v8/n6/full/nmeth.1613.html (2,JW Song)Computational methods for transcriptome annotation and quantification using RNA-seq]
|-
|align="center"|2012-29
|[http://genomebiology.com/2010/11/12/220 (3,HJ Han)From RNA-seq reads to differential expression results]
|-
|align="center"|2012-28
|[http://www.nature.com/nmeth/journal/v7/n9/full/nmeth.1491.html (4,AR Cho)Comprehensive comparative analysis of strand-specific RNA sequencing methods]
|-
|align="center"|2012-27
|[http://www.plosone.org/article/info:doi%2F10.1371%2Fjournal.pone.0026426 (5,T Lee)A Low-Cost Library Construction Protocol and Data Analysis Pipeline for Illumina-Based Strand-Specific Multiplex RNA-Seq]
|-
|align="center"|2012-26
|[http://www.nature.com/nbt/journal/v28/n5/abs/nbt.1621.html (6,So&Shin)Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation]
|-
|align="center"|2012-25
|[http://bioinformatics.oxfordjournals.org/content/25/9/1105.abstract (7,So&Shin)TopHat: discovering splice junctions with RNA-Seq]
|-
|rowspan = "5" |2012/02/06
| align="center"|2012-24
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3125733/ mirConnX: condition-specific mRNA-microRNA network integrator]
|-
|align="center"|2012-23
|[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002190 Construction and Analysis of an Integrated Regulatory Network Derived from High-Throughput Sequencing Data]
|-
|align="center"|2012-22
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002415 A Densely Interconnected Genome-Wide Network of MicroRNAs and Oncogenic Pathways Revealed Using Gene Expression Signatures]
|-
|align="center"|2012-21
|[http://genome.cshlp.org/content/20/5/589.short Reprogramming of miRNA networks in cancer and leukemia]
|-
|align="center"|2012-20
|[http://www.cell.com/abstract/S0092-8674(11)01152-4 An Extensive MicroRNA-Mediated Network of RNA-RNA Interactions Regulates Established Oncogenic Pathways in Glioblastoma]
|-
|rowspan = "5" |2012/01/30
|align="center"|2012-19
|[http://www.cell.com/abstract/S0092-8674(11)00237-6 Principles and Strategies for Developing Network Models in Cancer]
|-
|align="center"|2012-18
|[http://www.nature.com/ng/journal/v37/n4/full/ng1532.html Reverse engineering of regulatory networks in human B cells]
|-
|align="center"|2012-17
|[http://www.nature.com/nature/journal/v452/n7186/abs/nature06757.html Variations in DNA elucidate molecular networks that cause disease]
|-
|align="center"|2012-16
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2779083/ Harnessing gene expression to identify the genetic basis of drug resistance]
|-
|align="center"|2012-15
|[http://www.sciencedirect.com/science/article/pii/S0092867410012936 An Integrated Approach to Uncover Drivers of Cancer]
|-
|rowspan = "3" |2012/01/09
|align="center"|2012-14
|[http://www.ncbi.nlm.nih.gov/pubmed/18704161 Genetic variation in an individual human exome.]
|-
|align="center"|2012-13
|[http://www.ncbi.nlm.nih.gov/pubmed/22081227 Predicting phenotypic variation in yeast from individual genome sequences.]
|-
|align="center"|2012-12
|[http://www.ncbi.nlm.nih.gov/pubmed/20435227 Clinical assessment incorporating a personal genome.]
|-
|rowspan = "6" |2012/01/09 2012/01/16
| align="center"|2012-11
|[http://www.ncbi.nlm.nih.gov/pubmed/20399638 Human allelic variation: perspective from protein function, structure, and evolution.]
|-
|align="center"|2012-10
|[http://www.ncbi.nlm.nih.gov/pubmed/19561590 Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm.]
|-
|align="center"|2012-09
|[http://www.ncbi.nlm.nih.gov/pubmed/11230178 Prediction of deleterious human alleles.]
|-
|align="center"|2012-08
|[http://www.ncbi.nlm.nih.gov/pubmed/12202775 Human non-synonymous SNPs: server and survey.]
|-
|align="center"|2012-07
|[http://www.ncbi.nlm.nih.gov/pubmed/20354512 A method and server for predicting damaging missense mutations.]
|-
|align="center"|2012-06
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1920242/ SNAP: predict effect of non-synonymous polymorphisms on function]
|-
|rowspan = "3" |2012/01/09 2012/01/16
|align="center"|2012-05
|[http://www.ncbi.nlm.nih.gov/pubmed/21920052 Computational and statistical approaches to analyzing variants identified by exome sequencing.]
|-
|align="center"|2012-04
|[http://www.ncbi.nlm.nih.gov/pubmed/18179889 Shifting paradigm of association studies: value of rare single-nucleotide polymorphisms.]
|-
|align="center"|2012-03
|[http://www.ncbi.nlm.nih.gov/pubmed/19684571 Targeted capture and massively parallel sequencing of 12 human exomes.]
|-
|rowspan = "2" |2012/01/09 2012/01/16
|align="center"|2012-02
|[http://www.ncbi.nlm.nih.gov/pubmed/17637733 The distribution of fitness effects of new mutations.]
|-
|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]
|}
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
|align="center"|2011-49
|[http://bmir.stanford.edu/file_asset/index.php/1407/BMIR-2009-1355.pdf (Shin)Data-Driven Methods to Discover Molecular Determinants of Serious Adverse Drug Events]
|-
|align="center"|2011-48
|[http://www.nature.com/nchembio/journal/v4/n11/abs/nchembio.118.html (Shin)Network pharmacology: the next paradigm in drug discovery]
|-
|align="center"|2011-47
|[http://www.nature.com/msb/journal/v7/n1/full/msb201171.html (Oh)Systematic exploration of synergistic drug pairs]
|-
|align="center"|2011-46
|[http://www.nature.com/msb/journal/v5/n1/full/msb200995.html (Shim)Chemogenomic profiling predicts antifungal synergies]
|-
|align="center"|2011-45
|[http://www.pnas.org/content/104/32/13086 (Beck)A strategy for predicting the chemosensitivity of human cancers and its application to drug discovery]
|-
|align="center"|2011-44
|[http://www.nature.com/nchembio/journal/v1/n7/full/nchembio747.html (Hwang)Analysis of drug-induced effect patterns to link structure and side effects of medicines]
|-
|rowspan = "3" |2011/11/14
|align="center"|2011-43
|[http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000662 Network-Based Elucidation of Human Disease Similarities Reveals Common Functional Modules Enriched for Pluripotent Drug Targets]
|-
|align="center"|2011-42
|[http://www.nature.com/msb/journal/v7/n1/full/msb201131.html Cross-species discovery of syncretic drug combinations that potentiate the antifungal fluconazole]
|-
|align="center"|2011-41
|[http://www.nature.com/msb/journal/v7/n1/full/msb20115.html Analysis of multiple compound–protein interactions reveals novel bioactive molecules]
|-
|rowspan = "4" |2011/11/07
|align="center"|2011-40
|[http://www.nature.com/nbt/journal/v25/n10/abs/nbt1338.html Drug—target network]
|-
|align="center"|2011-39
|[http://www.nature.com/msb/journal/v6/n1/full/msb200998.html A side effect resource to capture phenotypic effects of drugs]
|-
|align="center"|2011-38
|[http://genome.cshlp.org/content/18/2/206.long Quantitative systems-level determinants of human genes targeted by successful drugs]
|-
|align="center"|2011-37
|[http://www.sciencemag.org/content/321/5886/263.short Drug Target Identification Using Side-Effect Similarity]
|-
|rowspan = "3" |2011/11/07
|align="center"|2011-36
|[http://stm.sciencemag.org/content/3/96/96ra77.short Discovery and Preclinical Validation of Drug Indications Using Compendia of Public Gene Expression Data]
|-
|align="center"|2011-35
|[http://bib.oxfordjournals.org/content/12/4/303.short Exploiting drug–disease relationships for computational drug repositioning]
|-
|align="center"|2011-34
|[http://www.springerlink.com/content/4489r051nu2t0ul1/ Drug Discovery in a Multidimensional World: Systems, Patterns, and Networks]
|-
|rowspan = "3" |2011/10/05
|align="center"|2011-33
|[http://genome.cshlp.org/content/20/7/960.full Analysis of membrane proteins in metagenomics: Networks of correlated environmental features and protein families]
|-
|align="center"|2011-32
|[http://www.pnas.org/content/106/5/1374.full Quantifying environmental adaptation of metabolic pathways in metagenomics]
|-
|align="center"|2011-31
|[http://www.pnas.org/content/104/35/13913.abstract Quantitative assessment of protein function prediction from metagenomics shotgun sequences]
|-
|rowspan = "4" |2011/10/04
|align="center"|2011-30
|[http://www.nature.com/nature/journal/v464/n7285/full/nature08821.html A human gut microbial gene catalogue established by metagenomic sequencing]
|-
|align="center"|2011-29
|[http://www.nature.com/nrmicro/journal/v6/n9/abs/nrmicro1935.html Molecular eco-systems biology: towards an understanding of community function]
|-
|align="center"|2011-28
|[http://genome.cshlp.org/content/early/2009/04/20/gr.085464.108 Microbial community profiling for human microbiome projects: Tools, techniques, and challenges]
|-
|align="center"|2011-27
|[http://www.nature.com/nrmicro/journal/v9/n4/full/nrmicro2540.html Unravelling the effects of the environment and host genotype on the gut microbiome]
|-
|rowspan = "2" |2011/09/19
|align="center"|2011-26
|[http://www.sciencemag.org/content/333/6042/596.full independently evolved virulence effectors converge onto hubs in a plant immune system Network]
|-
|align="center"|2011-25
|[http://www.sciencemag.org/content/333/6042/601.full Evidence for network evolution in an arabidopsis interactome Map]
|-
|rowspan = "2" |2011/09/05
|align="center"|2011-24
|[http://www.sciencedirect.com/science/article/pii/S0092867411005861 Exome Sequencing of Ion Channel Genes Reveals Complex Profiles Confounding Personal Risk Assessment in Epilepsy]
|-
|align="center"|2011-23
|[http://www.cell.com/abstract/S0092-8674(11)00543-5 Pluripotency factors in Embryonic stem cells Regulate Differentiation into Germ Layers]
|-
|rowspan = "2" |2011/09/22
|align="center"|2011-22
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002077 Integrated Genome-scale predition of Detrimental Mutations in Transcription Networks]
|-
|align="center"|2011-21
|[http://www.nature.com/nrg/journal/v12/n4/abs/nrg2969.html From expression QTLs to personalized transcriptomics]
|-
|2011/06/20
|align="center"|2011-20
|[http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001046 a user's guide to the encyclopedia of DNA elements(ENCODE)]
|-
|rowspan = "2" |2011/03/30
|align="center"|2011-19
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature09944.html enterotypes of the human gut microbiome]
|-
|align="center"|2011-18
|[http://www.nature.com/msb/journal/v7/n1/full/msb20116.html toward molecular trait-based ecology, through intergration of biogeochemical, geographical and metagenomic data]
|-
|rowspan = "2" |2011/03/16
|align="center"|2011-17
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002047 variable pathogenicity determines individual lifespan in ''caenorhabditis elegans'']
|-
|align="center"|2011-16
|[http://www.cell.com/abstract/S0092-8674(11)00371-0 a high-resolution ''c.elegans'' essential gene network based on phenotypic profiling of a complex tissue]
|-
|rowspan = "3" |2011/04/25
|align="center"|2011-15
|[http://www.ncbi.nlm.nih.gov/pubmed/21376230 Hallmarks of Cancer : The next generation]
|-
|align="center"|2011-14
|[http://www.cell.com/abstract/S0092-8674(11)00296-0 Mapping Cancer Origins]
|-
|align="center"|2011-13
|[http://www.cell.com/abstract/S0092-8674(11)00297-2 Genetic Interactions in Cancer Progression and Treatment]
|-
|rowspan = "2" |2011/04/11
|align="center"|2011-12
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1001273 Proteins encoded in genomic regions associated with immune-mediated disease physically interact and suggest underlying biology]
|-
|align="center"|2011-11
|***Changed!***
[http://www.ncbi.nlm.nih.gov/pubmed/19557189 Identifying relationships among genomic disease regions: predicting genes at pathogenic SNP associations and rare deletions]
|-
|rowspan = "2"|2011/03/28
|align="center"|2011-10
|[http://www.pnas.org/content/106/44/18843.long profiling translatomes of discrete cell populations resolves altered cellular priorities during hypoxia in Arabidopsis]
|-
|align="center"|2011-09
|[http://www.nature.com/msb/journal/v6/n1/full/msb201076.html cell-type specific analysis of translating RNAs in developing flowers reveals new levels of control]
|-
|rowspan = "2"|2011/03/14
|align="center"|2011-08
|[http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6WSN-51SFHJD-1&_user=44062&_coverDate=01%2F07%2F2011&_rdoc=1&_fmt=high&_orig=search&_origin=search&_sort=d&_docanchor=&view=c&_acct=C000004738&_version=1&_urlVersion=0&_userid=44062&md5=2f6017aab4c794ed7e78fbbd8447077a&searchtype=a phenotypic landscape of a bacterial cell]
|-
|align="center"|2011-07
|[http://www.nature.com/msb/journal/v6/n1/full/msb2010107.html cross-species chemogenomic profiling reveals evolutionarily conserved drug mode of action]
|-
|rowspan = "2"|2011/02/28
|align="center"|2011-06
|[http://www.pnas.org/content/early/2010/09/23/1004666107.abstract genomic patterns of pleiotropy and the evolution of complexity]
|-
|align="center"|2011-05
|[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1001009 simultaneous genome-wide inference of physical, genetic, regulatory, and functional pathway]
|-
|2011/02/21
|align="center"|2011-04
|[http://www.nature.com/msb/journal/v6/n1/full/msb201071.html dynamic interaction networks in a hierarchically organized tissue]
|-
|2011/02/14
|align="center"|2011-03
|[http://www.sciencemag.org/content/330/6009/1385.abstract rewiring of genetic networks in response to DNA damage]
|-
|rowspan = "2"|2011/01/31
|align="center"|2011-02
|[http://www.nature.com/nrg/journal/v10/n9/abs/nrg2633.html Applying mass spectrometry-based proteomics to genetics, genomics and network biology]
|-
|align="center"|2011-01
|[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
|align="center"|2010-29
|[http://www.nature.com/nature/journal/v467/n7312/full/nature09326.html Functional_roles_fornoise_in_genetic_circuits]
|-
|align="center"|2010-28
|[http://www.nature.com/ng/journal/v42/n7/full/ng.610.html Estimation_of_effect_size_distribution_from_genome-wide_association_studies_and_implications_for_future_discoveries]
|-
|rowspan = "2"|2010/11/15
|align="center"|2010-27
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1000932 Liver_and_Adipose_Expression_associated_SNPs_are_enriched_for_association_to_type_2_diabetes]
|-
|align="center"|2010-26
|[http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B8JDC-50J4MRJ-2&_user=44062&_coverDate=10%2F10%2F2010&_rdoc=1&_fmt=high&_orig=search&_origin=search&_sort=d&_docanchor=&view=c&_acct=C000004738&_version=1&_urlVersion=0&_userid=44062&md5=7667a67cf8ba3f68117b8d0ff85a8887&searchtype=a It's_the_machine_that_matters:_predicting_gene_function_and_phenotype_from_protein_networks]
|-
|rowspan = "2"|2010/11/01
|align="center"|2010-25
|[http://genome.cshlp.org/content/early/2010/06/15/gr.104216.109 A_genome-wide_map_of_human_genetic_interactions_inferred_from_radiation_hybrid_genotypes]
|-
|align="center"|2010-24
|[http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0012139 A_Genome-Wide_Gene_Function_Prediction_Resource_for_Drosophila_melanogaster]
|-
|2010/10/11
|align="center"|2010-23
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2913399/?tool=pubmed Dissecting_spatio-temporal_protein_networks_driving_human_heart_development_and_related_disorders]
|-
|rowspan = "2"|2010/09/13
|align="center"|2010-22
|[http://www.nature.com/ng/journal/v42/n7/full/ng.600.html Transposable_elements_have_rewired_the_core_regulatory_network_of_human_embryonic_stem_cells]
|-
|align="center"|2010-21
|[http://www.nature.com/ng/journal/v42/n7/full/ng0710-557.html Limits_of_sequence_and_functional_conservation]
|-
|2010/05/26
|align="center"|2010-20
|[[media:100428_network_based_elucidation_of_human_disease_similarities_reveals_common_functional_modules_enriched_for_pluripotent_drug_targets.pdf|network_based_elucidation_of_human_disease_similarities_reveals_common_functional_modules_enriched_for_pluripotent_drug_targets.pdf]]
|-
|2010/05/19
|align="center"|2010-19
|[[media:100421_interpreting_metabolomic_profiles_using_unbiased_pathway_models.pdf|interpreting_metabolomic_profiles_using_unbiased_pathway_models.pdf]]
|-
|2010/04/21
|align="center"|2010-18
|[[media:100414_identification_of_networks_of_co_occurring_tumor_related_dna_copy_number_changes_using_a_genome_wide_scoringapproach.pdf|identification_of_networks_of_co_occurring_tumor_related_dna_copy_number_changes_using_a_genome_wide_scoringapproach.pdf]]
|-
|2010/04/14
|align="center"|2010-17
|[http://www.cell.com/retrieve/pii/S0092867410000796 an_atlas_of_combinatorial_transcriptional_regulation_in_mouse_and_man]
|-
|rowspan = "2" |2010/04/07
|align="center"|2010-16
|[http://www.pnas.org/content/early/2010/03/11/0910200107.long systematic_discovery_of_nonobvious_human_disease_models_through_orthologous_phenotypes]
|-
|align="center"|2010-15
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature08800.html genome_side_association_study_of_107_phenotypes_in_Arabidopsis_thaliana_inbred_lines]
|-
|rowspan="2"|2010/03/31
|align="center"|2010-14
|[[media:100331_toward_stem_cell_systems_biology_from_molecules_to_networks_and_landscapes.pdf|toward_stem_cell_systems_biology_from_molecules_to_networks_and_landscapes.pdf]]
|-
|align="center"|2010-13
|[http://www.nature.com/nrm/journal/v10/n10/abs/nrm2766.html systems_biology_of_stem_cell_fate_and_cellular_reprogramming]
|-
|2010/03/24
|align="center"|2010-12
|[http://www.nature.com/nbt/journal/v27/n2/abs/nbt.1522.html dynamic_modularity_in_protein_interaction_networks_predicts_breast_cancer_outcome]
|-
|2010/01/18
|align="center"|2010-11
|JournalClub_100118_a_tutorial_on_statistical_methods_for_population_association_studies
|-
|2010/01/16
|align="center"|2010-10
|systems-level_dinamic_analyses_of_fate_change_in_murine_embryonic_stem_cells
|-
|2010/01/15
|align="center"|2010-09
|distinguishing_direct_versus_indirect_transcription_factor-DNA-interactions
|-
|2010/01/14
|align="center"|2010-08
|chemogenomic_profiling_predicts_antifungal_synergies
|-
|2010/01/13
|align="center"|2010-07
|edgetic_perturbation_models_of_human_inherited_disorders
|-
|2010/01/12
|align="center"|2010-06
|analysis_of_cell_fate_from_single-cell_gene_expression_profiles_in_C.elegans
|-
|2010/01/11
|align="center"|2010-05
|predicting_new_molecular_targets_for_known_drugs.pdf
Reference:SEA(Similarity Ensemble Approach)
|-
|2010/01/09
|align="center"|2010-04
|harnessing_gene_expression_to_identify_the_genetic_basis_of_drug_resistance
|-
|2010/01/08
|align="center"|2010-03
|an_integrative_approach_to_reveal_driver_gene_fusions_from_paired_end_sequencing_data_in_cancer
|-
|2010/01/07
|align="center"|2010-02
|a_phenotypic_profile_of_the_candida_albicans_regulatory_network
|-
|2010/01/06
|align="center"|2010-01
|a_global_view_of_protein_expression_in_human_cells_tissues_and_organs
|}

Journal Club

2025-02-21T08:48:43Z

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[https://doi.org/10.1101/2025.01.07.631644 A Single-Graph Visualization to Reveal Hidden Explainability Patterns of SHAP Feature Interactions in Machine Learning for Biomedical Issues]
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[https://doi.org/10.1101/2024.11.18.624166 Learning multi-cellular representations of single-cell transcriptomics data enables characterization of patient-level disease states]
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[https://doi.org/10.1016/j.cell.2024.08.037 Emergence of community behaviors in the gut microbiota upon drug treatment]
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[https://doi.org/10.1038/s41564-024-01832-5 Prediction of strain level phage–host interactions across the Escherichia genus using only genomic information]
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[https://doi.org/10.1101/2025.01.07.631807 Metagenomic estimation of absolute bacterial biomass in the mammalian gut through host-derived read normalization]
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[https://doi.org/10.1101/2024.10.02.616292 De novo discovery of conserved gene clusters in microbial genomes with Spacedust]
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|style="padding:.4em;"|SH Ahn
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[https://doi.org/10.1101/2024.04.14.589414 Rapid and Sensitive Protein Complex Alignment with Foldseek-Multimer]
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[https://www.medrxiv.org/content/10.1101/2024.04.04.24305313v1 Single-cell RNA sequencing of human tissue supports successful drug targets]
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[https://doi.org/10.1101/2024.12.30.630825 MGM as a large-scale pretrained foundation model for microbiome analyses in diverse contexts ]
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|style="padding:.4em;"|JY Ma
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[https://doi.org/10.1126/science.ado9336 Sequence modeling and design from molecular to genome scale with Evo]
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|style="padding:.4em;"|JH Cha
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[https://doi.org/10.1101/2024.12.18.629142 Human gut microbiome gene co-expression network reveals a loss in taxonomic and functional diversity in Parkinson’s disease]
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[https://doi.org/10.1101/2025.01.06.631550 Quantifying Metagenomic Strain Associations from Microbiomes with Anpan]
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[https://www.biorxiv.org/content/10.1101/2023.09.08.555192v7 Evaluating the Utilities of Foundation Models in Single-cell Data Analysis]
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[https://www.nature.com/articles/s41586-024-08328-6 Accurate predictions on small data with a tabular foundation model]
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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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[https://doi.org/10.1093/bioinformatics/btad663 TT3D: Leveraging precomputed protein 3D sequence models to predict protein–protein interactions]
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[https://doi.org/10.1093/bioinformatics/btac258 Topsy-Turvy: integrating a global view into sequence-based PPI prediction]
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[https://doi.org/10.1016/j.cels.2021.08.010 D-SCRIPT translates genome to phenome with sequence-based, structure-aware, genome-scale predictions of protein-protein interactions]
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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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[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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[https://doi.org/10.1038/s41592-024-02303-9 CellRank 2: unified fate mapping in multiview single-cell data]
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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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[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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[https://doi.org/10.1101/2024.12.13.628459 MaAsLin 3: Refining and extending generalized multivariable linear models for meta-omic association discovery]
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[https://doi.org/10.1038/s41564-024-01881-w Profiling lateral gene transfer events in the human microbiome using WAAFLE]
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[https://doi.org/10.1101/2024.05.30.596740 ProTrek: Navigating the Protein Universe through Tri-Modal Contrastive Learning]
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[https://doi.org/10.1101/2024.12.30.630844 The genetic diversity and populational specificity of the human gut virome at single nucleotide resolution]
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[https://doi.org/10.1186/s13059-024-03425-1 pan-Draft: automated reconstruction of species-representative metabolic models from multiple genomes]
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[https://doi.org/10.1016/j.cell.2024.09.019 A core microbiome signature as an indicator of health]
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[https://doi.org/10.1016/j.cell.2024.09.027 Using artificial intelligence to document the hidden RNA virosphere]
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[https://doi.org/10.1101/2023.07.17.549267 Learning a deep language model for microbiomes: the power of large scale unlabeled microbiome data]
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[https://doi.org/10.1126/science.adg7492 Accurate proteome-wide missense variant effect prediction with AlphaMissense]
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[https://doi.org/10.1038/s41591-024-02823-z A gut microbial signature for combination immune checkpoint blockade across cancer types]
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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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[https://doi.org/10.1038/s41564-024-01739-1 Multikingdom and functional gut microbiota markers for autism spectrum disorder]
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[https://doi.org/10.1186/s13059-024-03390-9 A realistic benchmark for differential abundance testing and confounder adjustment in human microbiome studies]
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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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[https://doi.org/10.1038/s41467-024-52561-6 Gut microbiota wellbeing index predicts overall health in a cohort of 1000 infants]
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[https://doi.org/10.1186/s13059-024-03320-9 VirRep: a hybrid language representation learning framework for identifying viruses from human gut metagenomes]
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[https://doi.org/10.1101/2024.06.27.601020 Ultrafast and accurate sequence alignment and clustering of viral genomes]
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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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[https://doi.org/10.1038/s41467-024-46947-9 Genomic language model predicts protein co-regulation and function]
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[https://doi.org/10.1101/2024.07.26.605391 Protein Set 1 Transformer: A protein-based genome language model to power high diversity viromics]
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[https://doi.org/10.1101/2024.07.11.603044 Prophage-DB: A comprehensive database to explore diversity,distribution, and ecology of prophages]
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[https://doi.org/10.1186/s40168-024-01904-y Strain‑resolved de‑novo metagenomic assembly of viral genomes and microbial 16S rRNAs]
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[https://doi.org/10.1186/s40168-024-01876-z Prokaryotic‑virus‑encoded auxiliary metabolic genes throughout the global oceans]
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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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[https://doi.org/10.1101/2024.04.17.589959 Pangenomes of Human Gut Microbiota Uncover Links Between Genetic Diversity and Stress Response]
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[https://doi.org/10.1101/2024.05.28.596318 vClassifier: a toolkit for species-level classification of prokaryotic viruses]
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[https://doi.org/10.1101/2024.07.26.605250 GRAViTy-V2: a grounded viral taxonomy application]
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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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[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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[https://doi.org/10.1126/science.adj4857 A blueprint for tumor-infiltrating B cells across human cancers]
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[https://doi.org/10.1038/s41467-024-48310-4 Systematic dissection of tumor-normal single-cell ecosystems across a thousand tumors of 30 cancer types]
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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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[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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[https://doi.org/10.1101/2024.06.04.597354 Cell-Graph Compass: Modeling Single Cells with Graph Structure Foundation Model]
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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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[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/10.1038/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/10.48550/arXiv.1806.00064 Efficient Low-rank Multimodal Fusion with Modality-Specific Factors]
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[https://doi.org/10.1016/j.cell.2024.03.034 Gut symbionts alleviate MASH through a secondary bile acid biosynthetic pathway]
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[https://pubmed.ncbi.nlm.nih.gov/31510656 Deep learning with multimodal representation for pancancer prognosis prediction]
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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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[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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[https://arxiv.org/abs/2103.00020 Learning Transferable Visual Models From Natural Language Supervision]
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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/10.1038/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/10.1038/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;"|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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[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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[https://doi.org/10.1038/s41467-023-44290-z Effective binning of metagenomic contigs using contrastive multi-view representation learning]
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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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[https://onlinelibrary.wiley.com/doi/full/10.1002/advs.202303925 Metagenomic Insight into The Global Dissemination of The Antibiotic Resistome]
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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="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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[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|Single-cell
|style="padding:.4em;"|24-30
|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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[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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[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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[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;" rowspan=1|Single-cell
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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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[https://doi.org/10.1038/s41588-022-01100-4 Single-cell and bulk transcriptome sequencing identifies two epithelial tumor cell states and refines the consensus molecular classification of colorectal cancer]
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|style="padding:.4em;"|HK Lee
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[https://doi.org/10.1038/s42255-023-00876-x Delineating mouse β-cell identity during lifetime and in diabetes with a single cell atlas]
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|style="padding:.4em;"|JI Lee
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[https://doi.org/10.1038/s41587-023-01747-2 Multimodal spatiotemporal phenotyping of human retinal organoid development]
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[https://doi.org/10.1038/s41586-024-07251-0 Immune microniches shape intestinal Treg function]
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[https://doi.org/10.1016/j.devcel.2021.02.021 A single-cell analysis of the Arabidopsis vegetative shoot apex]
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[https://doi.org/10.1038/s41467-023-40137-9 Droplet-based high-throughput single microbe RNA sequencing by smRandom-seq]
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[https://doi.org/10.1038/s41564-023-01462-3 Single-cell massively-parallel multiplexed microbial sequencing (M3-seq) identifies rare bacterial populations and profiles phage infection]
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[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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[https://doi.org/10.1038/s41587-023-01979-2 Spatial metatranscriptomics resolves host–bacteria–fungi interactomes]
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[https://doi.org/10.1038/s41467-023-36325-2 Dissecting the immune suppressive human prostate tumor microenvironment via integrated single-cell and spatial transcriptomic analyses]
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[https://doi.org/10.1038/s41593-023-01452-y Single-nucleus genomics in outbred rats with divergent cocaine addiction-like behaviors reveals changes in amygdala GABAergic inhibition]
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[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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[https://doi.org/10.1038/s41467-023-39933-0 Spatial cellular architecture predicts prognosis in glioblastoma]
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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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[https://doi.org/10.1038/s41467-022-30511-4 Transcriptional adaptation of olfactory sensory neurons to GPCR identity and activity]
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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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[https://doi.org/10.1021/acscentsci.3c01169 Single-Cell Analysis Reveals Cxcl14+ Fibroblast Accumulation in Regenerating Diabetic Wounds Treated by Hydrogel-Delivering Carbon Monoxide]
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[https://doi.org/10.1038/s41477-022-01291-y Single-cell RNA sequencing provides a high-resolution roadmap for understanding the multicellular compartmentation of specialized metabolism]
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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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[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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[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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[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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[https://doi.org/10.1038/s41592-023-02035-2 Population-level integration of single-cell datasets enables multi-scale analysis across samples]
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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;"|JH Cha
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[https://doi.org/10.1038/s41587-022-01467-z Modeling intercellular communication in tissues using spatial graphs of cells]
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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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[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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[https://doi.org/10.1038/s41588-022-01273-y MHC II immunogenicity shapes the neoepitope landscape in human tumors]
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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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[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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[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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[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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[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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[https://doi.org/10.1038/s41591-023-02324-5 An integrated tumor, immune and microbiome atlas of colon cancer]
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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|2023/09/05
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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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[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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[https://doi.org/10.1038/s41564-023-01439-2 A predicted CRISPR-mediated symbiosis between uncultivated archaea]
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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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[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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[https://doi.org/10.1038/s41586-023-06431-8 Mapping the T cell repertoire to a complex gut bacterial community]
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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|Microbiome
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|style="padding:.4em;"|JY Kim
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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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[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|Microbiome
|style="padding:.4em;"|23-58
|style="padding:.4em;"|G Koh
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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|Microbiome
|style="padding:.4em;"|23-57
|style="padding:.4em;"|SH Ahn
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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
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-56
|style="padding:.4em;"|HJ Kim
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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|Microbiome
|style="padding:.4em;"|23-55
|style="padding:.4em;"|JY Ma
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41591-023-02599-8 Bacterial SNPs in the human gut microbiome associate with host BMI]
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|style="padding:.4em;" rowspan=1|2023/01/03
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-54
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1080/19490976.2023.2245562 Multimodal metagenomic analysis reveals microbial single nucleotide variants as superior biomarkers for early detection of colorectal cancer]
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|style="padding:.4em;" rowspan=1|2023/01/03
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-53
|style="padding:.4em;"|NY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.chom.2023.10.005 Multi-kingdom gut microbiota analyses define bacterial-fungal interplay and microbial markers of pan-cancer immunotherapy across cohorts]
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|style="padding:.4em;" rowspan=1|2023/12/27
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-52
|style="padding:.4em;"|YR Kim
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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
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-51
|style="padding:.4em;"|JY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.cell.2023.05.046 Ultra-deep sequencing of Hadza hunter-gatherers recovers vanishing gut microbes]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-50
|style="padding:.4em;"|WJ Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1186/s40168-023-01472-7 Altered infective competence of the human gut microbiome in COVID-19]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-49
|style="padding:.4em;"|G Koh
|style="padding:.4em;text-align:left"|
[https://onlinelibrary.wiley.com/doi/full/10.1002/aisy.202300342 Host-Variable-Embedding Augmented Microbiome-Based Simultaneous Detection of Multiple Diseases by Deep Learning]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-48
|style="padding:.4em;"|SH Ahn
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41467-023-39264-0 A data-driven approach for predicting the impact of drugs on the human microbiome]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-47
|style="padding:.4em;"|HJ Kim
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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
|style="padding:.4em;"|23-46
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41467-023-39459-5 Top-down identification of keystone taxa in the microbiome]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-45
|style="padding:.4em;"|JY Ma
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.cels.2022.12.007 Pitfalls of genotyping microbial communities with rapidly growing genome collections]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-44
|style="padding:.4em;"|NY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1186/s13059-023-03028-2 Reconstruction of the last bacterial common ancestor from 183 pangenomes reveals a versatile ancient core genome]
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|style="padding:.4em;" rowspan=1|2023/11/22
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-43
|style="padding:.4em;"|SH Lee
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41587-023-01868-8 Generation of accurate, expandable phylogenomic trees with uDance]
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|style="padding:.4em;" rowspan=1|2023/11/08
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-42
|style="padding:.4em;"|WJ Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.immuni.2023.04.003 Phage display sequencing reveals that genetic, environmental, and intrinsic factors influence variation of human antibody epitope repertoire]
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|style="padding:.4em;" rowspan=1|2023/11/08
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-41
|style="padding:.4em;"|JY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.immuni.2023.04.017 Phage-display immunoprecipitation sequencing of the antibody epitope repertoire in inflammatory bowel disease reveals distinct antibody signatures]
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|style="padding:.4em;" rowspan=1|2023/11/01
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-40
|style="padding:.4em;"|G Koh
|style="padding:.4em;text-align:left"|
[https://doi.org/10.15252/msb.202311525 Consistency across multi-omics layers in a drug-perturbed gut microbial community]
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|style="padding:.4em;" rowspan=1|2023/11/01
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-39
|style="padding:.4em;"|HJ Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41587-023-01953-y Identification of mobile genetic elements with geNomad]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-38
|style="padding:.4em;"|SH Ahn
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41591-023-02407-3 Microbiome-derived cobalamin and succinyl-CoA as biomarkers for improved screening of anal cancer]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-37
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41591-023-02424-2 The airway microbiome mediates the interaction between environmental exposure and respiratory health in humans]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-36
|style="padding:.4em;"|JY Ma
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1080/19490976.2023.2224474 Ordering taxa in image convolution networks improves microbiome-based machine learning accuracy]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-35
|style="padding:.4em;"|NY Kim
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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|Microbiome
|style="padding:.4em;"|23-34
|style="padding:.4em;"|SH Lee
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.cell.2023.03.011 Dietary tryptophan metabolite released by intratumoral Lactobacillus reuteri facilitates immune checkpoint inhibitor treatment]
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|style="padding:.4em;" rowspan=1|2023/09/20
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-33
|style="padding:.4em;"|WJ Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s43587-022-00306-9 Toward an improved definition of a healthy microbiome for healthy aging]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-32
|style="padding:.4em;"|JY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s43587-022-00287-9 Associations of the skin, oral and gut microbiome with aging, frailty and infection risk reservoirs in older adults]
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|style="padding:.4em;" rowspan=1|2023/09/13
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-31
|style="padding:.4em;"|G Koh
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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|Microbiome
|style="padding:.4em;"|23-30
|style="padding:.4em;"|SH Ahn
|style="padding:.4em;text-align:left"|
[https://doi.org/10.7554/eLife.50240 Adjusting for age improves identification of gut microbiome alterations in multiple diseases]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-29
|style="padding:.4em;"|HJ Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41564-023-01370-6 Centenarians have a diverse gut virome with the potential to modulate metabolism and promote healthy lifespan]
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|style="padding:.4em;" rowspan=1|2023/09/06
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-28
|style="padding:.4em;"|JH Cha
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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;"
|+style="text-align:left;font-size:12pt" | 2023-1 ADVANCED MICROBIOME DATA ANALYSIS
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-24
|style="padding:.4em;"|JY Kim
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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|Microbiome
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|style="padding:.4em;"|WJ Kim
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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|Microbiome
|style="padding:.4em;"|23-22
|style="padding:.4em;"|SH Ahn
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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;" rowspan=1|Microbiome
|style="padding:.4em;"|23-21
|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;" rowspan=1|Microbiome
|style="padding:.4em;"|23-20
|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;" rowspan=1|Microbiome
|style="padding:.4em;"|23-19
|style="padding:.4em;"|JY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.chom.2023.01.004 Deficient butyrate-producing capacity in the gut microbiome is associated with bacterial network disturbances and fatigue symptoms in ME/CFS]
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|style="padding:.4em;" rowspan=1|2023/05/23
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-18
|style="padding:.4em;"|EJ Sung
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.ccell.2022.11.013 Gut microbiota-mediated nucleotide synthesis attenuates the response to neoadjuvant chemoradiotherapy in rectal cancer]
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|style="padding:.4em;" rowspan=1|2023/05/23
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-17
|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;" rowspan=1|2023/05/23
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-16
|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;" rowspan=1|2023/05/16
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-15
|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;" rowspan=1|2023/05/16
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-14
|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;" rowspan=1|2023/05/16
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-13
|style="padding:.4em;"|JW Yu
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.cell.2022.09.005 Pan-cancer analyses reveal cancer-type-specific fungal ecologies and bacteriome interactions]
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|style="padding:.4em;" rowspan=1|2023/05/09
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-12
|style="padding:.4em;"|NY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.cell.2022.09.015 A pan-cancer mycobiome analysis reveals fungal involvement in gastrointestinal and lung tumors]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-11
|style="padding:.4em;"|HR Shin
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41564-021-01030-7 Multi-kingdom microbiota analyses identify bacterial–fungal interactions and biomarkers of colorectal cancer across cohorts]
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|style="padding:.4em;" rowspan=1|2023/05/09
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-10
|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;" rowspan=1|2023/05/02
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-9
|style="padding:.4em;"|WJ Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41591-022-01964-3 Variability of strain engraftment and predictability of microbiome composition after fecal microbiota transplantation across different diseases]
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|style="padding:.4em;" rowspan=1|2023/05/02
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-8
|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;" rowspan=1|2023/05/02
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-7
|style="padding:.4em;"|YY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.cell.2022.11.023 Mobile genetic elements from the maternal microbiome shape infant gut microbial assembly and metabolism]
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|style="padding:.4em;" rowspan=1|2023/04/18
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-6
|style="padding:.4em;"|SH Heo
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.chom.2023.01.018 Mother-to-infant microbiota transmission and infant microbiota development across multiple body sites]
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|style="padding:.4em;" rowspan=1|2023/04/18
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-5
|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;" rowspan=1|Microbiome
|style="padding:.4em;"|23-4
|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;" rowspan=1|Microbiome
|style="padding:.4em;"|23-3
|style="padding:.4em;"|DH Lee
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[https://doi.org/10.1002/imt2.61 Targeting keystone species helps restore the dysbiosis of butyrate‐producing bacteria in nonalcoholic fatty liver disease]
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|YJ Roh
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[https://doi.org/10.1002/advs.202203115 Differential Oral Microbial Input Determines Two Microbiota Pneumo-Types Associated with Health Status]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-1
|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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{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2023-1 scOmics
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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/10.1101/2023.01.17.524482 Decoupling the correlation between cytotoxic and exhausted T lymphocyte transcriptomic signatures enhances melanoma immunotherapy response prediction from tumor expression]
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|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-23
|style="padding:.4em;"|IS Choi
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[https://doi.org/10.1101/2023.07.28.550993 Major data analysis errors invalidate cancer microbiome findings]
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|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|EJ Sung
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[https://doi.org/10.1038/s43018-022-00475-x A single-cell atlas of glioblastoma evolution under therapy reveals cell-intrinsic and cell-extrinsic therapeutic targets]
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|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|G Koh
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[https://doi.org/10.1038/s43018-022-00433-7 Single-cell meta-analyses reveal responses of tumor-reactive CXCL13+ T cells to immune-checkpoint blockade]
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|style="padding:.4em;" rowspan=1|Single-cell
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[https://doi.org/10.1038/s41587-022-01342-x Estimation of tumor cell total mRNA expression in 15 cancer types predicts disease progression]
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!scope="col" style="padding:.4em" | Date
!scope="col" style="padding:.4em" | Team
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!scope="col" style="padding:.4em" | Paper title
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|style="padding:.4em;" rowspan=1|2023/08/25
|style="padding:.4em;" rowspan=1|Microbiome
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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;" rowspan=1|Microbiome
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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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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|JY Kim
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|style="padding:.4em;" rowspan=1|2023/08/04
|style="padding:.4em;" rowspan=1|Microbiome
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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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[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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[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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[https://doi.org/10.1016/j.ccell.2022.09.009 Tumor microbiome links cellular programs and immunity in pancreatic cancer]
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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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[https://www.nature.com/articles/s41564-022-01157-1 Phage–host coevolution in natural populations]
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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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[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;"|JH Cha, SB Baek, IS Choi
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[https://www.pnas.org/doi/10.1073/pnas.2105859118 Representation learning of RNA velocity reveals robust cell transitions]
[https://www.nature.com/articles/s41467-022-34188-7 UniTVelo: temporally unified RNA velocity reinforces single-cell trajectory inference]
[https://www.sciencedirect.com/science/article/pii/S0092867421015774 Mapping transcriptomic vector fields of single cells]
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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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[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.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://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/s41596-020-00480-3 Tutorial: assessing metagenomics software with the CAMI benchmarking toolkit disease]
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[https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02195-w Functional and genetic markers of niche partitioning among enigmatic members of the human oral microbiome]
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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://genomebiology.biomedcentral.com/articles/10.1186/s13059-021-02473-1 Pandora: nucleotide-resolution bacterial pan-genomics with reference graphs]
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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://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://pubmed.ncbi.nlm.nih.gov/34912116/ Towards the biogeography of prokaryotic genes]
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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.sciencedirect.com/science/article/pii/S1931312821002365 Dispersal strategies shape persistence and evolution of human gut bacteria]
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[https://www.sciencedirect.com/science/article/pii/S0092867421003524 The long-term genetic stability and individual specificity of the human gut microbiome]
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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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[https://elifesciences.org/articles/42693 Extensive transmission of microbes along the gastrointestinal tract]
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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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[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/s41467-020-14766-3 Trajectory-based differential expression analysis for single-cell sequencing data]
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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://www.sciencedirect.com/science/article/pii/S0092867419308967?via%3Dihub Single-Cell Analysis of Crohn's Disease Lesions Identifies a Pathogenic Cellular Module Associated with Resistance to Anti-TNF Therapy]
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[https://www.nature.com/articles/s41467-020-15956-9 Multi-omic single-cell snapshots reveal multiple independent trajectories to drug tolerance in a melanoma cell line]
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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/31974247/ Single-cell Transcriptional Diversity Is a Hallmark of Developmental Potential]
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[https://pubmed.ncbi.nlm.nih.gov/30388456/ Defining T Cell States Associated With Response to Checkpoint Immunotherapy in Melanoma]
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[https://pubmed.ncbi.nlm.nih.gov/31915379/ Rapid Non-Uniform Adaptation to Conformation-Specific KRAS(G12C) Inhibition]
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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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[https://pubmed.ncbi.nlm.nih.gov/32066951/ Distinct Microbial and Immune Niches of the Human Colon]
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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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[https://pubmed.ncbi.nlm.nih.gov/30078704/ A Single-Cell Atlas of In Vivo Mammalian Chromatin Accessibility]
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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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[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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[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;"|SB Baek
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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
|style="padding:.4em;" rowspan=2|Microbiome
|style="padding:.4em;"|19-47
|style="padding:.4em;"|MY Lee
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S1931312819303026 Meta-Analysis Reveals Reproducible Gut Microbiome Alterations in Response to a High-Fat Diet]
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|style="padding:.4em;"|19-46
|style="padding:.4em;"|NY Kim
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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
|style="padding:.4em;" rowspan=2|Single-cell
|style="padding:.4em;"|19-45
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://genome.cshlp.org/content/early/2019/04/01/gr.243725.118 The accessible chromatin landscape of the murine hippocampus at single-cell resolution]
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|style="padding:.4em;"|19-44
|style="padding:.4em;"|SB Baek
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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
|style="padding:.4em;" rowspan=2|Microbiome
|style="padding:.4em;"|19-43
|style="padding:.4em;"|SH Lee
|style="padding:.4em;text-align:left"|
[https://science.sciencemag.org/content/365/6449/eaau4735 A sparse covarying unit that describes healthy and impaired human gut microbiota development]
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|style="padding:.4em;"|19-42
|style="padding:.4em;"|CY Kim
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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
|style="padding:.4em;" rowspan=2|Single-cell
|style="padding:.4em;"|19-41
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S0092867419307329?via%3Dihub Intra- and Inter-cellular Rewiring of the Human Colon during Ulcerative Colitis]
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|style="padding:.4em;"|19-40
|style="padding:.4em;"|IS Choi
|style="padding:.4em;text-align:left"|
[https://www.nature.com/articles/nbt.4042 Multiplexed droplet single-cell RNA-sequencing using natural genetic variation]
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|style="padding:.4em;" rowspan=2|2019/09/03
|style="padding:.4em;" rowspan=2|Microbiome
|style="padding:.4em;"|19-39
|style="padding:.4em;"|NY Kim
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S193131281930352X?via%3Dihub The Landscape of Genetic Content in the Gut and Oral Human Microbiome]
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|style="padding:.4em;"|19-38
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S0092867419307755?via%3Dihub Benchmarking Metagenomics Tools for Taxonomic Classification]
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{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2019
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|style="padding:.4em;" rowspan=2|2019/08/20
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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
|style="padding:.4em;"|MY Lee
|style="padding:.4em;text-align:left"|
[https://www.biorxiv.org/content/10.1101/586859v1 Single-cell interactomes of the human brain reveal cell-type specific convergence of brain disorders]
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|style="padding:.4em;" rowspan=3|2019/08/14
|style="padding:.4em;"|19-35
|style="padding:.4em;"|SH Lee
|style="padding:.4em;text-align:left"|
[https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004075 Proportionality: a valid alternative to correlation for relative data]
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|style="padding:.4em;"|19-34
|style="padding:.4em;"|SH Lee
|style="padding:.4em;text-align:left"|
[https://www.nature.com/articles/s41598-017-16520-0 propr: An R-package for Identifying Proportionally Abundant Features Using Compositional Data Analysis]
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|style="padding:.4em;"|19-33
|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
|style="padding:.4em;"|19-32
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[https://www.nature.com/articles/s41592-018-0254-1 A test metric for assessing single-cell RNA-seq batch correction]
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|style="padding:.4em;"|19-31
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S0092867419305598 Comprehensive Integration of Single-Cell Data]
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|style="padding:.4em;" rowspan=2|2019/08/08
|style="padding:.4em;"|19-30
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S0092867418313941 Defining T Cell States Associated with Response to Checkpoint Immunotherapy in Melanoma]
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|style="padding:.4em;"|19-29
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S009286741831242X High-Dimensional Analysis Delineates Myeloid and Lymphoid Compartment Remodeling during Successful Immune-Checkpoint Cancer Therapy]
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|style="padding:.4em;" rowspan=2|2019/08/07
|style="padding:.4em;"|19-28
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[https://www.biorxiv.org/content/10.1101/555557v1 A single-cell reference map for human blood and tissue T cell activation reveals functional states in health and disease]
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|style="padding:.4em;"|19-27
|style="padding:.4em;"|IS Choi
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S009286741831568X Dysfunctional CD8 T Cells Form a Proliferative, Dynamically Regulated Compartment within Human Melanoma]
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|style="padding:.4em;" rowspan=2|2019/07/30
|style="padding:.4em;"|19-26
|style="padding:.4em;"|IS Choi
|style="padding:.4em;text-align:left"|
[https://www.nature.com/articles/nm.4466 High-dimensional single-cell analysis predicts response to anti-PD-1 immunotherapy]
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|style="padding:.4em;"|19-25
|style="padding:.4em;"|JW Cho
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S0092867418311784 A Cancer Cell Program Promotes T Cell Exclusion and Resistance to Checkpoint Blockade]
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|style="padding:.4em;" rowspan=2|2019/07/23
|style="padding:.4em;"|19-24
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/26006008 COMPASS identifies T-cell subsets correlated with clinical outcomes.]
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|style="padding:.4em;"|19-23
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[https://www.nature.com/articles/ncomms14825 Sensitive detection of rare disease-associated cell subsets via representation learning]
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|style="padding:.4em;" rowspan=3|2019/05/30
|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
|style="padding:.4em;"|
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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
|style="padding:.4em;"|
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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
|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
|style="padding:.4em;"|
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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
|style="padding:.4em;"|
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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
|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
|style="padding:.4em;"|
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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
|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
|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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[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
|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
|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
|style="padding:.4em;"|19-6
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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
|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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|style="padding:.4em;"|19-3-1
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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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|style="padding:.4em;"|19-3
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[https://www.ncbi.nlm.nih.gov/pubmed/30787437 The single-cell transcriptional landscape of mammalian organogenesis]
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|style="padding:.4em;" rowspan=2|2019/3/14
|style="padding:.4em;"|19-2
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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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|style="padding:.4em;"|19-1
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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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{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2018
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|style="padding:.4em;" rowspan=2|2018/06/14
|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
|style="padding:.4em;"|18-10
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=Developmental+and+oncogenic+programs+in+H3K27M+gliomas+dissected+by+single-cell+RNA-seq Developmental and oncogenic programs in H3K27M gliomas dissected by single-cell RNA-seq.]
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|style="padding:.4em;"|18-9
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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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|style="padding:.4em;" rowspan=2|2018/05/31
|style="padding:.4em;"|18-8
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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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[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.]
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|style="padding:.4em;" rowspan=2|2018/05/17
|style="padding:.4em;"|18-4
|style="padding:.4em;"|KS Kim
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[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
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[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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|style="padding:.4em;" rowspan=2|2018/05/10
|style="padding:.4em;"|18-2
|style="padding:.4em;"|JW Cho
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[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
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[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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{|class=wikitable style="text-align:center;"
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|style="padding:.4em;" rowspan=2|2017/06/28
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|style="padding:.4em;"|HJ Han
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[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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|style="padding:.4em;"|17-35
|style="padding:.4em;"|JW Cho
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[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
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[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.]
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|style="padding:.4em;"|17-33
|style="padding:.4em;"|MY Lee
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[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
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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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|style="padding:.4em;"|17-31
|style="padding:.4em;"|MY Lee
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.celrep.2016.03.075 Genomic Correlates of Immune-Cell Infiltrates in Colorectal Carcinoma.]
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|style="padding:.4em;" rowspan=2|2017/05/31
|style="padding:.4em;"|17-30
|style="padding:.4em;"|DS Bae
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[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.]
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|style="padding:.4em;"|17-29
|style="padding:.4em;"|JW Cho
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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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|style="padding:.4em;" rowspan=2|2017/05/24
|style="padding:.4em;"|17-28
|style="padding:.4em;"|EB Kim
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[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
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[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
|style="padding:.4em;"|17-26
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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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[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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|style="padding:.4em;" rowspan=2|2017/04/26
|style="padding:.4em;"|17-24
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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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|style="padding:.4em;"|17-23
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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
|style="padding:.4em;"|17-22
|style="padding:.4em;"|SH Lee
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[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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|style="padding:.4em;"|17-21
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1038%2Fnbt.3569 Wishbone identifies bifurcating developmental trajectories from single-cell data.]
|-
|style="padding:.4em;" rowspan=2|2017/04/05
|style="padding:.4em;"|17-20
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[http://biorxiv.org/content/early/2017/02/21/110668 Reversed graph embedding resolves complex single-cell developmental trajectories.]
|-
|style="padding:.4em;"|17-19
|style="padding:.4em;"|SH Lee
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1038%2Fnmeth.4150 Single-cell mRNA quantification and differential analysis with Census.]
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|style="padding:.4em;" rowspan=2|2017/03/29
|style="padding:.4em;"|17-18
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.celrep.2016.12.060 Single-Cell Transcriptomic Analysis Defines Heterogeneity and Transcriptional Dynamics in the Adult Neural Stem Cell Lineage.]
|-
|style="padding:.4em;"|17-17
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/26051941 Single-Cell Network Analysis Identifies DDIT3 as a Nodal Lineage Regulator in Hematopoiesis.]
|-
|style="padding:.4em;" rowspan=2|2017/03/22
|style="padding:.4em;"|17-16
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/27580035 Single-cell analysis of mixed-lineage states leading to a binary cell fate choice.]
|-
|style="padding:.4em;"|17-15
|style="padding:.4em;"|EB Kim
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/27281220 Dissecting direct reprogramming from fibroblast to neuron using single-cell RNA-seq.]
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|style="padding:.4em;" rowspan=2|2017/03/15
|style="padding:.4em;"|17-14
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1126%2Fscience.aad0501 Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq.]
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|style="padding:.4em;"|17-13
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/26084335 Single-cell mRNA sequencing identifies subclonal heterogeneity in anti-cancer drug responses of lung adenocarcinoma cells.]
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|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.]
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|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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|style="padding:.4em;" rowspan=1|2017/01/31
|style="padding:.4em;"|17-06
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[//www.ncbi.nlm.nih.gov/pubmed/27912059 Microbiota Diurnal Rhythmicity Programs Host Transcriptome Oscillations.]
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|style="padding:.4em;" rowspan=1|2017/01/24
|style="padding:.4em;"|17-05
|style="padding:.4em;"|JW Cho
|style="padding:.4em;text-align:left"|
[//www.ncbi.nlm.nih.gov/pubmed/27851914 Transcriptional Landscape of Human Tissue Lymphocytes Unveils Uniqueness of Tumor-Infiltrating T Regulatory Cells.]
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|style="padding:.4em;" rowspan=2|2017/01/17
|style="padding:.4em;"|17-04
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[//www.ncbi.nlm.nih.gov/pubmed/25938943 Mapping long-range promoter contacts in human cells with high-resolution capture Hi-C]
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|style="padding:.4em;"|17-03
|style="padding:.4em;"|EB Kim
|style="padding:.4em;text-align:left"|
[//www.ncbi.nlm.nih.gov/pubmed/27306882 CHiCAGO: robust detection of DNA looping interactions in Capture Hi-C data.]
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|style="padding:.4em;" rowspan=1|2017/01/10
|style="padding:.4em;"|17-02
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[//www.ncbi.nlm.nih.gov/pubmed/26527291 ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis]
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|style="padding:.4em;" rowspan=1|2017/01/03
|style="padding:.4em;"|17-01
|style="padding:.4em;"|SH Lee
|style="padding:.4em;text-align:left"|
[//www.ncbi.nlm.nih.gov/pubmed/26287467 Single-cell messenger RNA sequencing reveals rare intestinal cell types]
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|}
{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2016
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!scope="col" style="padding:.4em" |Date
!scope="col" style="padding:.4em" | Paper index
!scope="col" style="padding:.4em" | Presenter
!scope="col" style="padding:.4em" | Paper title
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|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.]
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|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
|style="padding:.4em;"|2016-29
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/24658644 The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells.]
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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
|style="padding:.4em;"|2016-27
|style="padding:.4em;"|MY Lee
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26000487 Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells.]
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|style="padding:.4em;" rowspan=1|2016/11/8
|style="padding:.4em;"|2016-26
|style="padding:.4em;"|JW Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26000488 Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets.]
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|style="padding:.4em;" rowspan=1|2016/11/1
|style="padding:.4em;"|2016-25
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27426982 Causal Mechanistic Regulatory Network for Glioblastoma Deciphered Using Systems Genetics Network Analysis.]
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|style="padding:.4em;" rowspan=1|2016/10/25
|style="padding:.4em;"|2016-24
|style="padding:.4em;"|MY Lee
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27549193 Comprehensive analyses of tumor immunity: implications for cancer immunotherapy.]
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|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]
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|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]
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|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]
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|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.]
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|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
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27264179 A Computational Drug Repositioning Approach for Targeting Oncogenic Transcription Factors]
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|style="padding:.4em;" rowspan=1|2016/8/16
|style="padding:.4em;"|2016-17
|style="padding:.4em;"|JW Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27309802 The landscape of accessible chromatin in mammalian preimplantation embryos]
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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]
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|style="padding:.4em;" rowspan=1|2016/7/25
|style="padding:.4em;"|2016-14
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=integration+of+summary+data+from+gwas+and+eqtl+studies Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets]
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|style="padding:.4em;" rowspan=1|2016/7/18
|style="padding:.4em;"|2016-13
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/23624555 Identification of transcriptional regulators in the mouse immune system]
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|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]
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|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]
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|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]
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|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]
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|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]
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|style="padding:.4em;" rowspan=2|2016/5/11
|style="padding:.4em;"|2016-6
|style="padding:.4em;"|DS Bae, CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/25171417 Predicting Cancer-specific vulnerability via data-driven detection of synthetic lethality]
|-
|style="padding:.4em;"| 2016-5
|style="padding:.4em;"|DS Bae, CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/23467089 Dynamic regulatory network controlling Th17 cell differentiation]
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|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]
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|style="padding:.4em;" rowspan=2|2016/4/27
|style="padding:.4em;"|2016-2
|style="padding:.4em;"|CY Kim,SJ Kwon
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26780608 A predictive computational framework for direct reprogramming between human cell types]
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|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;"
|+style="text-align:left;font-size:12pt" | 2015
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!scope="col" style="padding:.4em" | Date
!scope="col" style="padding:.4em" | Paper index
!scope="col" style="padding:.4em" | Presenter
!scope="col" style="padding:.4em" | Paper title
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|style="padding:.4em;" rowspan=2|2015/06/11
|style="padding:.4em;"|2015-55
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://genome.cshlp.org/content/24/2/340.long Improved exome prioritization of disease genes through cross-species phenotype comparison.]
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|style="padding:.4em;"|2015-54
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0002929714001128 Phevor combines multiple biomedical ontologies for accurate identification of disease-causing alleles in single individuals and small nuclear families.]
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|style="padding:.4em;" rowspan=2|2015/06/04
|style="padding:.4em;"|2015-53
|style="padding:.4em;"|
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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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|style="padding:.4em;"|2015-52
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://genome.cshlp.org/content/21/9/1529.long A probabilistic disease-gene finder for personal genomes.]
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|style="padding:.4em;" rowspan=2|2015/05/28
|style="padding:.4em;"|2015-51
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/ng/journal/v46/n12/full/ng.3141.html Systematic analysis of noncoding somatic mutations and gene expression alterations across 14 tumor types.]
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|style="padding:.4em;"|2015-50
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nbt/journal/v28/n5/full/nbt.1630.html GREAT improves functional interpretation of cis-regulatory regions.]
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|style="padding:.4em;" rowspan=2|2015/05/21
|style="padding:.4em;"|2015-49
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nmeth/journal/v11/n3/full/nmeth.2832.html Functional annotation of noncoding sequence variants.]
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|style="padding:.4em;"|2015-48
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://genomebiology.com/content/15/10/480 FunSeq2: A framework for prioritizing noncoding regulatory variants in cancer.]
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|style="padding:.4em;" rowspan=2|2015/05/14
|style="padding:.4em;"|2015-47
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nmeth/journal/v12/n2/full/nmeth.3215.html Selecting causal genes from genome-wide association studies via functionally coherent subnetworks.]
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|style="padding:.4em;"|2015-46
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/ncomms/2015/150119/ncomms6890/full/ncomms6890.html Biological interpretation of genome-wide association studies using predicted gene functions.]
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|style="padding:.4em;" rowspan=3|2015/05/07
|style="padding:.4em;"|2015-45
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/ncomms/2014/140626/ncomms5212/full/ncomms5212.html Human symptoms-disease network.]
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|style="padding:.4em;"|2015-44
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867413010246 A nondegenerate code of deleterious variants in Mendelian loci contributes to complex disease risk.]
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|style="padding:.4em;"|2015-43
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.sciencemag.org/content/347/6224/1257601.long Uncovering disease-disease relationships through the incomplete interactome.]

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|style="padding:.4em;" rowspan=2|2015/04/30
|style="padding:.4em;"|2015-42
|style="padding:.4em;"|
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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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|style="padding:.4em;"|2015-41
|style="padding:.4em;"|
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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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|style="padding:.4em;" rowspan=3|2015/04/23
|style="padding:.4em;"|2015-40
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v518/n7539/full/nature13990.html Dissecting neural differentiation regulatory networks through epigenetic footprinting.]
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|style="padding:.4em;"|2015-39
|style="padding:.4em;"|
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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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|style="padding:.4em;"|2015-38
|style="padding:.4em;"|
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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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|style="padding:.4em;" rowspan=2|2015/04/09
|style="padding:.4em;"|2015-37
|style="padding:.4em;"|
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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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|style="padding:.4em;"|2015-36
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414014974 A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping.]
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|style="padding:.4em;" rowspan=2|2015/04/02
|style="padding:.4em;"|2015-35
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nrg/journal/v14/n6/full/nrg3454.html Exploring the three-dimensional organization of genomes: interpreting chromatin interaction data.]
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|style="padding:.4em;"|2015-34
|style="padding:.4em;"|
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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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|style="padding:.4em;" rowspan=2|2015/03/26
|style="padding:.4em;"|2015-33
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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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|style="padding:.4em;"|2015-32
|style="padding:.4em;"|
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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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|style="padding:.4em;" rowspan=2|2015/03/19
|style="padding:.4em;"|2015-31
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nbt/journal/vaop/ncurrent/full/nbt.3154.html Decoding the regulatory network of early blood development from single-cell gene expression measurements.]
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|style="padding:.4em;"|2015-30
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nrg/journal/v16/n3/full/nrg3833.html Computational and analytical challenges in single-cell transcriptomics.]
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|style="padding:.4em;" rowspan=3|2015/03/12
|style="padding:.4em;"|2015-29
|style="padding:.4em;"|
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[http://www.sciencedirect.com/science/article/pii/S0092867415000136 Extensive Strain-Level Copy-Number Variation across Human Gut Microbiome Species.]
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|style="padding:.4em;"|2015-28
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|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 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.]
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|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 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"
|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 title
|-
|rowspan = "1"|2013/01/11
|align ="center"|2012-81
|[http://genome.cshlp.org/content/22/8/1589.full (TH Kim) MuSiC: identifying mutational significance in cancer genomes.]
|-
|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"|2012-74
|[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]
|-
| 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]
|-
| align="center"|2012-72
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2063581/ (SH Hwang) Network-based classification of breast cancer metastasis]
|-
| 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]
|-
|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]
|-
| align="center"|2012-66
|[http://bioinformatics.oxfordjournals.org/content/25/16/2078.long (ER Kim&TH Kim)The Sequence Alignment/Map format and SAMtools]
|-
| 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]
|-
| align="center"|2012-64
|[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"|2012-63
|[http://www.sciencemag.org/content/336/6086/1262.abstract (T Lee&CY Kim)Host-Gut Microbiota Metabolic Interactions]
|-
|align="center"|2012-62
|[http://www.sciencemag.org/content/336/6086/1268.full (AR Cho,JH Ju)Interactions Between the Microbiota and the Immune System]
|-
|align="center"|2012-61
|[http://www.sciencemag.org/content/336/6086/1255.abstract (SH Hwang&HJ Cho)The Application of Ecological Theory Toward an Understanding of the Human Microbiome]
|-
|align="center"|2012-60
|[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
|[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]
|-
| align="center"|2012-58
|[http://www.nature.com/nature/journal/v486/n7402/full/nature11209.html (JH Shin&HJ Kim)A framework for human microbiome research]
|-
|align="center"|2012-57
|[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
|[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]
|-
|align="center"|2012-55
|[http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002511 (YH Go&HJ Han)Google Goes Cancer: Improving Outcome Prediction for Cancer Patients by Network-Based Ranking of Marker Genes]
|-
| align="center"|2012-54
|[http://www.nature.com/msb/journal/v7/n1/full/msb201147.html (YH Go&HJ Han)A pharmacogenomic method for individualized prediction of drug sensitivity]
|-
|align="center"|2012-53
|[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
|[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]
|-
|align="center"|2012-51
|[http://www.nature.com/nature/journal/v483/n7391/full/nature11005.html (ER Kim&TH Kim)Systematic identification of genomic markers of drug sensitivity in cancer cells]
|-
| align="center"|2012-50
|[http://www.pnas.org/content/early/2011/10/13/1018854108.abstract (ER Kim&TH Kim)Subtype and pathway specific responses to anticancer compounds in breast cancer]
|-
|align="center"|2012-49
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature10945.html (JE Shim&KS Kim)De novo mutations revealed by whole-exome sequencing are strongly associated with autism]
|-
|align="center"|2012-48
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature11011.html (JE Shim&KS Kim)Patterns and rates of exonic de novo mutations in autism spectrum disorders]
|-
|align="center"|2012-47
|[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
|[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)]
|-
|align="center"|2012-45
|[http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002621 (T Lee&CY Kim)Type 2 Diabetes Risk Alleles Demonstrate Extreme Directional Differentiation among Human Populations, Compared to Other Diseases]
|-
|align="center"|2012-44
|[http://www.nature.com/nature/journal/v480/n7376/full/nature10665.html (AR Cho&JH Ju)Predicting mutation outcome from early stochastic variation in genetic interaction partners]
|-
|align="center"|2012-43
|[http://www.sciencemag.org/content/335/6064/82 (AR Cho&JH Ju)Fitness Trade-Offs and Environmentally Induced Mutation Buffering in Isogenic C. elegans]
|-
| align="center"|2012-42
|[http://genome.cshlp.org/content/22/6/1163 (JE Shim&KS Kim)Identification of microRNA-regulated gene networks by expression analysis of target genes]
|-
|align="center"|2012-41
|[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
|[http://www.cell.com/abstract/S0092-8674(12)00573-9 (JH Soh)Functional Repurposing Revealed by Comparing S. pombe and S. cerevisiae Genetic Interactions]
|-
|align="center"|2012-39
|[http://genome.cshlp.org/content/22/2/375.long (ER Kim&TH Kim)De novo discovery of mutated driver pathways in cancer]
|-
| align="center"|2012-38
|[http://www.nature.com/msb/journal/v4/n1/full/msb20082.html (YH Go&HJ Han)A systems biology approach to prediction of oncogenes and molecular perturbation targets in B-cell lymphomas]
|-
|align="center"|2012-37
|[http://www.nature.com/msb/journal/v7/n1/full/msb201126.html (SH Hwang&HJ Cho)PREDICT: a method for inferring novel drug indications with application to personalized medicine]
|-
|align="center"|2012-36
|[http://www.nature.com/nature/journal/v453/n7198/full/nature06973.html (SH Hwang&HJ Cho)Synergistic response to oncogenic mutations defines gene class critical to cancer phenotype]
|-
|align="center"|2012-35
|[http://www.sciencemag.org/content/334/6062/1518.full (JH Shin&HJ Kim)Detecting Novel Associations in Large Data Sets]
|-
|rowspan = "3" |2012/03/05
| align="center"|2012-34
|[http://www.ncbi.nlm.nih.gov/pubmed/18516045 (8,HH Kim)Mapping and quantifying mammalian transcriptomes by RNA-Seq.]
|-
|align="center"|2012-33
|[http://genomebiology.com/2010/11/10/R106 (11,Go&Ju)Differential expression analysis for sequence count data]
|-
|align="center"|2012-32
|[http://bioinformatics.oxfordjournals.org/content/26/1/139.short (12,Go&Ju)edgeR: a Bioconductor package for differential expression analysis of digital gene expression data ]
|-
|rowspan = "7" |2012/02/27 2012/02/28
| align="center"|2012-31
|[http://www.nature.com/nrg/journal/v10/n1/full/nrg2484.html (1,JW Song)RNA-Seq: a revolutionary tool for transcriptomics]
|-
|align="center"|2012-30
|[http://www.nature.com/nmeth/journal/v8/n6/full/nmeth.1613.html (2,JW Song)Computational methods for transcriptome annotation and quantification using RNA-seq]
|-
|align="center"|2012-29
|[http://genomebiology.com/2010/11/12/220 (3,HJ Han)From RNA-seq reads to differential expression results]
|-
|align="center"|2012-28
|[http://www.nature.com/nmeth/journal/v7/n9/full/nmeth.1491.html (4,AR Cho)Comprehensive comparative analysis of strand-specific RNA sequencing methods]
|-
|align="center"|2012-27
|[http://www.plosone.org/article/info:doi%2F10.1371%2Fjournal.pone.0026426 (5,T Lee)A Low-Cost Library Construction Protocol and Data Analysis Pipeline for Illumina-Based Strand-Specific Multiplex RNA-Seq]
|-
|align="center"|2012-26
|[http://www.nature.com/nbt/journal/v28/n5/abs/nbt.1621.html (6,So&Shin)Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation]
|-
|align="center"|2012-25
|[http://bioinformatics.oxfordjournals.org/content/25/9/1105.abstract (7,So&Shin)TopHat: discovering splice junctions with RNA-Seq]
|-
|rowspan = "5" |2012/02/06
| align="center"|2012-24
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3125733/ mirConnX: condition-specific mRNA-microRNA network integrator]
|-
|align="center"|2012-23
|[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002190 Construction and Analysis of an Integrated Regulatory Network Derived from High-Throughput Sequencing Data]
|-
|align="center"|2012-22
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002415 A Densely Interconnected Genome-Wide Network of MicroRNAs and Oncogenic Pathways Revealed Using Gene Expression Signatures]
|-
|align="center"|2012-21
|[http://genome.cshlp.org/content/20/5/589.short Reprogramming of miRNA networks in cancer and leukemia]
|-
|align="center"|2012-20
|[http://www.cell.com/abstract/S0092-8674(11)01152-4 An Extensive MicroRNA-Mediated Network of RNA-RNA Interactions Regulates Established Oncogenic Pathways in Glioblastoma]
|-
|rowspan = "5" |2012/01/30
|align="center"|2012-19
|[http://www.cell.com/abstract/S0092-8674(11)00237-6 Principles and Strategies for Developing Network Models in Cancer]
|-
|align="center"|2012-18
|[http://www.nature.com/ng/journal/v37/n4/full/ng1532.html Reverse engineering of regulatory networks in human B cells]
|-
|align="center"|2012-17
|[http://www.nature.com/nature/journal/v452/n7186/abs/nature06757.html Variations in DNA elucidate molecular networks that cause disease]
|-
|align="center"|2012-16
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2779083/ Harnessing gene expression to identify the genetic basis of drug resistance]
|-
|align="center"|2012-15
|[http://www.sciencedirect.com/science/article/pii/S0092867410012936 An Integrated Approach to Uncover Drivers of Cancer]
|-
|rowspan = "3" |2012/01/09
|align="center"|2012-14
|[http://www.ncbi.nlm.nih.gov/pubmed/18704161 Genetic variation in an individual human exome.]
|-
|align="center"|2012-13
|[http://www.ncbi.nlm.nih.gov/pubmed/22081227 Predicting phenotypic variation in yeast from individual genome sequences.]
|-
|align="center"|2012-12
|[http://www.ncbi.nlm.nih.gov/pubmed/20435227 Clinical assessment incorporating a personal genome.]
|-
|rowspan = "6" |2012/01/09 2012/01/16
| align="center"|2012-11
|[http://www.ncbi.nlm.nih.gov/pubmed/20399638 Human allelic variation: perspective from protein function, structure, and evolution.]
|-
|align="center"|2012-10
|[http://www.ncbi.nlm.nih.gov/pubmed/19561590 Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm.]
|-
|align="center"|2012-09
|[http://www.ncbi.nlm.nih.gov/pubmed/11230178 Prediction of deleterious human alleles.]
|-
|align="center"|2012-08
|[http://www.ncbi.nlm.nih.gov/pubmed/12202775 Human non-synonymous SNPs: server and survey.]
|-
|align="center"|2012-07
|[http://www.ncbi.nlm.nih.gov/pubmed/20354512 A method and server for predicting damaging missense mutations.]
|-
|align="center"|2012-06
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1920242/ SNAP: predict effect of non-synonymous polymorphisms on function]
|-
|rowspan = "3" |2012/01/09 2012/01/16
|align="center"|2012-05
|[http://www.ncbi.nlm.nih.gov/pubmed/21920052 Computational and statistical approaches to analyzing variants identified by exome sequencing.]
|-
|align="center"|2012-04
|[http://www.ncbi.nlm.nih.gov/pubmed/18179889 Shifting paradigm of association studies: value of rare single-nucleotide polymorphisms.]
|-
|align="center"|2012-03
|[http://www.ncbi.nlm.nih.gov/pubmed/19684571 Targeted capture and massively parallel sequencing of 12 human exomes.]
|-
|rowspan = "2" |2012/01/09 2012/01/16
|align="center"|2012-02
|[http://www.ncbi.nlm.nih.gov/pubmed/17637733 The distribution of fitness effects of new mutations.]
|-
|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]
|}
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
|align="center"|2011-49
|[http://bmir.stanford.edu/file_asset/index.php/1407/BMIR-2009-1355.pdf (Shin)Data-Driven Methods to Discover Molecular Determinants of Serious Adverse Drug Events]
|-
|align="center"|2011-48
|[http://www.nature.com/nchembio/journal/v4/n11/abs/nchembio.118.html (Shin)Network pharmacology: the next paradigm in drug discovery]
|-
|align="center"|2011-47
|[http://www.nature.com/msb/journal/v7/n1/full/msb201171.html (Oh)Systematic exploration of synergistic drug pairs]
|-
|align="center"|2011-46
|[http://www.nature.com/msb/journal/v5/n1/full/msb200995.html (Shim)Chemogenomic profiling predicts antifungal synergies]
|-
|align="center"|2011-45
|[http://www.pnas.org/content/104/32/13086 (Beck)A strategy for predicting the chemosensitivity of human cancers and its application to drug discovery]
|-
|align="center"|2011-44
|[http://www.nature.com/nchembio/journal/v1/n7/full/nchembio747.html (Hwang)Analysis of drug-induced effect patterns to link structure and side effects of medicines]
|-
|rowspan = "3" |2011/11/14
|align="center"|2011-43
|[http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000662 Network-Based Elucidation of Human Disease Similarities Reveals Common Functional Modules Enriched for Pluripotent Drug Targets]
|-
|align="center"|2011-42
|[http://www.nature.com/msb/journal/v7/n1/full/msb201131.html Cross-species discovery of syncretic drug combinations that potentiate the antifungal fluconazole]
|-
|align="center"|2011-41
|[http://www.nature.com/msb/journal/v7/n1/full/msb20115.html Analysis of multiple compound–protein interactions reveals novel bioactive molecules]
|-
|rowspan = "4" |2011/11/07
|align="center"|2011-40
|[http://www.nature.com/nbt/journal/v25/n10/abs/nbt1338.html Drug—target network]
|-
|align="center"|2011-39
|[http://www.nature.com/msb/journal/v6/n1/full/msb200998.html A side effect resource to capture phenotypic effects of drugs]
|-
|align="center"|2011-38
|[http://genome.cshlp.org/content/18/2/206.long Quantitative systems-level determinants of human genes targeted by successful drugs]
|-
|align="center"|2011-37
|[http://www.sciencemag.org/content/321/5886/263.short Drug Target Identification Using Side-Effect Similarity]
|-
|rowspan = "3" |2011/11/07
|align="center"|2011-36
|[http://stm.sciencemag.org/content/3/96/96ra77.short Discovery and Preclinical Validation of Drug Indications Using Compendia of Public Gene Expression Data]
|-
|align="center"|2011-35
|[http://bib.oxfordjournals.org/content/12/4/303.short Exploiting drug–disease relationships for computational drug repositioning]
|-
|align="center"|2011-34
|[http://www.springerlink.com/content/4489r051nu2t0ul1/ Drug Discovery in a Multidimensional World: Systems, Patterns, and Networks]
|-
|rowspan = "3" |2011/10/05
|align="center"|2011-33
|[http://genome.cshlp.org/content/20/7/960.full Analysis of membrane proteins in metagenomics: Networks of correlated environmental features and protein families]
|-
|align="center"|2011-32
|[http://www.pnas.org/content/106/5/1374.full Quantifying environmental adaptation of metabolic pathways in metagenomics]
|-
|align="center"|2011-31
|[http://www.pnas.org/content/104/35/13913.abstract Quantitative assessment of protein function prediction from metagenomics shotgun sequences]
|-
|rowspan = "4" |2011/10/04
|align="center"|2011-30
|[http://www.nature.com/nature/journal/v464/n7285/full/nature08821.html A human gut microbial gene catalogue established by metagenomic sequencing]
|-
|align="center"|2011-29
|[http://www.nature.com/nrmicro/journal/v6/n9/abs/nrmicro1935.html Molecular eco-systems biology: towards an understanding of community function]
|-
|align="center"|2011-28
|[http://genome.cshlp.org/content/early/2009/04/20/gr.085464.108 Microbial community profiling for human microbiome projects: Tools, techniques, and challenges]
|-
|align="center"|2011-27
|[http://www.nature.com/nrmicro/journal/v9/n4/full/nrmicro2540.html Unravelling the effects of the environment and host genotype on the gut microbiome]
|-
|rowspan = "2" |2011/09/19
|align="center"|2011-26
|[http://www.sciencemag.org/content/333/6042/596.full independently evolved virulence effectors converge onto hubs in a plant immune system Network]
|-
|align="center"|2011-25
|[http://www.sciencemag.org/content/333/6042/601.full Evidence for network evolution in an arabidopsis interactome Map]
|-
|rowspan = "2" |2011/09/05
|align="center"|2011-24
|[http://www.sciencedirect.com/science/article/pii/S0092867411005861 Exome Sequencing of Ion Channel Genes Reveals Complex Profiles Confounding Personal Risk Assessment in Epilepsy]
|-
|align="center"|2011-23
|[http://www.cell.com/abstract/S0092-8674(11)00543-5 Pluripotency factors in Embryonic stem cells Regulate Differentiation into Germ Layers]
|-
|rowspan = "2" |2011/09/22
|align="center"|2011-22
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002077 Integrated Genome-scale predition of Detrimental Mutations in Transcription Networks]
|-
|align="center"|2011-21
|[http://www.nature.com/nrg/journal/v12/n4/abs/nrg2969.html From expression QTLs to personalized transcriptomics]
|-
|2011/06/20
|align="center"|2011-20
|[http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001046 a user's guide to the encyclopedia of DNA elements(ENCODE)]
|-
|rowspan = "2" |2011/03/30
|align="center"|2011-19
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature09944.html enterotypes of the human gut microbiome]
|-
|align="center"|2011-18
|[http://www.nature.com/msb/journal/v7/n1/full/msb20116.html toward molecular trait-based ecology, through intergration of biogeochemical, geographical and metagenomic data]
|-
|rowspan = "2" |2011/03/16
|align="center"|2011-17
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002047 variable pathogenicity determines individual lifespan in ''caenorhabditis elegans'']
|-
|align="center"|2011-16
|[http://www.cell.com/abstract/S0092-8674(11)00371-0 a high-resolution ''c.elegans'' essential gene network based on phenotypic profiling of a complex tissue]
|-
|rowspan = "3" |2011/04/25
|align="center"|2011-15
|[http://www.ncbi.nlm.nih.gov/pubmed/21376230 Hallmarks of Cancer : The next generation]
|-
|align="center"|2011-14
|[http://www.cell.com/abstract/S0092-8674(11)00296-0 Mapping Cancer Origins]
|-
|align="center"|2011-13
|[http://www.cell.com/abstract/S0092-8674(11)00297-2 Genetic Interactions in Cancer Progression and Treatment]
|-
|rowspan = "2" |2011/04/11
|align="center"|2011-12
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1001273 Proteins encoded in genomic regions associated with immune-mediated disease physically interact and suggest underlying biology]
|-
|align="center"|2011-11
|***Changed!***
[http://www.ncbi.nlm.nih.gov/pubmed/19557189 Identifying relationships among genomic disease regions: predicting genes at pathogenic SNP associations and rare deletions]
|-
|rowspan = "2"|2011/03/28
|align="center"|2011-10
|[http://www.pnas.org/content/106/44/18843.long profiling translatomes of discrete cell populations resolves altered cellular priorities during hypoxia in Arabidopsis]
|-
|align="center"|2011-09
|[http://www.nature.com/msb/journal/v6/n1/full/msb201076.html cell-type specific analysis of translating RNAs in developing flowers reveals new levels of control]
|-
|rowspan = "2"|2011/03/14
|align="center"|2011-08
|[http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6WSN-51SFHJD-1&_user=44062&_coverDate=01%2F07%2F2011&_rdoc=1&_fmt=high&_orig=search&_origin=search&_sort=d&_docanchor=&view=c&_acct=C000004738&_version=1&_urlVersion=0&_userid=44062&md5=2f6017aab4c794ed7e78fbbd8447077a&searchtype=a phenotypic landscape of a bacterial cell]
|-
|align="center"|2011-07
|[http://www.nature.com/msb/journal/v6/n1/full/msb2010107.html cross-species chemogenomic profiling reveals evolutionarily conserved drug mode of action]
|-
|rowspan = "2"|2011/02/28
|align="center"|2011-06
|[http://www.pnas.org/content/early/2010/09/23/1004666107.abstract genomic patterns of pleiotropy and the evolution of complexity]
|-
|align="center"|2011-05
|[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1001009 simultaneous genome-wide inference of physical, genetic, regulatory, and functional pathway]
|-
|2011/02/21
|align="center"|2011-04
|[http://www.nature.com/msb/journal/v6/n1/full/msb201071.html dynamic interaction networks in a hierarchically organized tissue]
|-
|2011/02/14
|align="center"|2011-03
|[http://www.sciencemag.org/content/330/6009/1385.abstract rewiring of genetic networks in response to DNA damage]
|-
|rowspan = "2"|2011/01/31
|align="center"|2011-02
|[http://www.nature.com/nrg/journal/v10/n9/abs/nrg2633.html Applying mass spectrometry-based proteomics to genetics, genomics and network biology]
|-
|align="center"|2011-01
|[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
|align="center"|2010-29
|[http://www.nature.com/nature/journal/v467/n7312/full/nature09326.html Functional_roles_fornoise_in_genetic_circuits]
|-
|align="center"|2010-28
|[http://www.nature.com/ng/journal/v42/n7/full/ng.610.html Estimation_of_effect_size_distribution_from_genome-wide_association_studies_and_implications_for_future_discoveries]
|-
|rowspan = "2"|2010/11/15
|align="center"|2010-27
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1000932 Liver_and_Adipose_Expression_associated_SNPs_are_enriched_for_association_to_type_2_diabetes]
|-
|align="center"|2010-26
|[http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B8JDC-50J4MRJ-2&_user=44062&_coverDate=10%2F10%2F2010&_rdoc=1&_fmt=high&_orig=search&_origin=search&_sort=d&_docanchor=&view=c&_acct=C000004738&_version=1&_urlVersion=0&_userid=44062&md5=7667a67cf8ba3f68117b8d0ff85a8887&searchtype=a It's_the_machine_that_matters:_predicting_gene_function_and_phenotype_from_protein_networks]
|-
|rowspan = "2"|2010/11/01
|align="center"|2010-25
|[http://genome.cshlp.org/content/early/2010/06/15/gr.104216.109 A_genome-wide_map_of_human_genetic_interactions_inferred_from_radiation_hybrid_genotypes]
|-
|align="center"|2010-24
|[http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0012139 A_Genome-Wide_Gene_Function_Prediction_Resource_for_Drosophila_melanogaster]
|-
|2010/10/11
|align="center"|2010-23
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2913399/?tool=pubmed Dissecting_spatio-temporal_protein_networks_driving_human_heart_development_and_related_disorders]
|-
|rowspan = "2"|2010/09/13
|align="center"|2010-22
|[http://www.nature.com/ng/journal/v42/n7/full/ng.600.html Transposable_elements_have_rewired_the_core_regulatory_network_of_human_embryonic_stem_cells]
|-
|align="center"|2010-21
|[http://www.nature.com/ng/journal/v42/n7/full/ng0710-557.html Limits_of_sequence_and_functional_conservation]
|-
|2010/05/26
|align="center"|2010-20
|[[media:100428_network_based_elucidation_of_human_disease_similarities_reveals_common_functional_modules_enriched_for_pluripotent_drug_targets.pdf|network_based_elucidation_of_human_disease_similarities_reveals_common_functional_modules_enriched_for_pluripotent_drug_targets.pdf]]
|-
|2010/05/19
|align="center"|2010-19
|[[media:100421_interpreting_metabolomic_profiles_using_unbiased_pathway_models.pdf|interpreting_metabolomic_profiles_using_unbiased_pathway_models.pdf]]
|-
|2010/04/21
|align="center"|2010-18
|[[media:100414_identification_of_networks_of_co_occurring_tumor_related_dna_copy_number_changes_using_a_genome_wide_scoringapproach.pdf|identification_of_networks_of_co_occurring_tumor_related_dna_copy_number_changes_using_a_genome_wide_scoringapproach.pdf]]
|-
|2010/04/14
|align="center"|2010-17
|[http://www.cell.com/retrieve/pii/S0092867410000796 an_atlas_of_combinatorial_transcriptional_regulation_in_mouse_and_man]
|-
|rowspan = "2" |2010/04/07
|align="center"|2010-16
|[http://www.pnas.org/content/early/2010/03/11/0910200107.long systematic_discovery_of_nonobvious_human_disease_models_through_orthologous_phenotypes]
|-
|align="center"|2010-15
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature08800.html genome_side_association_study_of_107_phenotypes_in_Arabidopsis_thaliana_inbred_lines]
|-
|rowspan="2"|2010/03/31
|align="center"|2010-14
|[[media:100331_toward_stem_cell_systems_biology_from_molecules_to_networks_and_landscapes.pdf|toward_stem_cell_systems_biology_from_molecules_to_networks_and_landscapes.pdf]]
|-
|align="center"|2010-13
|[http://www.nature.com/nrm/journal/v10/n10/abs/nrm2766.html systems_biology_of_stem_cell_fate_and_cellular_reprogramming]
|-
|2010/03/24
|align="center"|2010-12
|[http://www.nature.com/nbt/journal/v27/n2/abs/nbt.1522.html dynamic_modularity_in_protein_interaction_networks_predicts_breast_cancer_outcome]
|-
|2010/01/18
|align="center"|2010-11
|JournalClub_100118_a_tutorial_on_statistical_methods_for_population_association_studies
|-
|2010/01/16
|align="center"|2010-10
|systems-level_dinamic_analyses_of_fate_change_in_murine_embryonic_stem_cells
|-
|2010/01/15
|align="center"|2010-09
|distinguishing_direct_versus_indirect_transcription_factor-DNA-interactions
|-
|2010/01/14
|align="center"|2010-08
|chemogenomic_profiling_predicts_antifungal_synergies
|-
|2010/01/13
|align="center"|2010-07
|edgetic_perturbation_models_of_human_inherited_disorders
|-
|2010/01/12
|align="center"|2010-06
|analysis_of_cell_fate_from_single-cell_gene_expression_profiles_in_C.elegans
|-
|2010/01/11
|align="center"|2010-05
|predicting_new_molecular_targets_for_known_drugs.pdf
Reference:SEA(Similarity Ensemble Approach)
|-
|2010/01/09
|align="center"|2010-04
|harnessing_gene_expression_to_identify_the_genetic_basis_of_drug_resistance
|-
|2010/01/08
|align="center"|2010-03
|an_integrative_approach_to_reveal_driver_gene_fusions_from_paired_end_sequencing_data_in_cancer
|-
|2010/01/07
|align="center"|2010-02
|a_phenotypic_profile_of_the_candida_albicans_regulatory_network
|-
|2010/01/06
|align="center"|2010-01
|a_global_view_of_protein_expression_in_human_cells_tissues_and_organs
|}

People:Album:2025 Jan, 2025 KOGO

2025-02-12T12:12:26Z

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Album

2025-02-12T11:26:55Z

Sej4926:

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----
*[[People:Album:2024 Oct, 2024 BIOINFO | 2024 Oct, 2024 BIOINFO]]
----
*[[People:Album:2019 Nov, Thanksgiving Day | 2019 Nov, Thanksgiving Day]]
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*[[People:Album:2017 11th AYRCOB, GIW | 2017 Oct, 11th AYRCOB and GIW]]
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*[[People:Album:2016 10th AYRCOB, GIW | 2016 Oct, 10th AYRCOB and GIW]]
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*[[People:Album:Groupphoto_20161208_crop.jpg| 2016 Dec, Thanksgiving Day]]
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----
*[[People:Album:2016 9th AYRCOB | 2016 9th AYRCOB]]
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*[[People:Album:2015 Group photo | 2015 Group photo]]
----
*[[People:Album:2015 Teachers' day | 2015 Teacher's day]]
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*[[People:Album:2014 Summer MT | 2014 Summer MT]]
----
*[[People:Album:2014 Group photo | 2014 Group photo]]
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*[[People:Album:2013 Summer MT | 2013 Summer MT]]
----
*[[People:Album:2013 Group photo | 2013 Group photo]]
----
*[[People:Album:Teachers' day 2013| 2013 May, Teachers' day]]
----
*[[People:Album:ThanksGivingDay_2012| 2012 Nov, Thanksgiving Day]]
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----
*[[People:Album:winter_MT_2008|2008 December, Winter MT]]

Journal Club

2025-02-08T06:50:09Z

Sej4926:

{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2024-2 scOmics
|-
!scope="col" style="padding:.4em" | Date
!scope="col" style="padding:.4em" | Team
!scope="col" style="padding:.4em" | Paper index
!scope="col" style="padding:.4em" | Presenter
!scope="col" style="padding:.4em" | Paper title
|-
|style="padding:.4em;" rowspan=1|2025/3/12
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|25-8
|style="padding:.4em;"|YR Jung
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2024.11.18.624166 Learning multi-cellular representations of single-cell transcriptomics data enables characterization of patient-level disease states]
|-
|style="padding:.4em;" rowspan=1|2025/3/5
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|25-7
|style="padding:.4em;"|EJ Sung
|style="padding:.4em;text-align:left"|
[https://www.medrxiv.org/content/10.1101/2024.04.04.24305313v1 Single-cell RNA sequencing of human tissue supports successful drug targets]
|-
|style="padding:.4em;" rowspan=1|2025/2/24
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|25-6
|style="padding:.4em;"|IS Choi
|style="padding:.4em;text-align:left"|
[https://www.biorxiv.org/content/10.1101/2023.09.08.555192v7 Evaluating the Utilities of Foundation Models in Single-cell Data Analysis]
|-
|style="padding:.4em;" rowspan=1|2025/2/19
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|25-5
|style="padding:.4em;"|SB Baek
|style="padding:.4em;text-align:left"|
[https://www.nature.com/articles/s41586-024-08328-6 Accurate predictions on small data with a tabular foundation model]
|-
|style="padding:.4em;" rowspan=1|2025/1/15
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|25-4
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2024.09.24.614685 scEMB: Learning context representation of genes based on large-scale single-cell transcriptomics]
|-
|style="padding:.4em;" rowspan=1|2025/1/8
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|25-3
|style="padding:.4em;"|HB Lee
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1093/bioinformatics/btad663 TT3D: Leveraging precomputed protein 3D sequence models to predict protein–protein interactions]
|-
|style="padding:.4em;" rowspan=1|2025/1/8
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|25-2
|style="padding:.4em;"|HB Lee
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1093/bioinformatics/btac258 Topsy-Turvy: integrating a global view into sequence-based PPI prediction]
|-
|style="padding:.4em;" rowspan=1|2025/1/8
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|25-1
|style="padding:.4em;"|HB Lee
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.cels.2021.08.010 D-SCRIPT translates genome to phenome with sequence-based, structure-aware, genome-scale predictions of protein-protein interactions]
|-
|style="padding:.4em;" rowspan=1|2024/12/17
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|24-30
|style="padding:.4em;"|YR Jung
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2024.08.16.608180 Quantized multi-task learning for context-specific representations of gene network dynamics]
|-
|style="padding:.4em;" rowspan=1|2024/12/3
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|24-29
|style="padding:.4em;"|EJ Sung
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2023.11.21.568145 ANDES: a novel best-match approach for enhancing gene set analysis in embedding spaces]
|-
|style="padding:.4em;" rowspan=1|2024/11/26
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|24-28
|style="padding:.4em;"|IS Choi
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41592-024-02303-9 CellRank 2: unified fate mapping in multiview single-cell data]
|-
|style="padding:.4em;" rowspan=1|2024/11/19
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|24-27
|style="padding:.4em;"|SB Baek
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2024.07.29.605556 scPRINT: pre-training on 50 million cells allows robust gene network predictions]
|-
|style="padding:.4em;" rowspan=1|2024/11/12
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|24-26
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41467-024-46440-3 Bidirectional generation of structure and properties through a single molecular foundation model]
|}

{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2024-2 Microbiome
|-
!scope="col" style="padding:.4em" | Date
!scope="col" style="padding:.4em" | Team
!scope="col" style="padding:.4em" | Paper index
!scope="col" style="padding:.4em" | Presenter
!scope="col" style="padding:.4em" | Paper title
|-
|style="padding:.4em;" rowspan=1|2025/4/7
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|25-24
|style="padding:.4em;"|SB Lim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2025.01.07.631644 A Single-Graph Visualization to Reveal Hidden Explainability Patterns of SHAP Feature Interactions in Machine Learning for Biomedical Issues]
|-
|style="padding:.4em;" rowspan=1|2025/3/31
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|25-23
|style="padding:.4em;"|YR Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.cell.2024.08.037 Emergence of community behaviors in the gut microbiota upon drug treatment]
|-
|style="padding:.4em;" rowspan=1|2025/3/31
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|25-22
|style="padding:.4em;"|JY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41564-024-01832-5 Prediction of strain level phage–host interactions across the Escherichia genus using only genomic information]
|-
|style="padding:.4em;" rowspan=1|2025/3/24
|style="padding:.4em;" rowspan=1|Microbiome
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[https://doi.org/10.1101/2025.01.07.631807 Metagenomic estimation of absolute bacterial biomass in the mammalian gut through host-derived read normalization]
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[https://doi.org/10.1101/2024.04.14.589414 Rapid and Sensitive Protein Complex Alignment with Foldseek-Multimer]
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[https://doi.org/10.1101/2024.12.30.630825 MGM as a large-scale pretrained foundation model for microbiome analyses in diverse contexts ]
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[https://doi.org/10.1126/science.ado9336 Sequence modeling and design from molecular to genome scale with Evo]
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[https://doi.org/10.1101/2024.12.18.629142 Human gut microbiome gene co-expression network reveals a loss in taxonomic and functional diversity in Parkinson’s disease]
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[https://doi.org/10.1101/2025.01.06.631550 Quantifying Metagenomic Strain Associations from Microbiomes with Anpan]
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[https://doi.org/10.1101/2024.05.30.596740 ProTrek: Navigating the Protein Universe through Tri-Modal Contrastive Learning]
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[https://doi.org/10.1101/2024.12.30.630844 The genetic diversity and populational specificity of the human gut virome at single nucleotide resolution]
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[https://doi.org/10.1186/s13059-024-03425-1 pan-Draft: automated reconstruction of species-representative metabolic models from multiple genomes]
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[https://doi.org/10.1016/j.cell.2024.09.019 A core microbiome signature as an indicator of health]
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[https://doi.org/10.1101/2023.07.17.549267 Learning a deep language model for microbiomes: the power of large scale unlabeled microbiome data]
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[https://doi.org/10.1126/science.adg7492 Accurate proteome-wide missense variant effect prediction with AlphaMissense]
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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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[https://doi.org/10.1038/s41564-024-01739-1 Multikingdom and functional gut microbiota markers for autism spectrum disorder]
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[https://doi.org/10.1186/s13059-024-03390-9 A realistic benchmark for differential abundance testing and confounder adjustment in human microbiome studies]
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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="text-align:left;font-size:12pt" | 2024-1 Advanced scOmics Data Analysis
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[https://doi.org/10.1038/s41587-023-01747-2 Multimodal spatiotemporal phenotyping of human retinal organoid development]
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|style="padding:.4em;"|JH Lee
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[https://doi.org/10.1038/s41586-024-07251-0 Immune microniches shape intestinal Treg function]
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[https://doi.org/10.1016/j.devcel.2021.02.021 A single-cell analysis of the Arabidopsis vegetative shoot apex]
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[https://doi.org/10.1038/s41467-023-40137-9 Droplet-based high-throughput single microbe RNA sequencing by smRandom-seq]
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[https://doi.org/10.1038/s41564-023-01462-3 Single-cell massively-parallel multiplexed microbial sequencing (M3-seq) identifies rare bacterial populations and profiles phage infection]
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|style="padding:.4em;"|EB Yu
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[https://doi.org/10.1016/j.celrep.2022.111736 Spatial transcriptomics demonstrates the role of CD4 T cells in effector CD8 T cell differentiation during chronic viral infection]
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[https://doi.org/10.1038/s41587-023-01979-2 Spatial metatranscriptomics resolves host–bacteria–fungi interactomes]
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[https://doi.org/10.1038/s41467-023-36325-2 Dissecting the immune suppressive human prostate tumor microenvironment via integrated single-cell and spatial transcriptomic analyses]
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[https://doi.org/10.1038/s41593-023-01452-y Single-nucleus genomics in outbred rats with divergent cocaine addiction-like behaviors reveals changes in amygdala GABAergic inhibition]
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[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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[https://doi.org/10.1038/s41467-023-39933-0 Spatial cellular architecture predicts prognosis in glioblastoma]
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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;" rowspan=1|Single-cell
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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;" rowspan=1|2024/05/14
|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|Q Zhen
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[https://doi.org/10.1021/acscentsci.3c01169 Single-Cell Analysis Reveals Cxcl14+ Fibroblast Accumulation in Regenerating Diabetic Wounds Treated by Hydrogel-Delivering Carbon Monoxide]
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|style="padding:.4em;" rowspan=1|2024/05/07
|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|CR Leenaars
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[https://doi.org/10.1038/s41477-022-01291-y Single-cell RNA sequencing provides a high-resolution roadmap for understanding the multicellular compartmentation of specialized metabolism]
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|style="padding:.4em;"|YR Kim
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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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|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|WJ Kim
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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;" rowspan=1|Single-cell
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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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|style="padding:.4em;"|HJ Kim
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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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!scope="col" style="padding:.4em" | Date
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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/s41592-023-02035-2 Population-level integration of single-cell datasets enables multi-scale analysis across samples]
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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/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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[https://doi.org/10.1038/s41587-022-01467-z Modeling intercellular communication in tissues using spatial graphs of cells]
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|style="padding:.4em;" rowspan=1|Single-cell
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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;" rowspan=1|Single-cell
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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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|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/s41592-023-01971-3 Dictys: dynamic gene regulatory network dissects developmental continuum with single-cell multiomics]
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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://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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[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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|style="padding:.4em;" rowspan=1|Single-cell
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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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[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;"|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|2023/09/26
|style="padding:.4em;" rowspan=1|Single-cell
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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;"|IS Choi
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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;"|SB Baek
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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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[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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[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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[https://doi.org/10.1038/s41564-023-01439-2 A predicted CRISPR-mediated symbiosis between uncultivated archaea]
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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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[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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[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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[https://doi.org/10.1101/2023.07.03.547607 Multi-view integration of microbiome data for identifying disease-associated modules]
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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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[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;"|G Koh
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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|Microbiome
|style="padding:.4em;"|23-57
|style="padding:.4em;"|SH Ahn
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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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[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|Microbiome
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|style="padding:.4em;"|JY Ma
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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|Microbiome
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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;"|NY Kim
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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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[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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[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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[https://doi.org/10.1186/s40168-023-01472-7 Altered infective competence of the human gut microbiome in COVID-19]
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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;"|SH Ahn
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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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[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;"|JH Cha
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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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[https://doi.org/10.1016/j.cels.2022.12.007 Pitfalls of genotyping microbial communities with rapidly growing genome collections]
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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|Microbiome
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|style="padding:.4em;"|SH Lee
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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|Microbiome
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|style="padding:.4em;"|WJ Kim
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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|Microbiome
|style="padding:.4em;"|23-41
|style="padding:.4em;"|JY Kim
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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|Microbiome
|style="padding:.4em;"|23-40
|style="padding:.4em;"|G Koh
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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|Microbiome
|style="padding:.4em;"|23-39
|style="padding:.4em;"|HJ Kim
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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|Microbiome
|style="padding:.4em;"|23-38
|style="padding:.4em;"|SH Ahn
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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|Microbiome
|style="padding:.4em;"|23-37
|style="padding:.4em;"|JH Cha
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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
|style="padding:.4em;"|23-36
|style="padding:.4em;"|JY Ma
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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|Microbiome
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|style="padding:.4em;"|NY Kim
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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|Microbiome
|style="padding:.4em;"|23-34
|style="padding:.4em;"|SH Lee
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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|Microbiome
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|style="padding:.4em;"|WJ Kim
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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|Microbiome
|style="padding:.4em;"|23-32
|style="padding:.4em;"|JY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s43587-022-00287-9 Associations of the skin, oral and gut microbiome with aging, frailty and infection risk reservoirs in older adults]
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|style="padding:.4em;" rowspan=1|2023/09/13
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-31
|style="padding:.4em;"|G Koh
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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|Microbiome
|style="padding:.4em;"|23-30
|style="padding:.4em;"|SH Ahn
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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
|style="padding:.4em;"|23-29
|style="padding:.4em;"|HJ Kim
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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
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-28
|style="padding:.4em;"|JH Cha
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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;"
|+style="text-align:left;font-size:12pt" | 2023-1 ADVANCED MICROBIOME DATA ANALYSIS
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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/10.1016/j.chom.2020.03.005 Structure of the Mucosal and Stool Microbiome in Lynch Syndrome]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-23
|style="padding:.4em;"|WJ Kim
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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|Microbiome
|style="padding:.4em;"|23-22
|style="padding:.4em;"|SH Ahn
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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;" rowspan=1|Microbiome
|style="padding:.4em;"|23-21
|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;" rowspan=1|Microbiome
|style="padding:.4em;"|23-20
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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;" rowspan=1|Microbiome
|style="padding:.4em;"|23-19
|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;" rowspan=1|2023/05/23
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-18
|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;" rowspan=1|Microbiome
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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;" rowspan=1|2023/05/23
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-16
|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;" rowspan=1|Microbiome
|style="padding:.4em;"|23-15
|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;" rowspan=1|Microbiome
|style="padding:.4em;"|23-14
|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;" rowspan=1|2023/05/16
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-13
|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;" rowspan=1|2023/05/09
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-12
|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;" rowspan=1|Microbiome
|style="padding:.4em;"|23-11
|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;" rowspan=1|Microbiome
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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;" rowspan=1|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;" rowspan=1|Microbiome
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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;" rowspan=1|Microbiome
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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;" rowspan=1|2023/04/18
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-6
|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;" rowspan=1|Microbiome
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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;" rowspan=1|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;" rowspan=1|Microbiome
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|style="padding:.4em;"|DH Lee
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[https://doi.org/10.1002/imt2.61 Targeting keystone species helps restore the dysbiosis of butyrate‐producing bacteria in nonalcoholic fatty liver disease]
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[https://doi.org/10.1002/advs.202203115 Differential Oral Microbial Input Determines Two Microbiota Pneumo-Types Associated with Health Status]
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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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{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2023-1 scOmics
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|style="padding:.4em;" rowspan=1|2023/08/30
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|style="padding:.4em;"|JW Yu
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[https://doi.org/10.1101/2023.01.17.524482 Decoupling the correlation between cytotoxic and exhausted T lymphocyte transcriptomic signatures enhances melanoma immunotherapy response prediction from tumor expression]
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|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-23
|style="padding:.4em;"|IS Choi
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[https://doi.org/10.1101/2023.07.28.550993 Major data analysis errors invalidate cancer microbiome findings]
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|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|EJ Sung
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[https://doi.org/10.1038/s43018-022-00475-x A single-cell atlas of glioblastoma evolution under therapy reveals cell-intrinsic and cell-extrinsic therapeutic targets]
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|style="padding:.4em;"|G Koh
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[https://doi.org/10.1038/s43018-022-00433-7 Single-cell meta-analyses reveal responses of tumor-reactive CXCL13+ T cells to immune-checkpoint blockade]
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[https://doi.org/10.1038/s41587-022-01342-x Estimation of tumor cell total mRNA expression in 15 cancer types predicts disease progression]
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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/10.1038/s41587-022-01288-0 DIALOGUE maps multicellular programs in tissue from single-cell or spatial transcriptomics data]
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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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[https://doi.org/10.1186/s13059-022-02828-2 Parallel single-cell and bulk transcriptome analyses reveal key features of the gastric tumor microenvironment]
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|style="padding:.4em;"|JW Yu
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[https://doi.org/10.1038/s41467-022-32838-4 Mutated processes predict immune checkpoint inhibitor therapy benefit in metastatic melanoma]
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[https://doi.org/10.1038/s43018-021-00292-8 Temporal single-cell tracing reveals clonal revival and expansion of precursor exhausted T cells during anti-PD-1 therapy in lung cancer]
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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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[https://doi.org/10.1101/2022.12.20.521311 Supervised discovery of interpretable gene programs from single-cell data]
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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/s41586-022-05435-0 Effect of the intratumoral microbiota on spatial and cellular heterogeneity in cancer]
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|style="padding:.4em;" rowspan=1|Single-cell
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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;"|JW Yu
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[https://www.biorxiv.org/content/10.1101/2022.08.05.502989v1 MetaTiME: Meta-components of the Tumor Immune 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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|style="padding:.4em;"|SB Baek
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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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[https://www.nature.com/articles/s41591-022-01799-y A T cell resilience model associated with response to immunotherapy in multiple tumor types]
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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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[https://pubmed.ncbi.nlm.nih.gov/35649411/ Cross-tissue, single-cell stromal atlas identifies shared pathological fibroblast phenotypes in four chronic inflammatory diseases]
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|style="padding:.4em;"|JW Yu
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[https://www.sciencedirect.com/science/article/pii/S1535610822003178 Pan-cancer integrative histology-genomic analysis via multimodal deep learning]
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|style="padding:.4em;"|JH Cha
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[https://pubmed.ncbi.nlm.nih.gov/35803260/ Tissue-resident memory and circulating T cells are early responders to pre-surgical cancer immunotherapy]
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{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2023-1 Microbiome
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!scope="col" style="padding:.4em" | Date
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!scope="col" style="padding:.4em" | Paper title
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|style="padding:.4em;" rowspan=1|2023/08/25
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-27
|style="padding:.4em;"|JY Ma
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.chom.2023.05.024 Enterosignatures define common bacterial guilds in the human gut microbiome]
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|style="padding:.4em;" rowspan=1|2023/08/25
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-26
|style="padding:.4em;"|NY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1186/s13059-023-02902-3 PhyloMed: a phylogeny-based test of mediation effect in microbiome]
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|style="padding:.4em;" rowspan=1|2023/08/18
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-25
|style="padding:.4em;"|SH Lee
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.chom.2023.05.027 The TaxUMAP atlas: Efficient display of large clinical microbiome data reveals ecological competition in protection against bacteremia]
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|style="padding:.4em;" rowspan=1|2023/08/18
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-24
|style="padding:.4em;"|WJ Kim
|style="padding:.4em;text-align:left"|
[http://dx.doi.org/10.1038/nbt.3704 Measurement of bacterial replication rates in microbial communities]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-23
|style="padding:.4em;"|JY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1186/s40168-023-01564-4 Skin microbiome diferentiates into distinct cutotypes with unique metabolic functions upon exposure to polycyclic aromatic hydrocarbons]
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|style="padding:.4em;" rowspan=1|2023/08/11
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-22
|style="padding:.4em;"|SH Ahn
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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
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-21
|style="padding:.4em;"|HJ Kim
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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
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-20
|style="padding:.4em;"|JH Cha
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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
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-19
|style="padding:.4em;"|JY Ma
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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
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-18
|style="padding:.4em;"|NY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1186/s13059-022-02809-5 Gene fow and introgression are pervasive forces shaping the evolution of bacterial species]
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|style="padding:.4em;" rowspan=1|2023/06/30
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-17
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1186/s40168-022-01435-4 Alterations of oral microbiota and impact on the gut microbiome in type 1 diabetes mellitus revealed by integrated multi‑omic analyses]
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|style="padding:.4em;" rowspan=1|2023/06/23
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-16
|style="padding:.4em;"|HJ Kim
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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
|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|SH Lee
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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
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-14
|style="padding:.4em;"|JY Ma
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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|Microbiome
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|style="padding:.4em;"|NY Kim
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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
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-12
|style="padding:.4em;"|SH Lee
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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;"|JY Kim
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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
|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|WJ Kim
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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
|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|SH Ahn
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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;"|HJ Kim
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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
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-7
|style="padding:.4em;"|JH Cha
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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;"|JY Ma
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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;"|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|Microbiome
|style="padding:.4em;"|23-4
|style="padding:.4em;"|SH Lee
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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;"|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;"|HJ Kim
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|+style="text-align:left;font-size:12pt" | 2022 scOmics
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!scope="col" style="padding:.4em" | Date
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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
|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|JH Cha, SB Baek, IS Choi
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[https://www.pnas.org/doi/10.1073/pnas.2105859118 Representation learning of RNA velocity reveals robust cell transitions]
[https://www.nature.com/articles/s41467-022-34188-7 UniTVelo: temporally unified RNA velocity reinforces single-cell trajectory inference]
[https://www.sciencedirect.com/science/article/pii/S0092867421015774 Mapping transcriptomic vector fields of single cells]
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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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[https://www.biorxiv.org/content/10.1101/2022.05.04.490536v1 Modeling fragment counts improves single-cell ATAC-seq analysis]
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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;"|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;"|JH Cha
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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
|style="padding:.4em;" rowspan=1|Single-cell
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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;"|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;"|IS Choi
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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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[https://pubmed.ncbi.nlm.nih.gov/34462589/ Mapping single-cell data to reference atlases by transfer learning]
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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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[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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[https://www.biorxiv.org/content/10.1101/2021.06.07.447430v2 Metacells untangle large and complex single-cell transcriptome networks]
[https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1812-2 MetaCell: analysis of single-cell RNA-seq data using K-nn graph partitions]
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|style="padding:.4em;"|SB Baek
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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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[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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[https://pubmed.ncbi.nlm.nih.gov/35199064/ Effect of imputation on gene network reconstruction from single-cell RNA-seq data]
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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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[https://pubmed.ncbi.nlm.nih.gov/34594031/ Systematic investigation of cytokine signaling activity at the tissue and single-cell levels]
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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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|style="padding:.4em;"|IS Choi
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[https://pubmed.ncbi.nlm.nih.gov/34489465/ Single-cell ATAC and RNA sequencing reveal pre-existing and persistent cells associated with prostate cancer relapse]
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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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[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://journals.asm.org/doi/10.1128/msystems.00050-22 Compositionally Aware Phylogenetic Beta-Diversity Measures Better Resolve Microbiomes Associated with Phenotype]
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[https://www.nature.com/articles/s41596-020-00480-3 Tutorial: assessing metagenomics software with the CAMI benchmarking toolkit disease]
[https://www.nature.com/articles/s41592-022-01431-4 Critical Assessment of Metagenome Interpretation: the second round of challenges]
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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.nature.com/articles/s41591-022-01688-4 Microbiome and metabolome features of the cardiometabolic disease spectrum]
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[https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02195-w Functional and genetic markers of niche partitioning among enigmatic members of the human oral microbiome]
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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://genomebiology.biomedcentral.com/articles/10.1186/s13059-021-02473-1 Pandora: nucleotide-resolution bacterial pan-genomics with reference graphs]
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[https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009442 Multivariable association discovery in population-scale meta-omics 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://genomebiology.biomedcentral.com/articles/10.1186/s13059-022-02610-4 AGAMEMNON: an Accurate metaGenomics And MEtatranscriptoMics quaNtificatiON analysis suite]
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[https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02200-2 Metapangenomics of the oral microbiome provides insights into habitat adaptation and cultivar diversity]
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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/34893089/ Microbial co-occurrence complicates associations of gut microbiome with US immigration, dietary intake and obesity]
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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://pubmed.ncbi.nlm.nih.gov/34551799/ The influence of the gut microbiome on BCG-induced trained immunity]
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[https://pubmed.ncbi.nlm.nih.gov/34912116/ Towards the biogeography of prokaryotic genes]
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[https://pubmed.ncbi.nlm.nih.gov/29347966/ ReprDB and panDB: minimalist databases with maximal microbial representation]
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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/10.1101/2022.06.16.496510v2 MIDAS2: Metagenomic Intra-species Diversity Analysis System]
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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://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-022-01251-w Metagenomic strain detection with SameStr: identification of a persisting core gut microbiota transferable by fecal transplantation]
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[https://www.nature.com/articles/s41587-021-01102-3 Fast and accurate metagenotyping of the human gut microbiome with GT-Pro]
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[https://www.nature.com/articles/s41587-020-00797-0 inStrain profiles population microdiversity from metagenomic data and sensitively detects shared microbial strains]
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[https://genome.cshlp.org/content/early/2021/07/22/gr.265058.120 Longitudinal linked-read sequencing reveals ecological and evolutionary responses of a human gut microbiome during antibiotic treatment]
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[https://www.sciencedirect.com/science/article/pii/S1931312821002365 Dispersal strategies shape persistence and evolution of human gut bacteria]
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[https://www.sciencedirect.com/science/article/pii/S0092867421003524 The long-term genetic stability and individual specificity of the human gut microbiome]
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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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[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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[https://www.nature.com/articles/nmeth.3802 Strain-level microbial epidemiology and population genomics from shotgun metagenomics]
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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.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/s41467-020-14766-3 Trajectory-based differential expression analysis for single-cell sequencing data]
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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://www.sciencedirect.com/science/article/pii/S0092867419308967?via%3Dihub Single-Cell Analysis of Crohn's Disease Lesions Identifies a Pathogenic Cellular Module Associated with Resistance to Anti-TNF Therapy]
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[https://www.nature.com/articles/s41467-020-15956-9 Multi-omic single-cell snapshots reveal multiple independent trajectories to drug tolerance in a melanoma cell line]
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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/30595452/ Dysfunctional CD8 T Cells Form a Proliferative, Dynamically Regulated Compartment Within Human Melanoma]
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[https://pubmed.ncbi.nlm.nih.gov/30388455/ A Cancer Cell Program Promotes T Cell Exclusion and Resistance to Checkpoint Blockade]
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[https://pubmed.ncbi.nlm.nih.gov/31974247/ Single-cell Transcriptional Diversity Is a Hallmark of Developmental Potential]
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[https://pubmed.ncbi.nlm.nih.gov/31675496/ Landscape and Dynamics of Single Immune Cells in Hepatocellular Carcinoma]
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[https://pubmed.ncbi.nlm.nih.gov/32103181/ Peripheral T Cell Expansion Predicts Tumour Infiltration and Clinical Response]
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[https://pubmed.ncbi.nlm.nih.gov/30388456/ Defining T Cell States Associated With Response to Checkpoint Immunotherapy in Melanoma]
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[https://pubmed.ncbi.nlm.nih.gov/31915379/ Rapid Non-Uniform Adaptation to Conformation-Specific KRAS(G12C) Inhibition]
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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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[https://pubmed.ncbi.nlm.nih.gov/32066951/ Distinct Microbial and Immune Niches of the Human Colon]
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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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[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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[https://pubmed.ncbi.nlm.nih.gov/30078704/ A Single-Cell Atlas of In Vivo Mammalian Chromatin Accessibility]
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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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[https://pubmed.ncbi.nlm.nih.gov/31792411/ Single-cell Multiomic Analysis Identifies Regulatory Programs in Mixed-Phenotype Acute Leukemia]
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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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[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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[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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[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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[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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[https://www.sciencedirect.com/science/article/pii/S0092867419307731 Tumor Microbiome Diversity and Composition Influence Pancreatic Cancer Outcomes]
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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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[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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[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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[https://www.sciencedirect.com/science/article/pii/S0092867419307810 Large-Scale Analyses of Human Microbiomes Reveal Thousands of Small, Novel Genes]
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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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[https://www.nature.com/articles/nbt.4042 Multiplexed droplet single-cell RNA-sequencing using natural genetic variation]
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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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[https://www.sciencedirect.com/science/article/pii/S0092867419307755?via%3Dihub Benchmarking Metagenomics Tools for Taxonomic Classification]
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[https://www.biorxiv.org/content/10.1101/719088v1 Coexpression uncovers a unified single-cell transcriptomic landscape]
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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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[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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[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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[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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[https://www.nature.com/articles/s41592-018-0254-1 A test metric for assessing single-cell RNA-seq batch correction]
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[https://www.sciencedirect.com/science/article/pii/S0092867419305598 Comprehensive Integration of Single-Cell Data]
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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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[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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[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
|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
|style="padding:.4em;"|19-26
|style="padding:.4em;"|IS Choi
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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
|style="padding:.4em;"|JW Cho
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S0092867418311784 A Cancer Cell Program Promotes T Cell Exclusion and Resistance to Checkpoint Blockade]
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|style="padding:.4em;" rowspan=2|2019/07/23
|style="padding:.4em;"|19-24
|style="padding:.4em;"|HJ Han
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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
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[https://www.nature.com/articles/ncomms14825 Sensitive detection of rare disease-associated cell subsets via representation learning]
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|style="padding:.4em;" rowspan=3|2019/05/30
|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
|style="padding:.4em;"|
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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
|style="padding:.4em;"|
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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
|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
|style="padding:.4em;"|
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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
|style="padding:.4em;"|
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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
|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
|style="padding:.4em;"|
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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
|style="padding:.4em;"|19-14
|style="padding:.4em;"|
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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
|style="padding:.4em;"|19-12
|style="padding:.4em;"|
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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
|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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|style="padding:.4em;"|19-9
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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
|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
|style="padding:.4em;"|19-6
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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
|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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|style="padding:.4em;"|19-3-1
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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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|style="padding:.4em;"|19-3
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[https://www.ncbi.nlm.nih.gov/pubmed/30787437 The single-cell transcriptional landscape of mammalian organogenesis]
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|style="padding:.4em;" rowspan=2|2019/3/14
|style="padding:.4em;"|19-2
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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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|style="padding:.4em;"|19-1
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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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{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2018
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|style="padding:.4em;" rowspan=2|2018/06/14
|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
|style="padding:.4em;"|
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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
|style="padding:.4em;"|18-10
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=Developmental+and+oncogenic+programs+in+H3K27M+gliomas+dissected+by+single-cell+RNA-seq Developmental and oncogenic programs in H3K27M gliomas dissected by single-cell RNA-seq.]
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|style="padding:.4em;"|18-9
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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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|style="padding:.4em;" rowspan=2|2018/05/31
|style="padding:.4em;"|18-8
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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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|style="padding:.4em;"|18-7
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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.]
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|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
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[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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|style="padding:.4em;" rowspan=2|2018/05/10
|style="padding:.4em;"|18-2
|style="padding:.4em;"|JW Cho
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[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
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[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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{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2017
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!scope="col" style="padding:.4em" |Date
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|style="padding:.4em;" rowspan=2|2017/06/28
|style="padding:.4em;"|17-36
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/28104840 Tumor aneuploidy correlates with markers of immune evasion and with reduced response to immunotherapy.]
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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.]
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|style="padding:.4em;"|17-33
|style="padding:.4em;"|MY Lee
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[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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[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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|style="padding:.4em;"|17-31
|style="padding:.4em;"|MY Lee
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.celrep.2016.03.075 Genomic Correlates of Immune-Cell Infiltrates in Colorectal Carcinoma.]
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|style="padding:.4em;" rowspan=2|2017/05/31
|style="padding:.4em;"|17-30
|style="padding:.4em;"|DS Bae
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[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.]
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|style="padding:.4em;"|17-29
|style="padding:.4em;"|JW Cho
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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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|style="padding:.4em;" rowspan=2|2017/05/24
|style="padding:.4em;"|17-28
|style="padding:.4em;"|EB Kim
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[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
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[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
|style="padding:.4em;"|17-26
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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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[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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|style="padding:.4em;" rowspan=2|2017/04/26
|style="padding:.4em;"|17-24
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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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|style="padding:.4em;"|17-23
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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
|style="padding:.4em;"|17-22
|style="padding:.4em;"|SH Lee
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[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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|style="padding:.4em;"|17-21
|style="padding:.4em;"|CY Kim
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1038%2Fnbt.3569 Wishbone identifies bifurcating developmental trajectories from single-cell data.]
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|style="padding:.4em;" rowspan=2|2017/04/05
|style="padding:.4em;"|17-20
|style="padding:.4em;"|KS Kim
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[http://biorxiv.org/content/early/2017/02/21/110668 Reversed graph embedding resolves complex single-cell developmental trajectories.]
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|style="padding:.4em;"|17-19
|style="padding:.4em;"|SH Lee
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1038%2Fnmeth.4150 Single-cell mRNA quantification and differential analysis with Census.]
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|style="padding:.4em;" rowspan=2|2017/03/29
|style="padding:.4em;"|17-18
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[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.]
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|style="padding:.4em;"|17-17
|style="padding:.4em;"|DS Bae
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[https://www.ncbi.nlm.nih.gov/pubmed/26051941 Single-Cell Network Analysis Identifies DDIT3 as a Nodal Lineage Regulator in Hematopoiesis.]
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|style="padding:.4em;" rowspan=2|2017/03/22
|style="padding:.4em;"|17-16
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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.]
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|style="padding:.4em;"|17-15
|style="padding:.4em;"|EB Kim
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[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
|style="padding:.4em;"|17-14
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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.]
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|style="padding:.4em;"|17-13
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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.]
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|style="padding:.4em;" rowspan=2|2017/02/28
|style="padding:.4em;"|17-12
|style="padding:.4em;"|KS Kim
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[https://www.ncbi.nlm.nih.gov/pubmed/27824113 A Comprehensive Characterization of the Function of LincRNAs in Transcriptional Regulation Through Long-Range Chromatin Interactions.]
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|style="padding:.4em;"|17-11
|style="padding:.4em;"|JE Shim
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[//pubmed.gov/27851969 Epigenomic Deconvolution of Breast Tumors Reveals Metabolic Coupling between Constituent Cell Types.]
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|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.]
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|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.]
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|style="padding:.4em;" rowspan=2|2017/02/07
|style="padding:.4em;"|17-08
|style="padding:.4em;"|DS Bae
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[http://biorxiv.org/content/early/2016/11/17/088286 Somatic Mutations and Neoepitope Homology in Melanomas Treated with CTLA-4 Blockade.]
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|style="padding:.4em;"|17-07
|style="padding:.4em;"|MY Lee
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[http://biorxiv.org/content/early/2016/11/28/090134 Chemotherapy weakly contributes to predicted neoantigen expression in ovarian cancer.]
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|style="padding:.4em;" rowspan=1|2017/01/31
|style="padding:.4em;"|17-06
|style="padding:.4em;"|CY Kim
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[//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
|style="padding:.4em;"|17-05
|style="padding:.4em;"|JW Cho
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[//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
|style="padding:.4em;"|17-04
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[//www.ncbi.nlm.nih.gov/pubmed/25938943 Mapping long-range promoter contacts in human cells with high-resolution capture Hi-C]
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|style="padding:.4em;"|17-03
|style="padding:.4em;"|EB Kim
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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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|style="padding:.4em;" rowspan=1|2017/01/10
|style="padding:.4em;"|17-02
|style="padding:.4em;"|JE Shim
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[//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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[//www.ncbi.nlm.nih.gov/pubmed/26287467 Single-cell messenger RNA sequencing reveals rare intestinal cell types]
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|style="padding:.4em;" rowspan=1|2016/12/27
|style="padding:.4em;"|2016-31
|style="padding:.4em;"|EB Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/25664528 Decoding the regulatory network of early blood development from single-cell gene expression measurements.]
|-
|style="padding:.4em;" rowspan=1|2016/12/6
|style="padding:.4em;"|2016-30
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26299571 Single-Cell RNA-Seq with Waterfall Reveals Molecular Cascades underlying Adult Neurogenesis.]
|-
|style="padding:.4em;" rowspan=1|2016/11/29
|style="padding:.4em;"|2016-29
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/24658644 The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells.]
|-
|style="padding:.4em;" rowspan=1|2016/11/22
|style="padding:.4em;"|2016-28
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26887813 Classification of low quality cells from single-cell RNA-seq data.]
|-
|style="padding:.4em;" rowspan=1|2016/11/15
|style="padding:.4em;"|2016-27
|style="padding:.4em;"|MY Lee
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26000487 Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells.]
|-
|style="padding:.4em;" rowspan=1|2016/11/8
|style="padding:.4em;"|2016-26
|style="padding:.4em;"|JW Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26000488 Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets.]
|-
|style="padding:.4em;" rowspan=1|2016/11/1
|style="padding:.4em;"|2016-25
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27426982 Causal Mechanistic Regulatory Network for Glioblastoma Deciphered Using Systems Genetics Network Analysis.]
|-
|style="padding:.4em;" rowspan=1|2016/10/25
|style="padding:.4em;"|2016-24
|style="padding:.4em;"|MY Lee
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27549193 Comprehensive analyses of tumor immunity: implications for cancer immunotherapy.]
|-
|style="padding:.4em;" rowspan=1|2016/10/11
|style="padding:.4em;"|2016-23
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=sidespread+parainflammation Widespread parainflammation in human cancer]
|-
|style="padding:.4em;" rowspan=1|2016/9/27
|style="padding:.4em;"|2016-22
|style="padding:.4em;"|ER Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27535533 Analysis of protein-coding genetic variation in 60,706 humans]
|-
|style="padding:.4em;" rowspan=1|2016/9/20
|style="padding:.4em;"|2016-21
|style="padding:.4em;"|SM Yang
|style="padding:.4em;text-align:left"|
Functional characterization of somatic mutations in cancer using network-based inference of protein activity
[http://www.ncbi.nlm.nih.gov/pubmed/27322546 pubmed]
[http://www.nature.com/ng/journal/v48/n8/full/ng.3593.html fulltext]
|-
|style="padding:.4em;" rowspan=1|2016/9/13
|style="padding:.4em;"|2016-20
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=exploiting+single-cell+expression+to+characterize Exploiting single-cell expression to characterize co-expression replicability.]
|-
|style="padding:.4em;" rowspan=1|2016/9/6
|style="padding:.4em;"|2016-19
|style="padding:.4em;"|T Lee
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26940869 Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade]
|-
|style="padding:.4em;" rowspan=1|2016/8/31
|style="padding:.4em;"|2016-18
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27264179 A Computational Drug Repositioning Approach for Targeting Oncogenic Transcription Factors]
|-
|style="padding:.4em;" rowspan=1|2016/8/16
|style="padding:.4em;"|2016-17
|style="padding:.4em;"|JW Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27309802 The landscape of accessible chromatin in mammalian preimplantation embryos]
|-
|style="padding:.4em;" rowspan=1|2016/8/8
|style="padding:.4em;"|2016-16
|style="padding:.4em;"|EB Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27064255 Enhancer-promoter interactions are encoded by complex genomic signatures on looping chromatin]
|-
|style="padding:.4em;" rowspan=1|2016/8/1
|style="padding:.4em;"|2016-15
|style="padding:.4em;"|MY Lee
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27040498 Personalized Immunomonitoring Uncovers Molecular Networks that Stratify Lupus Patients]
|-
|style="padding:.4em;" rowspan=1|2016/7/25
|style="padding:.4em;"|2016-14
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=integration+of+summary+data+from+gwas+and+eqtl+studies Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets]
|-
|style="padding:.4em;" rowspan=1|2016/7/18
|style="padding:.4em;"|2016-13
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/23624555 Identification of transcriptional regulators in the mouse immune system]
|-
|style="padding:.4em;" rowspan=2|2016/6/8
|style="padding:.4em;"|2016-12
|style="padding:.4em;"|DS Bae, CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26619012 Mapping the effects of drugs on the immune system]
|-
|style="padding:.4em;"| 2016-11
|style="padding:.4em;"|DS Bae, CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26186195 Elucidating compound mechanism of action by network perturbation analysis]
|-
|style="padding:.4em;" rowspan=2|2016/6/1
|style="padding:.4em;"|2016-10
|style="padding:.4em;"|MY Lee,SM Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26854917 Integrative approaches for large-scale transcriptome-wide association studies]
|-
|style="padding:.4em;"| 2016-9
|style="padding:.4em;"|MY Lee,SM Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26950747 Tissue-specific regulatory circuits reveal variable modular perturbations across complex diseases]
|-
|style="padding:.4em;" rowspan=2|2016/5/18
|style="padding:.4em;"|2016-8
|style="padding:.4em;"|CY Kim,SJ Kwon
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27013732 Survey of variation in human transcription factors reveals prevalent DNA binding changes]
|-
|style="padding:.4em;"| 2016-7
|style="padding:.4em;"| CY Kim,SJ Kwon
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26502339 Large-scale identification of sequence variants influencing human transcription factor occupancy in vivo]
|-
|style="padding:.4em;" rowspan=2|2016/5/11
|style="padding:.4em;"|2016-6
|style="padding:.4em;"|DS Bae, CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/25171417 Predicting Cancer-specific vulnerability via data-driven detection of synthetic lethality]
|-
|style="padding:.4em;"| 2016-5
|style="padding:.4em;"|DS Bae, CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/23467089 Dynamic regulatory network controlling Th17 cell differentiation]
|-
|style="padding:.4em;" rowspan=2|2016/5/4
|style="padding:.4em;"|2016-4
|style="padding:.4em;"|MY Lee,SM Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/25853550 Characterization of the immunophenotypes and antigenomes of colorectal cancers reveals distinct tumor escape mechanisms and novel targets for immunotherapy]
|-
|style="padding:.4em;"| 2016-3
|style="padding:.4em;"|MY Lee,SM Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26618344 Regulators of genetic risk of breast cancer identified by integrative network analysis]
|-
|style="padding:.4em;" rowspan=2|2016/4/27
|style="padding:.4em;"|2016-2
|style="padding:.4em;"|CY Kim,SJ Kwon
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26780608 A predictive computational framework for direct reprogramming between human cell types]
|-
|style="padding:.4em;"| 2016-1
|style="padding:.4em;"| CY Kim,SJ Kwon
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/25126793 CellNet: Network biology applied to stem cell engineering]
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{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2015
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!scope="col" style="padding:.4em" | Date
!scope="col" style="padding:.4em" | Paper index
!scope="col" style="padding:.4em" | Presenter
!scope="col" style="padding:.4em" | Paper title
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|style="padding:.4em;" rowspan=2|2015/06/11
|style="padding:.4em;"|2015-55
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://genome.cshlp.org/content/24/2/340.long Improved exome prioritization of disease genes through cross-species phenotype comparison.]
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|style="padding:.4em;"|2015-54
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0002929714001128 Phevor combines multiple biomedical ontologies for accurate identification of disease-causing alleles in single individuals and small nuclear families.]
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|style="padding:.4em;" rowspan=2|2015/06/04
|style="padding:.4em;"|2015-53
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nmeth/journal/v10/n11/full/nmeth.2656.html eXtasy: variant prioritization by genomic data fusion.]
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|style="padding:.4em;"|2015-52
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://genome.cshlp.org/content/21/9/1529.long A probabilistic disease-gene finder for personal genomes.]
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|style="padding:.4em;" rowspan=2|2015/05/28
|style="padding:.4em;"|2015-51
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/ng/journal/v46/n12/full/ng.3141.html Systematic analysis of noncoding somatic mutations and gene expression alterations across 14 tumor types.]
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|style="padding:.4em;"|2015-50
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nbt/journal/v28/n5/full/nbt.1630.html GREAT improves functional interpretation of cis-regulatory regions.]
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|style="padding:.4em;" rowspan=2|2015/05/21
|style="padding:.4em;"|2015-49
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nmeth/journal/v11/n3/full/nmeth.2832.html Functional annotation of noncoding sequence variants.]
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|style="padding:.4em;"|2015-48
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://genomebiology.com/content/15/10/480 FunSeq2: A framework for prioritizing noncoding regulatory variants in cancer.]
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|style="padding:.4em;" rowspan=2|2015/05/14
|style="padding:.4em;"|2015-47
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nmeth/journal/v12/n2/full/nmeth.3215.html Selecting causal genes from genome-wide association studies via functionally coherent subnetworks.]
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|style="padding:.4em;"|2015-46
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/ncomms/2015/150119/ncomms6890/full/ncomms6890.html Biological interpretation of genome-wide association studies using predicted gene functions.]
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|style="padding:.4em;" rowspan=3|2015/05/07
|style="padding:.4em;"|2015-45
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/ncomms/2014/140626/ncomms5212/full/ncomms5212.html Human symptoms-disease network.]
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|style="padding:.4em;"|2015-44
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867413010246 A nondegenerate code of deleterious variants in Mendelian loci contributes to complex disease risk.]
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|style="padding:.4em;"|2015-43
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.sciencemag.org/content/347/6224/1257601.long Uncovering disease-disease relationships through the incomplete interactome.]

|-
|style="padding:.4em;" rowspan=2|2015/04/30
|style="padding:.4em;"|2015-42
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://genome.cshlp.org/content/25/1/142.long The discovery of integrated gene networks for autism and related disorders.]
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|style="padding:.4em;"|2015-41
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://msb.embopress.org/content/10/12/774.long Integrated systems analysis reveals a molecular network underlying autism spectrum disorders.]
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|style="padding:.4em;" rowspan=3|2015/04/23
|style="padding:.4em;"|2015-40
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v518/n7539/full/nature13990.html Dissecting neural differentiation regulatory networks through epigenetic footprinting.]
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|style="padding:.4em;"|2015-39
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v518/n7539/full/nature14221.html Cell-of-origin chromatin organization shapes the mutational landscape of cancer.]
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|style="padding:.4em;"|2015-38
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v518/n7539/full/nature14248.html Integrative analysis of 111 reference human epigenomes.]
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|style="padding:.4em;" rowspan=2|2015/04/09
|style="padding:.4em;"|2015-37
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://genome.cshlp.org/content/25/2/246.long Genome-wide analysis of local chromatin packing in Arabidopsis thaliana.]
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|style="padding:.4em;"|2015-36
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414014974 A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping.]
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|style="padding:.4em;" rowspan=2|2015/04/02
|style="padding:.4em;"|2015-35
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nrg/journal/v14/n6/full/nrg3454.html Exploring the three-dimensional organization of genomes: interpreting chromatin interaction data.]
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|style="padding:.4em;"|2015-34
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.sciencemag.org/content/347/6225/1010.long Transcribed enhancers lead waves of coordinated transcription in transitioning mammalian cells.]
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|style="padding:.4em;" rowspan=2|2015/03/26
|style="padding:.4em;"|2015-33
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://genome.cshlp.org/content/25/2/257.long Cupid: simultaneous reconstruction of microRNA-target and ceRNA networks.]
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|style="padding:.4em;"|2015-32
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://genome.cshlp.org/content/25/1/41.long Characterization of the neural stem cell gene regulatory network identifies OLIG2 as a multifunctional regulator of self-renewal.]
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|style="padding:.4em;" rowspan=2|2015/03/19
|style="padding:.4em;"|2015-31
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nbt/journal/vaop/ncurrent/full/nbt.3154.html Decoding the regulatory network of early blood development from single-cell gene expression measurements.]
|-
|style="padding:.4em;"|2015-30
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nrg/journal/v16/n3/full/nrg3833.html Computational and analytical challenges in single-cell transcriptomics.]
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|style="padding:.4em;" rowspan=3|2015/03/12
|style="padding:.4em;"|2015-29
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867415000136 Extensive Strain-Level Copy-Number Variation across Human Gut Microbiome Species.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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 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 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"
|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 title
|-
|rowspan = "1"|2013/01/11
|align ="center"|2012-81
|[http://genome.cshlp.org/content/22/8/1589.full (TH Kim) MuSiC: identifying mutational significance in cancer genomes.]
|-
|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"|2012-74
|[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]
|-
| 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]
|-
| align="center"|2012-72
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2063581/ (SH Hwang) Network-based classification of breast cancer metastasis]
|-
| 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]
|-
|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]
|-
| align="center"|2012-66
|[http://bioinformatics.oxfordjournals.org/content/25/16/2078.long (ER Kim&TH Kim)The Sequence Alignment/Map format and SAMtools]
|-
| 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]
|-
| align="center"|2012-64
|[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"|2012-63
|[http://www.sciencemag.org/content/336/6086/1262.abstract (T Lee&CY Kim)Host-Gut Microbiota Metabolic Interactions]
|-
|align="center"|2012-62
|[http://www.sciencemag.org/content/336/6086/1268.full (AR Cho,JH Ju)Interactions Between the Microbiota and the Immune System]
|-
|align="center"|2012-61
|[http://www.sciencemag.org/content/336/6086/1255.abstract (SH Hwang&HJ Cho)The Application of Ecological Theory Toward an Understanding of the Human Microbiome]
|-
|align="center"|2012-60
|[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
|[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]
|-
| align="center"|2012-58
|[http://www.nature.com/nature/journal/v486/n7402/full/nature11209.html (JH Shin&HJ Kim)A framework for human microbiome research]
|-
|align="center"|2012-57
|[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
|[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]
|-
|align="center"|2012-55
|[http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002511 (YH Go&HJ Han)Google Goes Cancer: Improving Outcome Prediction for Cancer Patients by Network-Based Ranking of Marker Genes]
|-
| align="center"|2012-54
|[http://www.nature.com/msb/journal/v7/n1/full/msb201147.html (YH Go&HJ Han)A pharmacogenomic method for individualized prediction of drug sensitivity]
|-
|align="center"|2012-53
|[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
|[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]
|-
|align="center"|2012-51
|[http://www.nature.com/nature/journal/v483/n7391/full/nature11005.html (ER Kim&TH Kim)Systematic identification of genomic markers of drug sensitivity in cancer cells]
|-
| align="center"|2012-50
|[http://www.pnas.org/content/early/2011/10/13/1018854108.abstract (ER Kim&TH Kim)Subtype and pathway specific responses to anticancer compounds in breast cancer]
|-
|align="center"|2012-49
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature10945.html (JE Shim&KS Kim)De novo mutations revealed by whole-exome sequencing are strongly associated with autism]
|-
|align="center"|2012-48
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature11011.html (JE Shim&KS Kim)Patterns and rates of exonic de novo mutations in autism spectrum disorders]
|-
|align="center"|2012-47
|[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
|[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)]
|-
|align="center"|2012-45
|[http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002621 (T Lee&CY Kim)Type 2 Diabetes Risk Alleles Demonstrate Extreme Directional Differentiation among Human Populations, Compared to Other Diseases]
|-
|align="center"|2012-44
|[http://www.nature.com/nature/journal/v480/n7376/full/nature10665.html (AR Cho&JH Ju)Predicting mutation outcome from early stochastic variation in genetic interaction partners]
|-
|align="center"|2012-43
|[http://www.sciencemag.org/content/335/6064/82 (AR Cho&JH Ju)Fitness Trade-Offs and Environmentally Induced Mutation Buffering in Isogenic C. elegans]
|-
| align="center"|2012-42
|[http://genome.cshlp.org/content/22/6/1163 (JE Shim&KS Kim)Identification of microRNA-regulated gene networks by expression analysis of target genes]
|-
|align="center"|2012-41
|[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
|[http://www.cell.com/abstract/S0092-8674(12)00573-9 (JH Soh)Functional Repurposing Revealed by Comparing S. pombe and S. cerevisiae Genetic Interactions]
|-
|align="center"|2012-39
|[http://genome.cshlp.org/content/22/2/375.long (ER Kim&TH Kim)De novo discovery of mutated driver pathways in cancer]
|-
| align="center"|2012-38
|[http://www.nature.com/msb/journal/v4/n1/full/msb20082.html (YH Go&HJ Han)A systems biology approach to prediction of oncogenes and molecular perturbation targets in B-cell lymphomas]
|-
|align="center"|2012-37
|[http://www.nature.com/msb/journal/v7/n1/full/msb201126.html (SH Hwang&HJ Cho)PREDICT: a method for inferring novel drug indications with application to personalized medicine]
|-
|align="center"|2012-36
|[http://www.nature.com/nature/journal/v453/n7198/full/nature06973.html (SH Hwang&HJ Cho)Synergistic response to oncogenic mutations defines gene class critical to cancer phenotype]
|-
|align="center"|2012-35
|[http://www.sciencemag.org/content/334/6062/1518.full (JH Shin&HJ Kim)Detecting Novel Associations in Large Data Sets]
|-
|rowspan = "3" |2012/03/05
| align="center"|2012-34
|[http://www.ncbi.nlm.nih.gov/pubmed/18516045 (8,HH Kim)Mapping and quantifying mammalian transcriptomes by RNA-Seq.]
|-
|align="center"|2012-33
|[http://genomebiology.com/2010/11/10/R106 (11,Go&Ju)Differential expression analysis for sequence count data]
|-
|align="center"|2012-32
|[http://bioinformatics.oxfordjournals.org/content/26/1/139.short (12,Go&Ju)edgeR: a Bioconductor package for differential expression analysis of digital gene expression data ]
|-
|rowspan = "7" |2012/02/27 2012/02/28
| align="center"|2012-31
|[http://www.nature.com/nrg/journal/v10/n1/full/nrg2484.html (1,JW Song)RNA-Seq: a revolutionary tool for transcriptomics]
|-
|align="center"|2012-30
|[http://www.nature.com/nmeth/journal/v8/n6/full/nmeth.1613.html (2,JW Song)Computational methods for transcriptome annotation and quantification using RNA-seq]
|-
|align="center"|2012-29
|[http://genomebiology.com/2010/11/12/220 (3,HJ Han)From RNA-seq reads to differential expression results]
|-
|align="center"|2012-28
|[http://www.nature.com/nmeth/journal/v7/n9/full/nmeth.1491.html (4,AR Cho)Comprehensive comparative analysis of strand-specific RNA sequencing methods]
|-
|align="center"|2012-27
|[http://www.plosone.org/article/info:doi%2F10.1371%2Fjournal.pone.0026426 (5,T Lee)A Low-Cost Library Construction Protocol and Data Analysis Pipeline for Illumina-Based Strand-Specific Multiplex RNA-Seq]
|-
|align="center"|2012-26
|[http://www.nature.com/nbt/journal/v28/n5/abs/nbt.1621.html (6,So&Shin)Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation]
|-
|align="center"|2012-25
|[http://bioinformatics.oxfordjournals.org/content/25/9/1105.abstract (7,So&Shin)TopHat: discovering splice junctions with RNA-Seq]
|-
|rowspan = "5" |2012/02/06
| align="center"|2012-24
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3125733/ mirConnX: condition-specific mRNA-microRNA network integrator]
|-
|align="center"|2012-23
|[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002190 Construction and Analysis of an Integrated Regulatory Network Derived from High-Throughput Sequencing Data]
|-
|align="center"|2012-22
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002415 A Densely Interconnected Genome-Wide Network of MicroRNAs and Oncogenic Pathways Revealed Using Gene Expression Signatures]
|-
|align="center"|2012-21
|[http://genome.cshlp.org/content/20/5/589.short Reprogramming of miRNA networks in cancer and leukemia]
|-
|align="center"|2012-20
|[http://www.cell.com/abstract/S0092-8674(11)01152-4 An Extensive MicroRNA-Mediated Network of RNA-RNA Interactions Regulates Established Oncogenic Pathways in Glioblastoma]
|-
|rowspan = "5" |2012/01/30
|align="center"|2012-19
|[http://www.cell.com/abstract/S0092-8674(11)00237-6 Principles and Strategies for Developing Network Models in Cancer]
|-
|align="center"|2012-18
|[http://www.nature.com/ng/journal/v37/n4/full/ng1532.html Reverse engineering of regulatory networks in human B cells]
|-
|align="center"|2012-17
|[http://www.nature.com/nature/journal/v452/n7186/abs/nature06757.html Variations in DNA elucidate molecular networks that cause disease]
|-
|align="center"|2012-16
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2779083/ Harnessing gene expression to identify the genetic basis of drug resistance]
|-
|align="center"|2012-15
|[http://www.sciencedirect.com/science/article/pii/S0092867410012936 An Integrated Approach to Uncover Drivers of Cancer]
|-
|rowspan = "3" |2012/01/09
|align="center"|2012-14
|[http://www.ncbi.nlm.nih.gov/pubmed/18704161 Genetic variation in an individual human exome.]
|-
|align="center"|2012-13
|[http://www.ncbi.nlm.nih.gov/pubmed/22081227 Predicting phenotypic variation in yeast from individual genome sequences.]
|-
|align="center"|2012-12
|[http://www.ncbi.nlm.nih.gov/pubmed/20435227 Clinical assessment incorporating a personal genome.]
|-
|rowspan = "6" |2012/01/09 2012/01/16
| align="center"|2012-11
|[http://www.ncbi.nlm.nih.gov/pubmed/20399638 Human allelic variation: perspective from protein function, structure, and evolution.]
|-
|align="center"|2012-10
|[http://www.ncbi.nlm.nih.gov/pubmed/19561590 Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm.]
|-
|align="center"|2012-09
|[http://www.ncbi.nlm.nih.gov/pubmed/11230178 Prediction of deleterious human alleles.]
|-
|align="center"|2012-08
|[http://www.ncbi.nlm.nih.gov/pubmed/12202775 Human non-synonymous SNPs: server and survey.]
|-
|align="center"|2012-07
|[http://www.ncbi.nlm.nih.gov/pubmed/20354512 A method and server for predicting damaging missense mutations.]
|-
|align="center"|2012-06
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1920242/ SNAP: predict effect of non-synonymous polymorphisms on function]
|-
|rowspan = "3" |2012/01/09 2012/01/16
|align="center"|2012-05
|[http://www.ncbi.nlm.nih.gov/pubmed/21920052 Computational and statistical approaches to analyzing variants identified by exome sequencing.]
|-
|align="center"|2012-04
|[http://www.ncbi.nlm.nih.gov/pubmed/18179889 Shifting paradigm of association studies: value of rare single-nucleotide polymorphisms.]
|-
|align="center"|2012-03
|[http://www.ncbi.nlm.nih.gov/pubmed/19684571 Targeted capture and massively parallel sequencing of 12 human exomes.]
|-
|rowspan = "2" |2012/01/09 2012/01/16
|align="center"|2012-02
|[http://www.ncbi.nlm.nih.gov/pubmed/17637733 The distribution of fitness effects of new mutations.]
|-
|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]
|}
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
|align="center"|2011-49
|[http://bmir.stanford.edu/file_asset/index.php/1407/BMIR-2009-1355.pdf (Shin)Data-Driven Methods to Discover Molecular Determinants of Serious Adverse Drug Events]
|-
|align="center"|2011-48
|[http://www.nature.com/nchembio/journal/v4/n11/abs/nchembio.118.html (Shin)Network pharmacology: the next paradigm in drug discovery]
|-
|align="center"|2011-47
|[http://www.nature.com/msb/journal/v7/n1/full/msb201171.html (Oh)Systematic exploration of synergistic drug pairs]
|-
|align="center"|2011-46
|[http://www.nature.com/msb/journal/v5/n1/full/msb200995.html (Shim)Chemogenomic profiling predicts antifungal synergies]
|-
|align="center"|2011-45
|[http://www.pnas.org/content/104/32/13086 (Beck)A strategy for predicting the chemosensitivity of human cancers and its application to drug discovery]
|-
|align="center"|2011-44
|[http://www.nature.com/nchembio/journal/v1/n7/full/nchembio747.html (Hwang)Analysis of drug-induced effect patterns to link structure and side effects of medicines]
|-
|rowspan = "3" |2011/11/14
|align="center"|2011-43
|[http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000662 Network-Based Elucidation of Human Disease Similarities Reveals Common Functional Modules Enriched for Pluripotent Drug Targets]
|-
|align="center"|2011-42
|[http://www.nature.com/msb/journal/v7/n1/full/msb201131.html Cross-species discovery of syncretic drug combinations that potentiate the antifungal fluconazole]
|-
|align="center"|2011-41
|[http://www.nature.com/msb/journal/v7/n1/full/msb20115.html Analysis of multiple compound–protein interactions reveals novel bioactive molecules]
|-
|rowspan = "4" |2011/11/07
|align="center"|2011-40
|[http://www.nature.com/nbt/journal/v25/n10/abs/nbt1338.html Drug—target network]
|-
|align="center"|2011-39
|[http://www.nature.com/msb/journal/v6/n1/full/msb200998.html A side effect resource to capture phenotypic effects of drugs]
|-
|align="center"|2011-38
|[http://genome.cshlp.org/content/18/2/206.long Quantitative systems-level determinants of human genes targeted by successful drugs]
|-
|align="center"|2011-37
|[http://www.sciencemag.org/content/321/5886/263.short Drug Target Identification Using Side-Effect Similarity]
|-
|rowspan = "3" |2011/11/07
|align="center"|2011-36
|[http://stm.sciencemag.org/content/3/96/96ra77.short Discovery and Preclinical Validation of Drug Indications Using Compendia of Public Gene Expression Data]
|-
|align="center"|2011-35
|[http://bib.oxfordjournals.org/content/12/4/303.short Exploiting drug–disease relationships for computational drug repositioning]
|-
|align="center"|2011-34
|[http://www.springerlink.com/content/4489r051nu2t0ul1/ Drug Discovery in a Multidimensional World: Systems, Patterns, and Networks]
|-
|rowspan = "3" |2011/10/05
|align="center"|2011-33
|[http://genome.cshlp.org/content/20/7/960.full Analysis of membrane proteins in metagenomics: Networks of correlated environmental features and protein families]
|-
|align="center"|2011-32
|[http://www.pnas.org/content/106/5/1374.full Quantifying environmental adaptation of metabolic pathways in metagenomics]
|-
|align="center"|2011-31
|[http://www.pnas.org/content/104/35/13913.abstract Quantitative assessment of protein function prediction from metagenomics shotgun sequences]
|-
|rowspan = "4" |2011/10/04
|align="center"|2011-30
|[http://www.nature.com/nature/journal/v464/n7285/full/nature08821.html A human gut microbial gene catalogue established by metagenomic sequencing]
|-
|align="center"|2011-29
|[http://www.nature.com/nrmicro/journal/v6/n9/abs/nrmicro1935.html Molecular eco-systems biology: towards an understanding of community function]
|-
|align="center"|2011-28
|[http://genome.cshlp.org/content/early/2009/04/20/gr.085464.108 Microbial community profiling for human microbiome projects: Tools, techniques, and challenges]
|-
|align="center"|2011-27
|[http://www.nature.com/nrmicro/journal/v9/n4/full/nrmicro2540.html Unravelling the effects of the environment and host genotype on the gut microbiome]
|-
|rowspan = "2" |2011/09/19
|align="center"|2011-26
|[http://www.sciencemag.org/content/333/6042/596.full independently evolved virulence effectors converge onto hubs in a plant immune system Network]
|-
|align="center"|2011-25
|[http://www.sciencemag.org/content/333/6042/601.full Evidence for network evolution in an arabidopsis interactome Map]
|-
|rowspan = "2" |2011/09/05
|align="center"|2011-24
|[http://www.sciencedirect.com/science/article/pii/S0092867411005861 Exome Sequencing of Ion Channel Genes Reveals Complex Profiles Confounding Personal Risk Assessment in Epilepsy]
|-
|align="center"|2011-23
|[http://www.cell.com/abstract/S0092-8674(11)00543-5 Pluripotency factors in Embryonic stem cells Regulate Differentiation into Germ Layers]
|-
|rowspan = "2" |2011/09/22
|align="center"|2011-22
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002077 Integrated Genome-scale predition of Detrimental Mutations in Transcription Networks]
|-
|align="center"|2011-21
|[http://www.nature.com/nrg/journal/v12/n4/abs/nrg2969.html From expression QTLs to personalized transcriptomics]
|-
|2011/06/20
|align="center"|2011-20
|[http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001046 a user's guide to the encyclopedia of DNA elements(ENCODE)]
|-
|rowspan = "2" |2011/03/30
|align="center"|2011-19
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature09944.html enterotypes of the human gut microbiome]
|-
|align="center"|2011-18
|[http://www.nature.com/msb/journal/v7/n1/full/msb20116.html toward molecular trait-based ecology, through intergration of biogeochemical, geographical and metagenomic data]
|-
|rowspan = "2" |2011/03/16
|align="center"|2011-17
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002047 variable pathogenicity determines individual lifespan in ''caenorhabditis elegans'']
|-
|align="center"|2011-16
|[http://www.cell.com/abstract/S0092-8674(11)00371-0 a high-resolution ''c.elegans'' essential gene network based on phenotypic profiling of a complex tissue]
|-
|rowspan = "3" |2011/04/25
|align="center"|2011-15
|[http://www.ncbi.nlm.nih.gov/pubmed/21376230 Hallmarks of Cancer : The next generation]
|-
|align="center"|2011-14
|[http://www.cell.com/abstract/S0092-8674(11)00296-0 Mapping Cancer Origins]
|-
|align="center"|2011-13
|[http://www.cell.com/abstract/S0092-8674(11)00297-2 Genetic Interactions in Cancer Progression and Treatment]
|-
|rowspan = "2" |2011/04/11
|align="center"|2011-12
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1001273 Proteins encoded in genomic regions associated with immune-mediated disease physically interact and suggest underlying biology]
|-
|align="center"|2011-11
|***Changed!***
[http://www.ncbi.nlm.nih.gov/pubmed/19557189 Identifying relationships among genomic disease regions: predicting genes at pathogenic SNP associations and rare deletions]
|-
|rowspan = "2"|2011/03/28
|align="center"|2011-10
|[http://www.pnas.org/content/106/44/18843.long profiling translatomes of discrete cell populations resolves altered cellular priorities during hypoxia in Arabidopsis]
|-
|align="center"|2011-09
|[http://www.nature.com/msb/journal/v6/n1/full/msb201076.html cell-type specific analysis of translating RNAs in developing flowers reveals new levels of control]
|-
|rowspan = "2"|2011/03/14
|align="center"|2011-08
|[http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6WSN-51SFHJD-1&_user=44062&_coverDate=01%2F07%2F2011&_rdoc=1&_fmt=high&_orig=search&_origin=search&_sort=d&_docanchor=&view=c&_acct=C000004738&_version=1&_urlVersion=0&_userid=44062&md5=2f6017aab4c794ed7e78fbbd8447077a&searchtype=a phenotypic landscape of a bacterial cell]
|-
|align="center"|2011-07
|[http://www.nature.com/msb/journal/v6/n1/full/msb2010107.html cross-species chemogenomic profiling reveals evolutionarily conserved drug mode of action]
|-
|rowspan = "2"|2011/02/28
|align="center"|2011-06
|[http://www.pnas.org/content/early/2010/09/23/1004666107.abstract genomic patterns of pleiotropy and the evolution of complexity]
|-
|align="center"|2011-05
|[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1001009 simultaneous genome-wide inference of physical, genetic, regulatory, and functional pathway]
|-
|2011/02/21
|align="center"|2011-04
|[http://www.nature.com/msb/journal/v6/n1/full/msb201071.html dynamic interaction networks in a hierarchically organized tissue]
|-
|2011/02/14
|align="center"|2011-03
|[http://www.sciencemag.org/content/330/6009/1385.abstract rewiring of genetic networks in response to DNA damage]
|-
|rowspan = "2"|2011/01/31
|align="center"|2011-02
|[http://www.nature.com/nrg/journal/v10/n9/abs/nrg2633.html Applying mass spectrometry-based proteomics to genetics, genomics and network biology]
|-
|align="center"|2011-01
|[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
|align="center"|2010-29
|[http://www.nature.com/nature/journal/v467/n7312/full/nature09326.html Functional_roles_fornoise_in_genetic_circuits]
|-
|align="center"|2010-28
|[http://www.nature.com/ng/journal/v42/n7/full/ng.610.html Estimation_of_effect_size_distribution_from_genome-wide_association_studies_and_implications_for_future_discoveries]
|-
|rowspan = "2"|2010/11/15
|align="center"|2010-27
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1000932 Liver_and_Adipose_Expression_associated_SNPs_are_enriched_for_association_to_type_2_diabetes]
|-
|align="center"|2010-26
|[http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B8JDC-50J4MRJ-2&_user=44062&_coverDate=10%2F10%2F2010&_rdoc=1&_fmt=high&_orig=search&_origin=search&_sort=d&_docanchor=&view=c&_acct=C000004738&_version=1&_urlVersion=0&_userid=44062&md5=7667a67cf8ba3f68117b8d0ff85a8887&searchtype=a It's_the_machine_that_matters:_predicting_gene_function_and_phenotype_from_protein_networks]
|-
|rowspan = "2"|2010/11/01
|align="center"|2010-25
|[http://genome.cshlp.org/content/early/2010/06/15/gr.104216.109 A_genome-wide_map_of_human_genetic_interactions_inferred_from_radiation_hybrid_genotypes]
|-
|align="center"|2010-24
|[http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0012139 A_Genome-Wide_Gene_Function_Prediction_Resource_for_Drosophila_melanogaster]
|-
|2010/10/11
|align="center"|2010-23
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2913399/?tool=pubmed Dissecting_spatio-temporal_protein_networks_driving_human_heart_development_and_related_disorders]
|-
|rowspan = "2"|2010/09/13
|align="center"|2010-22
|[http://www.nature.com/ng/journal/v42/n7/full/ng.600.html Transposable_elements_have_rewired_the_core_regulatory_network_of_human_embryonic_stem_cells]
|-
|align="center"|2010-21
|[http://www.nature.com/ng/journal/v42/n7/full/ng0710-557.html Limits_of_sequence_and_functional_conservation]
|-
|2010/05/26
|align="center"|2010-20
|[[media:100428_network_based_elucidation_of_human_disease_similarities_reveals_common_functional_modules_enriched_for_pluripotent_drug_targets.pdf|network_based_elucidation_of_human_disease_similarities_reveals_common_functional_modules_enriched_for_pluripotent_drug_targets.pdf]]
|-
|2010/05/19
|align="center"|2010-19
|[[media:100421_interpreting_metabolomic_profiles_using_unbiased_pathway_models.pdf|interpreting_metabolomic_profiles_using_unbiased_pathway_models.pdf]]
|-
|2010/04/21
|align="center"|2010-18
|[[media:100414_identification_of_networks_of_co_occurring_tumor_related_dna_copy_number_changes_using_a_genome_wide_scoringapproach.pdf|identification_of_networks_of_co_occurring_tumor_related_dna_copy_number_changes_using_a_genome_wide_scoringapproach.pdf]]
|-
|2010/04/14
|align="center"|2010-17
|[http://www.cell.com/retrieve/pii/S0092867410000796 an_atlas_of_combinatorial_transcriptional_regulation_in_mouse_and_man]
|-
|rowspan = "2" |2010/04/07
|align="center"|2010-16
|[http://www.pnas.org/content/early/2010/03/11/0910200107.long systematic_discovery_of_nonobvious_human_disease_models_through_orthologous_phenotypes]
|-
|align="center"|2010-15
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature08800.html genome_side_association_study_of_107_phenotypes_in_Arabidopsis_thaliana_inbred_lines]
|-
|rowspan="2"|2010/03/31
|align="center"|2010-14
|[[media:100331_toward_stem_cell_systems_biology_from_molecules_to_networks_and_landscapes.pdf|toward_stem_cell_systems_biology_from_molecules_to_networks_and_landscapes.pdf]]
|-
|align="center"|2010-13
|[http://www.nature.com/nrm/journal/v10/n10/abs/nrm2766.html systems_biology_of_stem_cell_fate_and_cellular_reprogramming]
|-
|2010/03/24
|align="center"|2010-12
|[http://www.nature.com/nbt/journal/v27/n2/abs/nbt.1522.html dynamic_modularity_in_protein_interaction_networks_predicts_breast_cancer_outcome]
|-
|2010/01/18
|align="center"|2010-11
|JournalClub_100118_a_tutorial_on_statistical_methods_for_population_association_studies
|-
|2010/01/16
|align="center"|2010-10
|systems-level_dinamic_analyses_of_fate_change_in_murine_embryonic_stem_cells
|-
|2010/01/15
|align="center"|2010-09
|distinguishing_direct_versus_indirect_transcription_factor-DNA-interactions
|-
|2010/01/14
|align="center"|2010-08
|chemogenomic_profiling_predicts_antifungal_synergies
|-
|2010/01/13
|align="center"|2010-07
|edgetic_perturbation_models_of_human_inherited_disorders
|-
|2010/01/12
|align="center"|2010-06
|analysis_of_cell_fate_from_single-cell_gene_expression_profiles_in_C.elegans
|-
|2010/01/11
|align="center"|2010-05
|predicting_new_molecular_targets_for_known_drugs.pdf
Reference:SEA(Similarity Ensemble Approach)
|-
|2010/01/09
|align="center"|2010-04
|harnessing_gene_expression_to_identify_the_genetic_basis_of_drug_resistance
|-
|2010/01/08
|align="center"|2010-03
|an_integrative_approach_to_reveal_driver_gene_fusions_from_paired_end_sequencing_data_in_cancer
|-
|2010/01/07
|align="center"|2010-02
|a_phenotypic_profile_of_the_candida_albicans_regulatory_network
|-
|2010/01/06
|align="center"|2010-01
|a_global_view_of_protein_expression_in_human_cells_tissues_and_organs
|}

Journal Club

2025-01-31T08:51:09Z

Sej4926:

{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2024-2 scOmics
|-
!scope="col" style="padding:.4em" | Date
!scope="col" style="padding:.4em" | Team
!scope="col" style="padding:.4em" | Paper index
!scope="col" style="padding:.4em" | Presenter
!scope="col" style="padding:.4em" | Paper title
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|style="padding:.4em;" rowspan=1|2025/3/5
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|25-8
|style="padding:.4em;"|YR Jung
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2024.11.18.624166 Learning multi-cellular representations of single-cell transcriptomics data enables characterization of patient-level disease states]
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|style="padding:.4em;" rowspan=1|2025/2/26
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|25-7
|style="padding:.4em;"|EJ Sung
|style="padding:.4em;text-align:left"|
[https://www.medrxiv.org/content/10.1101/2024.04.04.24305313v1 Single-cell RNA sequencing of human tissue supports successful drug targets]
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|style="padding:.4em;" rowspan=1|2025/2/19
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|25-6
|style="padding:.4em;"|IS Choi
|style="padding:.4em;text-align:left"|
[https://www.biorxiv.org/content/10.1101/2023.09.08.555192v7 Evaluating the Utilities of Foundation Models in Single-cell Data Analysis]
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|style="padding:.4em;" rowspan=1|2025/2/12
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|25-5
|style="padding:.4em;"|SB Baek
|style="padding:.4em;text-align:left"|
[https://www.nature.com/articles/s41586-024-08328-6 Accurate predictions on small data with a tabular foundation model]
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|style="padding:.4em;" rowspan=1|2025/1/15
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|25-4
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2024.09.24.614685 scEMB: Learning context representation of genes based on large-scale single-cell transcriptomics]
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|style="padding:.4em;" rowspan=1|2025/1/8
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|25-3
|style="padding:.4em;"|HB Lee
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1093/bioinformatics/btad663 TT3D: Leveraging precomputed protein 3D sequence models to predict protein–protein interactions]
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|style="padding:.4em;" rowspan=1|2025/1/8
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|25-2
|style="padding:.4em;"|HB Lee
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1093/bioinformatics/btac258 Topsy-Turvy: integrating a global view into sequence-based PPI prediction]
|-
|style="padding:.4em;" rowspan=1|2025/1/8
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|25-1
|style="padding:.4em;"|HB Lee
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.cels.2021.08.010 D-SCRIPT translates genome to phenome with sequence-based, structure-aware, genome-scale predictions of protein-protein interactions]
|-
|style="padding:.4em;" rowspan=1|2024/12/17
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|24-30
|style="padding:.4em;"|YR Jung
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2024.08.16.608180 Quantized multi-task learning for context-specific representations of gene network dynamics]
|-
|style="padding:.4em;" rowspan=1|2024/12/3
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|24-29
|style="padding:.4em;"|EJ Sung
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2023.11.21.568145 ANDES: a novel best-match approach for enhancing gene set analysis in embedding spaces]
|-
|style="padding:.4em;" rowspan=1|2024/11/26
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|24-28
|style="padding:.4em;"|IS Choi
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41592-024-02303-9 CellRank 2: unified fate mapping in multiview single-cell data]
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|style="padding:.4em;" rowspan=1|2024/11/19
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|24-27
|style="padding:.4em;"|SB Baek
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2024.07.29.605556 scPRINT: pre-training on 50 million cells allows robust gene network predictions]
|-
|style="padding:.4em;" rowspan=1|2024/11/12
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|24-26
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41467-024-46440-3 Bidirectional generation of structure and properties through a single molecular foundation model]
|}

{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2024-2 Microbiome
|-
!scope="col" style="padding:.4em" | Date
!scope="col" style="padding:.4em" | Team
!scope="col" style="padding:.4em" | Paper index
!scope="col" style="padding:.4em" | Presenter
!scope="col" style="padding:.4em" | Paper title
|-
|style="padding:.4em;" rowspan=1|2025/3/31
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|25-24
|style="padding:.4em;"|SB Lim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2025.01.07.631644 A Single-Graph Visualization to Reveal Hidden Explainability Patterns of SHAP Feature Interactions in Machine Learning for Biomedical Issues]
|-
|style="padding:.4em;" rowspan=1|2025/3/24
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|25-23
|style="padding:.4em;"|YR Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.cell.2024.08.037 Emergence of community behaviors in the gut microbiota upon drug treatment]
|-
|style="padding:.4em;" rowspan=1|2025/3/24
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|25-22
|style="padding:.4em;"|JY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41564-024-01832-5 Prediction of strain level phage–host interactions across the Escherichia genus using only genomic information]
|-
|style="padding:.4em;" rowspan=1|2025/3/17
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|25-21
|style="padding:.4em;"|WJ Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2025.01.07.631807 Metagenomic estimation of absolute bacterial biomass in the mammalian gut through host-derived read normalization]
|-
|style="padding:.4em;" rowspan=1|2025/3/17
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|25-20
|style="padding:.4em;"|SH Ahn
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2024.10.02.616292 De novo discovery of conserved gene clusters in microbial genomes with Spacedust]
|-
|style="padding:.4em;" rowspan=1|2025/3/17
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|25-19
|style="padding:.4em;"|SH Ahn
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2024.04.14.589414 Rapid and Sensitive Protein Complex Alignment with Foldseek-Multimer]
|-
|style="padding:.4em;" rowspan=1|2025/3/10
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|25-18
|style="padding:.4em;"|HJ Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2024.12.30.630825 MGM as a large-scale pretrained foundation model for microbiome analyses in diverse contexts ]
|-
|style="padding:.4em;" rowspan=1|2025/3/10
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|25-17
|style="padding:.4em;"|JY Ma
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1126/science.ado9336 Sequence modeling and design from molecular to genome scale with Evo]
|-
|style="padding:.4em;" rowspan=1|2025/2/24
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|25-16
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2024.12.18.629142 Human gut microbiome gene co-expression network reveals a loss in taxonomic and functional diversity in Parkinson’s disease]
|-
|style="padding:.4em;" rowspan=1|2025/2/24
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|25-15
|style="padding:.4em;"|NY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2025.01.06.631550 Quantifying Metagenomic Strain Associations from Microbiomes with Anpan]
|-
|style="padding:.4em;" rowspan=1|2025/2/17
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|25-14
|style="padding:.4em;"|SB Lim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2024.12.13.628459 MaAsLin 3: Refining and extending generalized multivariable linear models for meta-omic association discovery]
|-
|style="padding:.4em;" rowspan=1|2025/2/17
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|25-13
|style="padding:.4em;"|HB Lee
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41564-024-01881-w Profiling lateral gene transfer events in the human microbiome using WAAFLE]
|-
|style="padding:.4em;" rowspan=1|2025/2/10
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|25-12
|style="padding:.4em;"|JY Ma
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2024.05.30.596740 ProTrek: Navigating the Protein Universe through Tri-Modal Contrastive Learning]
|-
|style="padding:.4em;" rowspan=1|2025/2/10
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|25-11
|style="padding:.4em;"|JY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2024.12.30.630844 The genetic diversity and populational specificity of the human gut virome at single nucleotide resolution]
|-
|style="padding:.4em;" rowspan=1|2025/2/3
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|25-10
|style="padding:.4em;"|WJ Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1186/s13059-024-03425-1 pan-Draft: automated reconstruction of species-representative metabolic models from multiple genomes]
|-
|style="padding:.4em;" rowspan=1|2025/2/3
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|25-9
|style="padding:.4em;"|SH Ahn
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.cell.2024.09.019 A core microbiome signature as an indicator of health]
|-
|style="padding:.4em;" rowspan=1|2025/1/20
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|25-8
|style="padding:.4em;"|HJ Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.cell.2024.09.027 Using artificial intelligence to document the hidden RNA virosphere]
|-
|style="padding:.4em;" rowspan=1|2025/1/20
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|25-7
|style="padding:.4em;"|YR Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2023.07.17.549267 Learning a deep language model for microbiomes: the power of large scale unlabeled microbiome data]
|-
|style="padding:.4em;" rowspan=1|2025/1/13
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|25-6
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1126/science.adg7492 Accurate proteome-wide missense variant effect prediction with AlphaMissense]
|-
|style="padding:.4em;" rowspan=1|2025/1/13
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|25-5
|style="padding:.4em;"|NY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41591-024-02823-z A gut microbial signature for combination immune checkpoint blockade across cancer types]
|-
|style="padding:.4em;" rowspan=1|2025/1/6
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|25-4
|style="padding:.4em;"|YR Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1080/19490976.2024.2418984 Fecal microbial marker panel for aiding diagnosis of autism spectrum disorders]
|-
|style="padding:.4em;" rowspan=1|2025/1/6
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|25-3
|style="padding:.4em;"|YR Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41564-024-01739-1 Multikingdom and functional gut microbiota markers for autism spectrum disorder]
|-
|style="padding:.4em;" rowspan=1|2025/1/6
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|25-2
|style="padding:.4em;"|JY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1186/s13059-024-03390-9 A realistic benchmark for differential abundance testing and confounder adjustment in human microbiome studies]
|-
|style="padding:.4em;" rowspan=1|2024/12/30
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|25-1
|style="padding:.4em;"|WJ Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41564-024-01728-4 Microbial community-scale metabolic modelling predicts personalized short-chain fatty acid production profiles in the human gut]
|-
|style="padding:.4em;" rowspan=1|2024/12/30
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|24-65
|style="padding:.4em;"|SH Ahn
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41467-024-52561-6 Gut microbiota wellbeing index predicts overall health in a cohort of 1000 infants]
|-
|style="padding:.4em;" rowspan=1|2024/12/18
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|24-64
|style="padding:.4em;"|HJ Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1186/s13059-024-03320-9 VirRep: a hybrid language representation learning framework for identifying viruses from human gut metagenomes]
|-
|style="padding:.4em;" rowspan=1|2024/12/18
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|24-63
|style="padding:.4em;"|HJ Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2024.06.27.601020 Ultrafast and accurate sequence alignment and clustering of viral genomes]
|-
|style="padding:.4em;" rowspan=1|2024/12/18
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|24-62
|style="padding:.4em;"|JY Ma
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2024.08.14.607850 The OMG dataset: An Open MetaGenomic corpus for mixed-modality genomic language modeling]
|-
|style="padding:.4em;" rowspan=1|2024/12/4
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|24-61
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41467-024-46947-9 Genomic language model predicts protein co-regulation and function]
|-
|style="padding:.4em;" rowspan=1|2024/12/4
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|24-60
|style="padding:.4em;"|NY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2024.07.26.605391 Protein Set 1 Transformer: A protein-based genome language model to power high diversity viromics]
|-
|style="padding:.4em;" rowspan=1|2024/11/20
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|24-59
|style="padding:.4em;"|YR Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2024.07.11.603044 Prophage-DB: A comprehensive database to explore diversity,distribution, and ecology of prophages]
|-
|style="padding:.4em;" rowspan=1|2024/11/20
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|24-58
|style="padding:.4em;"|JY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1186/s40168-024-01904-y Strain‑resolved de‑novo metagenomic assembly of viral genomes and microbial 16S rRNAs]
|-
|style="padding:.4em;" rowspan=1|2024/11/13
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|24-57
|style="padding:.4em;"|WJ Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1186/s40168-024-01876-z Prokaryotic‑virus‑encoded auxiliary metabolic genes throughout the global oceans]
|-
|style="padding:.4em;" rowspan=1|2024/11/13
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|24-56
|style="padding:.4em;"|G Koh
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.cell.2024.07.039 Unexplored microbial diversity from 2,500 food metagenomes and links with the human microbiome]
|-
|style="padding:.4em;" rowspan=1|2024/11/6
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|24-55
|style="padding:.4em;"|SH Ahn
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2024.04.17.589959 Pangenomes of Human Gut Microbiota Uncover Links Between Genetic Diversity and Stress Response]
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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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[https://doi.org/10.1101/2024.07.26.605250 GRAViTy-V2: a grounded viral taxonomy application]
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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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[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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[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.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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[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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|style="padding:.4em;"|24-9
|style="padding:.4em;"|SH Ahn
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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;"|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;"|24-5
|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;"|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;"|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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[https://doi.org/10.1038/s41559-020-01353-4 Polarization of microbial communities between competitive and cooperative metabolism]
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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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[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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[https://doi.org/10.1038/s41586-023-07011-6 Spatial transcriptomics reveal neuron–astrocyte synergy in long-term memory]
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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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[https://doi.org/10.1126/science.abi4882 Spatial transcriptomics of planktonic and sessile bacterial populations at single-cell resolution]
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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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[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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[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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[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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[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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[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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[https://doi.org/10.1038/s41587-023-01747-2 Multimodal spatiotemporal phenotyping of human retinal organoid development]
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[https://doi.org/10.1038/s41586-024-07251-0 Immune microniches shape intestinal Treg function]
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[https://doi.org/10.1016/j.devcel.2021.02.021 A single-cell analysis of the Arabidopsis vegetative shoot apex]
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[https://doi.org/10.1038/s41467-023-40137-9 Droplet-based high-throughput single microbe RNA sequencing by smRandom-seq]
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[https://doi.org/10.1038/s41564-023-01462-3 Single-cell massively-parallel multiplexed microbial sequencing (M3-seq) identifies rare bacterial populations and profiles phage infection]
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[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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[https://doi.org/10.1038/s41587-023-01979-2 Spatial metatranscriptomics resolves host–bacteria–fungi interactomes]
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[https://doi.org/10.1038/s41467-023-36325-2 Dissecting the immune suppressive human prostate tumor microenvironment via integrated single-cell and spatial transcriptomic analyses]
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[https://doi.org/10.1038/s41593-023-01452-y Single-nucleus genomics in outbred rats with divergent cocaine addiction-like behaviors reveals changes in amygdala GABAergic inhibition]
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[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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[https://doi.org/10.1038/s41467-023-39933-0 Spatial cellular architecture predicts prognosis in glioblastoma]
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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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[https://doi.org/10.1038/s41467-022-30511-4 Transcriptional adaptation of olfactory sensory neurons to GPCR identity and activity]
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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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[https://doi.org/10.1021/acscentsci.3c01169 Single-Cell Analysis Reveals Cxcl14+ Fibroblast Accumulation in Regenerating Diabetic Wounds Treated by Hydrogel-Delivering Carbon Monoxide]
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[https://doi.org/10.1038/s41477-022-01291-y Single-cell RNA sequencing provides a high-resolution roadmap for understanding the multicellular compartmentation of specialized metabolism]
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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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[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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[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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[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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[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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[https://doi.org/10.1038/s41592-023-02035-2 Population-level integration of single-cell datasets enables multi-scale analysis across samples]
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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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[https://doi.org/10.1038/s41587-022-01467-z Modeling intercellular communication in tissues using spatial graphs of cells]
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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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[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;" rowspan=1|Single-cell
|style="padding:.4em;"|23-33
|style="padding:.4em;"|EJ Sung
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41588-022-01273-y MHC II immunogenicity shapes the neoepitope landscape in human tumors]
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|style="padding:.4em;" rowspan=1|2023/11/28
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-32
|style="padding:.4em;"|IS Choi
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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|2023/11/21
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-31
|style="padding:.4em;"|JW Yu
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41467-023-37353-8 Pan-cancer classification of single cells in the tumour microenvironment]
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|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-30
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41587-023-01686-y Single-cell mapping of combinatorial target antigens for CAR switches using logic gates]
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|style="padding:.4em;" rowspan=1|2023/10/24
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-29
|style="padding:.4em;"|SB Baek
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41587-023-01782-z Comparative analysis of cell–cell communication at single-cell resolution]
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|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-28
|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
|style="padding:.4em;"|23-27
|style="padding:.4em;"|IS Choi
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41591-023-02324-5 An integrated tumor, immune and microbiome atlas of colon cancer]
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|style="padding:.4em;" rowspan=1|2023/09/12
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-26
|style="padding:.4em;"|SB Baek
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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|2023/09/05
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|23-25
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.immuni.2023.04.010 Recruitment of epitope-specific T cell clones with a low-avidity threshold supports efficacy against mutational escape upon re-infection]
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{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2023-2 Microbiome
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!scope="col" style="padding:.4em" | Date
!scope="col" style="padding:.4em" | Team
!scope="col" style="padding:.4em" | Paper index
!scope="col" style="padding:.4em" | Presenter
!scope="col" style="padding:.4em" | Paper title

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|style="padding:.4em;" rowspan=1|2024/02/21
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-66
|style="padding:.4em;"|HJ Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1186/s40168-023-01607-w Phages are unrecognized players in the ecology of the oral pathogen Porphyromonas gingivalis]
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|style="padding:.4em;" rowspan=1|2024/02/21
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-65
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41564-023-01439-2 A predicted CRISPR-mediated symbiosis between uncultivated archaea]
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|style="padding:.4em;" rowspan=1|2024/02/14
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-64
|style="padding:.4em;"|SH Ahn
|style="padding:.4em;text-align:left"|
[https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-023-01692-x Integrating compositional and functional content to describe vaginal microbiomes in health and disease]
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|style="padding:.4em;" rowspan=1|2024/02/14
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-63
|style="padding:.4em;"|JY Ma
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41587-023-01696-w Contamination source modeling with SCRuB improves cancer phenotype prediction from microbiome data]
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|style="padding:.4em;" rowspan=1|2024/02/07
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-62
|style="padding:.4em;"|NY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41586-023-06431-8 Mapping the T cell repertoire to a complex gut bacterial community]
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|style="padding:.4em;" rowspan=1|2024/02/07
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-61
|style="padding:.4em;"|YR Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2023.07.03.547607 Multi-view integration of microbiome data for identifying disease-associated modules]
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|style="padding:.4em;" rowspan=1|2024/01/24
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-60
|style="padding:.4em;"|JY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2023.09.28.559994 Phage-bacteria dynamics during the first years of life revealed by trans-kingdom marker gene analysis]
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|style="padding:.4em;" rowspan=1|2024/01/24
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-59
|style="padding:.4em;"|WJ Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41593-023-01361-0 Multi-level analysis of the gut–brain axis shows autism spectrum disorder-associated molecular and microbial profiles]
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|style="padding:.4em;" rowspan=1|2024/01/17
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-58
|style="padding:.4em;"|G Koh
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2023.11.21.568153 Metagenomic Immunoglobulin Sequencing (MIG-Seq) Exposes Patterns of IgA Antibody Binding in the Healthy Human Gut Microbiome]
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|style="padding:.4em;" rowspan=1|2024/01/17
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-57
|style="padding:.4em;"|SH Ahn
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41467-023-41042-x Impact of dietary interventions on pre-diabetic oral and gut microbiome, metabolites and cytokines]
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|style="padding:.4em;" rowspan=1|2024/01/10
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-56
|style="padding:.4em;"|HJ Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41592-023-02018-3 Fast and robust metagenomic sequence comparison through sparse chaining with skani]
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|style="padding:.4em;" rowspan=1|2024/01/10
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-55
|style="padding:.4em;"|JY Ma
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41591-023-02599-8 Bacterial SNPs in the human gut microbiome associate with host BMI]
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|style="padding:.4em;" rowspan=1|2023/01/03
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-54
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1080/19490976.2023.2245562 Multimodal metagenomic analysis reveals microbial single nucleotide variants as superior biomarkers for early detection of colorectal cancer]
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|style="padding:.4em;" rowspan=1|2023/01/03
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-53
|style="padding:.4em;"|NY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.chom.2023.10.005 Multi-kingdom gut microbiota analyses define bacterial-fungal interplay and microbial markers of pan-cancer immunotherapy across cohorts]
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|style="padding:.4em;" rowspan=1|2023/12/27
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-52
|style="padding:.4em;"|YR Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.xcrm.2023.101251 Prior antibiotic administration disrupts anti-PD-1 responses in advanced gastric cancer by altering the gut microbiome and systemic immune response]
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|style="padding:.4em;" rowspan=1|2023/12/27
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-51
|style="padding:.4em;"|JY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.cell.2023.05.046 Ultra-deep sequencing of Hadza hunter-gatherers recovers vanishing gut microbes]
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|style="padding:.4em;" rowspan=1|2023/12/13
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-50
|style="padding:.4em;"|WJ Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1186/s40168-023-01472-7 Altered infective competence of the human gut microbiome in COVID-19]
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|style="padding:.4em;" rowspan=1|2023/12/13
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-49
|style="padding:.4em;"|G Koh
|style="padding:.4em;text-align:left"|
[https://onlinelibrary.wiley.com/doi/full/10.1002/aisy.202300342 Host-Variable-Embedding Augmented Microbiome-Based Simultaneous Detection of Multiple Diseases by Deep Learning]
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|style="padding:.4em;" rowspan=1|2023/12/06
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-48
|style="padding:.4em;"|SH Ahn
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41467-023-39264-0 A data-driven approach for predicting the impact of drugs on the human microbiome]
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|style="padding:.4em;" rowspan=1|2023/12/06
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-47
|style="padding:.4em;"|HJ Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2023.04.06.535777 Activation of programmed cell death and counter-defense functions of phage accessory genes]
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|style="padding:.4em;" rowspan=1|2023/11/29
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-46
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41467-023-39459-5 Top-down identification of keystone taxa in the microbiome]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-45
|style="padding:.4em;"|JY Ma
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.cels.2022.12.007 Pitfalls of genotyping microbial communities with rapidly growing genome collections]
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-44
|style="padding:.4em;"|NY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1186/s13059-023-03028-2 Reconstruction of the last bacterial common ancestor from 183 pangenomes reveals a versatile ancient core genome]
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|style="padding:.4em;" rowspan=1|2023/11/22
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-43
|style="padding:.4em;"|SH Lee
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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|Microbiome
|style="padding:.4em;"|23-42
|style="padding:.4em;"|WJ Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.immuni.2023.04.003 Phage display sequencing reveals that genetic, environmental, and intrinsic factors influence variation of human antibody epitope repertoire]
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|style="padding:.4em;" rowspan=1|2023/11/08
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-41
|style="padding:.4em;"|JY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.immuni.2023.04.017 Phage-display immunoprecipitation sequencing of the antibody epitope repertoire in inflammatory bowel disease reveals distinct antibody signatures]
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|style="padding:.4em;" rowspan=1|2023/11/01
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-40
|style="padding:.4em;"|G Koh
|style="padding:.4em;text-align:left"|
[https://doi.org/10.15252/msb.202311525 Consistency across multi-omics layers in a drug-perturbed gut microbial community]
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|style="padding:.4em;" rowspan=1|2023/11/01
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-39
|style="padding:.4em;"|HJ Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41587-023-01953-y Identification of mobile genetic elements with geNomad]
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|style="padding:.4em;" rowspan=1|2023/10/25
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-38
|style="padding:.4em;"|SH Ahn
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41591-023-02407-3 Microbiome-derived cobalamin and succinyl-CoA as biomarkers for improved screening of anal cancer]
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|style="padding:.4em;" rowspan=1|2023/10/11
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-37
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41591-023-02424-2 The airway microbiome mediates the interaction between environmental exposure and respiratory health in humans]
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|style="padding:.4em;" rowspan=1|2023/10/11
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-36
|style="padding:.4em;"|JY Ma
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1080/19490976.2023.2224474 Ordering taxa in image convolution networks improves microbiome-based machine learning accuracy]
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|style="padding:.4em;" rowspan=1|2023/09/27
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-35
|style="padding:.4em;"|NY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2023.08.12.553040 The defensome of complex bacterial communities]
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|style="padding:.4em;" rowspan=1|2023/09/27
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-34
|style="padding:.4em;"|SH Lee
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.cell.2023.03.011 Dietary tryptophan metabolite released by intratumoral Lactobacillus reuteri facilitates immune checkpoint inhibitor treatment]
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|style="padding:.4em;" rowspan=1|2023/09/20
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-33
|style="padding:.4em;"|WJ Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s43587-022-00306-9 Toward an improved definition of a healthy microbiome for healthy aging]
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|style="padding:.4em;" rowspan=1|2023/09/20
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-32
|style="padding:.4em;"|JY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s43587-022-00287-9 Associations of the skin, oral and gut microbiome with aging, frailty and infection risk reservoirs in older adults]
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|style="padding:.4em;" rowspan=1|2023/09/13
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-31
|style="padding:.4em;"|G Koh
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1186/s40168-023-01614-x Statistical modeling of gut microbiota for personalized health status monitoring]
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|style="padding:.4em;" rowspan=1|2023/09/13
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-30
|style="padding:.4em;"|SH Ahn
|style="padding:.4em;text-align:left"|
[https://doi.org/10.7554/eLife.50240 Adjusting for age improves identification of gut microbiome alterations in multiple diseases]
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|style="padding:.4em;" rowspan=1|2023/09/06
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-29
|style="padding:.4em;"|HJ Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41564-023-01370-6 Centenarians have a diverse gut virome with the potential to modulate metabolism and promote healthy lifespan]
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|style="padding:.4em;" rowspan=1|2023/09/06
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-28
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.chom.2023.01.003 Longitudinal comparison of the developing gut virome in infants and their mothers]
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{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2023-1 ADVANCED MICROBIOME DATA ANALYSIS
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!scope="col" style="padding:.4em" | Date
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!scope="col" style="padding:.4em" | Presenter
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|style="padding:.4em;" rowspan=1|2023/06/13
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-24
|style="padding:.4em;"|JY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.chom.2020.03.005 Structure of the Mucosal and Stool Microbiome in Lynch Syndrome]
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|style="padding:.4em;" rowspan=1|2023/06/13
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-23
|style="padding:.4em;"|WJ Kim
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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
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-22
|style="padding:.4em;"|SH Ahn
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.jare.2022.03.007 Roles of oral microbiota and oral-gut microbial transmission in hypertension]
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|style="padding:.4em;" rowspan=1|2023/05/30
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-21
|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;" rowspan=1|2023/05/30
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-20
|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;" rowspan=1|2023/05/30
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-19
|style="padding:.4em;"|JY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.chom.2023.01.004 Deficient butyrate-producing capacity in the gut microbiome is associated with bacterial network disturbances and fatigue symptoms in ME/CFS]
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|style="padding:.4em;" rowspan=1|2023/05/23
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-18
|style="padding:.4em;"|EJ Sung
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.ccell.2022.11.013 Gut microbiota-mediated nucleotide synthesis attenuates the response to neoadjuvant chemoradiotherapy in rectal cancer]
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|style="padding:.4em;" rowspan=1|2023/05/23
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-17
|style="padding:.4em;"|G Koh
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.chom.2021.06.019 Multi-omics reveal microbial determinants impacting responses to biologic therapies in inflammatory bowel disease]
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|style="padding:.4em;" rowspan=1|2023/05/23
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-16
|style="padding:.4em;"|SH Lee
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41586-022-05181-3 Identification of trypsin-degrading commensals in the large intestine]
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|style="padding:.4em;" rowspan=1|2023/05/16
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-15
|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;" rowspan=1|2023/05/16
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-14
|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;" rowspan=1|2023/05/16
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-13
|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;" rowspan=1|2023/05/09
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-12
|style="padding:.4em;"|NY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.cell.2022.09.015 A pan-cancer mycobiome analysis reveals fungal involvement in gastrointestinal and lung tumors]
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|style="padding:.4em;" rowspan=1|2023/05/09
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-11
|style="padding:.4em;"|HR Shin
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41564-021-01030-7 Multi-kingdom microbiota analyses identify bacterial–fungal interactions and biomarkers of colorectal cancer across cohorts]
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|style="padding:.4em;" rowspan=1|2023/05/09
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-10
|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;" rowspan=1|Microbiome
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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;" rowspan=1|Microbiome
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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;" rowspan=1|Microbiome
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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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[https://doi.org/10.1038/s41467-023-36633-7 Population-level impacts of antibiotic usage on the human gut microbiome]
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[https://doi.org/10.1002/imt2.61 Targeting keystone species helps restore the dysbiosis of butyrate‐producing bacteria in nonalcoholic fatty liver disease]
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[https://doi.org/10.1002/advs.202203115 Differential Oral Microbial Input Determines Two Microbiota Pneumo-Types Associated with Health Status]
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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;" rowspan=1|Single-cell
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|style="padding:.4em;" rowspan=1|2023/07/05
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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;" rowspan=1|2023/06/21
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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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[https://doi.org/10.1038/s41467-022-32838-4 Mutated processes predict immune checkpoint inhibitor therapy benefit in metastatic melanoma]
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[https://doi.org/10.1038/s43018-021-00292-8 Temporal single-cell tracing reveals clonal revival and expansion of precursor exhausted T cells during anti-PD-1 therapy in lung cancer]
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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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[https://doi.org/10.1038/s41586-022-05435-0 Effect of the intratumoral microbiota on spatial and cellular heterogeneity in cancer]
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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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[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.nature.com/articles/s41591-022-01799-y A T cell resilience model associated with response to immunotherapy in multiple tumor types]
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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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[https://www.sciencedirect.com/science/article/pii/S1535610822003178 Pan-cancer integrative histology-genomic analysis via multimodal deep learning]
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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;" rowspan=1|Microbiome
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|style="padding:.4em;"|NY Kim
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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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|style="padding:.4em;"|WJ Kim
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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;" rowspan=1|Microbiome
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|style="padding:.4em;"|JY Kim
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|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-22
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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
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-21
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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;"|23-20
|style="padding:.4em;"|JH Cha
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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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[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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[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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[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|Microbiome
|style="padding:.4em;"|23-14
|style="padding:.4em;"|JY Ma
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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;"|NY Kim
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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|Microbiome
|style="padding:.4em;"|23-12
|style="padding:.4em;"|SH Lee
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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;"|JY Kim
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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
|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|WJ Kim
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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
|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|SH Ahn
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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|2023/03/17
|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|HJ Kim
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|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|JH Cha
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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|Microbiome
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|style="padding:.4em;"|JY Ma
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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|Microbiome
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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|Microbiome
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|style="padding:.4em;"|SH Lee
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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;"|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;" rowspan=1|Microbiome
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|style="padding:.4em;"|HJ Kim
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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://pubmed.ncbi.nlm.nih.gov/34551799/ The influence of the gut microbiome on BCG-induced trained immunity]
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[https://pubmed.ncbi.nlm.nih.gov/34912116/ Towards the biogeography of prokaryotic genes]
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[https://pubmed.ncbi.nlm.nih.gov/29347966/ ReprDB and panDB: minimalist databases with maximal microbial representation]
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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/10.1101/2022.06.16.496510v2 MIDAS2: Metagenomic Intra-species Diversity Analysis System]
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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://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-022-01251-w Metagenomic strain detection with SameStr: identification of a persisting core gut microbiota transferable by fecal transplantation]
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[https://www.nature.com/articles/s41587-021-01102-3 Fast and accurate metagenotyping of the human gut microbiome with GT-Pro]
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[https://www.nature.com/articles/s41587-020-00797-0 inStrain profiles population microdiversity from metagenomic data and sensitively detects shared microbial strains]
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[https://genome.cshlp.org/content/early/2021/07/22/gr.265058.120 Longitudinal linked-read sequencing reveals ecological and evolutionary responses of a human gut microbiome during antibiotic treatment]
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[https://www.sciencedirect.com/science/article/pii/S1931312821002365 Dispersal strategies shape persistence and evolution of human gut bacteria]
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[https://www.sciencedirect.com/science/article/pii/S0092867421003524 The long-term genetic stability and individual specificity of the human gut microbiome]
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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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[https://elifesciences.org/articles/42693 Extensive transmission of microbes along the gastrointestinal tract]
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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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[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/s41467-020-14766-3 Trajectory-based differential expression analysis for single-cell sequencing data]
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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://www.sciencedirect.com/science/article/pii/S0092867419308967?via%3Dihub Single-Cell Analysis of Crohn's Disease Lesions Identifies a Pathogenic Cellular Module Associated with Resistance to Anti-TNF Therapy]
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[https://www.nature.com/articles/s41467-020-15956-9 Multi-omic single-cell snapshots reveal multiple independent trajectories to drug tolerance in a melanoma cell line]
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|style="padding:.4em;"|JW Choi
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[https://pubmed.ncbi.nlm.nih.gov/30595452/ Dysfunctional CD8 T Cells Form a Proliferative, Dynamically Regulated Compartment Within Human Melanoma]
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|style="padding:.4em;"|20-13
|style="padding:.4em;"|JW Seo
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[https://pubmed.ncbi.nlm.nih.gov/30388455/ A Cancer Cell Program Promotes T Cell Exclusion and Resistance to Checkpoint Blockade]
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|style="padding:.4em;" rowspan=2|2020/06/09
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|style="padding:.4em;"|JY Lee
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[https://pubmed.ncbi.nlm.nih.gov/31974247/ Single-cell Transcriptional Diversity Is a Hallmark of Developmental Potential]
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|style="padding:.4em;"|20-11
|style="padding:.4em;"|JH Kim
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[https://pubmed.ncbi.nlm.nih.gov/31675496/ Landscape and Dynamics of Single Immune Cells in Hepatocellular Carcinoma]
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|style="padding:.4em;"|HY Seo
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[https://pubmed.ncbi.nlm.nih.gov/32103181/ Peripheral T Cell Expansion Predicts Tumour Infiltration and Clinical Response]
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|style="padding:.4em;"|20-9
|style="padding:.4em;"|KH Hong
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[https://pubmed.ncbi.nlm.nih.gov/30388456/ Defining T Cell States Associated With Response to Checkpoint Immunotherapy in Melanoma]
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|style="padding:.4em;" rowspan=2|2020/05/26
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|style="padding:.4em;"|20-8
|style="padding:.4em;"|JY Seong
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[https://pubmed.ncbi.nlm.nih.gov/31915379/ Rapid Non-Uniform Adaptation to Conformation-Specific KRAS(G12C) Inhibition]
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|style="padding:.4em;"|20-7
|style="padding:.4em;"|OY Min
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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
|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
|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
|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
|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
|style="padding:.4em;" rowspan=2|Single-cell
|style="padding:.4em;"|20-2
|style="padding:.4em;"|SB Baek
|style="padding:.4em;text-align:left"|
[https://pubmed.ncbi.nlm.nih.gov/32014031/ scAI: An Unsupervised Approach for the Integrative Analysis of Parallel Single-Cell Transcriptomic and Epigenomic Profiles]
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|style="padding:.4em;"|20-1
|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;"
|+style="text-align:left;font-size:12pt" | 2019-2nd semester
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|style="padding:.4em;" rowspan=2|2019/10/15
|style="padding:.4em;" rowspan=2|Microbiome
|style="padding:.4em;"|19-51
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S1931312819303488 Obese Individuals with and without Type 2 Diabetes Show Different Gut Microbial Functional Capacity and Composition]
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|style="padding:.4em;"|19-50
|style="padding:.4em;"|SH Lee
|style="padding:.4em;text-align:left"|
[https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-019-0722-6 Clustering co-abundant genes identifies components of the gut microbiome that are reproducibly associated with colorectal cancer and inflammatory bowel disease]
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|style="padding:.4em;" rowspan=2|2019/10/08
|style="padding:.4em;" rowspan=2|Single-cell
|style="padding:.4em;"|19-49
|style="padding:.4em;"|SB Baek
|style="padding:.4em;text-align:left"|
[https://www.nature.com/articles/s41587-019-0206-z Massively parallel single-cell chromatin landscapes of human immune cell development and intratumoral T cell exhaustion]
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|style="padding:.4em;"|19-48
|style="padding:.4em;"|SB Baek
|style="padding:.4em;text-align:left"|
[https://www.biorxiv.org/content/10.1101/739011v1 Assessment of computational methods for the analysis of single-cell ATAC-seq data]
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|style="padding:.4em;" rowspan=2|2019/10/01
|style="padding:.4em;" rowspan=2|Microbiome
|style="padding:.4em;"|19-47
|style="padding:.4em;"|MY Lee
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S1931312819303026 Meta-Analysis Reveals Reproducible Gut Microbiome Alterations in Response to a High-Fat Diet]
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|style="padding:.4em;"|19-46
|style="padding:.4em;"|NY Kim
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S0092867419307731 Tumor Microbiome Diversity and Composition Influence Pancreatic Cancer Outcomes]
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|style="padding:.4em;" rowspan=2|2019/09/24
|style="padding:.4em;" rowspan=2|Single-cell
|style="padding:.4em;"|19-45
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://genome.cshlp.org/content/early/2019/04/01/gr.243725.118 The accessible chromatin landscape of the murine hippocampus at single-cell resolution]
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|style="padding:.4em;"|19-44
|style="padding:.4em;"|SB Baek
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S009286741830446X Integrated Single-Cell Analysis Maps the Continuous Regulatory Landscape of Human Hematopoietic Differentiation]
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|style="padding:.4em;" rowspan=2|2019/09/17
|style="padding:.4em;" rowspan=2|Microbiome
|style="padding:.4em;"|19-43
|style="padding:.4em;"|SH Lee
|style="padding:.4em;text-align:left"|
[https://science.sciencemag.org/content/365/6449/eaau4735 A sparse covarying unit that describes healthy and impaired human gut microbiota development]
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|style="padding:.4em;"|19-42
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S0092867419307810 Large-Scale Analyses of Human Microbiomes Reveal Thousands of Small, Novel Genes]
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|style="padding:.4em;" rowspan=2|2019/09/10
|style="padding:.4em;" rowspan=2|Single-cell
|style="padding:.4em;"|19-41
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S0092867419307329?via%3Dihub Intra- and Inter-cellular Rewiring of the Human Colon during Ulcerative Colitis]
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|style="padding:.4em;"|19-40
|style="padding:.4em;"|IS Choi
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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
|style="padding:.4em;" rowspan=2|Microbiome
|style="padding:.4em;"|19-39
|style="padding:.4em;"|NY Kim
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S193131281930352X?via%3Dihub The Landscape of Genetic Content in the Gut and Oral Human Microbiome]
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|style="padding:.4em;"|19-38
|style="padding:.4em;"|CY Kim
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[https://www.sciencedirect.com/science/article/pii/S0092867419307755?via%3Dihub Benchmarking Metagenomics Tools for Taxonomic Classification]
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{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2019
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|style="padding:.4em;" rowspan=2|2019/08/20
|style="padding:.4em;"|19-37
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://www.biorxiv.org/content/10.1101/719088v1 Coexpression uncovers a unified single-cell transcriptomic landscape]
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|style="padding:.4em;"|19-36
|style="padding:.4em;"|MY Lee
|style="padding:.4em;text-align:left"|
[https://www.biorxiv.org/content/10.1101/586859v1 Single-cell interactomes of the human brain reveal cell-type specific convergence of brain disorders]
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|style="padding:.4em;" rowspan=3|2019/08/14
|style="padding:.4em;"|19-35
|style="padding:.4em;"|SH Lee
|style="padding:.4em;text-align:left"|
[https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004075 Proportionality: a valid alternative to correlation for relative data]
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|style="padding:.4em;"|19-34
|style="padding:.4em;"|SH Lee
|style="padding:.4em;text-align:left"|
[https://www.nature.com/articles/s41598-017-16520-0 propr: An R-package for Identifying Proportionally Abundant Features Using Compositional Data Analysis]
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|style="padding:.4em;"|19-33
|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
|style="padding:.4em;"|19-32
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[https://www.nature.com/articles/s41592-018-0254-1 A test metric for assessing single-cell RNA-seq batch correction]
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|style="padding:.4em;"|19-31
|style="padding:.4em;"|KS Kim
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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
|style="padding:.4em;"|19-30
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S0092867418313941 Defining T Cell States Associated with Response to Checkpoint Immunotherapy in Melanoma]
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|style="padding:.4em;"|19-29
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S009286741831242X High-Dimensional Analysis Delineates Myeloid and Lymphoid Compartment Remodeling during Successful Immune-Checkpoint Cancer Therapy]
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|style="padding:.4em;" rowspan=2|2019/08/07
|style="padding:.4em;"|19-28
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[https://www.biorxiv.org/content/10.1101/555557v1 A single-cell reference map for human blood and tissue T cell activation reveals functional states in health and disease]
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|style="padding:.4em;"|19-27
|style="padding:.4em;"|IS Choi
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S009286741831568X Dysfunctional CD8 T Cells Form a Proliferative, Dynamically Regulated Compartment within Human Melanoma]
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|style="padding:.4em;" rowspan=2|2019/07/30
|style="padding:.4em;"|19-26
|style="padding:.4em;"|IS Choi
|style="padding:.4em;text-align:left"|
[https://www.nature.com/articles/nm.4466 High-dimensional single-cell analysis predicts response to anti-PD-1 immunotherapy]
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|style="padding:.4em;"|19-25
|style="padding:.4em;"|JW Cho
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S0092867418311784 A Cancer Cell Program Promotes T Cell Exclusion and Resistance to Checkpoint Blockade]
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|style="padding:.4em;" rowspan=2|2019/07/23
|style="padding:.4em;"|19-24
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/26006008 COMPASS identifies T-cell subsets correlated with clinical outcomes.]
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|style="padding:.4em;"|19-23
|style="padding:.4em;"|HJ Han
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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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[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
|style="padding:.4em;"|
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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
|style="padding:.4em;"|
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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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[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
|style="padding:.4em;"|
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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
|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
|style="padding:.4em;"|
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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
|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
|style="padding:.4em;"|19-12
|style="padding:.4em;"|
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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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[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;"|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;"|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;"|
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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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[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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[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
|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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|style="padding:.4em;"|19-2
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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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{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2018
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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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[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
|style="padding:.4em;"|18-10
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=Developmental+and+oncogenic+programs+in+H3K27M+gliomas+dissected+by+single-cell+RNA-seq Developmental and oncogenic programs in H3K27M gliomas dissected by single-cell RNA-seq.]
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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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|style="padding:.4em;" rowspan=2|2018/05/31
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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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[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
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[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
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[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.]
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|style="padding:.4em;" rowspan=2|2018/05/17
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|style="padding:.4em;"|KS Kim
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[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
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[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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|style="padding:.4em;" rowspan=2|2018/05/10
|style="padding:.4em;"|18-2
|style="padding:.4em;"|JW Cho
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=Inferring+regulatory+element+landscapes+and+transcription+factor+networks+from+cancer+methylomes Inferring regulatory element landscapes and transcription factor networks from cancer methylomes.]
|-
|style="padding:.4em;"|18-1
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/28129544 Genomic and Transcriptomic Features of Response to Anti-PD-1 Therapy in Metastatic Melanoma.]
|}
{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2017
|-
!scope="col" style="padding:.4em" |Date
!scope="col" style="padding:.4em" | Paper index
!scope="col" style="padding:.4em" | Presenter
!scope="col" style="padding:.4em" | Paper title
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|style="padding:.4em;" rowspan=2|2017/06/28
|style="padding:.4em;"|17-36
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/28104840 Tumor aneuploidy correlates with markers of immune evasion and with reduced response to immunotherapy.]
|-
|style="padding:.4em;"|17-35
|style="padding:.4em;"|JW Cho
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/27240091 Landscape of tumor-infiltrating T cell repertoire of human cancers.]
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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
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.tandfonline.com/doi/full/10.1080/2162402X.2016.1253654 Identification of genetic determinants of breast cancer immune phenotypes by integrative genome-scale analysis.]
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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.]
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|style="padding:.4em;" rowspan=2|2017/05/31
|style="padding:.4em;"|17-30
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.02.065 Genomic and Transcriptomic Features of Response to Anti-PD-1 Therapy in Metastatic Melanoma.]
|-
|style="padding:.4em;"|17-29
|style="padding:.4em;"|JW Cho
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.celrep.2016.12.019 Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade.]
|-
|style="padding:.4em;" rowspan=2|2017/05/24
|style="padding:.4em;"|17-28
|style="padding:.4em;"|EB Kim
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.12.022 Systemic Immunity Is Required for Effective Cencer Immunotherapy.]
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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
|style="padding:.4em;"|17-26
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.10.018 Host and Environmental Factors Influencing Individual Human Cytokine Responses.]
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|style="padding:.4em;"|17-25
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.10.017 A Functional Genomics Approach to Understand Variation in Cytokine Production in Humans.]
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|style="padding:.4em;" rowspan=2|2017/04/26
|style="padding:.4em;"|17-24
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1038%2FNMETH.4177 Pooled CRISPR screening with single-cell transcriptome readout.]
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|style="padding:.4em;"|17-23
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.11.039 Dissecting Immune Circuits by Linking CRISPR-Pooled Screens with Single-Cell RNA-Seq.]
|-
|style="padding:.4em;" rowspan=2|2017/04/12
|style="padding:.4em;"|17-22
|style="padding:.4em;"|SH Lee
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.11.038 Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens.]
|-
|style="padding:.4em;"|17-21
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1038%2Fnbt.3569 Wishbone identifies bifurcating developmental trajectories from single-cell data.]
|-
|style="padding:.4em;" rowspan=2|2017/04/05
|style="padding:.4em;"|17-20
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[http://biorxiv.org/content/early/2017/02/21/110668 Reversed graph embedding resolves complex single-cell developmental trajectories.]
|-
|style="padding:.4em;"|17-19
|style="padding:.4em;"|SH Lee
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1038%2Fnmeth.4150 Single-cell mRNA quantification and differential analysis with Census.]
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|style="padding:.4em;" rowspan=2|2017/03/29
|style="padding:.4em;"|17-18
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.celrep.2016.12.060 Single-Cell Transcriptomic Analysis Defines Heterogeneity and Transcriptional Dynamics in the Adult Neural Stem Cell Lineage.]
|-
|style="padding:.4em;"|17-17
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/26051941 Single-Cell Network Analysis Identifies DDIT3 as a Nodal Lineage Regulator in Hematopoiesis.]
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|style="padding:.4em;" rowspan=2|2017/03/22
|style="padding:.4em;"|17-16
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/27580035 Single-cell analysis of mixed-lineage states leading to a binary cell fate choice.]
|-
|style="padding:.4em;"|17-15
|style="padding:.4em;"|EB Kim
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/27281220 Dissecting direct reprogramming from fibroblast to neuron using single-cell RNA-seq.]
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|style="padding:.4em;" rowspan=2|2017/03/15
|style="padding:.4em;"|17-14
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1126%2Fscience.aad0501 Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq.]
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|style="padding:.4em;"|17-13
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[https://www.ncbi.nlm.nih.gov/pubmed/26084335 Single-cell mRNA sequencing identifies subclonal heterogeneity in anti-cancer drug responses of lung adenocarcinoma cells.]
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|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.]
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|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.]
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|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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|style="padding:.4em;" rowspan=1|2017/01/31
|style="padding:.4em;"|17-06
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[//www.ncbi.nlm.nih.gov/pubmed/27912059 Microbiota Diurnal Rhythmicity Programs Host Transcriptome Oscillations.]
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|style="padding:.4em;" rowspan=1|2017/01/24
|style="padding:.4em;"|17-05
|style="padding:.4em;"|JW Cho
|style="padding:.4em;text-align:left"|
[//www.ncbi.nlm.nih.gov/pubmed/27851914 Transcriptional Landscape of Human Tissue Lymphocytes Unveils Uniqueness of Tumor-Infiltrating T Regulatory Cells.]
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|style="padding:.4em;" rowspan=2|2017/01/17
|style="padding:.4em;"|17-04
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[//www.ncbi.nlm.nih.gov/pubmed/25938943 Mapping long-range promoter contacts in human cells with high-resolution capture Hi-C]
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|style="padding:.4em;"|17-03
|style="padding:.4em;"|EB Kim
|style="padding:.4em;text-align:left"|
[//www.ncbi.nlm.nih.gov/pubmed/27306882 CHiCAGO: robust detection of DNA looping interactions in Capture Hi-C data.]
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|style="padding:.4em;" rowspan=1|2017/01/10
|style="padding:.4em;"|17-02
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[//www.ncbi.nlm.nih.gov/pubmed/26527291 ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis]
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|style="padding:.4em;" rowspan=1|2017/01/03
|style="padding:.4em;"|17-01
|style="padding:.4em;"|SH Lee
|style="padding:.4em;text-align:left"|
[//www.ncbi.nlm.nih.gov/pubmed/26287467 Single-cell messenger RNA sequencing reveals rare intestinal cell types]
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{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2016
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!scope="col" style="padding:.4em" |Date
!scope="col" style="padding:.4em" | Paper index
!scope="col" style="padding:.4em" | Presenter
!scope="col" style="padding:.4em" | Paper title
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|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.]
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|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
|style="padding:.4em;"|2016-29
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/24658644 The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells.]
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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
|style="padding:.4em;"|2016-27
|style="padding:.4em;"|MY Lee
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26000487 Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells.]
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|style="padding:.4em;" rowspan=1|2016/11/8
|style="padding:.4em;"|2016-26
|style="padding:.4em;"|JW Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26000488 Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets.]
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|style="padding:.4em;" rowspan=1|2016/11/1
|style="padding:.4em;"|2016-25
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27426982 Causal Mechanistic Regulatory Network for Glioblastoma Deciphered Using Systems Genetics Network Analysis.]
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|style="padding:.4em;" rowspan=1|2016/10/25
|style="padding:.4em;"|2016-24
|style="padding:.4em;"|MY Lee
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27549193 Comprehensive analyses of tumor immunity: implications for cancer immunotherapy.]
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|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]
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|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]
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|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]
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|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.]
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|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
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27264179 A Computational Drug Repositioning Approach for Targeting Oncogenic Transcription Factors]
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|style="padding:.4em;" rowspan=1|2016/8/16
|style="padding:.4em;"|2016-17
|style="padding:.4em;"|JW Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27309802 The landscape of accessible chromatin in mammalian preimplantation embryos]
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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]
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|style="padding:.4em;" rowspan=1|2016/7/25
|style="padding:.4em;"|2016-14
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=integration+of+summary+data+from+gwas+and+eqtl+studies Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets]
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|style="padding:.4em;" rowspan=1|2016/7/18
|style="padding:.4em;"|2016-13
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/23624555 Identification of transcriptional regulators in the mouse immune system]
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|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]
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|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]
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|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]
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|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]
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|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]
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|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]
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|style="padding:.4em;" rowspan=2|2016/5/11
|style="padding:.4em;"|2016-6
|style="padding:.4em;"|DS Bae, CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/25171417 Predicting Cancer-specific vulnerability via data-driven detection of synthetic lethality]
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|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]
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|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]
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|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]
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|style="padding:.4em;" rowspan=2|2016/4/27
|style="padding:.4em;"|2016-2
|style="padding:.4em;"|CY Kim,SJ Kwon
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26780608 A predictive computational framework for direct reprogramming between human cell types]
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|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="padding:.4em;" rowspan=2|2015/06/11
|style="padding:.4em;"|2015-55
|style="padding:.4em;"|
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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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|style="padding:.4em;"|2015-54
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0002929714001128 Phevor combines multiple biomedical ontologies for accurate identification of disease-causing alleles in single individuals and small nuclear families.]
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|style="padding:.4em;" rowspan=2|2015/06/04
|style="padding:.4em;"|2015-53
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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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|style="padding:.4em;"|2015-52
|style="padding:.4em;"|
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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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|style="padding:.4em;" rowspan=2|2015/05/28
|style="padding:.4em;"|2015-51
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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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|style="padding:.4em;"|2015-50
|style="padding:.4em;"|
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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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|style="padding:.4em;" rowspan=2|2015/05/21
|style="padding:.4em;"|2015-49
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nmeth/journal/v11/n3/full/nmeth.2832.html Functional annotation of noncoding sequence variants.]
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|style="padding:.4em;"|2015-48
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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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|style="padding:.4em;" rowspan=2|2015/05/14
|style="padding:.4em;"|2015-47
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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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|style="padding:.4em;"|2015-46
|style="padding:.4em;"|
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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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|style="padding:.4em;" rowspan=3|2015/05/07
|style="padding:.4em;"|2015-45
|style="padding:.4em;"|
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[http://www.nature.com/ncomms/2014/140626/ncomms5212/full/ncomms5212.html Human symptoms-disease network.]
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|style="padding:.4em;"|2015-44
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867413010246 A nondegenerate code of deleterious variants in Mendelian loci contributes to complex disease risk.]
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|style="padding:.4em;"|2015-43
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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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|style="padding:.4em;" rowspan=2|2015/04/30
|style="padding:.4em;"|2015-42
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://genome.cshlp.org/content/25/1/142.long The discovery of integrated gene networks for autism and related disorders.]
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|style="padding:.4em;"|2015-41
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://msb.embopress.org/content/10/12/774.long Integrated systems analysis reveals a molecular network underlying autism spectrum disorders.]
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|style="padding:.4em;" rowspan=3|2015/04/23
|style="padding:.4em;"|2015-40
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v518/n7539/full/nature13990.html Dissecting neural differentiation regulatory networks through epigenetic footprinting.]
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|style="padding:.4em;"|2015-39
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v518/n7539/full/nature14221.html Cell-of-origin chromatin organization shapes the mutational landscape of cancer.]
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|style="padding:.4em;"|2015-38
|style="padding:.4em;"|
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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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|style="padding:.4em;" rowspan=2|2015/04/09
|style="padding:.4em;"|2015-37
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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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|style="padding:.4em;"|2015-36
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414014974 A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping.]
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|style="padding:.4em;" rowspan=2|2015/04/02
|style="padding:.4em;"|2015-35
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nrg/journal/v14/n6/full/nrg3454.html Exploring the three-dimensional organization of genomes: interpreting chromatin interaction data.]
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|style="padding:.4em;"|2015-34
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.sciencemag.org/content/347/6225/1010.long Transcribed enhancers lead waves of coordinated transcription in transitioning mammalian cells.]
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|style="padding:.4em;" rowspan=2|2015/03/26
|style="padding:.4em;"|2015-33
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|style="padding:.4em;text-align:left"|
[http://genome.cshlp.org/content/25/2/257.long Cupid: simultaneous reconstruction of microRNA-target and ceRNA networks.]
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|style="padding:.4em;"|2015-32
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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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|style="padding:.4em;" rowspan=2|2015/03/19
|style="padding:.4em;"|2015-31
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|style="padding:.4em;text-align:left"|
[http://www.nature.com/nbt/journal/vaop/ncurrent/full/nbt.3154.html Decoding the regulatory network of early blood development from single-cell gene expression measurements.]
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|style="padding:.4em;"|2015-30
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nrg/journal/v16/n3/full/nrg3833.html Computational and analytical challenges in single-cell transcriptomics.]
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|style="padding:.4em;" rowspan=3|2015/03/12
|style="padding:.4em;"|2015-29
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|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.]
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|style="padding:.4em;"|2015-28
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2014
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!scope="col" style="padding:.4em" | Date
!scope="col" style="padding:.4em" | Paper index
!scope="col" style="padding:.4em" | Presenter
!scope="col" style="padding:.4em" | Paper title
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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 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"
|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 title
|-
|rowspan = "1"|2013/01/11
|align ="center"|2012-81
|[http://genome.cshlp.org/content/22/8/1589.full (TH Kim) MuSiC: identifying mutational significance in cancer genomes.]
|-
|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"|2012-74
|[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]
|-
| 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]
|-
| align="center"|2012-72
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2063581/ (SH Hwang) Network-based classification of breast cancer metastasis]
|-
| 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]
|-
|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]
|-
| align="center"|2012-66
|[http://bioinformatics.oxfordjournals.org/content/25/16/2078.long (ER Kim&TH Kim)The Sequence Alignment/Map format and SAMtools]
|-
| 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]
|-
| align="center"|2012-64
|[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"|2012-63
|[http://www.sciencemag.org/content/336/6086/1262.abstract (T Lee&CY Kim)Host-Gut Microbiota Metabolic Interactions]
|-
|align="center"|2012-62
|[http://www.sciencemag.org/content/336/6086/1268.full (AR Cho,JH Ju)Interactions Between the Microbiota and the Immune System]
|-
|align="center"|2012-61
|[http://www.sciencemag.org/content/336/6086/1255.abstract (SH Hwang&HJ Cho)The Application of Ecological Theory Toward an Understanding of the Human Microbiome]
|-
|align="center"|2012-60
|[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
|[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]
|-
| align="center"|2012-58
|[http://www.nature.com/nature/journal/v486/n7402/full/nature11209.html (JH Shin&HJ Kim)A framework for human microbiome research]
|-
|align="center"|2012-57
|[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
|[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]
|-
|align="center"|2012-55
|[http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002511 (YH Go&HJ Han)Google Goes Cancer: Improving Outcome Prediction for Cancer Patients by Network-Based Ranking of Marker Genes]
|-
| align="center"|2012-54
|[http://www.nature.com/msb/journal/v7/n1/full/msb201147.html (YH Go&HJ Han)A pharmacogenomic method for individualized prediction of drug sensitivity]
|-
|align="center"|2012-53
|[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
|[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]
|-
|align="center"|2012-51
|[http://www.nature.com/nature/journal/v483/n7391/full/nature11005.html (ER Kim&TH Kim)Systematic identification of genomic markers of drug sensitivity in cancer cells]
|-
| align="center"|2012-50
|[http://www.pnas.org/content/early/2011/10/13/1018854108.abstract (ER Kim&TH Kim)Subtype and pathway specific responses to anticancer compounds in breast cancer]
|-
|align="center"|2012-49
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature10945.html (JE Shim&KS Kim)De novo mutations revealed by whole-exome sequencing are strongly associated with autism]
|-
|align="center"|2012-48
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature11011.html (JE Shim&KS Kim)Patterns and rates of exonic de novo mutations in autism spectrum disorders]
|-
|align="center"|2012-47
|[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
|[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)]
|-
|align="center"|2012-45
|[http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002621 (T Lee&CY Kim)Type 2 Diabetes Risk Alleles Demonstrate Extreme Directional Differentiation among Human Populations, Compared to Other Diseases]
|-
|align="center"|2012-44
|[http://www.nature.com/nature/journal/v480/n7376/full/nature10665.html (AR Cho&JH Ju)Predicting mutation outcome from early stochastic variation in genetic interaction partners]
|-
|align="center"|2012-43
|[http://www.sciencemag.org/content/335/6064/82 (AR Cho&JH Ju)Fitness Trade-Offs and Environmentally Induced Mutation Buffering in Isogenic C. elegans]
|-
| align="center"|2012-42
|[http://genome.cshlp.org/content/22/6/1163 (JE Shim&KS Kim)Identification of microRNA-regulated gene networks by expression analysis of target genes]
|-
|align="center"|2012-41
|[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
|[http://www.cell.com/abstract/S0092-8674(12)00573-9 (JH Soh)Functional Repurposing Revealed by Comparing S. pombe and S. cerevisiae Genetic Interactions]
|-
|align="center"|2012-39
|[http://genome.cshlp.org/content/22/2/375.long (ER Kim&TH Kim)De novo discovery of mutated driver pathways in cancer]
|-
| align="center"|2012-38
|[http://www.nature.com/msb/journal/v4/n1/full/msb20082.html (YH Go&HJ Han)A systems biology approach to prediction of oncogenes and molecular perturbation targets in B-cell lymphomas]
|-
|align="center"|2012-37
|[http://www.nature.com/msb/journal/v7/n1/full/msb201126.html (SH Hwang&HJ Cho)PREDICT: a method for inferring novel drug indications with application to personalized medicine]
|-
|align="center"|2012-36
|[http://www.nature.com/nature/journal/v453/n7198/full/nature06973.html (SH Hwang&HJ Cho)Synergistic response to oncogenic mutations defines gene class critical to cancer phenotype]
|-
|align="center"|2012-35
|[http://www.sciencemag.org/content/334/6062/1518.full (JH Shin&HJ Kim)Detecting Novel Associations in Large Data Sets]
|-
|rowspan = "3" |2012/03/05
| align="center"|2012-34
|[http://www.ncbi.nlm.nih.gov/pubmed/18516045 (8,HH Kim)Mapping and quantifying mammalian transcriptomes by RNA-Seq.]
|-
|align="center"|2012-33
|[http://genomebiology.com/2010/11/10/R106 (11,Go&Ju)Differential expression analysis for sequence count data]
|-
|align="center"|2012-32
|[http://bioinformatics.oxfordjournals.org/content/26/1/139.short (12,Go&Ju)edgeR: a Bioconductor package for differential expression analysis of digital gene expression data ]
|-
|rowspan = "7" |2012/02/27 2012/02/28
| align="center"|2012-31
|[http://www.nature.com/nrg/journal/v10/n1/full/nrg2484.html (1,JW Song)RNA-Seq: a revolutionary tool for transcriptomics]
|-
|align="center"|2012-30
|[http://www.nature.com/nmeth/journal/v8/n6/full/nmeth.1613.html (2,JW Song)Computational methods for transcriptome annotation and quantification using RNA-seq]
|-
|align="center"|2012-29
|[http://genomebiology.com/2010/11/12/220 (3,HJ Han)From RNA-seq reads to differential expression results]
|-
|align="center"|2012-28
|[http://www.nature.com/nmeth/journal/v7/n9/full/nmeth.1491.html (4,AR Cho)Comprehensive comparative analysis of strand-specific RNA sequencing methods]
|-
|align="center"|2012-27
|[http://www.plosone.org/article/info:doi%2F10.1371%2Fjournal.pone.0026426 (5,T Lee)A Low-Cost Library Construction Protocol and Data Analysis Pipeline for Illumina-Based Strand-Specific Multiplex RNA-Seq]
|-
|align="center"|2012-26
|[http://www.nature.com/nbt/journal/v28/n5/abs/nbt.1621.html (6,So&Shin)Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation]
|-
|align="center"|2012-25
|[http://bioinformatics.oxfordjournals.org/content/25/9/1105.abstract (7,So&Shin)TopHat: discovering splice junctions with RNA-Seq]
|-
|rowspan = "5" |2012/02/06
| align="center"|2012-24
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3125733/ mirConnX: condition-specific mRNA-microRNA network integrator]
|-
|align="center"|2012-23
|[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002190 Construction and Analysis of an Integrated Regulatory Network Derived from High-Throughput Sequencing Data]
|-
|align="center"|2012-22
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002415 A Densely Interconnected Genome-Wide Network of MicroRNAs and Oncogenic Pathways Revealed Using Gene Expression Signatures]
|-
|align="center"|2012-21
|[http://genome.cshlp.org/content/20/5/589.short Reprogramming of miRNA networks in cancer and leukemia]
|-
|align="center"|2012-20
|[http://www.cell.com/abstract/S0092-8674(11)01152-4 An Extensive MicroRNA-Mediated Network of RNA-RNA Interactions Regulates Established Oncogenic Pathways in Glioblastoma]
|-
|rowspan = "5" |2012/01/30
|align="center"|2012-19
|[http://www.cell.com/abstract/S0092-8674(11)00237-6 Principles and Strategies for Developing Network Models in Cancer]
|-
|align="center"|2012-18
|[http://www.nature.com/ng/journal/v37/n4/full/ng1532.html Reverse engineering of regulatory networks in human B cells]
|-
|align="center"|2012-17
|[http://www.nature.com/nature/journal/v452/n7186/abs/nature06757.html Variations in DNA elucidate molecular networks that cause disease]
|-
|align="center"|2012-16
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2779083/ Harnessing gene expression to identify the genetic basis of drug resistance]
|-
|align="center"|2012-15
|[http://www.sciencedirect.com/science/article/pii/S0092867410012936 An Integrated Approach to Uncover Drivers of Cancer]
|-
|rowspan = "3" |2012/01/09
|align="center"|2012-14
|[http://www.ncbi.nlm.nih.gov/pubmed/18704161 Genetic variation in an individual human exome.]
|-
|align="center"|2012-13
|[http://www.ncbi.nlm.nih.gov/pubmed/22081227 Predicting phenotypic variation in yeast from individual genome sequences.]
|-
|align="center"|2012-12
|[http://www.ncbi.nlm.nih.gov/pubmed/20435227 Clinical assessment incorporating a personal genome.]
|-
|rowspan = "6" |2012/01/09 2012/01/16
| align="center"|2012-11
|[http://www.ncbi.nlm.nih.gov/pubmed/20399638 Human allelic variation: perspective from protein function, structure, and evolution.]
|-
|align="center"|2012-10
|[http://www.ncbi.nlm.nih.gov/pubmed/19561590 Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm.]
|-
|align="center"|2012-09
|[http://www.ncbi.nlm.nih.gov/pubmed/11230178 Prediction of deleterious human alleles.]
|-
|align="center"|2012-08
|[http://www.ncbi.nlm.nih.gov/pubmed/12202775 Human non-synonymous SNPs: server and survey.]
|-
|align="center"|2012-07
|[http://www.ncbi.nlm.nih.gov/pubmed/20354512 A method and server for predicting damaging missense mutations.]
|-
|align="center"|2012-06
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1920242/ SNAP: predict effect of non-synonymous polymorphisms on function]
|-
|rowspan = "3" |2012/01/09 2012/01/16
|align="center"|2012-05
|[http://www.ncbi.nlm.nih.gov/pubmed/21920052 Computational and statistical approaches to analyzing variants identified by exome sequencing.]
|-
|align="center"|2012-04
|[http://www.ncbi.nlm.nih.gov/pubmed/18179889 Shifting paradigm of association studies: value of rare single-nucleotide polymorphisms.]
|-
|align="center"|2012-03
|[http://www.ncbi.nlm.nih.gov/pubmed/19684571 Targeted capture and massively parallel sequencing of 12 human exomes.]
|-
|rowspan = "2" |2012/01/09 2012/01/16
|align="center"|2012-02
|[http://www.ncbi.nlm.nih.gov/pubmed/17637733 The distribution of fitness effects of new mutations.]
|-
|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]
|}
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
|align="center"|2011-49
|[http://bmir.stanford.edu/file_asset/index.php/1407/BMIR-2009-1355.pdf (Shin)Data-Driven Methods to Discover Molecular Determinants of Serious Adverse Drug Events]
|-
|align="center"|2011-48
|[http://www.nature.com/nchembio/journal/v4/n11/abs/nchembio.118.html (Shin)Network pharmacology: the next paradigm in drug discovery]
|-
|align="center"|2011-47
|[http://www.nature.com/msb/journal/v7/n1/full/msb201171.html (Oh)Systematic exploration of synergistic drug pairs]
|-
|align="center"|2011-46
|[http://www.nature.com/msb/journal/v5/n1/full/msb200995.html (Shim)Chemogenomic profiling predicts antifungal synergies]
|-
|align="center"|2011-45
|[http://www.pnas.org/content/104/32/13086 (Beck)A strategy for predicting the chemosensitivity of human cancers and its application to drug discovery]
|-
|align="center"|2011-44
|[http://www.nature.com/nchembio/journal/v1/n7/full/nchembio747.html (Hwang)Analysis of drug-induced effect patterns to link structure and side effects of medicines]
|-
|rowspan = "3" |2011/11/14
|align="center"|2011-43
|[http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000662 Network-Based Elucidation of Human Disease Similarities Reveals Common Functional Modules Enriched for Pluripotent Drug Targets]
|-
|align="center"|2011-42
|[http://www.nature.com/msb/journal/v7/n1/full/msb201131.html Cross-species discovery of syncretic drug combinations that potentiate the antifungal fluconazole]
|-
|align="center"|2011-41
|[http://www.nature.com/msb/journal/v7/n1/full/msb20115.html Analysis of multiple compound–protein interactions reveals novel bioactive molecules]
|-
|rowspan = "4" |2011/11/07
|align="center"|2011-40
|[http://www.nature.com/nbt/journal/v25/n10/abs/nbt1338.html Drug—target network]
|-
|align="center"|2011-39
|[http://www.nature.com/msb/journal/v6/n1/full/msb200998.html A side effect resource to capture phenotypic effects of drugs]
|-
|align="center"|2011-38
|[http://genome.cshlp.org/content/18/2/206.long Quantitative systems-level determinants of human genes targeted by successful drugs]
|-
|align="center"|2011-37
|[http://www.sciencemag.org/content/321/5886/263.short Drug Target Identification Using Side-Effect Similarity]
|-
|rowspan = "3" |2011/11/07
|align="center"|2011-36
|[http://stm.sciencemag.org/content/3/96/96ra77.short Discovery and Preclinical Validation of Drug Indications Using Compendia of Public Gene Expression Data]
|-
|align="center"|2011-35
|[http://bib.oxfordjournals.org/content/12/4/303.short Exploiting drug–disease relationships for computational drug repositioning]
|-
|align="center"|2011-34
|[http://www.springerlink.com/content/4489r051nu2t0ul1/ Drug Discovery in a Multidimensional World: Systems, Patterns, and Networks]
|-
|rowspan = "3" |2011/10/05
|align="center"|2011-33
|[http://genome.cshlp.org/content/20/7/960.full Analysis of membrane proteins in metagenomics: Networks of correlated environmental features and protein families]
|-
|align="center"|2011-32
|[http://www.pnas.org/content/106/5/1374.full Quantifying environmental adaptation of metabolic pathways in metagenomics]
|-
|align="center"|2011-31
|[http://www.pnas.org/content/104/35/13913.abstract Quantitative assessment of protein function prediction from metagenomics shotgun sequences]
|-
|rowspan = "4" |2011/10/04
|align="center"|2011-30
|[http://www.nature.com/nature/journal/v464/n7285/full/nature08821.html A human gut microbial gene catalogue established by metagenomic sequencing]
|-
|align="center"|2011-29
|[http://www.nature.com/nrmicro/journal/v6/n9/abs/nrmicro1935.html Molecular eco-systems biology: towards an understanding of community function]
|-
|align="center"|2011-28
|[http://genome.cshlp.org/content/early/2009/04/20/gr.085464.108 Microbial community profiling for human microbiome projects: Tools, techniques, and challenges]
|-
|align="center"|2011-27
|[http://www.nature.com/nrmicro/journal/v9/n4/full/nrmicro2540.html Unravelling the effects of the environment and host genotype on the gut microbiome]
|-
|rowspan = "2" |2011/09/19
|align="center"|2011-26
|[http://www.sciencemag.org/content/333/6042/596.full independently evolved virulence effectors converge onto hubs in a plant immune system Network]
|-
|align="center"|2011-25
|[http://www.sciencemag.org/content/333/6042/601.full Evidence for network evolution in an arabidopsis interactome Map]
|-
|rowspan = "2" |2011/09/05
|align="center"|2011-24
|[http://www.sciencedirect.com/science/article/pii/S0092867411005861 Exome Sequencing of Ion Channel Genes Reveals Complex Profiles Confounding Personal Risk Assessment in Epilepsy]
|-
|align="center"|2011-23
|[http://www.cell.com/abstract/S0092-8674(11)00543-5 Pluripotency factors in Embryonic stem cells Regulate Differentiation into Germ Layers]
|-
|rowspan = "2" |2011/09/22
|align="center"|2011-22
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002077 Integrated Genome-scale predition of Detrimental Mutations in Transcription Networks]
|-
|align="center"|2011-21
|[http://www.nature.com/nrg/journal/v12/n4/abs/nrg2969.html From expression QTLs to personalized transcriptomics]
|-
|2011/06/20
|align="center"|2011-20
|[http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001046 a user's guide to the encyclopedia of DNA elements(ENCODE)]
|-
|rowspan = "2" |2011/03/30
|align="center"|2011-19
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature09944.html enterotypes of the human gut microbiome]
|-
|align="center"|2011-18
|[http://www.nature.com/msb/journal/v7/n1/full/msb20116.html toward molecular trait-based ecology, through intergration of biogeochemical, geographical and metagenomic data]
|-
|rowspan = "2" |2011/03/16
|align="center"|2011-17
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002047 variable pathogenicity determines individual lifespan in ''caenorhabditis elegans'']
|-
|align="center"|2011-16
|[http://www.cell.com/abstract/S0092-8674(11)00371-0 a high-resolution ''c.elegans'' essential gene network based on phenotypic profiling of a complex tissue]
|-
|rowspan = "3" |2011/04/25
|align="center"|2011-15
|[http://www.ncbi.nlm.nih.gov/pubmed/21376230 Hallmarks of Cancer : The next generation]
|-
|align="center"|2011-14
|[http://www.cell.com/abstract/S0092-8674(11)00296-0 Mapping Cancer Origins]
|-
|align="center"|2011-13
|[http://www.cell.com/abstract/S0092-8674(11)00297-2 Genetic Interactions in Cancer Progression and Treatment]
|-
|rowspan = "2" |2011/04/11
|align="center"|2011-12
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1001273 Proteins encoded in genomic regions associated with immune-mediated disease physically interact and suggest underlying biology]
|-
|align="center"|2011-11
|***Changed!***
[http://www.ncbi.nlm.nih.gov/pubmed/19557189 Identifying relationships among genomic disease regions: predicting genes at pathogenic SNP associations and rare deletions]
|-
|rowspan = "2"|2011/03/28
|align="center"|2011-10
|[http://www.pnas.org/content/106/44/18843.long profiling translatomes of discrete cell populations resolves altered cellular priorities during hypoxia in Arabidopsis]
|-
|align="center"|2011-09
|[http://www.nature.com/msb/journal/v6/n1/full/msb201076.html cell-type specific analysis of translating RNAs in developing flowers reveals new levels of control]
|-
|rowspan = "2"|2011/03/14
|align="center"|2011-08
|[http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6WSN-51SFHJD-1&_user=44062&_coverDate=01%2F07%2F2011&_rdoc=1&_fmt=high&_orig=search&_origin=search&_sort=d&_docanchor=&view=c&_acct=C000004738&_version=1&_urlVersion=0&_userid=44062&md5=2f6017aab4c794ed7e78fbbd8447077a&searchtype=a phenotypic landscape of a bacterial cell]
|-
|align="center"|2011-07
|[http://www.nature.com/msb/journal/v6/n1/full/msb2010107.html cross-species chemogenomic profiling reveals evolutionarily conserved drug mode of action]
|-
|rowspan = "2"|2011/02/28
|align="center"|2011-06
|[http://www.pnas.org/content/early/2010/09/23/1004666107.abstract genomic patterns of pleiotropy and the evolution of complexity]
|-
|align="center"|2011-05
|[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1001009 simultaneous genome-wide inference of physical, genetic, regulatory, and functional pathway]
|-
|2011/02/21
|align="center"|2011-04
|[http://www.nature.com/msb/journal/v6/n1/full/msb201071.html dynamic interaction networks in a hierarchically organized tissue]
|-
|2011/02/14
|align="center"|2011-03
|[http://www.sciencemag.org/content/330/6009/1385.abstract rewiring of genetic networks in response to DNA damage]
|-
|rowspan = "2"|2011/01/31
|align="center"|2011-02
|[http://www.nature.com/nrg/journal/v10/n9/abs/nrg2633.html Applying mass spectrometry-based proteomics to genetics, genomics and network biology]
|-
|align="center"|2011-01
|[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
|align="center"|2010-29
|[http://www.nature.com/nature/journal/v467/n7312/full/nature09326.html Functional_roles_fornoise_in_genetic_circuits]
|-
|align="center"|2010-28
|[http://www.nature.com/ng/journal/v42/n7/full/ng.610.html Estimation_of_effect_size_distribution_from_genome-wide_association_studies_and_implications_for_future_discoveries]
|-
|rowspan = "2"|2010/11/15
|align="center"|2010-27
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1000932 Liver_and_Adipose_Expression_associated_SNPs_are_enriched_for_association_to_type_2_diabetes]
|-
|align="center"|2010-26
|[http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B8JDC-50J4MRJ-2&_user=44062&_coverDate=10%2F10%2F2010&_rdoc=1&_fmt=high&_orig=search&_origin=search&_sort=d&_docanchor=&view=c&_acct=C000004738&_version=1&_urlVersion=0&_userid=44062&md5=7667a67cf8ba3f68117b8d0ff85a8887&searchtype=a It's_the_machine_that_matters:_predicting_gene_function_and_phenotype_from_protein_networks]
|-
|rowspan = "2"|2010/11/01
|align="center"|2010-25
|[http://genome.cshlp.org/content/early/2010/06/15/gr.104216.109 A_genome-wide_map_of_human_genetic_interactions_inferred_from_radiation_hybrid_genotypes]
|-
|align="center"|2010-24
|[http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0012139 A_Genome-Wide_Gene_Function_Prediction_Resource_for_Drosophila_melanogaster]
|-
|2010/10/11
|align="center"|2010-23
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2913399/?tool=pubmed Dissecting_spatio-temporal_protein_networks_driving_human_heart_development_and_related_disorders]
|-
|rowspan = "2"|2010/09/13
|align="center"|2010-22
|[http://www.nature.com/ng/journal/v42/n7/full/ng.600.html Transposable_elements_have_rewired_the_core_regulatory_network_of_human_embryonic_stem_cells]
|-
|align="center"|2010-21
|[http://www.nature.com/ng/journal/v42/n7/full/ng0710-557.html Limits_of_sequence_and_functional_conservation]
|-
|2010/05/26
|align="center"|2010-20
|[[media:100428_network_based_elucidation_of_human_disease_similarities_reveals_common_functional_modules_enriched_for_pluripotent_drug_targets.pdf|network_based_elucidation_of_human_disease_similarities_reveals_common_functional_modules_enriched_for_pluripotent_drug_targets.pdf]]
|-
|2010/05/19
|align="center"|2010-19
|[[media:100421_interpreting_metabolomic_profiles_using_unbiased_pathway_models.pdf|interpreting_metabolomic_profiles_using_unbiased_pathway_models.pdf]]
|-
|2010/04/21
|align="center"|2010-18
|[[media:100414_identification_of_networks_of_co_occurring_tumor_related_dna_copy_number_changes_using_a_genome_wide_scoringapproach.pdf|identification_of_networks_of_co_occurring_tumor_related_dna_copy_number_changes_using_a_genome_wide_scoringapproach.pdf]]
|-
|2010/04/14
|align="center"|2010-17
|[http://www.cell.com/retrieve/pii/S0092867410000796 an_atlas_of_combinatorial_transcriptional_regulation_in_mouse_and_man]
|-
|rowspan = "2" |2010/04/07
|align="center"|2010-16
|[http://www.pnas.org/content/early/2010/03/11/0910200107.long systematic_discovery_of_nonobvious_human_disease_models_through_orthologous_phenotypes]
|-
|align="center"|2010-15
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature08800.html genome_side_association_study_of_107_phenotypes_in_Arabidopsis_thaliana_inbred_lines]
|-
|rowspan="2"|2010/03/31
|align="center"|2010-14
|[[media:100331_toward_stem_cell_systems_biology_from_molecules_to_networks_and_landscapes.pdf|toward_stem_cell_systems_biology_from_molecules_to_networks_and_landscapes.pdf]]
|-
|align="center"|2010-13
|[http://www.nature.com/nrm/journal/v10/n10/abs/nrm2766.html systems_biology_of_stem_cell_fate_and_cellular_reprogramming]
|-
|2010/03/24
|align="center"|2010-12
|[http://www.nature.com/nbt/journal/v27/n2/abs/nbt.1522.html dynamic_modularity_in_protein_interaction_networks_predicts_breast_cancer_outcome]
|-
|2010/01/18
|align="center"|2010-11
|JournalClub_100118_a_tutorial_on_statistical_methods_for_population_association_studies
|-
|2010/01/16
|align="center"|2010-10
|systems-level_dinamic_analyses_of_fate_change_in_murine_embryonic_stem_cells
|-
|2010/01/15
|align="center"|2010-09
|distinguishing_direct_versus_indirect_transcription_factor-DNA-interactions
|-
|2010/01/14
|align="center"|2010-08
|chemogenomic_profiling_predicts_antifungal_synergies
|-
|2010/01/13
|align="center"|2010-07
|edgetic_perturbation_models_of_human_inherited_disorders
|-
|2010/01/12
|align="center"|2010-06
|analysis_of_cell_fate_from_single-cell_gene_expression_profiles_in_C.elegans
|-
|2010/01/11
|align="center"|2010-05
|predicting_new_molecular_targets_for_known_drugs.pdf
Reference:SEA(Similarity Ensemble Approach)
|-
|2010/01/09
|align="center"|2010-04
|harnessing_gene_expression_to_identify_the_genetic_basis_of_drug_resistance
|-
|2010/01/08
|align="center"|2010-03
|an_integrative_approach_to_reveal_driver_gene_fusions_from_paired_end_sequencing_data_in_cancer
|-
|2010/01/07
|align="center"|2010-02
|a_phenotypic_profile_of_the_candida_albicans_regulatory_network
|-
|2010/01/06
|align="center"|2010-01
|a_global_view_of_protein_expression_in_human_cells_tissues_and_organs
|}

Journal Club

2025-01-14T00:08:10Z

Sej4926:

{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2024-2 scOmics
|-
!scope="col" style="padding:.4em" | Date
!scope="col" style="padding:.4em" | Team
!scope="col" style="padding:.4em" | Paper index
!scope="col" style="padding:.4em" | Presenter
!scope="col" style="padding:.4em" | Paper title
|-
|style="padding:.4em;" rowspan=1|2025/3/5
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|25-8
|style="padding:.4em;"|YR Jung
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2024.11.18.624166 Learning multi-cellular representations of single-cell transcriptomics data enables characterization of patient-level disease states]
|-
|style="padding:.4em;" rowspan=1|2025/2/26
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|25-7
|style="padding:.4em;"|EJ Sung
|style="padding:.4em;text-align:left"|
[https://www.medrxiv.org/content/10.1101/2024.04.04.24305313v1 Single-cell RNA sequencing of human tissue supports successful drug targets]
|-
|style="padding:.4em;" rowspan=1|2025/2/19
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|25-6
|style="padding:.4em;"|IS Choi
|style="padding:.4em;text-align:left"|
[https://www.biorxiv.org/content/10.1101/2023.09.08.555192v7 Evaluating the Utilities of Foundation Models in Single-cell Data Analysis]
|-
|style="padding:.4em;" rowspan=1|2025/2/12
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|25-5
|style="padding:.4em;"|SB Baek
|style="padding:.4em;text-align:left"|
[https://www.nature.com/articles/s41586-024-08328-6 Accurate predictions on small data with a tabular foundation model]
|-
|style="padding:.4em;" rowspan=1|2025/1/15
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|25-4
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2024.09.24.614685 scEMB: Learning context representation of genes based on large-scale single-cell transcriptomics]
|-
|style="padding:.4em;" rowspan=1|2025/1/8
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|25-3
|style="padding:.4em;"|HB Lee
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1093/bioinformatics/btad663 TT3D: Leveraging precomputed protein 3D sequence models to predict protein–protein interactions]
|-
|style="padding:.4em;" rowspan=1|2025/1/8
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|25-2
|style="padding:.4em;"|HB Lee
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1093/bioinformatics/btac258 Topsy-Turvy: integrating a global view into sequence-based PPI prediction]
|-
|style="padding:.4em;" rowspan=1|2025/1/8
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|25-1
|style="padding:.4em;"|HB Lee
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.cels.2021.08.010 D-SCRIPT translates genome to phenome with sequence-based, structure-aware, genome-scale predictions of protein-protein interactions]
|-
|style="padding:.4em;" rowspan=1|2024/12/17
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|24-30
|style="padding:.4em;"|YR Jung
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2024.08.16.608180 Quantized multi-task learning for context-specific representations of gene network dynamics]
|-
|style="padding:.4em;" rowspan=1|2024/12/3
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|24-29
|style="padding:.4em;"|EJ Sung
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2023.11.21.568145 ANDES: a novel best-match approach for enhancing gene set analysis in embedding spaces]
|-
|style="padding:.4em;" rowspan=1|2024/11/26
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|24-28
|style="padding:.4em;"|IS Choi
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41592-024-02303-9 CellRank 2: unified fate mapping in multiview single-cell data]
|-
|style="padding:.4em;" rowspan=1|2024/11/19
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|24-27
|style="padding:.4em;"|SB Baek
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2024.07.29.605556 scPRINT: pre-training on 50 million cells allows robust gene network predictions]
|-
|style="padding:.4em;" rowspan=1|2024/11/12
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|24-26
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1038/s41467-024-46440-3 Bidirectional generation of structure and properties through a single molecular foundation model]
|}

{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2024-2 Microbiome
|-
!scope="col" style="padding:.4em" | Date
!scope="col" style="padding:.4em" | Team
!scope="col" style="padding:.4em" | Paper index
!scope="col" style="padding:.4em" | Presenter
!scope="col" style="padding:.4em" | Paper title
|-
|style="padding:.4em;" rowspan=1|2025/3/31
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|25-24
|style="padding:.4em;"|SB Lim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1101/2025.01.07.631644 A Single-Graph Visualization to Reveal Hidden Explainability Patterns of SHAP Feature Interactions in Machine Learning for Biomedical Issues]
|-
|style="padding:.4em;" rowspan=1|2025/3/24
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|25-23
|style="padding:.4em;"|YR Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.cell.2024.08.037 Emergence of community behaviors in the gut microbiota upon drug treatment]
|-
|style="padding:.4em;" rowspan=1|2025/3/24
|style="padding:.4em;" rowspan=1|Microbiome
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[https://doi.org/10.1038/s41564-024-01832-5 Prediction of strain level phage–host interactions across the Escherichia genus using only genomic information]
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[https://doi.org/10.1101/2025.01.07.631807 Metagenomic estimation of absolute bacterial biomass in the mammalian gut through host-derived read normalization]
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[https://doi.org/10.1101/2024.04.14.589414 Rapid and Sensitive Protein Complex Alignment with Foldseek-Multimer]
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[https://doi.org/10.1101/2024.12.30.630825 MGM as a large-scale pretrained foundation model for microbiome analyses in diverse contexts ]
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[https://doi.org/10.1126/science.ado9336 Sequence modeling and design from molecular to genome scale with Evo]
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[https://doi.org/10.1101/2024.12.18.629142 Human gut microbiome gene co-expression network reveals a loss in taxonomic and functional diversity in Parkinson’s disease]
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[https://doi.org/10.1101/2025.01.06.631550 Quantifying Metagenomic Strain Associations from Microbiomes with Anpan]
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[https://doi.org/10.1101/2024.12.13.628459 MaAsLin 3: Refining and extending generalized multivariable linear models for meta-omic association discovery]
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[https://doi.org/10.1101/2024.11.05.622169 PLM-interact: extending protein language models to predict protein-protein interactions]
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[https://doi.org/10.1101/2024.05.30.596740 ProTrek: Navigating the Protein Universe through Tri-Modal Contrastive Learning]
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[https://doi.org/10.1101/2024.12.30.630844 The genetic diversity and populational specificity of the human gut virome at single nucleotide resolution]
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[https://doi.org/10.1186/s13059-024-03425-1 pan-Draft: automated reconstruction of species-representative metabolic models from multiple genomes]
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[https://doi.org/10.1016/j.cell.2024.09.019 A core microbiome signature as an indicator of health]
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[https://doi.org/10.1016/j.cell.2024.09.027 Using artificial intelligence to document the hidden RNA virosphere]
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[https://doi.org/10.1101/2023.07.17.549267 Learning a deep language model for microbiomes: the power of large scale unlabeled microbiome data]
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[https://doi.org/10.1126/science.adg7492 Accurate proteome-wide missense variant effect prediction with AlphaMissense]
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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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[https://doi.org/10.1038/s41564-024-01739-1 Multikingdom and functional gut microbiota markers for autism spectrum disorder]
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[https://doi.org/10.1186/s13059-024-03390-9 A realistic benchmark for differential abundance testing and confounder adjustment in human microbiome studies]
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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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[https://doi.org/10.1101/2024.07.11.603044 Prophage-DB: A comprehensive database to explore diversity,distribution, and ecology of prophages]
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[https://doi.org/10.1016/j.cell.2024.07.039 Unexplored microbial diversity from 2,500 food metagenomes and links with the human microbiome]
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[https://doi.org/10.1038/s41467-024-52533-w Accurately predicting enzyme functions through geometric graph learning on ESMFold-predicted structures]
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|+style="text-align:left;font-size:12pt" | 2024-1 Advanced scOmics Data Analysis
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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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[https://doi.org/10.1038/s41586-024-07251-0 Immune microniches shape intestinal Treg function]
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[https://doi.org/10.1016/j.devcel.2021.02.021 A single-cell analysis of the Arabidopsis vegetative shoot apex]
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[https://doi.org/10.1038/s41467-023-40137-9 Droplet-based high-throughput single microbe RNA sequencing by smRandom-seq]
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[https://doi.org/10.1038/s41564-023-01462-3 Single-cell massively-parallel multiplexed microbial sequencing (M3-seq) identifies rare bacterial populations and profiles phage infection]
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[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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[https://doi.org/10.1038/s41587-023-01979-2 Spatial metatranscriptomics resolves host–bacteria–fungi interactomes]
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[https://doi.org/10.1038/s41467-023-36325-2 Dissecting the immune suppressive human prostate tumor microenvironment via integrated single-cell and spatial transcriptomic analyses]
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[https://doi.org/10.1038/s41593-023-01452-y Single-nucleus genomics in outbred rats with divergent cocaine addiction-like behaviors reveals changes in amygdala GABAergic inhibition]
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[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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[https://doi.org/10.1038/s41467-023-39933-0 Spatial cellular architecture predicts prognosis in glioblastoma]
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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;" rowspan=1|Single-cell
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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;" rowspan=1|Single-cell
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|style="padding:.4em;"|Q Zhen
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[https://doi.org/10.1021/acscentsci.3c01169 Single-Cell Analysis Reveals Cxcl14+ Fibroblast Accumulation in Regenerating Diabetic Wounds Treated by Hydrogel-Delivering Carbon Monoxide]
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|style="padding:.4em;" rowspan=1|2024/05/07
|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|CR Leenaars
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[https://doi.org/10.1038/s41477-022-01291-y Single-cell RNA sequencing provides a high-resolution roadmap for understanding the multicellular compartmentation of specialized metabolism]
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|style="padding:.4em;"|YR Kim
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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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|style="padding:.4em;" rowspan=1|Single-cell
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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;" rowspan=1|Single-cell
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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;" rowspan=1|Single-cell
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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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|style="padding:.4em;"|HJ Kim
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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="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|IS Choi
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[https://doi.org/10.1038/s41592-023-02035-2 Population-level integration of single-cell datasets enables multi-scale analysis across samples]
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|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|SB Baek
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|style="padding:.4em;" rowspan=1|Single-cell
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|style="padding:.4em;"|EJ Sung
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|style="padding:.4em;" rowspan=1|Single-cell
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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;"|SB Baek
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|style="padding:.4em;" rowspan=1|Single-cell
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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;" rowspan=1|Single-cell
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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;" rowspan=1|Single-cell
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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;"|SB Baek
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|style="padding:.4em;" rowspan=1|Single-cell
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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;"|IS Choi
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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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[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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[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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[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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[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;"|G Koh
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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|Microbiome
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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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[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|Microbiome
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|style="padding:.4em;"|JY Ma
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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|Microbiome
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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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[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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[https://doi.org/10.1186/s40168-023-01472-7 Altered infective competence of the human gut microbiome in COVID-19]
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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;"|SH Ahn
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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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[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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[https://doi.org/10.1038/s41467-023-39459-5 Top-down identification of keystone taxa in the microbiome]
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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;"|NY Kim
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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|Microbiome
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|style="padding:.4em;"|SH Lee
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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;"|WJ Kim
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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;"|23-41
|style="padding:.4em;"|JY Kim
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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|Microbiome
|style="padding:.4em;"|23-40
|style="padding:.4em;"|G Koh
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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|Microbiome
|style="padding:.4em;"|23-39
|style="padding:.4em;"|HJ Kim
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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|Microbiome
|style="padding:.4em;"|23-38
|style="padding:.4em;"|SH Ahn
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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|Microbiome
|style="padding:.4em;"|23-37
|style="padding:.4em;"|JH Cha
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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
|style="padding:.4em;"|23-36
|style="padding:.4em;"|JY Ma
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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|Microbiome
|style="padding:.4em;"|23-35
|style="padding:.4em;"|NY Kim
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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|Microbiome
|style="padding:.4em;"|23-34
|style="padding:.4em;"|SH Lee
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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;"|WJ Kim
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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|Microbiome
|style="padding:.4em;"|23-32
|style="padding:.4em;"|JY Kim
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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|Microbiome
|style="padding:.4em;"|23-31
|style="padding:.4em;"|G Koh
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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|Microbiome
|style="padding:.4em;"|23-30
|style="padding:.4em;"|SH Ahn
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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;"|HJ Kim
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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|Microbiome
|style="padding:.4em;"|23-28
|style="padding:.4em;"|JH Cha
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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;"
|+style="text-align:left;font-size:12pt" | 2023-1 ADVANCED MICROBIOME DATA ANALYSIS
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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;"|WJ Kim
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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|Microbiome
|style="padding:.4em;"|23-22
|style="padding:.4em;"|SH Ahn
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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;"|23-21
|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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[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;" rowspan=1|Microbiome
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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;"|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;"|23-15
|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;" rowspan=1|Microbiome
|style="padding:.4em;"|23-14
|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;"|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;" rowspan=1|2023/05/09
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-12
|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;" rowspan=1|Microbiome
|style="padding:.4em;"|23-11
|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;" rowspan=1|Microbiome
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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;" rowspan=1|Microbiome
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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;" rowspan=1|Microbiome
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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;" rowspan=1|Microbiome
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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;" rowspan=1|Microbiome
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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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[https://doi.org/10.1038/s41467-023-36633-7 Population-level impacts of antibiotic usage on the human gut microbiome]
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[https://doi.org/10.1038/s41586-022-05438-x Enterococci enhance Clostridioides difficile pathogenesis]
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[https://doi.org/10.1002/imt2.61 Targeting keystone species helps restore the dysbiosis of butyrate‐producing bacteria in nonalcoholic fatty liver disease]
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[https://doi.org/10.1002/advs.202203115 Differential Oral Microbial Input Determines Two Microbiota Pneumo-Types Associated with Health Status]
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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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{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2023-1 scOmics
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[https://doi.org/10.1101/2023.01.17.524482 Decoupling the correlation between cytotoxic and exhausted T lymphocyte transcriptomic signatures enhances melanoma immunotherapy response prediction from tumor expression]
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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/10.1101/2023.07.28.550993 Major data analysis errors invalidate cancer microbiome findings]
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|style="padding:.4em;"|EJ Sung
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[https://doi.org/10.1038/s43018-022-00475-x A single-cell atlas of glioblastoma evolution under therapy reveals cell-intrinsic and cell-extrinsic therapeutic targets]
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[https://doi.org/10.1038/s43018-022-00433-7 Single-cell meta-analyses reveal responses of tumor-reactive CXCL13+ T cells to immune-checkpoint blockade]
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[https://doi.org/10.1038/s41587-022-01342-x Estimation of tumor cell total mRNA expression in 15 cancer types predicts disease progression]
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|style="padding:.4em;"|JH Cha
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[https://doi.org/10.1038/s41587-022-01288-0 DIALOGUE maps multicellular programs in tissue from single-cell or spatial transcriptomics data]
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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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[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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[https://doi.org/10.1186/s13059-022-02828-2 Parallel single-cell and bulk transcriptome analyses reveal key features of the gastric tumor microenvironment]
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[https://doi.org/10.1038/s41467-022-32838-4 Mutated processes predict immune checkpoint inhibitor therapy benefit in metastatic melanoma]
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[https://doi.org/10.1038/s43018-021-00292-8 Temporal single-cell tracing reveals clonal revival and expansion of precursor exhausted T cells during anti-PD-1 therapy in lung cancer]
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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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[https://doi.org/10.1101/2022.12.20.521311 Supervised discovery of interpretable gene programs from single-cell data]
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[https://doi.org/10.1038/s41586-022-05435-0 Effect of the intratumoral microbiota on spatial and cellular heterogeneity in cancer]
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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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[https://www.biorxiv.org/content/10.1101/2022.08.05.502989v1 MetaTiME: Meta-components of the Tumor Immune Microenvironment]
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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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[https://www.nature.com/articles/s41591-022-01799-y A T cell resilience model associated with response to immunotherapy in multiple tumor types]
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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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[https://pubmed.ncbi.nlm.nih.gov/35649411/ Cross-tissue, single-cell stromal atlas identifies shared pathological fibroblast phenotypes in four chronic inflammatory diseases]
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|style="padding:.4em;"|JW Yu
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[https://www.sciencedirect.com/science/article/pii/S1535610822003178 Pan-cancer integrative histology-genomic analysis via multimodal deep learning]
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|style="padding:.4em;" rowspan=1|2023/01/11
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|style="padding:.4em;"|JH Cha
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[https://pubmed.ncbi.nlm.nih.gov/35803260/ Tissue-resident memory and circulating T cells are early responders to pre-surgical cancer immunotherapy]
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{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2023-1 Microbiome
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!scope="col" style="padding:.4em" | Date
!scope="col" style="padding:.4em" | Team
!scope="col" style="padding:.4em" | Paper index
!scope="col" style="padding:.4em" | Presenter
!scope="col" style="padding:.4em" | Paper title
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|style="padding:.4em;" rowspan=1|2023/08/25
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-27
|style="padding:.4em;"|JY Ma
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.chom.2023.05.024 Enterosignatures define common bacterial guilds in the human gut microbiome]
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|style="padding:.4em;" rowspan=1|2023/08/25
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-26
|style="padding:.4em;"|NY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1186/s13059-023-02902-3 PhyloMed: a phylogeny-based test of mediation effect in microbiome]
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|style="padding:.4em;" rowspan=1|2023/08/18
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-25
|style="padding:.4em;"|SH Lee
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1016/j.chom.2023.05.027 The TaxUMAP atlas: Efficient display of large clinical microbiome data reveals ecological competition in protection against bacteremia]
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|style="padding:.4em;" rowspan=1|2023/08/18
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-24
|style="padding:.4em;"|WJ Kim
|style="padding:.4em;text-align:left"|
[http://dx.doi.org/10.1038/nbt.3704 Measurement of bacterial replication rates in microbial communities]
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|style="padding:.4em;" rowspan=1|2023/08/11
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-23
|style="padding:.4em;"|JY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1186/s40168-023-01564-4 Skin microbiome diferentiates into distinct cutotypes with unique metabolic functions upon exposure to polycyclic aromatic hydrocarbons]
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|style="padding:.4em;" rowspan=1|2023/08/11
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-22
|style="padding:.4em;"|SH Ahn
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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
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-21
|style="padding:.4em;"|HJ Kim
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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
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-20
|style="padding:.4em;"|JH Cha
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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
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-19
|style="padding:.4em;"|JY Ma
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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
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-18
|style="padding:.4em;"|NY Kim
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1186/s13059-022-02809-5 Gene fow and introgression are pervasive forces shaping the evolution of bacterial species]
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|style="padding:.4em;" rowspan=1|2023/06/30
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-17
|style="padding:.4em;"|JH Cha
|style="padding:.4em;text-align:left"|
[https://doi.org/10.1186/s40168-022-01435-4 Alterations of oral microbiota and impact on the gut microbiome in type 1 diabetes mellitus revealed by integrated multi‑omic analyses]
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|style="padding:.4em;" rowspan=1|2023/06/23
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-16
|style="padding:.4em;"|HJ Kim
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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
|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|SH Lee
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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
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-14
|style="padding:.4em;"|JY Ma
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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|Microbiome
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|style="padding:.4em;"|NY Kim
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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
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-12
|style="padding:.4em;"|SH Lee
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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
|style="padding:.4em;"|23-11
|style="padding:.4em;"|JY Kim
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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
|style="padding:.4em;" rowspan=1|Microbiome
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|style="padding:.4em;"|WJ Kim
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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
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-9
|style="padding:.4em;"|SH Ahn
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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;"|HJ Kim
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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
|style="padding:.4em;" rowspan=1|Microbiome
|style="padding:.4em;"|23-7
|style="padding:.4em;"|JH Cha
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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|Microbiome
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|style="padding:.4em;"|JY Ma
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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;"|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|Microbiome
|style="padding:.4em;"|23-4
|style="padding:.4em;"|SH Lee
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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;"|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;"|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="text-align:left;font-size:12pt" | 2022 scOmics
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!scope="col" style="padding:.4em" | Date
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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
|style="padding:.4em;" rowspan=1|Single-cell
|style="padding:.4em;"|22-31
|style="padding:.4em;"|JH Cha, SB Baek, IS Choi
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[https://www.pnas.org/doi/10.1073/pnas.2105859118 Representation learning of RNA velocity reveals robust cell transitions]
[https://www.nature.com/articles/s41467-022-34188-7 UniTVelo: temporally unified RNA velocity reinforces single-cell trajectory inference]
[https://www.sciencedirect.com/science/article/pii/S0092867421015774 Mapping transcriptomic vector fields of single cells]
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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;"|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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[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;"|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;"|JH Cha
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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
|style="padding:.4em;" rowspan=1|Single-cell
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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;"|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;"|IS Choi
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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;"|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;"|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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[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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[https://www.biorxiv.org/content/10.1101/2021.06.07.447430v2 Metacells untangle large and complex single-cell transcriptome networks]
[https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1812-2 MetaCell: analysis of single-cell RNA-seq data using K-nn graph partitions]
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|style="padding:.4em;"|SB Baek
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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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[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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[https://pubmed.ncbi.nlm.nih.gov/35199064/ Effect of imputation on gene network reconstruction from single-cell RNA-seq data]
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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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[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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[https://pubmed.ncbi.nlm.nih.gov/34594031/ Systematic investigation of cytokine signaling activity at the tissue and single-cell levels]
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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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[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;"|IS Choi
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[https://pubmed.ncbi.nlm.nih.gov/34597583/ Atlas of clinically distinct cell states and ecosystems across human solid tumors]
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[https://pubmed.ncbi.nlm.nih.gov/34489465/ Single-cell ATAC and RNA sequencing reveal pre-existing and persistent cells associated with prostate cancer relapse]
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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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[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://journals.asm.org/doi/10.1128/msystems.00050-22 Compositionally Aware Phylogenetic Beta-Diversity Measures Better Resolve Microbiomes Associated with Phenotype]
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[https://www.nature.com/articles/s41596-020-00480-3 Tutorial: assessing metagenomics software with the CAMI benchmarking toolkit disease]
[https://www.nature.com/articles/s41592-022-01431-4 Critical Assessment of Metagenome Interpretation: the second round of challenges]
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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.nature.com/articles/s41591-022-01688-4 Microbiome and metabolome features of the cardiometabolic disease spectrum]
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[https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02195-w Functional and genetic markers of niche partitioning among enigmatic members of the human oral microbiome]
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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://genomebiology.biomedcentral.com/articles/10.1186/s13059-021-02473-1 Pandora: nucleotide-resolution bacterial pan-genomics with reference graphs]
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[https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009442 Multivariable association discovery in population-scale meta-omics 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://genomebiology.biomedcentral.com/articles/10.1186/s13059-022-02610-4 AGAMEMNON: an Accurate metaGenomics And MEtatranscriptoMics quaNtificatiON analysis suite]
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[https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02200-2 Metapangenomics of the oral microbiome provides insights into habitat adaptation and cultivar diversity]
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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/34893089/ Microbial co-occurrence complicates associations of gut microbiome with US immigration, dietary intake and obesity]
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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://pubmed.ncbi.nlm.nih.gov/34551799/ The influence of the gut microbiome on BCG-induced trained immunity]
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[https://pubmed.ncbi.nlm.nih.gov/34912116/ Towards the biogeography of prokaryotic genes]
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[https://pubmed.ncbi.nlm.nih.gov/29347966/ ReprDB and panDB: minimalist databases with maximal microbial representation]
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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/10.1101/2022.06.16.496510v2 MIDAS2: Metagenomic Intra-species Diversity Analysis System]
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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://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-022-01251-w Metagenomic strain detection with SameStr: identification of a persisting core gut microbiota transferable by fecal transplantation]
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[https://www.nature.com/articles/s41587-021-01102-3 Fast and accurate metagenotyping of the human gut microbiome with GT-Pro]
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[https://www.nature.com/articles/s41587-020-00797-0 inStrain profiles population microdiversity from metagenomic data and sensitively detects shared microbial strains]
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[https://genome.cshlp.org/content/early/2021/07/22/gr.265058.120 Longitudinal linked-read sequencing reveals ecological and evolutionary responses of a human gut microbiome during antibiotic treatment]
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[https://www.sciencedirect.com/science/article/pii/S1931312821002365 Dispersal strategies shape persistence and evolution of human gut bacteria]
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[https://www.sciencedirect.com/science/article/pii/S0092867421003524 The long-term genetic stability and individual specificity of the human gut microbiome]
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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://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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[https://www.nature.com/articles/nmeth.3802 Strain-level microbial epidemiology and population genomics from shotgun metagenomics]
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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.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/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.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/s41467-020-14766-3 Trajectory-based differential expression analysis for single-cell sequencing data]
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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://www.sciencedirect.com/science/article/pii/S0092867419308967?via%3Dihub Single-Cell Analysis of Crohn's Disease Lesions Identifies a Pathogenic Cellular Module Associated with Resistance to Anti-TNF Therapy]
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[https://www.nature.com/articles/s41467-020-15956-9 Multi-omic single-cell snapshots reveal multiple independent trajectories to drug tolerance in a melanoma cell line]
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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/30595452/ Dysfunctional CD8 T Cells Form a Proliferative, Dynamically Regulated Compartment Within Human Melanoma]
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[https://pubmed.ncbi.nlm.nih.gov/30388455/ A Cancer Cell Program Promotes T Cell Exclusion and Resistance to Checkpoint Blockade]
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[https://pubmed.ncbi.nlm.nih.gov/31974247/ Single-cell Transcriptional Diversity Is a Hallmark of Developmental Potential]
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[https://pubmed.ncbi.nlm.nih.gov/31675496/ Landscape and Dynamics of Single Immune Cells in Hepatocellular Carcinoma]
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[https://pubmed.ncbi.nlm.nih.gov/32103181/ Peripheral T Cell Expansion Predicts Tumour Infiltration and Clinical Response]
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[https://pubmed.ncbi.nlm.nih.gov/30388456/ Defining T Cell States Associated With Response to Checkpoint Immunotherapy in Melanoma]
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[https://pubmed.ncbi.nlm.nih.gov/31915379/ Rapid Non-Uniform Adaptation to Conformation-Specific KRAS(G12C) Inhibition]
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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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[https://pubmed.ncbi.nlm.nih.gov/32066951/ Distinct Microbial and Immune Niches of the Human Colon]
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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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[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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[https://pubmed.ncbi.nlm.nih.gov/30078704/ A Single-Cell Atlas of In Vivo Mammalian Chromatin Accessibility]
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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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[https://pubmed.ncbi.nlm.nih.gov/31792411/ Single-cell Multiomic Analysis Identifies Regulatory Programs in Mixed-Phenotype Acute Leukemia]
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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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[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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[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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[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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[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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[https://www.sciencedirect.com/science/article/pii/S0092867419307731 Tumor Microbiome Diversity and Composition Influence Pancreatic Cancer Outcomes]
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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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[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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[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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[https://www.sciencedirect.com/science/article/pii/S0092867419307810 Large-Scale Analyses of Human Microbiomes Reveal Thousands of Small, Novel Genes]
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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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[https://www.nature.com/articles/nbt.4042 Multiplexed droplet single-cell RNA-sequencing using natural genetic variation]
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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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[https://www.sciencedirect.com/science/article/pii/S0092867419307755?via%3Dihub Benchmarking Metagenomics Tools for Taxonomic Classification]
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[https://www.biorxiv.org/content/10.1101/719088v1 Coexpression uncovers a unified single-cell transcriptomic landscape]
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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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[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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[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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[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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[https://www.nature.com/articles/s41592-018-0254-1 A test metric for assessing single-cell RNA-seq batch correction]
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[https://www.sciencedirect.com/science/article/pii/S0092867419305598 Comprehensive Integration of Single-Cell Data]
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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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[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;"|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
|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
|style="padding:.4em;"|19-26
|style="padding:.4em;"|IS Choi
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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
|style="padding:.4em;"|JW Cho
|style="padding:.4em;text-align:left"|
[https://www.sciencedirect.com/science/article/pii/S0092867418311784 A Cancer Cell Program Promotes T Cell Exclusion and Resistance to Checkpoint Blockade]
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|style="padding:.4em;" rowspan=2|2019/07/23
|style="padding:.4em;"|19-24
|style="padding:.4em;"|HJ Han
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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
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[https://www.nature.com/articles/ncomms14825 Sensitive detection of rare disease-associated cell subsets via representation learning]
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|style="padding:.4em;" rowspan=3|2019/05/30
|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
|style="padding:.4em;"|
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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
|style="padding:.4em;"|
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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
|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
|style="padding:.4em;"|
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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
|style="padding:.4em;"|
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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
|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
|style="padding:.4em;"|
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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
|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
|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
|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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|style="padding:.4em;"|19-9
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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
|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
|style="padding:.4em;"|19-6
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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
|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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|style="padding:.4em;"|19-3-1
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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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|style="padding:.4em;"|19-3
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[https://www.ncbi.nlm.nih.gov/pubmed/30787437 The single-cell transcriptional landscape of mammalian organogenesis]
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|style="padding:.4em;" rowspan=2|2019/3/14
|style="padding:.4em;"|19-2
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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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|style="padding:.4em;"|19-1
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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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{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2018
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|style="padding:.4em;" rowspan=2|2018/06/14
|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
|style="padding:.4em;"|
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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
|style="padding:.4em;"|18-10
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=Developmental+and+oncogenic+programs+in+H3K27M+gliomas+dissected+by+single-cell+RNA-seq Developmental and oncogenic programs in H3K27M gliomas dissected by single-cell RNA-seq.]
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|style="padding:.4em;"|18-9
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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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|style="padding:.4em;" rowspan=2|2018/05/31
|style="padding:.4em;"|18-8
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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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|style="padding:.4em;"|18-7
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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.]
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|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
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[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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|style="padding:.4em;" rowspan=2|2018/05/10
|style="padding:.4em;"|18-2
|style="padding:.4em;"|JW Cho
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[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
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[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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{|class=wikitable style="text-align:center;"
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|style="padding:.4em;" rowspan=2|2017/06/28
|style="padding:.4em;"|17-36
|style="padding:.4em;"|HJ Han
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[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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|style="padding:.4em;"|17-35
|style="padding:.4em;"|JW Cho
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[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.]
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|style="padding:.4em;"|17-33
|style="padding:.4em;"|MY Lee
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[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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[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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|style="padding:.4em;"|17-31
|style="padding:.4em;"|MY Lee
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.celrep.2016.03.075 Genomic Correlates of Immune-Cell Infiltrates in Colorectal Carcinoma.]
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|style="padding:.4em;" rowspan=2|2017/05/31
|style="padding:.4em;"|17-30
|style="padding:.4em;"|DS Bae
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[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.]
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|style="padding:.4em;"|17-29
|style="padding:.4em;"|JW Cho
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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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|style="padding:.4em;" rowspan=2|2017/05/24
|style="padding:.4em;"|17-28
|style="padding:.4em;"|EB Kim
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[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
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[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
|style="padding:.4em;"|17-26
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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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[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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|style="padding:.4em;" rowspan=2|2017/04/26
|style="padding:.4em;"|17-24
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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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|style="padding:.4em;"|17-23
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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
|style="padding:.4em;"|17-22
|style="padding:.4em;"|SH Lee
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[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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|style="padding:.4em;"|17-21
|style="padding:.4em;"|CY Kim
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1038%2Fnbt.3569 Wishbone identifies bifurcating developmental trajectories from single-cell data.]
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|style="padding:.4em;" rowspan=2|2017/04/05
|style="padding:.4em;"|17-20
|style="padding:.4em;"|KS Kim
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[http://biorxiv.org/content/early/2017/02/21/110668 Reversed graph embedding resolves complex single-cell developmental trajectories.]
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|style="padding:.4em;"|17-19
|style="padding:.4em;"|SH Lee
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[https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1038%2Fnmeth.4150 Single-cell mRNA quantification and differential analysis with Census.]
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|style="padding:.4em;" rowspan=2|2017/03/29
|style="padding:.4em;"|17-18
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[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.]
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|style="padding:.4em;"|17-17
|style="padding:.4em;"|DS Bae
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[https://www.ncbi.nlm.nih.gov/pubmed/26051941 Single-Cell Network Analysis Identifies DDIT3 as a Nodal Lineage Regulator in Hematopoiesis.]
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|style="padding:.4em;" rowspan=2|2017/03/22
|style="padding:.4em;"|17-16
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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.]
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|style="padding:.4em;"|17-15
|style="padding:.4em;"|EB Kim
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[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
|style="padding:.4em;"|17-14
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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.]
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|style="padding:.4em;"|17-13
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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.]
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|style="padding:.4em;" rowspan=2|2017/02/28
|style="padding:.4em;"|17-12
|style="padding:.4em;"|KS Kim
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[https://www.ncbi.nlm.nih.gov/pubmed/27824113 A Comprehensive Characterization of the Function of LincRNAs in Transcriptional Regulation Through Long-Range Chromatin Interactions.]
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|style="padding:.4em;"|17-11
|style="padding:.4em;"|JE Shim
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[//pubmed.gov/27851969 Epigenomic Deconvolution of Breast Tumors Reveals Metabolic Coupling between Constituent Cell Types.]
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|style="padding:.4em;" rowspan=2|2017/02/21
|style="padding:.4em;"|17-10
|style="padding:.4em;"|EB Kim
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[http://biorxiv.org/content/early/2016/12/30/097451 Convergence of dispersed regulatory mutations predicts driver genes in prostate cancer.]
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|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.]
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|style="padding:.4em;" rowspan=2|2017/02/07
|style="padding:.4em;"|17-08
|style="padding:.4em;"|DS Bae
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[http://biorxiv.org/content/early/2016/11/17/088286 Somatic Mutations and Neoepitope Homology in Melanomas Treated with CTLA-4 Blockade.]
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|style="padding:.4em;"|17-07
|style="padding:.4em;"|MY Lee
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[http://biorxiv.org/content/early/2016/11/28/090134 Chemotherapy weakly contributes to predicted neoantigen expression in ovarian cancer.]
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|style="padding:.4em;" rowspan=1|2017/01/31
|style="padding:.4em;"|17-06
|style="padding:.4em;"|CY Kim
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[//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
|style="padding:.4em;"|17-05
|style="padding:.4em;"|JW Cho
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[//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
|style="padding:.4em;"|17-04
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[//www.ncbi.nlm.nih.gov/pubmed/25938943 Mapping long-range promoter contacts in human cells with high-resolution capture Hi-C]
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|style="padding:.4em;"|17-03
|style="padding:.4em;"|EB Kim
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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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|style="padding:.4em;" rowspan=1|2017/01/10
|style="padding:.4em;"|17-02
|style="padding:.4em;"|JE Shim
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[//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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[//www.ncbi.nlm.nih.gov/pubmed/26287467 Single-cell messenger RNA sequencing reveals rare intestinal cell types]
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!scope="col" style="padding:.4em" | Paper title
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|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.]
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|style="padding:.4em;" rowspan=1|2016/12/6
|style="padding:.4em;"|2016-30
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26299571 Single-Cell RNA-Seq with Waterfall Reveals Molecular Cascades underlying Adult Neurogenesis.]
|-
|style="padding:.4em;" rowspan=1|2016/11/29
|style="padding:.4em;"|2016-29
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/24658644 The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells.]
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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.]
|-
|style="padding:.4em;" rowspan=1|2016/11/15
|style="padding:.4em;"|2016-27
|style="padding:.4em;"|MY Lee
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26000487 Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells.]
|-
|style="padding:.4em;" rowspan=1|2016/11/8
|style="padding:.4em;"|2016-26
|style="padding:.4em;"|JW Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26000488 Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets.]
|-
|style="padding:.4em;" rowspan=1|2016/11/1
|style="padding:.4em;"|2016-25
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27426982 Causal Mechanistic Regulatory Network for Glioblastoma Deciphered Using Systems Genetics Network Analysis.]
|-
|style="padding:.4em;" rowspan=1|2016/10/25
|style="padding:.4em;"|2016-24
|style="padding:.4em;"|MY Lee
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27549193 Comprehensive analyses of tumor immunity: implications for cancer immunotherapy.]
|-
|style="padding:.4em;" rowspan=1|2016/10/11
|style="padding:.4em;"|2016-23
|style="padding:.4em;"|JE Shim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=sidespread+parainflammation Widespread parainflammation in human cancer]
|-
|style="padding:.4em;" rowspan=1|2016/9/27
|style="padding:.4em;"|2016-22
|style="padding:.4em;"|ER Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27535533 Analysis of protein-coding genetic variation in 60,706 humans]
|-
|style="padding:.4em;" rowspan=1|2016/9/20
|style="padding:.4em;"|2016-21
|style="padding:.4em;"|SM Yang
|style="padding:.4em;text-align:left"|
Functional characterization of somatic mutations in cancer using network-based inference of protein activity
[http://www.ncbi.nlm.nih.gov/pubmed/27322546 pubmed]
[http://www.nature.com/ng/journal/v48/n8/full/ng.3593.html fulltext]
|-
|style="padding:.4em;" rowspan=1|2016/9/13
|style="padding:.4em;"|2016-20
|style="padding:.4em;"|KS Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=exploiting+single-cell+expression+to+characterize Exploiting single-cell expression to characterize co-expression replicability.]
|-
|style="padding:.4em;" rowspan=1|2016/9/6
|style="padding:.4em;"|2016-19
|style="padding:.4em;"|T Lee
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26940869 Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade]
|-
|style="padding:.4em;" rowspan=1|2016/8/31
|style="padding:.4em;"|2016-18
|style="padding:.4em;"|DS Bae
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27264179 A Computational Drug Repositioning Approach for Targeting Oncogenic Transcription Factors]
|-
|style="padding:.4em;" rowspan=1|2016/8/16
|style="padding:.4em;"|2016-17
|style="padding:.4em;"|JW Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27309802 The landscape of accessible chromatin in mammalian preimplantation embryos]
|-
|style="padding:.4em;" rowspan=1|2016/8/8
|style="padding:.4em;"|2016-16
|style="padding:.4em;"|EB Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27064255 Enhancer-promoter interactions are encoded by complex genomic signatures on looping chromatin]
|-
|style="padding:.4em;" rowspan=1|2016/8/1
|style="padding:.4em;"|2016-15
|style="padding:.4em;"|MY Lee
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27040498 Personalized Immunomonitoring Uncovers Molecular Networks that Stratify Lupus Patients]
|-
|style="padding:.4em;" rowspan=1|2016/7/25
|style="padding:.4em;"|2016-14
|style="padding:.4em;"|CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/?term=integration+of+summary+data+from+gwas+and+eqtl+studies Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets]
|-
|style="padding:.4em;" rowspan=1|2016/7/18
|style="padding:.4em;"|2016-13
|style="padding:.4em;"|HJ Han
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/23624555 Identification of transcriptional regulators in the mouse immune system]
|-
|style="padding:.4em;" rowspan=2|2016/6/8
|style="padding:.4em;"|2016-12
|style="padding:.4em;"|DS Bae, CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26619012 Mapping the effects of drugs on the immune system]
|-
|style="padding:.4em;"| 2016-11
|style="padding:.4em;"|DS Bae, CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26186195 Elucidating compound mechanism of action by network perturbation analysis]
|-
|style="padding:.4em;" rowspan=2|2016/6/1
|style="padding:.4em;"|2016-10
|style="padding:.4em;"|MY Lee,SM Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26854917 Integrative approaches for large-scale transcriptome-wide association studies]
|-
|style="padding:.4em;"| 2016-9
|style="padding:.4em;"|MY Lee,SM Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26950747 Tissue-specific regulatory circuits reveal variable modular perturbations across complex diseases]
|-
|style="padding:.4em;" rowspan=2|2016/5/18
|style="padding:.4em;"|2016-8
|style="padding:.4em;"|CY Kim,SJ Kwon
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/27013732 Survey of variation in human transcription factors reveals prevalent DNA binding changes]
|-
|style="padding:.4em;"| 2016-7
|style="padding:.4em;"| CY Kim,SJ Kwon
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26502339 Large-scale identification of sequence variants influencing human transcription factor occupancy in vivo]
|-
|style="padding:.4em;" rowspan=2|2016/5/11
|style="padding:.4em;"|2016-6
|style="padding:.4em;"|DS Bae, CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/25171417 Predicting Cancer-specific vulnerability via data-driven detection of synthetic lethality]
|-
|style="padding:.4em;"| 2016-5
|style="padding:.4em;"|DS Bae, CY Kim
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/23467089 Dynamic regulatory network controlling Th17 cell differentiation]
|-
|style="padding:.4em;" rowspan=2|2016/5/4
|style="padding:.4em;"|2016-4
|style="padding:.4em;"|MY Lee,SM Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/25853550 Characterization of the immunophenotypes and antigenomes of colorectal cancers reveals distinct tumor escape mechanisms and novel targets for immunotherapy]
|-
|style="padding:.4em;"| 2016-3
|style="padding:.4em;"|MY Lee,SM Cho
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26618344 Regulators of genetic risk of breast cancer identified by integrative network analysis]
|-
|style="padding:.4em;" rowspan=2|2016/4/27
|style="padding:.4em;"|2016-2
|style="padding:.4em;"|CY Kim,SJ Kwon
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/26780608 A predictive computational framework for direct reprogramming between human cell types]
|-
|style="padding:.4em;"| 2016-1
|style="padding:.4em;"| CY Kim,SJ Kwon
|style="padding:.4em;text-align:left"|
[http://www.ncbi.nlm.nih.gov/pubmed/25126793 CellNet: Network biology applied to stem cell engineering]
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{|class=wikitable style="text-align:center;"
|+style="text-align:left;font-size:12pt" | 2015
|-
!scope="col" style="padding:.4em" | Date
!scope="col" style="padding:.4em" | Paper index
!scope="col" style="padding:.4em" | Presenter
!scope="col" style="padding:.4em" | Paper title
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|style="padding:.4em;" rowspan=2|2015/06/11
|style="padding:.4em;"|2015-55
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://genome.cshlp.org/content/24/2/340.long Improved exome prioritization of disease genes through cross-species phenotype comparison.]
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|style="padding:.4em;"|2015-54
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0002929714001128 Phevor combines multiple biomedical ontologies for accurate identification of disease-causing alleles in single individuals and small nuclear families.]
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|style="padding:.4em;" rowspan=2|2015/06/04
|style="padding:.4em;"|2015-53
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nmeth/journal/v10/n11/full/nmeth.2656.html eXtasy: variant prioritization by genomic data fusion.]
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|style="padding:.4em;"|2015-52
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://genome.cshlp.org/content/21/9/1529.long A probabilistic disease-gene finder for personal genomes.]
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|style="padding:.4em;" rowspan=2|2015/05/28
|style="padding:.4em;"|2015-51
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/ng/journal/v46/n12/full/ng.3141.html Systematic analysis of noncoding somatic mutations and gene expression alterations across 14 tumor types.]
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|style="padding:.4em;"|2015-50
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nbt/journal/v28/n5/full/nbt.1630.html GREAT improves functional interpretation of cis-regulatory regions.]
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|style="padding:.4em;" rowspan=2|2015/05/21
|style="padding:.4em;"|2015-49
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nmeth/journal/v11/n3/full/nmeth.2832.html Functional annotation of noncoding sequence variants.]
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|style="padding:.4em;"|2015-48
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://genomebiology.com/content/15/10/480 FunSeq2: A framework for prioritizing noncoding regulatory variants in cancer.]
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|style="padding:.4em;" rowspan=2|2015/05/14
|style="padding:.4em;"|2015-47
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nmeth/journal/v12/n2/full/nmeth.3215.html Selecting causal genes from genome-wide association studies via functionally coherent subnetworks.]
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|style="padding:.4em;"|2015-46
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/ncomms/2015/150119/ncomms6890/full/ncomms6890.html Biological interpretation of genome-wide association studies using predicted gene functions.]
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|style="padding:.4em;" rowspan=3|2015/05/07
|style="padding:.4em;"|2015-45
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/ncomms/2014/140626/ncomms5212/full/ncomms5212.html Human symptoms-disease network.]
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|style="padding:.4em;"|2015-44
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867413010246 A nondegenerate code of deleterious variants in Mendelian loci contributes to complex disease risk.]
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|style="padding:.4em;"|2015-43
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.sciencemag.org/content/347/6224/1257601.long Uncovering disease-disease relationships through the incomplete interactome.]

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|style="padding:.4em;" rowspan=2|2015/04/30
|style="padding:.4em;"|2015-42
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://genome.cshlp.org/content/25/1/142.long The discovery of integrated gene networks for autism and related disorders.]
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|style="padding:.4em;"|2015-41
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://msb.embopress.org/content/10/12/774.long Integrated systems analysis reveals a molecular network underlying autism spectrum disorders.]
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|style="padding:.4em;" rowspan=3|2015/04/23
|style="padding:.4em;"|2015-40
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v518/n7539/full/nature13990.html Dissecting neural differentiation regulatory networks through epigenetic footprinting.]
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|style="padding:.4em;"|2015-39
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v518/n7539/full/nature14221.html Cell-of-origin chromatin organization shapes the mutational landscape of cancer.]
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|style="padding:.4em;"|2015-38
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nature/journal/v518/n7539/full/nature14248.html Integrative analysis of 111 reference human epigenomes.]
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|style="padding:.4em;" rowspan=2|2015/04/09
|style="padding:.4em;"|2015-37
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://genome.cshlp.org/content/25/2/246.long Genome-wide analysis of local chromatin packing in Arabidopsis thaliana.]
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|style="padding:.4em;"|2015-36
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867414014974 A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping.]
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|style="padding:.4em;" rowspan=2|2015/04/02
|style="padding:.4em;"|2015-35
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nrg/journal/v14/n6/full/nrg3454.html Exploring the three-dimensional organization of genomes: interpreting chromatin interaction data.]
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|style="padding:.4em;"|2015-34
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.sciencemag.org/content/347/6225/1010.long Transcribed enhancers lead waves of coordinated transcription in transitioning mammalian cells.]
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|style="padding:.4em;" rowspan=2|2015/03/26
|style="padding:.4em;"|2015-33
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://genome.cshlp.org/content/25/2/257.long Cupid: simultaneous reconstruction of microRNA-target and ceRNA networks.]
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|style="padding:.4em;"|2015-32
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://genome.cshlp.org/content/25/1/41.long Characterization of the neural stem cell gene regulatory network identifies OLIG2 as a multifunctional regulator of self-renewal.]
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|style="padding:.4em;" rowspan=2|2015/03/19
|style="padding:.4em;"|2015-31
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nbt/journal/vaop/ncurrent/full/nbt.3154.html Decoding the regulatory network of early blood development from single-cell gene expression measurements.]
|-
|style="padding:.4em;"|2015-30
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.nature.com/nrg/journal/v16/n3/full/nrg3833.html Computational and analytical challenges in single-cell transcriptomics.]
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|style="padding:.4em;" rowspan=3|2015/03/12
|style="padding:.4em;"|2015-29
|style="padding:.4em;"|
|style="padding:.4em;text-align:left"|
[http://www.sciencedirect.com/science/article/pii/S0092867415000136 Extensive Strain-Level Copy-Number Variation across Human Gut Microbiome Species.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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.]
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|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 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 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"
|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 title
|-
|rowspan = "1"|2013/01/11
|align ="center"|2012-81
|[http://genome.cshlp.org/content/22/8/1589.full (TH Kim) MuSiC: identifying mutational significance in cancer genomes.]
|-
|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"|2012-74
|[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]
|-
| 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]
|-
| align="center"|2012-72
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2063581/ (SH Hwang) Network-based classification of breast cancer metastasis]
|-
| 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]
|-
|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]
|-
| align="center"|2012-66
|[http://bioinformatics.oxfordjournals.org/content/25/16/2078.long (ER Kim&TH Kim)The Sequence Alignment/Map format and SAMtools]
|-
| 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]
|-
| align="center"|2012-64
|[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"|2012-63
|[http://www.sciencemag.org/content/336/6086/1262.abstract (T Lee&CY Kim)Host-Gut Microbiota Metabolic Interactions]
|-
|align="center"|2012-62
|[http://www.sciencemag.org/content/336/6086/1268.full (AR Cho,JH Ju)Interactions Between the Microbiota and the Immune System]
|-
|align="center"|2012-61
|[http://www.sciencemag.org/content/336/6086/1255.abstract (SH Hwang&HJ Cho)The Application of Ecological Theory Toward an Understanding of the Human Microbiome]
|-
|align="center"|2012-60
|[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
|[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]
|-
| align="center"|2012-58
|[http://www.nature.com/nature/journal/v486/n7402/full/nature11209.html (JH Shin&HJ Kim)A framework for human microbiome research]
|-
|align="center"|2012-57
|[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
|[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]
|-
|align="center"|2012-55
|[http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002511 (YH Go&HJ Han)Google Goes Cancer: Improving Outcome Prediction for Cancer Patients by Network-Based Ranking of Marker Genes]
|-
| align="center"|2012-54
|[http://www.nature.com/msb/journal/v7/n1/full/msb201147.html (YH Go&HJ Han)A pharmacogenomic method for individualized prediction of drug sensitivity]
|-
|align="center"|2012-53
|[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
|[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]
|-
|align="center"|2012-51
|[http://www.nature.com/nature/journal/v483/n7391/full/nature11005.html (ER Kim&TH Kim)Systematic identification of genomic markers of drug sensitivity in cancer cells]
|-
| align="center"|2012-50
|[http://www.pnas.org/content/early/2011/10/13/1018854108.abstract (ER Kim&TH Kim)Subtype and pathway specific responses to anticancer compounds in breast cancer]
|-
|align="center"|2012-49
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature10945.html (JE Shim&KS Kim)De novo mutations revealed by whole-exome sequencing are strongly associated with autism]
|-
|align="center"|2012-48
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature11011.html (JE Shim&KS Kim)Patterns and rates of exonic de novo mutations in autism spectrum disorders]
|-
|align="center"|2012-47
|[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
|[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)]
|-
|align="center"|2012-45
|[http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002621 (T Lee&CY Kim)Type 2 Diabetes Risk Alleles Demonstrate Extreme Directional Differentiation among Human Populations, Compared to Other Diseases]
|-
|align="center"|2012-44
|[http://www.nature.com/nature/journal/v480/n7376/full/nature10665.html (AR Cho&JH Ju)Predicting mutation outcome from early stochastic variation in genetic interaction partners]
|-
|align="center"|2012-43
|[http://www.sciencemag.org/content/335/6064/82 (AR Cho&JH Ju)Fitness Trade-Offs and Environmentally Induced Mutation Buffering in Isogenic C. elegans]
|-
| align="center"|2012-42
|[http://genome.cshlp.org/content/22/6/1163 (JE Shim&KS Kim)Identification of microRNA-regulated gene networks by expression analysis of target genes]
|-
|align="center"|2012-41
|[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
|[http://www.cell.com/abstract/S0092-8674(12)00573-9 (JH Soh)Functional Repurposing Revealed by Comparing S. pombe and S. cerevisiae Genetic Interactions]
|-
|align="center"|2012-39
|[http://genome.cshlp.org/content/22/2/375.long (ER Kim&TH Kim)De novo discovery of mutated driver pathways in cancer]
|-
| align="center"|2012-38
|[http://www.nature.com/msb/journal/v4/n1/full/msb20082.html (YH Go&HJ Han)A systems biology approach to prediction of oncogenes and molecular perturbation targets in B-cell lymphomas]
|-
|align="center"|2012-37
|[http://www.nature.com/msb/journal/v7/n1/full/msb201126.html (SH Hwang&HJ Cho)PREDICT: a method for inferring novel drug indications with application to personalized medicine]
|-
|align="center"|2012-36
|[http://www.nature.com/nature/journal/v453/n7198/full/nature06973.html (SH Hwang&HJ Cho)Synergistic response to oncogenic mutations defines gene class critical to cancer phenotype]
|-
|align="center"|2012-35
|[http://www.sciencemag.org/content/334/6062/1518.full (JH Shin&HJ Kim)Detecting Novel Associations in Large Data Sets]
|-
|rowspan = "3" |2012/03/05
| align="center"|2012-34
|[http://www.ncbi.nlm.nih.gov/pubmed/18516045 (8,HH Kim)Mapping and quantifying mammalian transcriptomes by RNA-Seq.]
|-
|align="center"|2012-33
|[http://genomebiology.com/2010/11/10/R106 (11,Go&Ju)Differential expression analysis for sequence count data]
|-
|align="center"|2012-32
|[http://bioinformatics.oxfordjournals.org/content/26/1/139.short (12,Go&Ju)edgeR: a Bioconductor package for differential expression analysis of digital gene expression data ]
|-
|rowspan = "7" |2012/02/27 2012/02/28
| align="center"|2012-31
|[http://www.nature.com/nrg/journal/v10/n1/full/nrg2484.html (1,JW Song)RNA-Seq: a revolutionary tool for transcriptomics]
|-
|align="center"|2012-30
|[http://www.nature.com/nmeth/journal/v8/n6/full/nmeth.1613.html (2,JW Song)Computational methods for transcriptome annotation and quantification using RNA-seq]
|-
|align="center"|2012-29
|[http://genomebiology.com/2010/11/12/220 (3,HJ Han)From RNA-seq reads to differential expression results]
|-
|align="center"|2012-28
|[http://www.nature.com/nmeth/journal/v7/n9/full/nmeth.1491.html (4,AR Cho)Comprehensive comparative analysis of strand-specific RNA sequencing methods]
|-
|align="center"|2012-27
|[http://www.plosone.org/article/info:doi%2F10.1371%2Fjournal.pone.0026426 (5,T Lee)A Low-Cost Library Construction Protocol and Data Analysis Pipeline for Illumina-Based Strand-Specific Multiplex RNA-Seq]
|-
|align="center"|2012-26
|[http://www.nature.com/nbt/journal/v28/n5/abs/nbt.1621.html (6,So&Shin)Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation]
|-
|align="center"|2012-25
|[http://bioinformatics.oxfordjournals.org/content/25/9/1105.abstract (7,So&Shin)TopHat: discovering splice junctions with RNA-Seq]
|-
|rowspan = "5" |2012/02/06
| align="center"|2012-24
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3125733/ mirConnX: condition-specific mRNA-microRNA network integrator]
|-
|align="center"|2012-23
|[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002190 Construction and Analysis of an Integrated Regulatory Network Derived from High-Throughput Sequencing Data]
|-
|align="center"|2012-22
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002415 A Densely Interconnected Genome-Wide Network of MicroRNAs and Oncogenic Pathways Revealed Using Gene Expression Signatures]
|-
|align="center"|2012-21
|[http://genome.cshlp.org/content/20/5/589.short Reprogramming of miRNA networks in cancer and leukemia]
|-
|align="center"|2012-20
|[http://www.cell.com/abstract/S0092-8674(11)01152-4 An Extensive MicroRNA-Mediated Network of RNA-RNA Interactions Regulates Established Oncogenic Pathways in Glioblastoma]
|-
|rowspan = "5" |2012/01/30
|align="center"|2012-19
|[http://www.cell.com/abstract/S0092-8674(11)00237-6 Principles and Strategies for Developing Network Models in Cancer]
|-
|align="center"|2012-18
|[http://www.nature.com/ng/journal/v37/n4/full/ng1532.html Reverse engineering of regulatory networks in human B cells]
|-
|align="center"|2012-17
|[http://www.nature.com/nature/journal/v452/n7186/abs/nature06757.html Variations in DNA elucidate molecular networks that cause disease]
|-
|align="center"|2012-16
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2779083/ Harnessing gene expression to identify the genetic basis of drug resistance]
|-
|align="center"|2012-15
|[http://www.sciencedirect.com/science/article/pii/S0092867410012936 An Integrated Approach to Uncover Drivers of Cancer]
|-
|rowspan = "3" |2012/01/09
|align="center"|2012-14
|[http://www.ncbi.nlm.nih.gov/pubmed/18704161 Genetic variation in an individual human exome.]
|-
|align="center"|2012-13
|[http://www.ncbi.nlm.nih.gov/pubmed/22081227 Predicting phenotypic variation in yeast from individual genome sequences.]
|-
|align="center"|2012-12
|[http://www.ncbi.nlm.nih.gov/pubmed/20435227 Clinical assessment incorporating a personal genome.]
|-
|rowspan = "6" |2012/01/09 2012/01/16
| align="center"|2012-11
|[http://www.ncbi.nlm.nih.gov/pubmed/20399638 Human allelic variation: perspective from protein function, structure, and evolution.]
|-
|align="center"|2012-10
|[http://www.ncbi.nlm.nih.gov/pubmed/19561590 Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm.]
|-
|align="center"|2012-09
|[http://www.ncbi.nlm.nih.gov/pubmed/11230178 Prediction of deleterious human alleles.]
|-
|align="center"|2012-08
|[http://www.ncbi.nlm.nih.gov/pubmed/12202775 Human non-synonymous SNPs: server and survey.]
|-
|align="center"|2012-07
|[http://www.ncbi.nlm.nih.gov/pubmed/20354512 A method and server for predicting damaging missense mutations.]
|-
|align="center"|2012-06
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1920242/ SNAP: predict effect of non-synonymous polymorphisms on function]
|-
|rowspan = "3" |2012/01/09 2012/01/16
|align="center"|2012-05
|[http://www.ncbi.nlm.nih.gov/pubmed/21920052 Computational and statistical approaches to analyzing variants identified by exome sequencing.]
|-
|align="center"|2012-04
|[http://www.ncbi.nlm.nih.gov/pubmed/18179889 Shifting paradigm of association studies: value of rare single-nucleotide polymorphisms.]
|-
|align="center"|2012-03
|[http://www.ncbi.nlm.nih.gov/pubmed/19684571 Targeted capture and massively parallel sequencing of 12 human exomes.]
|-
|rowspan = "2" |2012/01/09 2012/01/16
|align="center"|2012-02
|[http://www.ncbi.nlm.nih.gov/pubmed/17637733 The distribution of fitness effects of new mutations.]
|-
|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]
|}
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
|align="center"|2011-49
|[http://bmir.stanford.edu/file_asset/index.php/1407/BMIR-2009-1355.pdf (Shin)Data-Driven Methods to Discover Molecular Determinants of Serious Adverse Drug Events]
|-
|align="center"|2011-48
|[http://www.nature.com/nchembio/journal/v4/n11/abs/nchembio.118.html (Shin)Network pharmacology: the next paradigm in drug discovery]
|-
|align="center"|2011-47
|[http://www.nature.com/msb/journal/v7/n1/full/msb201171.html (Oh)Systematic exploration of synergistic drug pairs]
|-
|align="center"|2011-46
|[http://www.nature.com/msb/journal/v5/n1/full/msb200995.html (Shim)Chemogenomic profiling predicts antifungal synergies]
|-
|align="center"|2011-45
|[http://www.pnas.org/content/104/32/13086 (Beck)A strategy for predicting the chemosensitivity of human cancers and its application to drug discovery]
|-
|align="center"|2011-44
|[http://www.nature.com/nchembio/journal/v1/n7/full/nchembio747.html (Hwang)Analysis of drug-induced effect patterns to link structure and side effects of medicines]
|-
|rowspan = "3" |2011/11/14
|align="center"|2011-43
|[http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000662 Network-Based Elucidation of Human Disease Similarities Reveals Common Functional Modules Enriched for Pluripotent Drug Targets]
|-
|align="center"|2011-42
|[http://www.nature.com/msb/journal/v7/n1/full/msb201131.html Cross-species discovery of syncretic drug combinations that potentiate the antifungal fluconazole]
|-
|align="center"|2011-41
|[http://www.nature.com/msb/journal/v7/n1/full/msb20115.html Analysis of multiple compound–protein interactions reveals novel bioactive molecules]
|-
|rowspan = "4" |2011/11/07
|align="center"|2011-40
|[http://www.nature.com/nbt/journal/v25/n10/abs/nbt1338.html Drug—target network]
|-
|align="center"|2011-39
|[http://www.nature.com/msb/journal/v6/n1/full/msb200998.html A side effect resource to capture phenotypic effects of drugs]
|-
|align="center"|2011-38
|[http://genome.cshlp.org/content/18/2/206.long Quantitative systems-level determinants of human genes targeted by successful drugs]
|-
|align="center"|2011-37
|[http://www.sciencemag.org/content/321/5886/263.short Drug Target Identification Using Side-Effect Similarity]
|-
|rowspan = "3" |2011/11/07
|align="center"|2011-36
|[http://stm.sciencemag.org/content/3/96/96ra77.short Discovery and Preclinical Validation of Drug Indications Using Compendia of Public Gene Expression Data]
|-
|align="center"|2011-35
|[http://bib.oxfordjournals.org/content/12/4/303.short Exploiting drug–disease relationships for computational drug repositioning]
|-
|align="center"|2011-34
|[http://www.springerlink.com/content/4489r051nu2t0ul1/ Drug Discovery in a Multidimensional World: Systems, Patterns, and Networks]
|-
|rowspan = "3" |2011/10/05
|align="center"|2011-33
|[http://genome.cshlp.org/content/20/7/960.full Analysis of membrane proteins in metagenomics: Networks of correlated environmental features and protein families]
|-
|align="center"|2011-32
|[http://www.pnas.org/content/106/5/1374.full Quantifying environmental adaptation of metabolic pathways in metagenomics]
|-
|align="center"|2011-31
|[http://www.pnas.org/content/104/35/13913.abstract Quantitative assessment of protein function prediction from metagenomics shotgun sequences]
|-
|rowspan = "4" |2011/10/04
|align="center"|2011-30
|[http://www.nature.com/nature/journal/v464/n7285/full/nature08821.html A human gut microbial gene catalogue established by metagenomic sequencing]
|-
|align="center"|2011-29
|[http://www.nature.com/nrmicro/journal/v6/n9/abs/nrmicro1935.html Molecular eco-systems biology: towards an understanding of community function]
|-
|align="center"|2011-28
|[http://genome.cshlp.org/content/early/2009/04/20/gr.085464.108 Microbial community profiling for human microbiome projects: Tools, techniques, and challenges]
|-
|align="center"|2011-27
|[http://www.nature.com/nrmicro/journal/v9/n4/full/nrmicro2540.html Unravelling the effects of the environment and host genotype on the gut microbiome]
|-
|rowspan = "2" |2011/09/19
|align="center"|2011-26
|[http://www.sciencemag.org/content/333/6042/596.full independently evolved virulence effectors converge onto hubs in a plant immune system Network]
|-
|align="center"|2011-25
|[http://www.sciencemag.org/content/333/6042/601.full Evidence for network evolution in an arabidopsis interactome Map]
|-
|rowspan = "2" |2011/09/05
|align="center"|2011-24
|[http://www.sciencedirect.com/science/article/pii/S0092867411005861 Exome Sequencing of Ion Channel Genes Reveals Complex Profiles Confounding Personal Risk Assessment in Epilepsy]
|-
|align="center"|2011-23
|[http://www.cell.com/abstract/S0092-8674(11)00543-5 Pluripotency factors in Embryonic stem cells Regulate Differentiation into Germ Layers]
|-
|rowspan = "2" |2011/09/22
|align="center"|2011-22
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002077 Integrated Genome-scale predition of Detrimental Mutations in Transcription Networks]
|-
|align="center"|2011-21
|[http://www.nature.com/nrg/journal/v12/n4/abs/nrg2969.html From expression QTLs to personalized transcriptomics]
|-
|2011/06/20
|align="center"|2011-20
|[http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001046 a user's guide to the encyclopedia of DNA elements(ENCODE)]
|-
|rowspan = "2" |2011/03/30
|align="center"|2011-19
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature09944.html enterotypes of the human gut microbiome]
|-
|align="center"|2011-18
|[http://www.nature.com/msb/journal/v7/n1/full/msb20116.html toward molecular trait-based ecology, through intergration of biogeochemical, geographical and metagenomic data]
|-
|rowspan = "2" |2011/03/16
|align="center"|2011-17
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002047 variable pathogenicity determines individual lifespan in ''caenorhabditis elegans'']
|-
|align="center"|2011-16
|[http://www.cell.com/abstract/S0092-8674(11)00371-0 a high-resolution ''c.elegans'' essential gene network based on phenotypic profiling of a complex tissue]
|-
|rowspan = "3" |2011/04/25
|align="center"|2011-15
|[http://www.ncbi.nlm.nih.gov/pubmed/21376230 Hallmarks of Cancer : The next generation]
|-
|align="center"|2011-14
|[http://www.cell.com/abstract/S0092-8674(11)00296-0 Mapping Cancer Origins]
|-
|align="center"|2011-13
|[http://www.cell.com/abstract/S0092-8674(11)00297-2 Genetic Interactions in Cancer Progression and Treatment]
|-
|rowspan = "2" |2011/04/11
|align="center"|2011-12
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1001273 Proteins encoded in genomic regions associated with immune-mediated disease physically interact and suggest underlying biology]
|-
|align="center"|2011-11
|***Changed!***
[http://www.ncbi.nlm.nih.gov/pubmed/19557189 Identifying relationships among genomic disease regions: predicting genes at pathogenic SNP associations and rare deletions]
|-
|rowspan = "2"|2011/03/28
|align="center"|2011-10
|[http://www.pnas.org/content/106/44/18843.long profiling translatomes of discrete cell populations resolves altered cellular priorities during hypoxia in Arabidopsis]
|-
|align="center"|2011-09
|[http://www.nature.com/msb/journal/v6/n1/full/msb201076.html cell-type specific analysis of translating RNAs in developing flowers reveals new levels of control]
|-
|rowspan = "2"|2011/03/14
|align="center"|2011-08
|[http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6WSN-51SFHJD-1&_user=44062&_coverDate=01%2F07%2F2011&_rdoc=1&_fmt=high&_orig=search&_origin=search&_sort=d&_docanchor=&view=c&_acct=C000004738&_version=1&_urlVersion=0&_userid=44062&md5=2f6017aab4c794ed7e78fbbd8447077a&searchtype=a phenotypic landscape of a bacterial cell]
|-
|align="center"|2011-07
|[http://www.nature.com/msb/journal/v6/n1/full/msb2010107.html cross-species chemogenomic profiling reveals evolutionarily conserved drug mode of action]
|-
|rowspan = "2"|2011/02/28
|align="center"|2011-06
|[http://www.pnas.org/content/early/2010/09/23/1004666107.abstract genomic patterns of pleiotropy and the evolution of complexity]
|-
|align="center"|2011-05
|[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1001009 simultaneous genome-wide inference of physical, genetic, regulatory, and functional pathway]
|-
|2011/02/21
|align="center"|2011-04
|[http://www.nature.com/msb/journal/v6/n1/full/msb201071.html dynamic interaction networks in a hierarchically organized tissue]
|-
|2011/02/14
|align="center"|2011-03
|[http://www.sciencemag.org/content/330/6009/1385.abstract rewiring of genetic networks in response to DNA damage]
|-
|rowspan = "2"|2011/01/31
|align="center"|2011-02
|[http://www.nature.com/nrg/journal/v10/n9/abs/nrg2633.html Applying mass spectrometry-based proteomics to genetics, genomics and network biology]
|-
|align="center"|2011-01
|[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
|align="center"|2010-29
|[http://www.nature.com/nature/journal/v467/n7312/full/nature09326.html Functional_roles_fornoise_in_genetic_circuits]
|-
|align="center"|2010-28
|[http://www.nature.com/ng/journal/v42/n7/full/ng.610.html Estimation_of_effect_size_distribution_from_genome-wide_association_studies_and_implications_for_future_discoveries]
|-
|rowspan = "2"|2010/11/15
|align="center"|2010-27
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1000932 Liver_and_Adipose_Expression_associated_SNPs_are_enriched_for_association_to_type_2_diabetes]
|-
|align="center"|2010-26
|[http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B8JDC-50J4MRJ-2&_user=44062&_coverDate=10%2F10%2F2010&_rdoc=1&_fmt=high&_orig=search&_origin=search&_sort=d&_docanchor=&view=c&_acct=C000004738&_version=1&_urlVersion=0&_userid=44062&md5=7667a67cf8ba3f68117b8d0ff85a8887&searchtype=a It's_the_machine_that_matters:_predicting_gene_function_and_phenotype_from_protein_networks]
|-
|rowspan = "2"|2010/11/01
|align="center"|2010-25
|[http://genome.cshlp.org/content/early/2010/06/15/gr.104216.109 A_genome-wide_map_of_human_genetic_interactions_inferred_from_radiation_hybrid_genotypes]
|-
|align="center"|2010-24
|[http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0012139 A_Genome-Wide_Gene_Function_Prediction_Resource_for_Drosophila_melanogaster]
|-
|2010/10/11
|align="center"|2010-23
|[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2913399/?tool=pubmed Dissecting_spatio-temporal_protein_networks_driving_human_heart_development_and_related_disorders]
|-
|rowspan = "2"|2010/09/13
|align="center"|2010-22
|[http://www.nature.com/ng/journal/v42/n7/full/ng.600.html Transposable_elements_have_rewired_the_core_regulatory_network_of_human_embryonic_stem_cells]
|-
|align="center"|2010-21
|[http://www.nature.com/ng/journal/v42/n7/full/ng0710-557.html Limits_of_sequence_and_functional_conservation]
|-
|2010/05/26
|align="center"|2010-20
|[[media:100428_network_based_elucidation_of_human_disease_similarities_reveals_common_functional_modules_enriched_for_pluripotent_drug_targets.pdf|network_based_elucidation_of_human_disease_similarities_reveals_common_functional_modules_enriched_for_pluripotent_drug_targets.pdf]]
|-
|2010/05/19
|align="center"|2010-19
|[[media:100421_interpreting_metabolomic_profiles_using_unbiased_pathway_models.pdf|interpreting_metabolomic_profiles_using_unbiased_pathway_models.pdf]]
|-
|2010/04/21
|align="center"|2010-18
|[[media:100414_identification_of_networks_of_co_occurring_tumor_related_dna_copy_number_changes_using_a_genome_wide_scoringapproach.pdf|identification_of_networks_of_co_occurring_tumor_related_dna_copy_number_changes_using_a_genome_wide_scoringapproach.pdf]]
|-
|2010/04/14
|align="center"|2010-17
|[http://www.cell.com/retrieve/pii/S0092867410000796 an_atlas_of_combinatorial_transcriptional_regulation_in_mouse_and_man]
|-
|rowspan = "2" |2010/04/07
|align="center"|2010-16
|[http://www.pnas.org/content/early/2010/03/11/0910200107.long systematic_discovery_of_nonobvious_human_disease_models_through_orthologous_phenotypes]
|-
|align="center"|2010-15
|[http://www.nature.com/nature/journal/vaop/ncurrent/full/nature08800.html genome_side_association_study_of_107_phenotypes_in_Arabidopsis_thaliana_inbred_lines]
|-
|rowspan="2"|2010/03/31
|align="center"|2010-14
|[[media:100331_toward_stem_cell_systems_biology_from_molecules_to_networks_and_landscapes.pdf|toward_stem_cell_systems_biology_from_molecules_to_networks_and_landscapes.pdf]]
|-
|align="center"|2010-13
|[http://www.nature.com/nrm/journal/v10/n10/abs/nrm2766.html systems_biology_of_stem_cell_fate_and_cellular_reprogramming]
|-
|2010/03/24
|align="center"|2010-12
|[http://www.nature.com/nbt/journal/v27/n2/abs/nbt.1522.html dynamic_modularity_in_protein_interaction_networks_predicts_breast_cancer_outcome]
|-
|2010/01/18
|align="center"|2010-11
|JournalClub_100118_a_tutorial_on_statistical_methods_for_population_association_studies
|-
|2010/01/16
|align="center"|2010-10
|systems-level_dinamic_analyses_of_fate_change_in_murine_embryonic_stem_cells
|-
|2010/01/15
|align="center"|2010-09
|distinguishing_direct_versus_indirect_transcription_factor-DNA-interactions
|-
|2010/01/14
|align="center"|2010-08
|chemogenomic_profiling_predicts_antifungal_synergies
|-
|2010/01/13
|align="center"|2010-07
|edgetic_perturbation_models_of_human_inherited_disorders
|-
|2010/01/12
|align="center"|2010-06
|analysis_of_cell_fate_from_single-cell_gene_expression_profiles_in_C.elegans
|-
|2010/01/11
|align="center"|2010-05
|predicting_new_molecular_targets_for_known_drugs.pdf
Reference:SEA(Similarity Ensemble Approach)
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|2010/01/09
|align="center"|2010-04
|harnessing_gene_expression_to_identify_the_genetic_basis_of_drug_resistance
|-
|2010/01/08
|align="center"|2010-03
|an_integrative_approach_to_reveal_driver_gene_fusions_from_paired_end_sequencing_data_in_cancer
|-
|2010/01/07
|align="center"|2010-02
|a_phenotypic_profile_of_the_candida_albicans_regulatory_network
|-
|2010/01/06
|align="center"|2010-01
|a_global_view_of_protein_expression_in_human_cells_tissues_and_organs
|}