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+ | |+style="text-align:left;font-size:12pt" | 2024-2 scOmics | ||
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+ | [https://doi.org/10.1101/2024.04.04.24305313 Single-cell RNA sequencing of human tissue supports successful drug targets] | ||
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+ | [https://doi.org/10.1038/s41587-023-02082-2 Prediction of single-cell RNA expression profiles in live cells by Raman microscopy with Raman2RNA] | ||
+ | |- | ||
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+ | [https://doi.org/10.1101/2024.09.24.614685 Evaluating the Utilities of Foundation Models in Single-cell Data Analysis] | ||
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+ | [https://doi.org/10.1101/2024.09.24.614685 Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation] | ||
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+ | |style="padding:.4em;"|JH Cha | ||
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+ | [https://doi.org/10.1101/2024.09.24.614685 scEMB: Learning context representation of genes based on large-scale single-cell transcriptomics] | ||
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+ | [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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+ | |style="padding:.4em;"|YR Jung | ||
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+ | [https://doi.org/10.1101/2024.08.16.608180 Quantized multi-task learning for context-specific representations of gene network dynamics] | ||
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+ | [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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+ | |+style="text-align:left;font-size:12pt" | 2024-2 Microbiome | ||
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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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+ | |style="padding:.4em;"|SH Ahn | ||
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+ | [https://doi.org/10.1038/s41467-024-52561-6 Gut microbiota wellbeing index predicts overall health in a cohort of 1000 infants] | ||
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+ | [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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+ | |style="padding:.4em;"|JY Ma | ||
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+ | [https://doi.org/10.1101/2024.08.14.607850 The OMG dataset: An Open MetaGenomic corpus for mixed-modality genomic language modeling] | ||
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+ | |style="padding:.4em;"|JH Cha | ||
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+ | [https://doi.org/10.1038/s41467-024-46947-9 Genomic language model predicts protein co-regulation and function] | ||
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+ | |style="padding:.4em;"|NY Kim | ||
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+ | [https://doi.org/10.1101/2024.07.26.605391 Protein Set 1 Transformer: A protein-based genome language model to power high diversity viromics] | ||
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+ | [https://doi.org/10.1101/2024.07.11.603044 Prophage-DB: A comprehensive database to explore diversity,distribution, and ecology of prophages] | ||
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+ | |style="padding:.4em;"|JY Kim | ||
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+ | [https://doi.org/10.1186/s40168-024-01904-y Strain‑resolved de‑novo metagenomic assembly of viral genomes and microbial 16S rRNAs] | ||
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+ | |style="padding:.4em;"|WJ Kim | ||
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+ | [https://doi.org/10.1186/s40168-024-01876-z Prokaryotic‑virus‑encoded auxiliary metabolic genes throughout the global oceans] | ||
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+ | |style="padding:.4em;"|G Koh | ||
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+ | [https://doi.org/10.1016/j.cell.2024.07.039 Unexplored microbial diversity from 2,500 food metagenomes and links with the human microbiome] | ||
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+ | |style="padding:.4em;"|SH Ahn | ||
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+ | [https://doi.org/10.1101/2024.04.17.589959 Pangenomes of Human Gut Microbiota Uncover Links Between Genetic Diversity and Stress Response] | ||
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+ | [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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+ | |style="padding:.4em;"|JY Ma | ||
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+ | [https://doi.org/10.1038/s41467-024-52533-w Accurately predicting enzyme functions through geometric graph learning on ESMFold-predicted structures] | ||
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+ | |style="padding:.4em;"|JH Cha | ||
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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)] | ||
+ | |} | ||
+ | {|class=wikitable style="text-align:center;" | ||
+ | |+style="text-align:left;font-size:12pt" | 2024-1 scOmics | ||
+ | |- | ||
+ | !scope="col" style="padding:.4em" | Date | ||
+ | !scope="col" style="padding:.4em" | Team | ||
+ | !scope="col" style="padding:.4em" | Paper<br/>index | ||
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+ | !scope="col" style="padding:.4em" | Paper title | ||
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+ | |style="padding:.4em;"|HB Lee | ||
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+ | [https://doi.org/10.1126/science.adj4857 A blueprint for tumor-infiltrating B cells across human cancers] | ||
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+ | |style="padding:.4em;"|YR Jung | ||
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+ | [https://doi.org/10.1038/s41467-024-48310-4 Systematic dissection of tumor-normal single-cell ecosystems across a thousand tumors of 30 cancer types] | ||
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+ | [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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+ | |style="padding:.4em;"|YR Jung | ||
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+ | [https://doi.org/10.1016/j.xgen.2023.100473 Single-cell transcriptome landscape of circulating CD4+ T cell populations in autoimmune diseases] | ||
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+ | |style="padding:.4em;"|EJ Sung | ||
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+ | [https://doi.org/10.1038/s43588-024-00597-5 Population-level comparisons of gene regulatory networks modeled on highthroughput single-cell transcriptomics data] | ||
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+ | [https://doi.org/10.1101/2024.06.16.599201 node2vec2rank: Large Scale and Stable Graph Differential Analysis via Multi-Layer Node Embeddings and Ranking] | ||
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+ | [https://doi.org/10.1016/j.xgen.2024.100553 Unified cross-modality integration and analysis of T cell receptors and T cell transcriptomes by low-resource-aware representation learning] | ||
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+ | |style="padding:.4em;"|JH Cha | ||
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+ | [https://doi.org/10.1101/2023.07.18.549602 Contextual AI models for single-cell protein biology] | ||
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+ | [https://doi.org/10.1101/2024.04.15.589472 Nicheformer: a foundation model for single-cell and spatial omics] | ||
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+ | [https://doi.org/10.1101/2023.05.29.542705 Large Scale Foundation Model on Single-cell Transcriptomics] | ||
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+ | [https://doi.org/10.1038/s41592-024-02201-0 scGPT: toward building a foundation modelfor single-cell multi-omics using generative AI] | ||
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+ | |style="padding:.4em;"|JH Cha | ||
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+ | [https://doi.org/10.1038/s41586-023-06139-9 Transfer learning enables predictions in network biology] | ||
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+ | |- | ||
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+ | [https://doi.org/10.1038/s41587-023-01728-5 A relay velocity model infers cell-dependent RNA velocity] | ||
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+ | [https://doi.org/10.1038/s41467-023-44206-x Pathway centric analysis for single-cell RNA-seq and spatial transcriptomics data with GSDensity] | ||
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+ | [https://doi.org/10.1038/s41587-023-01734-7 Dandelion uses the single-cell adaptive immune receptor repertoire to explore lymphocyte developmental origins] | ||
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+ | [https://doi.org/10.1016/j.cell.2023.11.026 Automatic cell-type harmonization and integration across Human Cell Atlas datasets] | ||
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+ | [https://doi.org/10.1038/s41592-023-01994-w Deep generative modeling of transcriptional dynamics for RNA velocity analysis in single cells] | ||
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+ | [https://doi.org/10.1038/s41587-021-00896-6 Gene signature extraction and cell identity recognition at the single-cell level with Cell-ID] | ||
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+ | [https://doi.org/10.1038/s41591-024-03067-7 Strain-specific gut microbial signatures in type 2 diabetes identified in a cross-cohort analysis of 8,117 metagenomes] | ||
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+ | [https://doi.org/10.1186/s40168-024-01832-x Gut virome-wide association analysis identifes cross-population viral signatures for infammatory bowel disease] | ||
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+ | [https://doi.org/10.48550/arXiv.1806.00064 Efficient Low-rank Multimodal Fusion with Modality-Specific Factors] | ||
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+ | [https://doi.org/10.48550/arXiv.1707.07250 Tensor Fusion Network for Multimodal Sentiment Analysis] | ||
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+ | [https://doi.org/10.1016/j.cell.2024.03.034 Gut symbionts alleviate MASH through a secondary bile acid biosynthetic pathway] | ||
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+ | |style="padding:.4em;"|G Koh | ||
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+ | [https://doi.org/10.1186/s13059-024-03325-4 Gut microbiota DPP4-like enzymes are increased in type-2 diabetes and contribute to incretin inactivation] | ||
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+ | |style="padding:.4em;"|SH Ahn | ||
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+ | [https://pubmed.ncbi.nlm.nih.gov/31510656 Deep learning with multimodal representation for pancancer prognosis prediction] | ||
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+ | [https://doi.org/10.1016/j.chom.2024.03.005 A metagenomics pipeline reveals insertion sequence-driven evolution of the microbiota] | ||
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+ | |style="padding:.4em;"|JH Cha | ||
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+ | [https://arxiv.org/abs/2103.00020 Learning Transferable Visual Models From Natural Language Supervision] | ||
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+ | |style="padding:.4em;"|SH Ahn | ||
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+ | |style="padding:.4em;"|JY Cha | ||
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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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+ | |style="padding:.4em;"|JH Cha | ||
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+ | [https://doi.org/10.1038/s41592-023-02092-7 Multigroup analysis of compositions of microbiomes with covariate adjustments and repeated measures] | ||
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+ | [https://doi.org/10.1038/s41467-023-40719-7 Microdiversity of the vaginal microbiome is associated with preterm birth] | ||
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+ | [https://doi.org/10.1038/s41564-023-01584-8 Large language models improve annotation of prokaryotic viral proteins] | ||
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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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+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2024/01/02 | ||
+ | |style="padding:.4em;" rowspan=1|Single-cell | ||
+ | |style="padding:.4em;"|23-35 | ||
+ | |style="padding:.4em;"|SB Baek | ||
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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;"|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;"|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;"|23-31 | ||
+ | |style="padding:.4em;"|JW Yu | ||
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+ | |- | ||
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+ | |style="padding:.4em;"|23-30 | ||
+ | |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;"|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;"|23-28 | ||
+ | |style="padding:.4em;"|EJ Sung | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
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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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+ | |style="padding:.4em;"|JH Cha | ||
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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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+ | |style="padding:.4em;"|HJ Kim | ||
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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;"|JH Cha | ||
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+ | [https://doi.org/10.1038/s41564-023-01439-2 A predicted CRISPR-mediated symbiosis between uncultivated archaea] | ||
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+ | |style="padding:.4em;"|23-64 | ||
+ | |style="padding:.4em;"|SH Ahn | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-023-01692-x Integrating compositional and functional content to describe vaginal microbiomes in health and disease] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2024/02/14 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|23-63 | ||
+ | |style="padding:.4em;"|JY Ma | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1038/s41587-023-01696-w Contamination source modeling with SCRuB improves cancer phenotype prediction from microbiome data] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2024/02/07 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|23-62 | ||
+ | |style="padding:.4em;"|NY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1038/s41586-023-06431-8 Mapping the T cell repertoire to a complex gut bacterial community] | ||
+ | |- | ||
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+ | |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;"|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;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|23-59 | ||
+ | |style="padding:.4em;"|WJ Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1038/s41593-023-01361-0 Multi-level analysis of the gut–brain axis shows autism spectrum disorder-associated molecular and microbial profiles] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2024/01/17 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|23-58 | ||
+ | |style="padding:.4em;"|G Koh | ||
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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;"|23-57 | ||
+ | |style="padding:.4em;"|SH Ahn | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1038/s41467-023-41042-x Impact of dietary interventions on pre-diabetic oral and gut microbiome, metabolites and cytokines] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2024/01/10 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|23-56 | ||
+ | |style="padding:.4em;"|HJ Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1038/s41592-023-02018-3 Fast and robust metagenomic sequence comparison through sparse chaining with skani] | ||
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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] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/01/03 | ||
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+ | |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;"|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;"|23-52 | ||
+ | |style="padding:.4em;"|YR Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1016/j.xcrm.2023.101251 Prior antibiotic administration disrupts anti-PD-1 responses in advanced gastric cancer by altering the gut microbiome and systemic immune response] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/12/27 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|23-51 | ||
+ | |style="padding:.4em;"|JY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1016/j.cell.2023.05.046 Ultra-deep sequencing of Hadza hunter-gatherers recovers vanishing gut microbes] | ||
+ | |- | ||
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+ | |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;"|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;"|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;"|HJ Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |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;"|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;"|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;"|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 | ||
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+ | |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;"|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;"|23-40 | ||
+ | |style="padding:.4em;"|G Koh | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |style="padding:.4em;" rowspan=1|2023/11/01 | ||
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+ | |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;"|SH Ahn | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1038/s41591-023-02407-3 Microbiome-derived cobalamin and succinyl-CoA as biomarkers for improved screening of anal cancer] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/10/11 | ||
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+ | |style="padding:.4em;"|JH Cha | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1038/s41591-023-02424-2 The airway microbiome mediates the interaction between environmental exposure and respiratory health in humans] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/10/11 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|23-36 | ||
+ | |style="padding:.4em;"|JY Ma | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1080/19490976.2023.2224474 Ordering taxa in image convolution networks improves microbiome-based machine learning accuracy] | ||
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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] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/09/27 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|23-34 | ||
+ | |style="padding:.4em;"|SH Lee | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1016/j.cell.2023.03.011 Dietary tryptophan metabolite released by intratumoral Lactobacillus reuteri facilitates immune checkpoint inhibitor treatment] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/09/20 | ||
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+ | |style="padding:.4em;"|23-33 | ||
+ | |style="padding:.4em;"|WJ Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1038/s43587-022-00306-9 Toward an improved definition of a healthy microbiome for healthy aging] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/09/20 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|23-32 | ||
+ | |style="padding:.4em;"|JY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1038/s43587-022-00287-9 Associations of the skin, oral and gut microbiome with aging, frailty and infection risk reservoirs in older adults] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/09/13 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|23-31 | ||
+ | |style="padding:.4em;"|G Koh | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1186/s40168-023-01614-x Statistical modeling of gut microbiota for personalized health status monitoring] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/09/13 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
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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;" rowspan=1|Microbiome | ||
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+ | |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 | ||
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+ | |style="padding:.4em;"|JH Cha | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1016/j.chom.2023.01.003 Longitudinal comparison of the developing gut virome in infants and their mothers] | ||
+ | |} | ||
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+ | |||
+ | |||
+ | |||
+ | {|class=wikitable style="text-align:center;" | ||
+ | |+style="text-align:left;font-size:12pt" | 2023-1 ADVANCED MICROBIOME DATA ANALYSIS | ||
+ | |- | ||
+ | !scope="col" style="padding:.4em" | Date | ||
+ | !scope="col" style="padding:.4em" | Team | ||
+ | !scope="col" style="padding:.4em" | Paper<br/>index | ||
+ | !scope="col" style="padding:.4em" | Presenter | ||
+ | !scope="col" style="padding:.4em" | Paper title | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/06/13 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|23-24 | ||
+ | |style="padding:.4em;"|JY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1016/j.chom.2020.03.005 Structure of the Mucosal and Stool Microbiome in Lynch Syndrome] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/06/13 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|23-23 | ||
+ | |style="padding:.4em;"|WJ Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1038/s41586-019-1237-9 Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/06/13 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|23-22 | ||
+ | |style="padding:.4em;"|SH Ahn | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1016/j.jare.2022.03.007 Roles of oral microbiota and oral-gut microbial transmission in hypertension] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/05/30 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|23-21 | ||
+ | |style="padding:.4em;"|SY Lim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1016/j.chom.2022.08.009 Human gut microbiota stimulate defined innate immune responses that vary from phylum to strain] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/05/30 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|23-20 | ||
+ | |style="padding:.4em;"|BS Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1038/s41591-023-02217-7 Gut microbial metabolism of 5-ASA diminishes its clinical efficacy in inflammatory bowel disease] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/05/30 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|23-19 | ||
+ | |style="padding:.4em;"|JY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1016/j.chom.2023.01.004 Deficient butyrate-producing capacity in the gut microbiome is associated with bacterial network disturbances and fatigue symptoms in ME/CFS] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/05/23 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|23-18 | ||
+ | |style="padding:.4em;"|EJ Sung | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1016/j.ccell.2022.11.013 Gut microbiota-mediated nucleotide synthesis attenuates the response to neoadjuvant chemoradiotherapy in rectal cancer] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/05/23 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|23-17 | ||
+ | |style="padding:.4em;"|G Koh | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1016/j.chom.2021.06.019 Multi-omics reveal microbial determinants impacting responses to biologic therapies in inflammatory bowel disease] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/05/23 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|23-16 | ||
+ | |style="padding:.4em;"|SH Lee | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1038/s41586-022-05181-3 Identification of trypsin-degrading commensals in the large intestine] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/05/16 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|23-15 | ||
+ | |style="padding:.4em;"|JP Hong | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1038/s41586-022-05546-8 Questioning the fetal microbiome illustrates pitfalls of low-biomass microbial studies] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/05/16 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|23-14 | ||
+ | |style="padding:.4em;"|MR Jang | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1016/j.chom.2023.01.013 Tissue-resident Lachnospiraceae family bacteria protect against colorectal carcinogenesis by promoting tumor immune surveillance] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/05/16 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|23-13 | ||
+ | |style="padding:.4em;"|JW Yu | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1016/j.cell.2022.09.005 Pan-cancer analyses reveal cancer-type-specific fungal ecologies and bacteriome interactions] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/05/09 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|23-12 | ||
+ | |style="padding:.4em;"|NY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1016/j.cell.2022.09.015 A pan-cancer mycobiome analysis reveals fungal involvement in gastrointestinal and lung tumors] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/05/09 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|23-11 | ||
+ | |style="padding:.4em;"|HR Shin | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1038/s41564-021-01030-7 Multi-kingdom microbiota analyses identify bacterial–fungal interactions and biomarkers of colorectal cancer across cohorts] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/05/09 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|23-10 | ||
+ | |style="padding:.4em;"|SG Oh | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1038/s42255-022-00716-4 The antitumour effects of caloric restriction are mediated by the gut microbiome] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/05/02 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|23-9 | ||
+ | |style="padding:.4em;"|WJ Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1038/s41591-022-01964-3 Variability of strain engraftment and predictability of microbiome composition after fecal microbiota transplantation across different diseases] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/05/02 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|23-8 | ||
+ | |style="padding:.4em;"|SM Han | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1038/s41591-022-01913-0 Drivers and determinants of strain dynamics following fecal microbiota transplantation] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/05/02 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|23-7 | ||
+ | |style="padding:.4em;"|YY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1016/j.cell.2022.11.023 Mobile genetic elements from the maternal microbiome shape infant gut microbial assembly and metabolism] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/04/18 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|23-6 | ||
+ | |style="padding:.4em;"|SH Heo | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1016/j.chom.2023.01.018 Mother-to-infant microbiota transmission and infant microbiota development across multiple body sites] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/04/18 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|23-5 | ||
+ | |style="padding:.4em;"|SY Yang | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1038/s41467-023-36633-7 Population-level impacts of antibiotic usage on the human gut microbiome] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/04/18 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|23-4 | ||
+ | |style="padding:.4em;"|YH Yoon | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1038/s41586-022-05438-x Enterococci enhance Clostridioides difficile pathogenesis] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/04/11 | ||
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+ | |style="padding:.4em;"|23-3 | ||
+ | |style="padding:.4em;"|DH Lee | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1002/imt2.61 Targeting keystone species helps restore the dysbiosis of butyrate‐producing bacteria in nonalcoholic fatty liver disease] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/04/11 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|23-2 | ||
+ | |style="padding:.4em;"|YJ Roh | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1002/advs.202203115 Differential Oral Microbial Input Determines Two Microbiota Pneumo-Types Associated with Health Status] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/04/11 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|23-1 | ||
+ | |style="padding:.4em;"|SH Ahn | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1016/j.cell.2022.08.021 Gut microbiome of multiple sclerosis patients and paired household healthy controls reveal associations with disease risk and course] | ||
+ | |} | ||
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+ | |+style="text-align:left;font-size:12pt" | 2023-1 scOmics | ||
+ | |- | ||
+ | !scope="col" style="padding:.4em" | Date | ||
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+ | !scope="col" style="padding:.4em" | Paper<br/>index | ||
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+ | |style="padding:.4em;" rowspan=1|Single-cell | ||
+ | |style="padding:.4em;"|23-24 | ||
+ | |style="padding:.4em;"|JW Yu | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1101/2023.01.17.524482 Decoupling the correlation between cytotoxic and exhausted T lymphocyte transcriptomic signatures enhances melanoma immunotherapy response prediction from tumor expression] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/08/09 | ||
+ | |style="padding:.4em;" rowspan=1|Single-cell | ||
+ | |style="padding:.4em;"|23-23 | ||
+ | |style="padding:.4em;"|IS Choi | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1101/2023.07.28.550993 Major data analysis errors invalidate cancer microbiome findings] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/08/02 | ||
+ | |style="padding:.4em;" rowspan=1|Single-cell | ||
+ | |style="padding:.4em;"|23-22 | ||
+ | |style="padding:.4em;"|EJ Sung | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1038/s43018-022-00475-x A single-cell atlas of glioblastoma evolution under therapy reveals cell-intrinsic and cell-extrinsic therapeutic targets] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/07/26 | ||
+ | |style="padding:.4em;" rowspan=1|Single-cell | ||
+ | |style="padding:.4em;"|23-21 | ||
+ | |style="padding:.4em;"|G Koh | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1038/s43018-022-00433-7 Single-cell meta-analyses reveal responses of tumor-reactive CXCL13+ T cells to immune-checkpoint blockade] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/07/19 | ||
+ | |style="padding:.4em;" rowspan=1|Single-cell | ||
+ | |style="padding:.4em;"|23-20 | ||
+ | |style="padding:.4em;"|JW Yu | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1038/s41587-022-01342-x Estimation of tumor cell total mRNA expression in 15 cancer types predicts disease progression] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/07/12 | ||
+ | |style="padding:.4em;" rowspan=1|Single-cell | ||
+ | |style="padding:.4em;"|23-19 | ||
+ | |style="padding:.4em;"|JH Cha | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1038/s41587-022-01288-0 DIALOGUE maps multicellular programs in tissue from single-cell or spatial transcriptomics data] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/07/05 | ||
+ | |style="padding:.4em;" rowspan=1|Single-cell | ||
+ | |style="padding:.4em;"|23-18 | ||
+ | |style="padding:.4em;"|SB Baek | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1038/s41591-023-02371-y Pan-cancer T cell atlas links a cellular stress response state to immunotherapy resistance] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/06/28 | ||
+ | |style="padding:.4em;" rowspan=1|Single-cell | ||
+ | |style="padding:.4em;"|23-17 | ||
+ | |style="padding:.4em;"|EJ Sung | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1016/j.patter.2022.100651 Self-supervised graph representation learning integrates multiple molecular networks and decodes gene-disease relationships] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/06/21 | ||
+ | |style="padding:.4em;" rowspan=1|Single-cell | ||
+ | |style="padding:.4em;"|23-16 | ||
+ | |style="padding:.4em;"|IS Choi | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1016/j.ccell.2022.10.008 High-resolution single-cell atlas reveals diversity and plasticity of tissue-resident neutrophils in non-small cell lung cancer] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/06/14 | ||
+ | |style="padding:.4em;" rowspan=1|Single-cell | ||
+ | |style="padding:.4em;"|23-15 | ||
+ | |style="padding:.4em;"|G Koh | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1186/s13059-022-02828-2 Parallel single-cell and bulk transcriptome analyses reveal key features of the gastric tumor microenvironment] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/05/31 | ||
+ | |style="padding:.4em;" rowspan=1|Single-cell | ||
+ | |style="padding:.4em;"|23-14 | ||
+ | |style="padding:.4em;"|JW Yu | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1038/s41467-022-32838-4 Mutated processes predict immune checkpoint inhibitor therapy benefit in metastatic melanoma] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/05/24 | ||
+ | |style="padding:.4em;" rowspan=1|Single-cell | ||
+ | |style="padding:.4em;"|23-13 | ||
+ | |style="padding:.4em;"|JH Cha | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1038/s43018-021-00292-8 Temporal single-cell tracing reveals clonal revival and expansion of precursor exhausted T cells during anti-PD-1 therapy in lung cancer] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/05/17 | ||
+ | |style="padding:.4em;" rowspan=1|Single-cell | ||
+ | |style="padding:.4em;"|23-12 | ||
+ | |style="padding:.4em;"|SB Baek | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1016/j.cell.2022.11.028 Integrative single-cell analysis of cardiogenesis indentifies developmental trajectories and non-conding mutations in congenital heart disease] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/05/10 | ||
+ | |style="padding:.4em;" rowspan=1|Single-cell | ||
+ | |style="padding:.4em;"|23-11 | ||
+ | |style="padding:.4em;"|EJ Sung | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1101/2022.12.20.521311 Supervised discovery of interpretable gene programs from single-cell data] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/05/03 | ||
+ | |style="padding:.4em;" rowspan=1|Single-cell | ||
+ | |style="padding:.4em;"|23-10 | ||
+ | |style="padding:.4em;"|IS Choi | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1038/s41586-022-05435-0 Effect of the intratumoral microbiota on spatial and cellular heterogeneity in cancer] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/04/26 | ||
+ | |style="padding:.4em;" rowspan=1|Single-cell | ||
+ | |style="padding:.4em;"|23-9 | ||
+ | |style="padding:.4em;"|G Koh | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.nature.com/articles/s41588-022-01141-9 Cancer cell states recur across tumor types and form specific interactions with the tumor microenvironment] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/03/22 | ||
+ | |style="padding:.4em;" rowspan=1|Single-cell | ||
+ | |style="padding:.4em;"|23-8 | ||
+ | |style="padding:.4em;"|JW Yu | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.biorxiv.org/content/10.1101/2022.08.05.502989v1 MetaTiME: Meta-components of the Tumor Immune Microenvironment] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/03/08 | ||
+ | |style="padding:.4em;" rowspan=1|Single-cell | ||
+ | |style="padding:.4em;"|23-7 | ||
+ | |style="padding:.4em;"|JH Cha | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.nature.com/articles/s41590-022-01262-7 Pre-encoded responsiveness to type I interferon in the peripheral immune system defines outcome of PD1 blockade therapy] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/02/21 | ||
+ | |style="padding:.4em;" rowspan=1|Single-cell | ||
+ | |style="padding:.4em;"|23-6 | ||
+ | |style="padding:.4em;"|SB Baek | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.biorxiv.org/content/10.1101/2022.03.16.484513v1 Integrated single-cell profiling dissects cell-state-specific enhancer landscapes of human tumor-infiltrating T cells] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/02/14 | ||
+ | |style="padding:.4em;" rowspan=1|Single-cell | ||
+ | |style="padding:.4em;"|23-5 | ||
+ | |style="padding:.4em;"|EJ Sung | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.nature.com/articles/s41591-022-01799-y A T cell resilience model associated with response to immunotherapy in multiple tumor types] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2022/01/31 | ||
+ | |style="padding:.4em;" rowspan=1|Single-cell | ||
+ | |style="padding:.4em;"|23-4 | ||
+ | |style="padding:.4em;"|IS Choi | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.nature.com/articles/s41588-022-01134-8 Single-nucleus and spatial transcriptome profiling of pancreatic cancer identifies multicellular dynamics associated with neoadjuvant treatment] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/01/25 | ||
+ | |style="padding:.4em;" rowspan=1|Single-cell | ||
+ | |style="padding:.4em;"|23-3 | ||
+ | |style="padding:.4em;"|G Koh | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://pubmed.ncbi.nlm.nih.gov/35649411/ Cross-tissue, single-cell stromal atlas identifies shared pathological fibroblast phenotypes in four chronic inflammatory diseases] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/01/17 | ||
+ | |style="padding:.4em;" rowspan=1|Single-cell | ||
+ | |style="padding:.4em;"|23-2 | ||
+ | |style="padding:.4em;"|JW Yu | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.sciencedirect.com/science/article/pii/S1535610822003178 Pan-cancer integrative histology-genomic analysis via multimodal deep learning] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/01/11 | ||
+ | |style="padding:.4em;" rowspan=1|Single-cell | ||
+ | |style="padding:.4em;"|23-1 | ||
+ | |style="padding:.4em;"|JH Cha | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://pubmed.ncbi.nlm.nih.gov/35803260/ Tissue-resident memory and circulating T cells are early responders to pre-surgical cancer immunotherapy] | ||
+ | |} | ||
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+ | |+style="text-align:left;font-size:12pt" | 2023-1 Microbiome | ||
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+ | |- | ||
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+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|23-26 | ||
+ | |style="padding:.4em;"|NY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
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+ | |style="padding:.4em;"|23-25 | ||
+ | |style="padding:.4em;"|SH Lee | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/08/18 | ||
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+ | |style="padding:.4em;"|WJ Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/08/11 | ||
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+ | |style="padding:.4em;"|JY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/08/11 | ||
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+ | |style="padding:.4em;"|23-22 | ||
+ | |style="padding:.4em;"|SH Ahn | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/08/04 | ||
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+ | |style="padding:.4em;"|HJ Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
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+ | |style="padding:.4em;"|JH Cha | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/07/14 | ||
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+ | |style="padding:.4em;"|23-19 | ||
+ | |style="padding:.4em;"|JY Ma | ||
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+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/07/07 | ||
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+ | |style="padding:.4em;"|23-18 | ||
+ | |style="padding:.4em;"|NY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
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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"| | ||
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+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/06/23 | ||
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+ | |style="padding:.4em;"|23-16 | ||
+ | |style="padding:.4em;"|HJ Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/06/16 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|23-15 | ||
+ | |style="padding:.4em;"|SH Lee | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/06/02 | ||
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+ | |style="padding:.4em;"|23-14 | ||
+ | |style="padding:.4em;"|JY Ma | ||
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+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/05/26 | ||
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+ | |style="padding:.4em;"|23-13 | ||
+ | |style="padding:.4em;"|NY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |style="padding:.4em;" rowspan=1|2023/05/19 | ||
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+ | |style="padding:.4em;"|23-12 | ||
+ | |style="padding:.4em;"|SH Lee | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |style="padding:.4em;" rowspan=1|2023/05/12 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|23-11 | ||
+ | |style="padding:.4em;"|JY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/04/28 | ||
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+ | |style="padding:.4em;"|23-10 | ||
+ | |style="padding:.4em;"|WJ Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
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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 | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
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+ | |style="padding:.4em;"|23-8 | ||
+ | |style="padding:.4em;"|HJ Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
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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 | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |style="padding:.4em;"|23-6 | ||
+ | |style="padding:.4em;"|JY Ma | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |style="padding:.4em;"|23-5 | ||
+ | |style="padding:.4em;"|NY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |style="padding:.4em;"|23-4 | ||
+ | |style="padding:.4em;"|SH Lee | ||
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+ | |- | ||
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+ | |style="padding:.4em;"|23-3 | ||
+ | |style="padding:.4em;"|SH Ahn | ||
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+ | |style="padding:.4em;"|JH Cha | ||
+ | |style="padding:.4em;text-align:left"| | ||
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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|2022/11/29 | ||
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+ | |style="padding:.4em;"|JH Cha, SB Baek, IS Choi | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |style="padding:.4em;" rowspan=1|2022/11/22 | ||
+ | |style="padding:.4em;" rowspan=1|Single-cell | ||
+ | |style="padding:.4em;"|22-30 | ||
+ | |style="padding:.4em;"|IS Choi | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |style="padding:.4em;" rowspan=1|2022/11/08 | ||
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+ | |style="padding:.4em;"|22-29 | ||
+ | |style="padding:.4em;"|SB Baek | ||
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+ | |style="padding:.4em;" rowspan=1|2022/10/11 | ||
+ | |style="padding:.4em;" rowspan=1|Single-cell | ||
+ | |style="padding:.4em;"|22-28 | ||
+ | |style="padding:.4em;"|G Koh | ||
+ | |style="padding:.4em;text-align:left"| | ||
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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 | ||
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+ | |style="padding:.4em;"|22-26 | ||
+ | |style="padding:.4em;"|JH Cha | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |style="padding:.4em;" rowspan=1|2022/09/01 | ||
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+ | |style="padding:.4em;"|SB Baek | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |style="padding:.4em;"|EJ Sung | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |style="padding:.4em;"|IS Choi | ||
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+ | |style="padding:.4em;"|SB Baek | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |style="padding:.4em;"|JH Cha | ||
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+ | |style="padding:.4em;"|JW Yu | ||
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+ | |style="padding:.4em;"|22-19 | ||
+ | |style="padding:.4em;"|IS Choi | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |style="padding:.4em;"|EJ Sung | ||
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+ | |style="padding:.4em;"|SB Baek | ||
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+ | |style="padding:.4em;"|JH Cha | ||
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+ | |style="padding:.4em;"|JW Yu | ||
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+ | |style="padding:.4em;"|EJ Sung | ||
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+ | |style="padding:.4em;"|IS Choi | ||
+ | |style="padding:.4em;text-align:left"| | ||
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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/34930412/ MultiMAP: dimensionality reduction and integration of multimodal data] | ||
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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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+ | |style="padding:.4em;"|SH Lee | ||
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+ | [https://www.sciencedirect.com/science/article/pii/S0092867422009199?via%3Dihub Personalized microbiome-driven effects of non-nutritive sweeteners on human glucose tolerance] | ||
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+ | |style="padding:.4em;"|SH Ahn | ||
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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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+ | |style="padding:.4em;"|JH Cha | ||
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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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+ | |style="padding:.4em;"|JY Ma | ||
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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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+ | |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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+ | [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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+ | |style="padding:.4em;"|JY Ma | ||
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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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+ | |style="padding:.4em;"|SH Ann | ||
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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://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://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/s13059-020-02200-2 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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+ | |style="padding:.4em;"|SH Lee | ||
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+ | |style="padding:.4em;"|SH Ahn | ||
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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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+ | |} | ||
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+ | !scope="col" style="padding:.4em" | Paper<br/>index | ||
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+ | !scope="col" style="padding:.4em" | Paper title | ||
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+ | [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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+ | |style="padding:.4em;"|SH Lee | ||
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+ | [https://www.nature.com/articles/nmeth.3802 Strain-level microbial epidemiology and population genomics from shotgun metagenomics] | ||
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+ | [https://www.cell.com/cell/fulltext/S0092-8674(21)00942-9#secsectitle0025 Chromatin and gene-regulatory dynamics of the developing human cerebral cortex at single-cell resolution] | ||
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+ | [https://www.biorxiv.org/content/10.1101/2021.02.09.430114v2 Single-cell ATAC and RNA sequencing reveal pre-existing and persistent subpopulations of cells associated with relapse of prostate cancer] | ||
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+ | [https://www.biorxiv.org/content/10.1101/2021.03.16.435578v1 Integrated single-cell transcriptomics and epigenomics reveals strong germinal center-associated etiology of autoimmune risk loci] | ||
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+ | [https://www.biorxiv.org/content/10.1101/2021.07.28.453784v1 Functional Inference of Gene Regulation using Single-Cell Multi-Omics] | ||
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+ | [https://www.biorxiv.org/content/10.1101/2021.03.24.436532v1 Single-cell analyses reveal a continuum of cell state and composition changes in the malignant transformation of polyps to colorectal cancer] | ||
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+ | [https://www.cell.com/cancer-cell/fulltext/S1535-6108(21)00165-3 Single-cell sequencing links multiregional immune landscapes and tissue-resident T cells in ccRCC to tumor topology and therapy efficacy] | ||
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+ | [https://www.sciencedirect.com/science/article/pii/S1535610821001173 Tumor and immune reprogramming during immunotherapy in advanced renal cell carcinoma] | ||
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+ | [https://www.sciencedirect.com/science/article/pii/S1074761321001199 Single-cell chromatin accessibility landscape identifies tissue repair program in human regulatory T cells] | ||
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+ | [https://www.nature.com/articles/s41591-021-01232-w Identification of resistance pathways and therapeutic targets in relapsed multiple myeloma patients through single-cell sequencing] | ||
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+ | [https://www.nature.com/articles/s41591-021-01323-8 A single-cell map of intratumoral changes during anti-PD1 treatment of patients with breast cancer] | ||
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+ | [https://www.sciencedirect.com/science/article/abs/pii/S0092867420316135 Single-cell landscape of the ecosystem in early-relapse hepatocellular carcinoma] | ||
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+ | [https://www.nature.com/articles/s41586-021-03552-w Interpreting type 1 diabetes risk with genetics and single-cell epigenomics] | ||
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+ | [https://www.sciencedirect.com/science/article/pii/S0092867421000726 Massive expansion of human gut bacteriophage diversity] | ||
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+ | [https://www.sciencedirect.com/science/article/pii/S1931312821001451 The infant gut resistome associates with E. coli, environmental exposures, gut microbiome maturity, and asthma-associated bacterial composition] | ||
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+ | [https://www.sciencedirect.com/science/article/pii/S1931312820306703?dgcid=rss_sd_all Methotrexate impacts conserved pathways in diverse human gut bacteria leading to decreased host immune activation] | ||
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+ | [https://science.sciencemag.org/content/366/6471/eaax9176 A metagenomic strategy for harnessing the chemical repertoire of the human microbiome] | ||
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+ | [https://www.nature.com/articles/s41467-019-10927-1 Predictive metabolomic profiling of microbial communities using amplicon or metagenomic sequences] | ||
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+ | [https://www.nature.com/articles/s41564-018-0306-4 Gut microbiome structure and metabolic activity in inflammatory bowel disease] | ||
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+ | [https://www.nature.com/articles/s41591-020-01183-8 Microbiome connections with host metabolism and habitual diet from 1,098 deeply phenotyped individuals] | ||
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+ | [https://www.nature.com/articles/s41467-020-18476-8 A predictive index for health status using species-level gut microbiome profiling] | ||
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+ | [https://www.nature.com/articles/s41586-019-1237-9 Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases] | ||
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+ | [https://www.nature.com/articles/s41467-021-21475-y Gastrointestinal microbiota composition predicts peripheral inflammatory state during treatment of human tuberculosis] | ||
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+ | [https://www.sciencedirect.com/science/article/pii/S1931312820301694 Structure of the Mucosal and Stool Microbiome in Lynch Syndrome] | ||
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+ | [https://science.sciencemag.org/content/369/6506/936 Cross-reactivity between tumor MHC class 1-restricted antigens and an enterococcal bacteriophage] | ||
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+ | |style="padding:.4em;"|JH Park | ||
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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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+ | |} | ||
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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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+ | |style="padding:.4em;"|20-14 | ||
+ | |style="padding:.4em;"|JW Choi | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;"|20-13 | ||
+ | |style="padding:.4em;"|JW Seo | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://pubmed.ncbi.nlm.nih.gov/30388455/ A Cancer Cell Program Promotes T Cell Exclusion and Resistance to Checkpoint Blockade] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2020/06/09 | ||
+ | |style="padding:.4em;" rowspan=2|- | ||
+ | |style="padding:.4em;"|20-12 | ||
+ | |style="padding:.4em;"|JY Lee | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://pubmed.ncbi.nlm.nih.gov/31974247/ Single-cell Transcriptional Diversity Is a Hallmark of Developmental Potential] | ||
+ | |- | ||
+ | |style="padding:.4em;"|20-11 | ||
+ | |style="padding:.4em;"|JH Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2020/06/02 | ||
+ | |style="padding:.4em;" rowspan=2|- | ||
+ | |style="padding:.4em;"|20-10 | ||
+ | |style="padding:.4em;"|HY Seo | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;"|20-9 | ||
+ | |style="padding:.4em;"|KH Hong | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2020/05/26 | ||
+ | |style="padding:.4em;" rowspan=2|- | ||
+ | |style="padding:.4em;"|20-8 | ||
+ | |style="padding:.4em;"|JY Seong | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;"|20-7 | ||
+ | |style="padding:.4em;"|OY Min | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2020/05/19 | ||
+ | |style="padding:.4em;" rowspan=2|- | ||
+ | |style="padding:.4em;"|20-6 | ||
+ | |style="padding:.4em;"|SN Lee | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;"|20-5 | ||
+ | |style="padding:.4em;"|DJ Park | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2020/05/12 | ||
+ | |style="padding:.4em;" rowspan=2|- | ||
+ | |style="padding:.4em;"|20-4 | ||
+ | |style="padding:.4em;"|SY Park | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;"|20-3 | ||
+ | |style="padding:.4em;"|NY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
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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"| | ||
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+ | |- | ||
+ | |style="padding:.4em;"|20-1 | ||
+ | |style="padding:.4em;"|JH Cha | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | {|class=wikitable style="text-align:center;" | ||
+ | |+style="text-align:left;font-size:12pt" | 2019-2nd semester | ||
+ | |- | ||
+ | !scope="col" style="padding:.4em" |Date | ||
+ | !scope="col" stype="padding:.4em" | Team | ||
+ | !scope="col" style="padding:.4em" | Paper<br/>index | ||
+ | !scope="col" style="padding:.4em" | Presenter | ||
+ | !scope="col" style="padding:.4em" | Paper title | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2019/10/15 | ||
+ | |style="padding:.4em;" rowspan=2|Microbiome | ||
+ | |style="padding:.4em;"|19-51 | ||
+ | |style="padding:.4em;"|CY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.sciencedirect.com/science/article/pii/S1931312819303488 Obese Individuals with and without Type 2 Diabetes Show Different Gut Microbial Functional Capacity and Composition] | ||
+ | |- | ||
+ | |style="padding:.4em;"|19-50 | ||
+ | |style="padding:.4em;"|SH Lee | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-019-0722-6 Clustering co-abundant genes identifies components of the gut microbiome that are reproducibly associated with colorectal cancer and inflammatory bowel disease] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2019/10/08 | ||
+ | |style="padding:.4em;" rowspan=2|Single-cell | ||
+ | |style="padding:.4em;"|19-49 | ||
+ | |style="padding:.4em;"|SB Baek | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;"|19-48 | ||
+ | |style="padding:.4em;"|SB Baek | ||
+ | |style="padding:.4em;text-align:left"| | ||
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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"| | ||
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+ | |- | ||
+ | |style="padding:.4em;"|19-46 | ||
+ | |style="padding:.4em;"|NY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
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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"| | ||
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+ | |- | ||
+ | |style="padding:.4em;"|19-44 | ||
+ | |style="padding:.4em;"|SB Baek | ||
+ | |style="padding:.4em;text-align:left"| | ||
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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"| | ||
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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] | ||
+ | |- | ||
+ | |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"| | ||
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+ | |- | ||
+ | |style="padding:.4em;"|19-40 | ||
+ | |style="padding:.4em;"|IS Choi | ||
+ | |style="padding:.4em;text-align:left"| | ||
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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] | ||
+ | |- | ||
+ | |style="padding:.4em;"|19-38 | ||
+ | |style="padding:.4em;"|CY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.sciencedirect.com/science/article/pii/S0092867419307755?via%3Dihub Benchmarking Metagenomics Tools for Taxonomic Classification] | ||
+ | |} | ||
+ | {|class=wikitable style="text-align:center;" | ||
+ | |+style="text-align:left;font-size:12pt" | 2019 | ||
+ | |- | ||
+ | !scope="col" style="padding:.4em" |Date | ||
+ | !scope="col" style="padding:.4em" | Paper<br/>index | ||
+ | !scope="col" style="padding:.4em" | Presenter | ||
+ | !scope="col" style="padding:.4em" | Paper title | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2019/08/20 | ||
+ | |style="padding:.4em;"|19-37 | ||
+ | |style="padding:.4em;"|JH Cha | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;"|19-36 | ||
+ | |style="padding:.4em;"|MY Lee | ||
+ | |style="padding:.4em;text-align:left"| | ||
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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"| | ||
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+ | |- | ||
+ | |style="padding:.4em;"|19-34 | ||
+ | |style="padding:.4em;"|SH Lee | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;"|19-33 | ||
+ | |style="padding:.4em;"|HJ Han | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.nature.com/articles/s41591-018-0157-9 Robust prediction of response to immune checkpoint blockade therapy in metastatic melanoma] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2019/08/13 | ||
+ | |style="padding:.4em;"|19-32 | ||
+ | |style="padding:.4em;"|KS Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
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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] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2019/08/08 | ||
+ | |style="padding:.4em;"|19-30 | ||
+ | |style="padding:.4em;"|JH Cha | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.sciencedirect.com/science/article/pii/S0092867418313941 Defining T Cell States Associated with Response to Checkpoint Immunotherapy in Melanoma] | ||
+ | |- | ||
+ | |style="padding:.4em;"|19-29 | ||
+ | |style="padding:.4em;"|KS Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.sciencedirect.com/science/article/pii/S009286741831242X High-Dimensional Analysis Delineates Myeloid and Lymphoid Compartment Remodeling during Successful Immune-Checkpoint Cancer Therapy] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2019/08/07 | ||
+ | |style="padding:.4em;"|19-28 | ||
+ | |style="padding:.4em;"|HJ Han | ||
+ | |style="padding:.4em;text-align:left"| | ||
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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] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2019/07/30 | ||
+ | |style="padding:.4em;"|19-26 | ||
+ | |style="padding:.4em;"|IS Choi | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.nature.com/articles/nm.4466 High-dimensional single-cell analysis predicts response to anti-PD-1 immunotherapy] | ||
+ | |- | ||
+ | |style="padding:.4em;"|19-25 | ||
+ | |style="padding:.4em;"|JW Cho | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.sciencedirect.com/science/article/pii/S0092867418311784 A Cancer Cell Program Promotes T Cell Exclusion and Resistance to Checkpoint Blockade] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2019/07/23 | ||
+ | |style="padding:.4em;"|19-24 | ||
+ | |style="padding:.4em;"|HJ Han | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/26006008 COMPASS identifies T-cell subsets correlated with clinical outcomes.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|19-23 | ||
+ | |style="padding:.4em;"|HJ Han | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;" rowspan=3|2019/05/30 | ||
+ | |style="padding:.4em;"|19-22 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/30936547 Meta-analysis of fecal metagenomes reveals global microbial signatures that are specific for colorectal cancer] | ||
+ | |- | ||
+ | |style="padding:.4em;"|19-21 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;"|19-20 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/30664783 Microbial network disturbances in relapsing refractory Crohn's disease.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=3|2019/05/23 | ||
+ | |style="padding:.4em;"|19-19 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/30867587 New insights from uncultivated genomes of the global human gut microbiome] | ||
+ | |- | ||
+ | |style="padding:.4em;"|19-18 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/30745586 A new genomic blueprint of the human gut microbiota] | ||
+ | |- | ||
+ | |style="padding:.4em;"|19-17 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/30661755 Extensive Unexplored Human Microbiome Diversity Revealed by Over 150,000 Genomes from Metagenomes Spanning Age, Geography, and Lifestyle.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2019/05/16 | ||
+ | |style="padding:.4em;"|19-16 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/29311644 Dynamics of metatranscription in the inflammatory bowel disease gut microbiome.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|19-15 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/29335555 Metatranscriptome of human faecal microbial communities in a cohort of adult men.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2019/05/09 | ||
+ | |style="padding:.4em;"|19-14 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/30193113 Post-Antibiotic Gut Mucosal Microbiome Reconstitution Is Impaired by Probiotics and Improved by Autologous FMT.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|19-13 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/30193112 Personalized Gut Mucosal Colonization Resistance to Empiric Probiotics Is Associated with Unique Host and Microbiome Features.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2019/05/02 | ||
+ | |style="padding:.4em;"|19-12 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/30753825 Distinct Immune Cell Populations Define Response to Anti-PD-1 Monotherapy and Anti-PD-1/Anti-CTLA-4 Combined Therapy.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|19-11 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/30778252 Subsets of exhausted CD8+ T cells differentially mediate tumor control and respond to checkpoint blockade.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2019/4/11 | ||
+ | |style="padding:.4em;"|19-10 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/30479382 Lineage tracking reveals dynamic relationships of T cells in colorectal cancer.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|19-9 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/30523328 Single cell dissection of plasma cell heterogeneity in symptomatic and asymptomatic myeloma.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2019/4/4 | ||
+ | |style="padding:.4em;"|19-8 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/29942092 Single-cell profiling of breast cancer T cells reveals a tissue-resident memory subset associated with improved prognosis.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|19-7 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2019/3/28 | ||
+ | |style="padding:.4em;"|19-6 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/28319088 Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|19-5 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/28622514 Landscape of Infiltrating T Cells in Liver Cancer Revealed by Single-Cell Sequencing.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=3|2019/3/21 | ||
+ | |style="padding:.4em;"|19-4 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/29988129 Phenotype molding of stromal cells in the lung tumor microenvironment.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|19-3-1 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;"|19-3 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/30787437 The single-cell transcriptional landscape of mammalian organogenesis] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2019/3/14 | ||
+ | |style="padding:.4em;"|19-2 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/29961579 Single-Cell Map of Diverse Immune Phenotypes in the Breast Tumor Microenvironment] | ||
+ | |- | ||
+ | |style="padding:.4em;"|19-1 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/29198524 Single-Cell Transcriptomic Analysis of Primary and Metastatic Tumor Ecosystems in Head and Neck Cancer] | ||
+ | |} | ||
+ | {|class=wikitable style="text-align:center;" | ||
+ | |+style="text-align:left;font-size:12pt" | 2018 | ||
+ | |- | ||
+ | !scope="col" style="padding:.4em" |Date | ||
+ | !scope="col" style="padding:.4em" | Paper<br/>index | ||
+ | !scope="col" style="padding:.4em" | Presenter | ||
+ | !scope="col" style="padding:.4em" | Paper title | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2018/06/14 | ||
+ | |style="padding:.4em;"|18-12 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/?term=A+pan-cancer+analysis+of+enhancer+expression+in+nearly+9000+patient+samples A Pan-Cancer Analysis of Enhancer Expression in Nearly 9000 Patient Samples.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|18-11 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/?term=Machine+learning+identifies+stemness+features+associated+with+oncogenic+dedifferentiation Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2018/06/07 | ||
+ | |style="padding:.4em;"|18-10 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/?term=Developmental+and+oncogenic+programs+in+H3K27M+gliomas+dissected+by+single-cell+RNA-seq Developmental and oncogenic programs in H3K27M gliomas dissected by single-cell RNA-seq.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|18-9 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/?term=Chemoresistance+evolution+in+triple-negative+breast+cancer+delineated+by+single-cell+sequencing Chemoresistance Evolution in Triple-Negative Breast Cancer Delineated by Single-Cell Sequencing.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2018/05/31 | ||
+ | |style="padding:.4em;"|18-8 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/?term=Mapping+human+pluripotent+stem+cell+differentiation+pathways+using+high+throughput+single-cell+RNA-sequencing Mapping human pluripotent stem cell differentiation pathways using high throughput single-cell RNA-sequencing.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|18-7 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/?term=A+single-cell+RNA-seq+survey+of+the+developmental+landscape+of+the+human+prefrontal+cortex A single-cell RNA-seq survey of the developmental landscape of the human prefrontal cortex.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2018/05/24 | ||
+ | |style="padding:.4em;"|18-6 | ||
+ | |style="padding:.4em;"|CY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/?term=Single-cell+RNA+sequencing+identifies+celltype-specific+cis-eQTLs+and+co-expression+QTLs Single-cell RNA sequencing identifies celltype-specific cis-eQTLs and co-expression QTLs.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|18-5 | ||
+ | |style="padding:.4em;"|HJ Han | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/?term=FOCS%3A+a+novel+method+for+analyzing+enhancer+and+gene+activity+patterns+infers+an+extensive+enhancer-promoter+map FOCS: a novel method for analyzing enhancer and gene activity patterns infers an extensive enhancer-promoter map.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2018/05/17 | ||
+ | |style="padding:.4em;"|18-4 | ||
+ | |style="padding:.4em;"|KS Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/29149608 A Distinct Gene Module for Dysfunction Uncoupled from Activation in Tumor-Infiltrating T Cells.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|18-3 | ||
+ | |style="padding:.4em;"|SH Lee | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/29610481 A global transcriptional network connecting noncoding mutations to changes in tumor gene expression.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2018/05/10 | ||
+ | |style="padding:.4em;"|18-2 | ||
+ | |style="padding:.4em;"|JW Cho | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/?term=Inferring+regulatory+element+landscapes+and+transcription+factor+networks+from+cancer+methylomes Inferring regulatory element landscapes and transcription factor networks from cancer methylomes.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|18-1 | ||
+ | |style="padding:.4em;"|DS Bae | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/28129544 Genomic and Transcriptomic Features of Response to Anti-PD-1 Therapy in Metastatic Melanoma.] | ||
+ | |} | ||
+ | {|class=wikitable style="text-align:center;" | ||
+ | |+style="text-align:left;font-size:12pt" | 2017 | ||
+ | |- | ||
+ | !scope="col" style="padding:.4em" |Date | ||
+ | !scope="col" style="padding:.4em" | Paper<br/>index | ||
+ | !scope="col" style="padding:.4em" | Presenter | ||
+ | !scope="col" style="padding:.4em" | Paper title | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2017/06/28 | ||
+ | |style="padding:.4em;"|17-36 | ||
+ | |style="padding:.4em;"|HJ Han | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/28104840 Tumor aneuploidy correlates with markers of immune evasion and with reduced response to immunotherapy.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|17-35 | ||
+ | |style="padding:.4em;"|JW Cho | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/27240091 Landscape of tumor-infiltrating T cell repertoire of human cancers.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2017/06/14 | ||
+ | |style="padding:.4em;"|17-34 | ||
+ | |style="padding:.4em;"|JW Cho | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://biorxiv.org/content/early/2015/09/01/025908 The landscape of T cell infiltration in human cancer and its association with antigen presenting gene expression.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|17-33 | ||
+ | |style="padding:.4em;"|MY Lee | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.08.052 A Distinct Gene Module for Dysfunction Uncoupled from Activation in Tumor-Infiltrating T Cells.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2017/06/07 | ||
+ | |style="padding:.4em;"|17-32 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.tandfonline.com/doi/full/10.1080/2162402X.2016.1253654 Identification of genetic determinants of breast cancer immune phenotypes by integrative genome-scale analysis.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|17-31 | ||
+ | |style="padding:.4em;"|MY Lee | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.celrep.2016.03.075 Genomic Correlates of Immune-Cell Infiltrates in Colorectal Carcinoma.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2017/05/31 | ||
+ | |style="padding:.4em;"|17-30 | ||
+ | |style="padding:.4em;"|DS Bae | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.02.065 Genomic and Transcriptomic Features of Response to Anti-PD-1 Therapy in Metastatic Melanoma.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|17-29 | ||
+ | |style="padding:.4em;"|JW Cho | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.celrep.2016.12.019 Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2017/05/24 | ||
+ | |style="padding:.4em;"|17-28 | ||
+ | |style="padding:.4em;"|EB Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.12.022 Systemic Immunity Is Required for Effective Cencer Immunotherapy.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|17-27 | ||
+ | |style="padding:.4em;"|CY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.10.020 Linking the Human Gut Microbiome to Inflammatory Cytokine Production Capacity.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2017/05/17 | ||
+ | |style="padding:.4em;"|17-26 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.10.018 Host and Environmental Factors Influencing Individual Human Cytokine Responses.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|17-25 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.10.017 A Functional Genomics Approach to Understand Variation in Cytokine Production in Humans.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2017/04/26 | ||
+ | |style="padding:.4em;"|17-24 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1038%2FNMETH.4177 Pooled CRISPR screening with single-cell transcriptome readout.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|17-23 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.11.039 Dissecting Immune Circuits by Linking CRISPR-Pooled Screens with Single-Cell RNA-Seq.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2017/04/12 | ||
+ | |style="padding:.4em;"|17-22 | ||
+ | |style="padding:.4em;"|SH Lee | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.cell.2016.11.038 Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|17-21 | ||
+ | |style="padding:.4em;"|CY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1038%2Fnbt.3569 Wishbone identifies bifurcating developmental trajectories from single-cell data.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2017/04/05 | ||
+ | |style="padding:.4em;"|17-20 | ||
+ | |style="padding:.4em;"|KS Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://biorxiv.org/content/early/2017/02/21/110668 Reversed graph embedding resolves complex single-cell developmental trajectories.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|17-19 | ||
+ | |style="padding:.4em;"|SH Lee | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1038%2Fnmeth.4150 Single-cell mRNA quantification and differential analysis with Census.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2017/03/29 | ||
+ | |style="padding:.4em;"|17-18 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1016%2Fj.celrep.2016.12.060 Single-Cell Transcriptomic Analysis Defines Heterogeneity and Transcriptional Dynamics in the Adult Neural Stem Cell Lineage.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|17-17 | ||
+ | |style="padding:.4em;"|DS Bae | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/26051941 Single-Cell Network Analysis Identifies DDIT3 as a Nodal Lineage Regulator in Hematopoiesis.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2017/03/22 | ||
+ | |style="padding:.4em;"|17-16 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/27580035 Single-cell analysis of mixed-lineage states leading to a binary cell fate choice.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|17-15 | ||
+ | |style="padding:.4em;"|EB Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/27281220 Dissecting direct reprogramming from fibroblast to neuron using single-cell RNA-seq.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2017/03/15 | ||
+ | |style="padding:.4em;"|17-14 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/?term=10.1126%2Fscience.aad0501 Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|17-13 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/26084335 Single-cell mRNA sequencing identifies subclonal heterogeneity in anti-cancer drug responses of lung adenocarcinoma cells.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2017/02/28 | ||
+ | |style="padding:.4em;"|17-12 | ||
+ | |style="padding:.4em;"|KS Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/27824113 A Comprehensive Characterization of the Function of LincRNAs in Transcriptional Regulation Through Long-Range Chromatin Interactions.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|17-11 | ||
+ | |style="padding:.4em;"|JE Shim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [//pubmed.gov/27851969 Epigenomic Deconvolution of Breast Tumors Reveals Metabolic Coupling between Constituent Cell Types.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2017/02/21 | ||
+ | |style="padding:.4em;"|17-10 | ||
+ | |style="padding:.4em;"|EB Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://biorxiv.org/content/early/2016/12/30/097451 Convergence of dispersed regulatory mutations predicts driver genes in prostate cancer.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|17-09 | ||
+ | |style="padding:.4em;"|EB Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [//pubmed.gov/27723759 Chromatin structure-based prediction of recurrent noncoding mutations in cancer.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2017/02/07 | ||
+ | |style="padding:.4em;"|17-08 | ||
+ | |style="padding:.4em;"|DS Bae | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://biorxiv.org/content/early/2016/11/17/088286 Somatic Mutations and Neoepitope Homology in Melanomas Treated with CTLA-4 Blockade.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|17-07 | ||
+ | |style="padding:.4em;"|MY Lee | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://biorxiv.org/content/early/2016/11/28/090134 Chemotherapy weakly contributes to predicted neoantigen expression in ovarian cancer.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2017/01/31 | ||
+ | |style="padding:.4em;"|17-06 | ||
+ | |style="padding:.4em;"|CY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [//www.ncbi.nlm.nih.gov/pubmed/27912059 Microbiota Diurnal Rhythmicity Programs Host Transcriptome Oscillations.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2017/01/24 | ||
+ | |style="padding:.4em;"|17-05 | ||
+ | |style="padding:.4em;"|JW Cho | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [//www.ncbi.nlm.nih.gov/pubmed/27851914 Transcriptional Landscape of Human Tissue Lymphocytes Unveils Uniqueness of Tumor-Infiltrating T Regulatory Cells.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2017/01/17 | ||
+ | |style="padding:.4em;"|17-04 | ||
+ | |style="padding:.4em;"|HJ Han | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [//www.ncbi.nlm.nih.gov/pubmed/25938943 Mapping long-range promoter contacts in human cells with high-resolution capture Hi-C] | ||
+ | |- | ||
+ | |style="padding:.4em;"|17-03 | ||
+ | |style="padding:.4em;"|EB Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [//www.ncbi.nlm.nih.gov/pubmed/27306882 CHiCAGO: robust detection of DNA looping interactions in Capture Hi-C data.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2017/01/10 | ||
+ | |style="padding:.4em;"|17-02 | ||
+ | |style="padding:.4em;"|JE Shim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [//www.ncbi.nlm.nih.gov/pubmed/26527291 ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2017/01/03 | ||
+ | |style="padding:.4em;"|17-01 | ||
+ | |style="padding:.4em;"|SH Lee | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [//www.ncbi.nlm.nih.gov/pubmed/26287467 Single-cell messenger RNA sequencing reveals rare intestinal cell types] | ||
+ | |- | ||
+ | |} | ||
+ | {|class=wikitable style="text-align:center;" | ||
+ | |+style="text-align:left;font-size:12pt" | 2016 | ||
+ | |- | ||
+ | !scope="col" style="padding:.4em" |Date | ||
+ | !scope="col" style="padding:.4em" | Paper<br/>index | ||
+ | !scope="col" style="padding:.4em" | Presenter | ||
+ | !scope="col" style="padding:.4em" | Paper title | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2016/12/27 | ||
+ | |style="padding:.4em;"|2016-31 | ||
+ | |style="padding:.4em;"|EB Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.ncbi.nlm.nih.gov/pubmed/25664528 Decoding the regulatory network of early blood development from single-cell gene expression measurements.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2016/12/6 | ||
+ | |style="padding:.4em;"|2016-30 | ||
+ | |style="padding:.4em;"|KS Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.ncbi.nlm.nih.gov/pubmed/26299571 Single-Cell RNA-Seq with Waterfall Reveals Molecular Cascades underlying Adult Neurogenesis.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2016/11/29 | ||
+ | |style="padding:.4em;"|2016-29 | ||
+ | |style="padding:.4em;"|DS Bae | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.ncbi.nlm.nih.gov/pubmed/24658644 The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2016/11/22 | ||
+ | |style="padding:.4em;"|2016-28 | ||
+ | |style="padding:.4em;"|CY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.ncbi.nlm.nih.gov/pubmed/26887813 Classification of low quality cells from single-cell RNA-seq data.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2016/11/15 | ||
+ | |style="padding:.4em;"|2016-27 | ||
+ | |style="padding:.4em;"|MY Lee | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.ncbi.nlm.nih.gov/pubmed/26000487 Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2016/11/8 | ||
+ | |style="padding:.4em;"|2016-26 | ||
+ | |style="padding:.4em;"|JW Cho | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.ncbi.nlm.nih.gov/pubmed/26000488 Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2016/11/1 | ||
+ | |style="padding:.4em;"|2016-25 | ||
+ | |style="padding:.4em;"|HJ Han | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.ncbi.nlm.nih.gov/pubmed/27426982 Causal Mechanistic Regulatory Network for Glioblastoma Deciphered Using Systems Genetics Network Analysis.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2016/10/25 | ||
+ | |style="padding:.4em;"|2016-24 | ||
+ | |style="padding:.4em;"|MY Lee | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.ncbi.nlm.nih.gov/pubmed/27549193 Comprehensive analyses of tumor immunity: implications for cancer immunotherapy.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2016/10/11 | ||
+ | |style="padding:.4em;"|2016-23 | ||
+ | |style="padding:.4em;"|JE Shim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.ncbi.nlm.nih.gov/pubmed/?term=sidespread+parainflammation Widespread parainflammation in human cancer] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2016/9/27 | ||
+ | |style="padding:.4em;"|2016-22 | ||
+ | |style="padding:.4em;"|ER Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.ncbi.nlm.nih.gov/pubmed/27535533 Analysis of protein-coding genetic variation in 60,706 humans] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2016/9/20 | ||
+ | |style="padding:.4em;"|2016-21 | ||
+ | |style="padding:.4em;"|SM Yang | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | Functional characterization of somatic mutations in cancer using network-based inference of protein activity | ||
+ | [http://www.ncbi.nlm.nih.gov/pubmed/27322546 pubmed] | ||
+ | [http://www.nature.com/ng/journal/v48/n8/full/ng.3593.html fulltext] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2016/9/13 | ||
+ | |style="padding:.4em;"|2016-20 | ||
+ | |style="padding:.4em;"|KS Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.ncbi.nlm.nih.gov/pubmed/?term=exploiting+single-cell+expression+to+characterize Exploiting single-cell expression to characterize co-expression replicability.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2016/9/6 | ||
+ | |style="padding:.4em;"|2016-19 | ||
+ | |style="padding:.4em;"|T Lee | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.ncbi.nlm.nih.gov/pubmed/26940869 Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2016/8/31 | ||
+ | |style="padding:.4em;"|2016-18 | ||
+ | |style="padding:.4em;"|DS Bae | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.ncbi.nlm.nih.gov/pubmed/27264179 A Computational Drug Repositioning Approach for Targeting Oncogenic Transcription Factors] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2016/8/16 | ||
+ | |style="padding:.4em;"|2016-17 | ||
+ | |style="padding:.4em;"|JW Cho | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.ncbi.nlm.nih.gov/pubmed/27309802 The landscape of accessible chromatin in mammalian preimplantation embryos] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2016/8/8 | ||
+ | |style="padding:.4em;"|2016-16 | ||
+ | |style="padding:.4em;"|EB Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.ncbi.nlm.nih.gov/pubmed/27064255 Enhancer-promoter interactions are encoded by complex genomic signatures on looping chromatin] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2016/8/1 | ||
+ | |style="padding:.4em;"|2016-15 | ||
+ | |style="padding:.4em;"|MY Lee | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.ncbi.nlm.nih.gov/pubmed/27040498 Personalized Immunomonitoring Uncovers Molecular Networks that Stratify Lupus Patients] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2016/7/25 | ||
+ | |style="padding:.4em;"|2016-14 | ||
+ | |style="padding:.4em;"|CY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.ncbi.nlm.nih.gov/pubmed/?term=integration+of+summary+data+from+gwas+and+eqtl+studies Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2016/7/18 | ||
+ | |style="padding:.4em;"|2016-13 | ||
+ | |style="padding:.4em;"|HJ Han | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.ncbi.nlm.nih.gov/pubmed/23624555 Identification of transcriptional regulators in the mouse immune system] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2016/6/8 | ||
+ | |style="padding:.4em;"|2016-12 | ||
+ | |style="padding:.4em;"|DS Bae, CY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.ncbi.nlm.nih.gov/pubmed/26619012 Mapping the effects of drugs on the immune system] | ||
+ | |- | ||
+ | |style="padding:.4em;"| 2016-11 | ||
+ | |style="padding:.4em;"|DS Bae, CY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.ncbi.nlm.nih.gov/pubmed/26186195 Elucidating compound mechanism of action by network perturbation analysis] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2016/6/1 | ||
+ | |style="padding:.4em;"|2016-10 | ||
+ | |style="padding:.4em;"|MY Lee,SM Cho | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.ncbi.nlm.nih.gov/pubmed/26854917 Integrative approaches for large-scale transcriptome-wide association studies] | ||
+ | |- | ||
+ | |style="padding:.4em;"| 2016-9 | ||
+ | |style="padding:.4em;"|MY Lee,SM Cho | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.ncbi.nlm.nih.gov/pubmed/26950747 Tissue-specific regulatory circuits reveal variable modular perturbations across complex diseases] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2016/5/18 | ||
+ | |style="padding:.4em;"|2016-8 | ||
+ | |style="padding:.4em;"|CY Kim,SJ Kwon | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.ncbi.nlm.nih.gov/pubmed/27013732 Survey of variation in human transcription factors reveals prevalent DNA binding changes] | ||
+ | |- | ||
+ | |style="padding:.4em;"| 2016-7 | ||
+ | |style="padding:.4em;"| CY Kim,SJ Kwon | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.ncbi.nlm.nih.gov/pubmed/26502339 Large-scale identification of sequence variants influencing human transcription factor occupancy in vivo] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2016/5/11 | ||
+ | |style="padding:.4em;"|2016-6 | ||
+ | |style="padding:.4em;"|DS Bae, CY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.ncbi.nlm.nih.gov/pubmed/25171417 Predicting Cancer-specific vulnerability via data-driven detection of synthetic lethality] | ||
+ | |- | ||
+ | |style="padding:.4em;"| 2016-5 | ||
+ | |style="padding:.4em;"|DS Bae, CY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.ncbi.nlm.nih.gov/pubmed/23467089 Dynamic regulatory network controlling Th17 cell differentiation] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2016/5/4 | ||
+ | |style="padding:.4em;"|2016-4 | ||
+ | |style="padding:.4em;"|MY Lee,SM Cho | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.ncbi.nlm.nih.gov/pubmed/25853550 Characterization of the immunophenotypes and antigenomes of colorectal cancers reveals distinct tumor escape mechanisms and novel targets for immunotherapy] | ||
+ | |- | ||
+ | |style="padding:.4em;"| 2016-3 | ||
+ | |style="padding:.4em;"|MY Lee,SM Cho | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.ncbi.nlm.nih.gov/pubmed/26618344 Regulators of genetic risk of breast cancer identified by integrative network analysis] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2016/4/27 | ||
+ | |style="padding:.4em;"|2016-2 | ||
+ | |style="padding:.4em;"|CY Kim,SJ Kwon | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.ncbi.nlm.nih.gov/pubmed/26780608 A predictive computational framework for direct reprogramming between human cell types] | ||
+ | |- | ||
+ | |style="padding:.4em;"| 2016-1 | ||
+ | |style="padding:.4em;"| CY Kim,SJ Kwon | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.ncbi.nlm.nih.gov/pubmed/25126793 CellNet: Network biology applied to stem cell engineering] | ||
+ | |} | ||
+ | |||
+ | {|class=wikitable style="text-align:center;" | ||
+ | |+style="text-align:left;font-size:12pt" | 2015 | ||
+ | |- | ||
+ | !scope="col" style="padding:.4em" | Date | ||
+ | !scope="col" style="padding:.4em" | Paper<br/>index | ||
+ | !scope="col" style="padding:.4em" | Presenter | ||
+ | !scope="col" style="padding:.4em" | Paper title | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2015/06/11 | ||
+ | |style="padding:.4em;"|2015-55 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://genome.cshlp.org/content/24/2/340.long Improved exome prioritization of disease genes through cross-species phenotype comparison.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2015-54 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.sciencedirect.com/science/article/pii/S0002929714001128 Phevor combines multiple biomedical ontologies for accurate identification of disease-causing alleles in single individuals and small nuclear families.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2015/06/04 | ||
+ | |style="padding:.4em;"|2015-53 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.nature.com/nmeth/journal/v10/n11/full/nmeth.2656.html eXtasy: variant prioritization by genomic data fusion.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2015-52 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://genome.cshlp.org/content/21/9/1529.long A probabilistic disease-gene finder for personal genomes.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2015/05/28 | ||
+ | |style="padding:.4em;"|2015-51 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.nature.com/ng/journal/v46/n12/full/ng.3141.html Systematic analysis of noncoding somatic mutations and gene expression alterations across 14 tumor types.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2015-50 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.nature.com/nbt/journal/v28/n5/full/nbt.1630.html GREAT improves functional interpretation of cis-regulatory regions.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2015/05/21 | ||
+ | |style="padding:.4em;"|2015-49 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.nature.com/nmeth/journal/v11/n3/full/nmeth.2832.html Functional annotation of noncoding sequence variants.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2015-48 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://genomebiology.com/content/15/10/480 FunSeq2: A framework for prioritizing noncoding regulatory variants in cancer.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2015/05/14 | ||
+ | |style="padding:.4em;"|2015-47 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.nature.com/nmeth/journal/v12/n2/full/nmeth.3215.html Selecting causal genes from genome-wide association studies via functionally coherent subnetworks.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2015-46 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.nature.com/ncomms/2015/150119/ncomms6890/full/ncomms6890.html Biological interpretation of genome-wide association studies using predicted gene functions.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=3|2015/05/07 | ||
+ | |style="padding:.4em;"|2015-45 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.nature.com/ncomms/2014/140626/ncomms5212/full/ncomms5212.html Human symptoms-disease network.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2015-44 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.sciencedirect.com/science/article/pii/S0092867413010246 A nondegenerate code of deleterious variants in Mendelian loci contributes to complex disease risk.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2015-43 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.sciencemag.org/content/347/6224/1257601.long Uncovering disease-disease relationships through the incomplete interactome.] | ||
+ | |||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2015/04/30 | ||
+ | |style="padding:.4em;"|2015-42 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://genome.cshlp.org/content/25/1/142.long The discovery of integrated gene networks for autism and related disorders.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2015-41 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://msb.embopress.org/content/10/12/774.long Integrated systems analysis reveals a molecular network underlying autism spectrum disorders.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=3|2015/04/23 | ||
+ | |style="padding:.4em;"|2015-40 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.nature.com/nature/journal/v518/n7539/full/nature13990.html Dissecting neural differentiation regulatory networks through epigenetic footprinting.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2015-39 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.nature.com/nature/journal/v518/n7539/full/nature14221.html Cell-of-origin chromatin organization shapes the mutational landscape of cancer.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2015-38 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.nature.com/nature/journal/v518/n7539/full/nature14248.html Integrative analysis of 111 reference human epigenomes.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2015/04/09 | ||
+ | |style="padding:.4em;"|2015-37 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://genome.cshlp.org/content/25/2/246.long Genome-wide analysis of local chromatin packing in Arabidopsis thaliana.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2015-36 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.sciencedirect.com/science/article/pii/S0092867414014974 A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2015/04/02 | ||
+ | |style="padding:.4em;"|2015-35 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.nature.com/nrg/journal/v14/n6/full/nrg3454.html Exploring the three-dimensional organization of genomes: interpreting chromatin interaction data.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2015-34 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.sciencemag.org/content/347/6225/1010.long Transcribed enhancers lead waves of coordinated transcription in transitioning mammalian cells.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2015/03/26 | ||
+ | |style="padding:.4em;"|2015-33 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://genome.cshlp.org/content/25/2/257.long Cupid: simultaneous reconstruction of microRNA-target and ceRNA networks.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2015-32 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://genome.cshlp.org/content/25/1/41.long Characterization of the neural stem cell gene regulatory network identifies OLIG2 as a multifunctional regulator of self-renewal.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2015/03/19 | ||
+ | |style="padding:.4em;"|2015-31 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.nature.com/nbt/journal/vaop/ncurrent/full/nbt.3154.html Decoding the regulatory network of early blood development from single-cell gene expression measurements.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2015-30 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.nature.com/nrg/journal/v16/n3/full/nrg3833.html Computational and analytical challenges in single-cell transcriptomics.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=3|2015/03/12 | ||
+ | |style="padding:.4em;"|2015-29 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.sciencedirect.com/science/article/pii/S0092867415000136 Extensive Strain-Level Copy-Number Variation across Human Gut Microbiome Species.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2015-28 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.nature.com/nature/journal/v500/n7464/full/nature12506.html Richness of human gut microbiome correlates with metabolic markers.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2015-27 | ||
+ | |style="padding:.4em;"| | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.nature.com/nature/journal/v490/n7418/full/nature11450.html A metagenome-wide association study of gut microbiota in type 2 diabetes.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=3|2015/03/09 | ||
+ | |style="padding:.4em;"|2015-26 | ||
+ | |style="padding:.4em;"|HJ Han | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003326 Practical guidelines for the comprehensive analysis of ChIP-seq data.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2015-25 | ||
+ | |style="padding:.4em;"|T Lee | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://genome.cshlp.org/content/23/5/777.long Patterns of regulatory activity across diverse human cell types predict tissue identity, transcription factor binding, and long-range interactions.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2015-24 | ||
+ | |style="padding:.4em;"|ER Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://msb.embopress.org/content/10/11/760.long Rapid neurogenesis through transcriptional activation in human stem cells.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=3|2015/03/02 | ||
+ | |style="padding:.4em;"|2015-23 | ||
+ | |style="padding:.4em;"|DS Bae | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.sciencedirect.com/science/article/pii/S0092867414011787 Genome-Scale CRISPR-Mediated Control of Gene Repression and Activation.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2015-22 | ||
+ | |style="padding:.4em;"|CY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1004237 Integrating multiple genomic data to predict disease-causing nonsynonymous single nucleotide variants in exome sequencing studies.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2015-21 | ||
+ | |style="padding:.4em;"|ER Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.nature.com/ng/journal/v45/n6/full/ng.2653.html The Genotype-Tissue Expression (GTEx) project.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=3|2015/02/24 | ||
+ | |style="padding:.4em;"|2015-20 | ||
+ | |style="padding:.4em;"|ER Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.sciencemag.org/content/346/6212/1007.long Mouse regulatory DNA landscapes reveal global principles of cis-regulatory evolution.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2015-19 | ||
+ | |style="padding:.4em;"|KS Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.nature.com/nature/journal/v515/n7527/full/nature13985.html Principles of regulatory information conservation between mouse and human.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2015-18 | ||
+ | |style="padding:.4em;"|BH Kang | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.nature.com/nature/journal/v515/n7527/full/nature13972.html Conservation of trans-acting circuitry during mammalian regulatory evolution.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=3|2015/02/16 | ||
+ | |style="padding:.4em;"|2015-17 | ||
+ | |style="padding:.4em;"|T Lee | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.nature.com/nature/journal/vaop/ncurrent/full/nature13835.html Genetic and epigenetic fine mapping of causal autoimmune disease variants.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2015-16 | ||
+ | |style="padding:.4em;"|CY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.nature.com/ng/journal/v46/n3/full/ng.2892.html A general framework for estimating the relative pathogenicity of human genetic variants.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2015-15 | ||
+ | |style="padding:.4em;"|CY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003825 A probabilistic model to predict clinical phenotypic traits from genome sequencing.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=4|2015/02/02 | ||
+ | |style="padding:.4em;"|2015-14 | ||
+ | |style="padding:.4em;"|BH Kang | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.pnas.org/content/111/22/E2329.long Relating the metatranscriptome and metagenome of the human gut.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2015-13 | ||
+ | |style="padding:.4em;"|BH Kang | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.nature.com/nature/journal/v513/n7516/full/nature13568.html Alterations of the human gut microbiome in liver cirrhosis.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2015-12 | ||
+ | |style="padding:.4em;"|CY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.nature.com/nbt/journal/v32/n8/full/nbt.2942.html An integrated catalog of reference genes in the human gut microbiome.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2015-11 | ||
+ | |style="padding:.4em;"|CY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.nature.com/nbt/journal/v32/n8/full/nbt.2939.html Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=5|2015/01/26 | ||
+ | |style="padding:.4em;"|2015-10 | ||
+ | |style="padding:.4em;"|HH Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://msb.embopress.org/content/9/1/666.long Computational meta'omics for microbial community studies.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2015-09 | ||
+ | |style="padding:.4em;"|HH Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://genomemedicine.com/content/5/7/65 Functional profiling of the gut microbiome in disease-associated inflammation.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2015-08 | ||
+ | |style="padding:.4em;"|BH Kang | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.sciencedirect.com/science/article/pii/S016895251200145X Biodiversity and functional genomics in the human microbiome.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2015-07 | ||
+ | |style="padding:.4em;"|BH Kang | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002808 Chapter 12: Human Microbiome Analysis.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2015-06 | ||
+ | |style="padding:.4em;"|BH Kang | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.cell.com/cell/abstract/S0092-8674%2814%2900864-2 Conducting a Microbiome Study.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=3|2015/01/12 | ||
+ | |style="padding:.4em;"|2015-05 | ||
+ | |style="padding:.4em;"|KS Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.nature.com/ncomms/2014/141210/ncomms6522/full/ncomms6522.html Small RNA changes en route to distinct cellular states of induced pluripotency.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2015-04 | ||
+ | |style="padding:.4em;"|DS Bae | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.nature.com/nature/journal/v516/n7530/full/nature14046.html Genome-wide characterization of the routes to pluripotency.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2015-03 | ||
+ | |style="padding:.4em;"|DS Bae | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.nature.com/nature/journal/v516/n7530/full/nature14047.html Divergent reprogramming routes lead to alternative stem-cell states.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2015/01/05 | ||
+ | |style="padding:.4em;"|2015-02 | ||
+ | |style="padding:.4em;"|HJ Han | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.pnas.org/content/111/21/E2191.long Global view of enhancer-promoter interactome in human cells.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2015-01 | ||
+ | |style="padding:.4em;"|HJ Han | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://nar.oxfordjournals.org/content/41/22/10391.long Combining Hi-C data with phylogenetic correlation to predict the target genes of distal regulatory elements in human genome.] | ||
+ | |} | ||
+ | {|class=wikitable style="text-align:center;" | ||
+ | |+style="text-align:left;font-size:12pt" | 2014 | ||
+ | |- | ||
+ | !scope="col" style="padding:.4em" | Date | ||
+ | !scope="col" style="padding:.4em" | Paper<br/>index | ||
+ | !scope="col" style="padding:.4em" | Presenter | ||
+ | !scope="col" style="padding:.4em" | Paper title | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2014/12/23 | ||
+ | |style="padding:.4em;"|2014-41 | ||
+ | |style="padding:.4em;"|CY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.sciencedirect.com/science/article/pii/S0092867413012270 Super-enhancers in the control of cell identity and disease.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2014-40 | ||
+ | |style="padding:.4em;"|CY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.sciencedirect.com/science/article/pii/S0092867413003929 Master transcription factors and mediator establish super-enhancers at key cell identity genes.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=4|2014/12/09 | ||
+ | |style="padding:.4em;"|2014-39 | ||
+ | |style="padding:.4em;"|HH Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.sciencedirect.com/science/article/pii/S0092867414013713 Unraveling the biology of a fungal meningitis pathogen using chemical genetics.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2014-38 | ||
+ | |style="padding:.4em;"|JE Shim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.sciencedirect.com/science/article/pii/S0092867414014226 A proteome-scale map of the human interactome network.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2014-37 | ||
+ | |style="padding:.4em;"|JE Shim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://msb.embopress.org/content/10/9/752.long The role of the interactome in the maintenance of deleterious variability in human populations.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2014-36 | ||
+ | |style="padding:.4em;"|HS Shim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.sciencemag.org/content/342/6154/1235587.long Integrative annotation of variants from 1092 humans: application to cancer genomics.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2014/12/02 | ||
+ | |style="padding:.4em;"|2014-35 | ||
+ | |style="padding:.4em;"|HJ Han | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://genome.cshlp.org/content/23/8/1319.long Mapping functional transcription factor networks from gene expression data.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2014-34 | ||
+ | |style="padding:.4em;"|KS Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.nature.com/nrg/journal/v15/n7/full/nrg3684.html In pursuit of design principles of regulatory sequences.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2014/11/25 | ||
+ | |style="padding:.4em;"|2014-33 | ||
+ | |style="padding:.4em;"|KS Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://genomemedicine.com/content/3/6/36 Epigenomics of human embryonic stem cells and induced pluripotent stem cells: insights into pluripotency and implications for disease.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2014-32 | ||
+ | |style="padding:.4em;"|HJ Han | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.sciencedirect.com/science/article/pii/S009286741300891X Developmental fate and cellular maturity encoded in human regulatory DNA landscapes.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2014/11/18 | ||
+ | |style="padding:.4em;"|2014-31 | ||
+ | |style="padding:.4em;"|SM Yang | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://genomemedicine.com/content/6/8/64 The 'dnet' approach promotes emerging research on cancer patient survival.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2014-30 | ||
+ | |style="padding:.4em;"|HJ Han | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.sciencedirect.com/science/article/pii/S0092867414010368 Determination and inference of eukaryotic transcription factor sequence specificity.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=3|2014/11/11 | ||
+ | |style="padding:.4em;"|2014-29 | ||
+ | |style="padding:.4em;"|DS Bae | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.pnas.org/content/110/16/6412.long Transcription factors interfering with dedifferentiation induce cell type-specific transcriptional profiles.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2014-28 | ||
+ | |style="padding:.4em;"|HH Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.sciencedirect.com/science/article/pii/S0092867414009350 Dissecting engineered cell types and enhancing cell fate conversion via CellNet.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2014-27 | ||
+ | |style="padding:.4em;"|HH Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.sciencedirect.com/science/article/pii/S0092867414009349 CellNet: network biology applied to stem cell engineering.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2014/11/04 | ||
+ | |style="padding:.4em;"|2014-26 | ||
+ | |style="padding:.4em;"|HS Shim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.sciencedirect.com/science/article/pii/S0092867414009775 Predicting Cancer-Specific Vulnerability via Data-Driven Detection of Synthetic Lethality.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2014-25 | ||
+ | |style="padding:.4em;"|JH Shin | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.nature.com/nmeth/journal/v11/n9/full/nmeth.3046.html Phen-Gen: combining phenotype and genotype to analyze rare disorders.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2014/10/28 | ||
+ | |style="padding:.4em;"|2014-24 | ||
+ | |style="padding:.4em;"|HJ Han | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.nature.com/nbt/journal/vaop/ncurrent/full/nbt.2877.html A community effort to assess and improve drug sensitivity prediction algorithms.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2014-23 | ||
+ | |style="padding:.4em;"|HS Shim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.nature.com/ng/journal/v46/n9/full/ng.3051.html Multi-tiered genomic analysis of head and neck cancer ties TP53 mutation to 3p loss.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2014/09/30 | ||
+ | |style="padding:.4em;"|2014-22 | ||
+ | |style="padding:.4em;"|JE Shim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.nature.com/nmeth/journal/v10/n11/full/nmeth.2651.html Network-based stratification of tumor mutations.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2014-21 | ||
+ | |style="padding:.4em;"|KS Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.sciencedirect.com/science/article/pii/S0092867414001457 Synonymous mutations frequently act as driver mutations in human cancers.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2014/09/23 | ||
+ | |style="padding:.4em;"|2014-20 | ||
+ | |style="padding:.4em;"|CY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003460 VarWalker: Personalized Mutation Network Analysis of Putative Cancer Genes from Next-Generation Sequencing Data.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2014-19 | ||
+ | |style="padding:.4em;"|HJ Han | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://genomebiology.com/content/13/12/R124 DriverNet: uncovering the impact of somatic driver mutations on transcriptional networks in cancer.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2014/09/16 | ||
+ | |style="padding:.4em;"|2014-18 | ||
+ | |style="padding:.4em;"|JH Shin | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://genomebiology.com/content/14/5/R52 Integrated analysis of recurrent properties of cancer genes to identify novel drivers.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2014-17 | ||
+ | |style="padding:.4em;"|AR Cho | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://genomemedicine.com/content/4/11/89 Improving the prediction of the functional impact of cancer mutations by baseline tolerance transformation.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2014/09/02 | ||
+ | |style="padding:.4em;"|2014-16 | ||
+ | |style="padding:.4em;"|JE Shim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.ncbi.nlm.nih.gov/pubmed/?term=23228031 A network module-based method for identifying cancer prognostic signatures.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2014-15 | ||
+ | |style="padding:.4em;"|AR Cho | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.ncbi.nlm.nih.gov/pubmed/?term=24429628 Realizing the promise of cancer predisposition genes.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2014/08/19 | ||
+ | |style="padding:.4em;"|2014-14 | ||
+ | |style="padding:.4em;"|JE Shim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.ncbi.nlm.nih.gov/pubmed/?term=24952901 Assessing the clinical utility of cancer genomic and proteomic data across tumor types.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2014-13 | ||
+ | |style="padding:.4em;"|HS Shim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.ncbi.nlm.nih.gov/pubmed/?term=24132290 Mutational landscape and significance across 12 major cancer types.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2014/08/12 | ||
+ | |style="padding:.4em;"|2014-12 | ||
+ | |style="padding:.4em;"|HS Shim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.ncbi.nlm.nih.gov/pubmed/23945592 Signatures of mutational processes in human cancer.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2014-11 | ||
+ | |style="padding:.4em;"|HJ Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.ncbi.nlm.nih.gov/pubmed/24390350 Discovery and saturation analysis of cancer genes across 21 tumour types.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2014/08/05 | ||
+ | |style="padding:.4em;"|2014-10 | ||
+ | |style="padding:.4em;"|CY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.ncbi.nlm.nih.gov/pubmed/?term=24084849 Comprehensive identification of mutational cancer driver genes across 12 tumor types.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2014-9 | ||
+ | |style="padding:.4em;"|JE Shim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.ncbi.nlm.nih.gov/pubmed/?term=24037244 IntOGen-mutations identifies cancer drivers across tumor types.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=3|2014/07/29 | ||
+ | |style="padding:.4em;"|2014-8 | ||
+ | |style="padding:.4em;"|CY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.ncbi.nlm.nih.gov/pubmed/23900255 Computational approaches to identify functional genetic variants in cancer genomes.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2014-7 | ||
+ | |style="padding:.4em;"|AR Cho | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.ncbi.nlm.nih.gov/pubmed/?term=23770567 Mutational heterogeneity in cancer and the search for new cancer-associated genes.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2014-6 | ||
+ | |style="padding:.4em;"|AR Cho | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.nature.com/nmeth/journal/v11/n4/abs/nmeth.2891.html Cancer genomes: discerning drivers from passengers] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=3|2014/07/22 | ||
+ | |style="padding:.4em;"|2014-5 | ||
+ | |style="padding:.4em;"|AR Cho | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.ncbi.nlm.nih.gov/pubmed/24071849 The Cancer Genome Atlas Pan-Cancer analysis project.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2014-4 | ||
+ | |style="padding:.4em;"|AR Cho | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.ncbi.nlm.nih.gov/pubmed/23539594 Cancer genome landscapes.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2014-3 | ||
+ | |style="padding:.4em;"|AR Cho | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.ncbi.nlm.nih.gov/pubmed/?term=21900272 Distinguishing driver and passenger mutations in an evolutionary history categorized by interference.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2014/07/15 | ||
+ | |style="padding:.4em;"|2014-2 | ||
+ | |style="padding:.4em;"|HJ Han | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.ncbi.nlm.nih.gov/pubmed/?term=24670764 A promoter-level mammalian expression atlas.] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2014-1 | ||
+ | |style="padding:.4em;"|HJ Han | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://www.ncbi.nlm.nih.gov/pubmed/?term=24670763 An atlas of active enhancers across human cell types and tissues.] | ||
+ | |} | ||
+ | {|class=wikitable style="text-align:center;" | ||
+ | |+style="text-align:left;font-size:12pt" | 2013 | ||
+ | |- | ||
+ | !scope="col" style="padding:.4em" | Date | ||
+ | !scope="col" style="padding:.4em" | Paper<br/>index | ||
+ | !scope="col" style="padding:.4em" | Presenter | ||
+ | !scope="col" style="padding:.4em" | Paper title | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2013/06/26 | ||
+ | |style="padding:.4em;"|2013-31 | ||
+ | |style="padding:.4em;"|HJ Han | ||
+ | |style="padding:.4em;text-align:left"|[http://nar.oxfordjournals.org/content/40/16/7690.long Integration of Hi-C and ChIP-seq data reveals distinct types of chromatin linkages] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2013-30 | ||
+ | |style="padding:.4em;"|YH Ko | ||
+ | |style="padding:.4em;text-align:left"|[http://www.nature.com/nbt/journal/v31/n4/full/nbt.2519.html Deciphering molecular circuits from genetic variation underlying transcriptional responsiveness to stimuli] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2013/06/04 | ||
+ | |style="padding:.4em;"|2013-29 | ||
+ | |style="padding:.4em;"|KS Kim | ||
+ | |style="padding:.4em;text-align:left"|[https://www.cell.com/abstract/S0092-8674(13)00439-X Mapping the Human miRNA Interactome by CLASH Reveals Frequent Noncanonical Binding] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2013-28 | ||
+ | |style="padding:.4em;"|ER Kim | ||
+ | |style="padding:.4em;text-align:left"|[http://www.nature.com/nbt/journal/v31/n3/full/nbt.2514.html Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2013/05/28 | ||
+ | |style="padding:.4em;"|2013-27 | ||
+ | |style="padding:.4em;"|YH Ko | ||
+ | |style="padding:.4em;text-align:left"|[http://www.pnas.org/content/102/38/13544.long Discovering statistically significant pathways in expression profiling studies] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2013-26 | ||
+ | |style="padding:.4em;"|JE Shim | ||
+ | |style="padding:.4em;text-align:left"|[http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0058977 Prediction and Validation of Gene-Disease Associations Using Methods Inspired by Social Network Analyses] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2013/05/21 | ||
+ | |style="padding:.4em;"|2013-25 | ||
+ | |style="padding:.4em;"|HJ Han | ||
+ | |style="padding:.4em;text-align:left"|[http://www.nature.com/nature/journal/v496/n7446/full/nature11981.html Dynamic regulatory network controlling TH17 cell differentiation] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2013-24 | ||
+ | |style="padding:.4em;"|T Lee | ||
+ | |style="padding:.4em;text-align:left"|[http://www.sciencedirect.com/science/article/pii/S0092867412013529 Deciphering and Prediction of Transcriptome Dynamics under Fluctuating Field Conditions] | ||
+ | |||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2013/05/14 | ||
+ | |style="padding:.4em;"|2013-23 | ||
+ | |style="padding:.4em;"|JE Shim | ||
+ | |style="padding:.4em;text-align:left"|[http://www.sciencedirect.com/science/article/pii/S0092867412015565 Integrative eQTL-Based Analyses Reveal the Biology of Breast Cancer Risk Loci] | ||
+ | |||
+ | |- | ||
+ | |style="padding:.4em;"|2013-22 | ||
+ | |style="padding:.4em;"|HJ Kim | ||
+ | |style="padding:.4em;text-align:left"|[http://www.nature.com/ng/journal/v45/n2/full/ng.2504.html Chromatin marks identify critical cell types for fine mapping complex trait variants] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|'''2013/05/07''' | ||
+ | |style="padding:.4em;"|2013-21 | ||
+ | |style="padding:.4em;"|ER Kim | ||
+ | |style="padding:.4em;text-align:left"|[http://www.cell.com/abstract/S0092-8674(12)01555-3 Genome-wide Chromatin State Transitions Associated with Developmental and Environmental Cues] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2013-20 | ||
+ | |style="padding:.4em;"|HS Shim | ||
+ | |style="padding:.4em;text-align:left"|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1003201 Human Disease-Associated Genetic Variation Impacts Large Intergenic Non-Coding RNA Expression] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2013/04/09 | ||
+ | |style="padding:.4em;"|2013-19 | ||
+ | |style="padding:.4em;"|HJ Kim | ||
+ | |style="padding:.4em;text-align:left"|[http://genome.cshlp.org/content/22/9/1790.long Annotation of functional variation in personal genomes using RegulomeDB] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2013/04/02 | ||
+ | |style="padding:.4em;"|2013-18 | ||
+ | |style="padding:.4em;"|HJ Kim | ||
+ | |style="padding:.4em;text-align:left"|[http://genome.cshlp.org/content/22/9/1775.long The GENCODE v7 catalog of human long noncoding RNAs: Analysis of their gene structure, evolution, and expression] | ||
+ | |- | ||
+ | |style="padding:.4em;" |2013/03/12 | ||
+ | |style="padding:.4em;"|2013-17 | ||
+ | |style="padding:.4em;"| ER Kim | ||
+ | |style="padding:.4em;text-align:left"|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002311 Global Mapping of Cell Type–Specific Open Chromatin by FAIRE-seq Reveals the Regulatory Role of the NFI Family in Adipocyte Differentiation] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2013/03/05 | ||
+ | |style="padding:.4em;"|2013-16 | ||
+ | |style="padding:.4em;"| ER Kim | ||
+ | |style="padding:.4em;text-align:left"|[http://www.nature.com/nrg/journal/v10/n10/full/nrg2641.html ChIP–seq: advantages and challenges of a maturing technology] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=3|2013/02/21 | ||
+ | |style="padding:.4em;"|2013-15 | ||
+ | |style="padding:.4em;"| KS Kim | ||
+ | |style="padding:.4em;text-align:left"|[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002830 Novel Modeling of Combinatorial miRNA Targeting Identifies SNP with Potential Role in Bone Density] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2013-14 | ||
+ | |style="padding:.4em;"| KS Kim | ||
+ | |style="padding:.4em;text-align:left"|[http://www.cell.com/abstract/S0092-8674(12)01424-9 A Molecular Roadmap of Reprogramming Somatic Cells into iPS Cells] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2013-13 | ||
+ | |style="padding:.4em;"| HJ Han | ||
+ | |style="padding:.4em;text-align:left"|[http://genome.cshlp.org/content/22/6/1015.long Differential DNase I hypersensitivity reveals factor-dependent chromatin dynamics.] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=3|2013/02/15 | ||
+ | |style="padding:.4em;"|2013-12 | ||
+ | |style="padding:.4em;"| HJ Han | ||
+ | |style="padding:.4em;text-align:left"|[http://www.nature.com/ng/journal/v43/n3/full/ng.759.html Chromatin accessibility pre-determines glucocorticoid receptor binding patterns] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2013-11 | ||
+ | |style="padding:.4em;"| JE Shim, CY Kim | ||
+ | |style="padding:.4em;text-align:left"|[http://www.nature.com/nbt/journal/v30/n11/full/nbt.2422.html Interpreting noncoding genetic variation in complex traits and human disease] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2013-10 | ||
+ | |style="padding:.4em;"|HJ Han, JH Kim | ||
+ | |style="padding:.4em;text-align:left"|[http://genome.cshlp.org/content/21/3/447.full.pdf+html Accurate inference of transcription factor binding from DNA sequence and chromatin accessibility data] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=3|2013/02/08 | ||
+ | |style="padding:.4em;"|2013-09 | ||
+ | |style="padding:.4em;"|HJ Kim | ||
+ | |style="padding:.4em;text-align:left"|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002599 Widespread Site-Dependent Buffering of Human Regulatory Polymorphism] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2013-08 | ||
+ | |style="padding:.4em;"|JE Shim, CY Kim | ||
+ | |style="padding:.4em;text-align:left;"|[http://genome.cshlp.org/content/22/9/1748.long Linking disease associations with regulatory information in the human genome] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2013-07 | ||
+ | |style="padding:.4em;"|HJ Han, JH Kim | ||
+ | |style="padding:.4em;text-align:left"|[http://genome.cshlp.org/content/22/9/1658.long Understanding transcriptional regulation by integrative analysis of transcription factor binding data] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=3|2013/01/25 | ||
+ | |style="padding:.4em;"|2013-06 | ||
+ | |style="padding:.4em;"|HJ Han, '''JH Kim''' | ||
+ | |style="padding:.4em;text-align:left;"|[http://www.nature.com/nature/journal/v489/n7414/full/nature11279.html The long-range interaction landscape of gene promoters] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2013-05 | ||
+ | |style="padding:.4em;"|'''ER Kim''', HS Shim | ||
+ | |style="padding:.4em;text-align:left;"|[http://www.nature.com/nature/journal/v489/n7414/full/nature11233.html Landscape of transcription in human cells] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2013-04 | ||
+ | |style="padding:.4em;"|'''HJ Han''', JH Kim | ||
+ | |style="padding:.4em;text-align:left;"|[http://www.nature.com/nature/journal/v489/n7414/full/nature11245.html Architecture of the human regulatory network derived from ENCODE data] | ||
+ | |||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2013/01/18 | ||
+ | |style="padding:.4em;"|2013-03 | ||
+ | |style="padding:.4em;"|KS Kim, '''TH Kim''' | ||
+ | |style="padding:.4em;text-align:left;"|[http://www.nature.com/nature/journal/v489/n7414/full/nature11212.html An expansive human regulatory lexicon encoded in transcription factor footprints] | ||
+ | |- | ||
+ | |style="padding:.4em;"|2013-02 | ||
+ | |style="padding:.4em;"|'''HJ Han''', JH Kim | ||
+ | |style="padding:.4em;text-align:left;"|[http://www.nature.com/nature/journal/v489/n7414/full/nature11232.html The accessible chromatin landscape of the human genome] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2013/01/11 | ||
+ | |style="padding:.4em;"|2013-01 | ||
+ | |style="padding:.4em;"| '''JE Shim''', CY Kim | ||
+ | |style="padding:.4em;text-align:left"|[http://www.nature.com/nature/journal/v489/n7414/full/nature11247.html An integrated encyclopedia of DNA elements in the human genome] | ||
+ | |} | ||
+ | |||
+ | 2012 | ||
{|border ="1" | {|border ="1" | ||
− | |style="color:black; background-color:# | + | |style="color:black; background-color:#dcdcdc; padding:5px;" align="center" |Date |
− | |style="color:black; background-color:# | + | |style="color:black; background-color:#dcdcdc;" align="center"|Paper index |
− | |style="color:black; background-color:# | + | |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 | |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.] | |[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] | |[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 ] | |[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<br />2012/02/28 | |rowspan = "7" |2012/02/27<br />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] | |[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] | |[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] | |[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] | |[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] | |[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] | |[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] | |[http://bioinformatics.oxfordjournals.org/content/25/9/1105.abstract (7,So&Shin)TopHat: discovering splice junctions with RNA-Seq] | ||
|- | |- | ||
|rowspan = "5" |2012/02/06 | |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] | |[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] | |[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] | |[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] | |[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] | |[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 | |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] | |[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] | |[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] | |[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] | |[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] | |[http://www.sciencedirect.com/science/article/pii/S0092867410012936 An Integrated Approach to Uncover Drivers of Cancer] | ||
|- | |- | ||
|rowspan = "3" |2012/01/09 | |rowspan = "3" |2012/01/09 | ||
− | | | + | |align="center"|2012-14 |
|[http://www.ncbi.nlm.nih.gov/pubmed/18704161 Genetic variation in an individual human exome.] | |[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.] | |[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.] | |[http://www.ncbi.nlm.nih.gov/pubmed/20435227 Clinical assessment incorporating a personal genome.] | ||
|- | |- | ||
|rowspan = "6" |2012/01/09<br />2012/01/16 | |rowspan = "6" |2012/01/09<br />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.] | |[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.] | |[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.] | |[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.] | |[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.] | |[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] | |[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1920242/ SNAP: predict effect of non-synonymous polymorphisms on function] | ||
|- | |- | ||
|rowspan = "3" |2012/01/09<br />2012/01/16 | |rowspan = "3" |2012/01/09<br />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.] | |[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.] | |[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.] | |[http://www.ncbi.nlm.nih.gov/pubmed/19684571 Targeted capture and massively parallel sequencing of 12 human exomes.] | ||
|- | |- | ||
|rowspan = "2" |2012/01/09<br />2012/01/16 | |rowspan = "2" |2012/01/09<br />2012/01/16 | ||
− | | | + | |align="center"|2012-02 |
|[http://www.ncbi.nlm.nih.gov/pubmed/17637733 The distribution of fitness effects of new mutations.] | |[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] | |[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" | | + | |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] | |[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] | |[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] | |[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] | |[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] | |[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] | |[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" | | + | |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] | |[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] | |[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] | |[http://www.nature.com/msb/journal/v7/n1/full/msb20115.html Analysis of multiple compound–protein interactions reveals novel bioactive molecules] | ||
|- | |- | ||
− | |rowspan = "4" | | + | |rowspan = "4" |2011/11/07 |
− | | | + | |align="center"|2011-40 |
− | + | ||
|[http://www.nature.com/nbt/journal/v25/n10/abs/nbt1338.html Drug—target network] | |[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] | |[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] | |[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] | |[http://www.sciencemag.org/content/321/5886/263.short Drug Target Identification Using Side-Effect Similarity] | ||
|- | |- | ||
− | |rowspan = "3" | | + | |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] | |[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] | |[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] | |[http://www.springerlink.com/content/4489r051nu2t0ul1/ Drug Discovery in a Multidimensional World: Systems, Patterns, and Networks] | ||
|- | |- | ||
− | |rowspan = "3" | | + | |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] | |[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] | |[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] | |[http://www.pnas.org/content/104/35/13913.abstract Quantitative assessment of protein function prediction from metagenomics shotgun sequences] | ||
|- | |- | ||
− | |rowspan = "4" | | + | |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] | |[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] | |[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] | |[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] | |[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" | | + | |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] | |[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] | |[http://www.sciencemag.org/content/333/6042/601.full Evidence for network evolution in an arabidopsis interactome Map] | ||
|- | |- | ||
− | |rowspan = "2" | | + | |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] | |[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] | |[http://www.cell.com/abstract/S0092-8674(11)00543-5 Pluripotency factors in Embryonic stem cells Regulate Differentiation into Germ Layers] | ||
|- | |- | ||
− | |rowspan = "2" | | + | |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] | |[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] | |[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)] | |[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" | | + | |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] | |[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] | |[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 | |
− | |rowspan = "2" | | + | |align="center"|2011-17 |
− | | | + | |
− | + | ||
|[http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002047 variable pathogenicity determines individual lifespan in ''caenorhabditis elegans''] | |[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] | |[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" | | + | |rowspan = "3" |2011/04/25 |
− | | | + | |align="center"|2011-15 |
− | + | ||
|[http://www.ncbi.nlm.nih.gov/pubmed/21376230 Hallmarks of Cancer : The next generation] | |[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] | |[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] | |[http://www.cell.com/abstract/S0092-8674(11)00297-2 Genetic Interactions in Cancer Progression and Treatment] | ||
|- | |- | ||
− | |rowspan = "2" | | + | |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] | |[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!*** | |***Changed!*** | ||
[http://www.ncbi.nlm.nih.gov/pubmed/19557189 Identifying relationships among genomic disease regions: predicting genes at pathogenic SNP associations and rare deletions] | [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"| | + | |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] | |[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] | |[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"| | + | |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] | |[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] | |[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 | |
− | |rowspan = "2"| | + | |align="center"|2011-06 |
− | | | + | |
− | + | ||
|[http://www.pnas.org/content/early/2010/09/23/1004666107.abstract genomic patterns of pleiotropy and the evolution of complexity] | |[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] | |[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] | |[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] | |[http://www.sciencemag.org/content/330/6009/1385.abstract rewiring of genetic networks in response to DNA damage] | ||
|- | |- | ||
− | |rowspan = "2"| | + | |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] | |[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] | |[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"| | + | |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] | |[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] | |[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"| | + | |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] | |[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] | |[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"| | + | |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] | |[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] | |[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] | |[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] | |[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]] | |[[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]] | |[[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]] | |[[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] | |[http://www.cell.com/retrieve/pii/S0092867410000796 an_atlas_of_combinatorial_transcriptional_regulation_in_mouse_and_man] | ||
|- | |- | ||
− | |rowspan = "2" | | + | |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] |
− | + | ||
− | [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] |
− | [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]] | |[[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] | |[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 | |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 | |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 | |distinguishing_direct_versus_indirect_transcription_factor-DNA-interactions | ||
|- | |- | ||
− | | | + | |2010/01/14 |
− | + | |align="center"|2010-08 | |
− | | | + | |
|chemogenomic_profiling_predicts_antifungal_synergies | |chemogenomic_profiling_predicts_antifungal_synergies | ||
|- | |- | ||
− | | | + | |2010/01/13 |
− | + | |align="center"|2010-07 | |
− | | | + | |
|edgetic_perturbation_models_of_human_inherited_disorders | |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 | |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 | |predicting_new_molecular_targets_for_known_drugs.pdf | ||
Reference:SEA(Similarity Ensemble Approach) | Reference:SEA(Similarity Ensemble Approach) | ||
|- | |- | ||
− | | | + | |2010/01/09 |
− | + | |align="center"|2010-04 | |
− | | | + | |
|harnessing_gene_expression_to_identify_the_genetic_basis_of_drug_resistance | |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 | |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 | |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 | |a_global_view_of_protein_expression_in_human_cells_tissues_and_organs | ||
|} | |} | ||
− | |||
+ | <!-- | ||
{| border ="1" | {| border ="1" | ||
{| border ="1" | {| border ="1" |
Latest revision as of 18:01, 26 December 2024
Date | Team | Paper index |
Presenter | Paper title |
---|---|---|---|---|
2024/06/18 | Single-cell | 24-32 | EB Hong |
Spatial transcriptomics reveal neuron–astrocyte synergy in long-term memory |
2024/06/18 | Single-cell | 24-31 | JJ Heo |
scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses |
2024/06/18 | Single-cell | 24-30 | SM Han |
Spatial transcriptomics of planktonic and sessile bacterial populations at single-cell resolution |
2024/06/18 | Single-cell | 24-29 | HJ Choi | |
2024/06/11 | Single-cell | 24-28 | SA Choi | |
2024/06/11 | Single-cell | 24-27 | HJ Cha |
Cell-type-specific responses to fungal infection in plants revealed by single-cell transcriptomics |
2024/06/11 | Single-cell | 24-26 | YK Jung | |
2024/06/11 | Single-cell | 24-25 | HJ Lee | |
2024/06/04 | Single-cell | 24-24 | HK Lee |
Delineating mouse β-cell identity during lifetime and in diabetes with a single cell atlas |
2024/06/04 | Single-cell | 24-23 | JI Lee |
Multimodal spatiotemporal phenotyping of human retinal organoid development |
2024/06/04 | Single-cell | 24-22 | JH Lee | |
2024/06/04 | Single-cell | 24-21 | JH Lee |
A single-cell analysis of the Arabidopsis vegetative shoot apex |
2024/05/28 | Single-cell | 24-20 | JH Lee |
Droplet-based high-throughput single microbe RNA sequencing by smRandom-seq |
2024/05/28 | Single-cell | 24-19 | YH Lee | |
2024/05/28 | Single-cell | 24-18 | EB Yu | |
2024/05/28 | Single-cell | 24-17 | DY Won |
Spatial metatranscriptomics resolves host–bacteria–fungi interactomes |
2024/05/21 | Single-cell | 24-16 | SG Oh | |
2024/05/21 | Single-cell | 24-15 | SY Park | |
2024/05/21 | Single-cell | 24-14 | HS Moon | |
2024/05/21 | Single-cell | 24-13 | JH Nam |
Spatial cellular architecture predicts prognosis in glioblastoma |
2024/05/14 | Single-cell | 24-12 | HS Na | |
2024/05/14 | Single-cell | 24-11 | PK Kim |
Transcriptional adaptation of olfactory sensory neurons to GPCR identity and activity |
2024/05/14 | Single-cell | 24-10 | SH Kwon | |
2024/05/14 | Single-cell | 24-9 | Q Zhen | |
2024/05/07 | Single-cell | 24-8 | CR Leenaars | |
2024/05/07 | Single-cell | 24-7 | YR Kim | |
2024/05/07 | Single-cell | 24-6 | JY Kim |
Spatial transcriptomics landscape of lesions from non-communicable inflammatory skin diseases |
2024/05/07 | Single-cell | 24-5 | WJ Kim |
Neuregulin 4 suppresses NASH-HCC development by restraining tumor-prone liver microenvironment |
2024/04/23 | Single-cell | 24-4 | G Koh |
Single-nucleus multiregion transcriptomic analysis of brain vasculature in Alzheimer’s disease |
2024/04/23 | Single-cell | 24-3 | SH Ahn | |
2024/04/23 | Single-cell | 24-2 | EJ Sung | |
2024/04/23 | Single-cell | 24-1 | HJ Kim |
Date | Team | Paper index |
Presenter | Paper title |
---|---|---|---|---|
2023/08/30 | Single-cell | 23-24 | JW Yu | |
2023/08/09 | Single-cell | 23-23 | IS Choi |
Major data analysis errors invalidate cancer microbiome findings |
2023/08/02 | Single-cell | 23-22 | EJ Sung | |
2023/07/26 | Single-cell | 23-21 | G Koh | |
2023/07/19 | Single-cell | 23-20 | JW Yu |
Estimation of tumor cell total mRNA expression in 15 cancer types predicts disease progression |
2023/07/12 | Single-cell | 23-19 | JH Cha |
DIALOGUE maps multicellular programs in tissue from single-cell or spatial transcriptomics data |
2023/07/05 | Single-cell | 23-18 | SB Baek |
Pan-cancer T cell atlas links a cellular stress response state to immunotherapy resistance |
2023/06/28 | Single-cell | 23-17 | EJ Sung | |
2023/06/21 | Single-cell | 23-16 | IS Choi | |
2023/06/14 | Single-cell | 23-15 | G Koh | |
2023/05/31 | Single-cell | 23-14 | JW Yu |
Mutated processes predict immune checkpoint inhibitor therapy benefit in metastatic melanoma |
2023/05/24 | Single-cell | 23-13 | JH Cha | |
2023/05/17 | Single-cell | 23-12 | SB Baek | |
2023/05/10 | Single-cell | 23-11 | EJ Sung |
Supervised discovery of interpretable gene programs from single-cell data |
2023/05/03 | Single-cell | 23-10 | IS Choi |
Effect of the intratumoral microbiota on spatial and cellular heterogeneity in cancer |
2023/04/26 | Single-cell | 23-9 | G Koh | |
2023/03/22 | Single-cell | 23-8 | JW Yu |
MetaTiME: Meta-components of the Tumor Immune Microenvironment |
2023/03/08 | Single-cell | 23-7 | JH Cha | |
2023/02/21 | Single-cell | 23-6 | SB Baek | |
2023/02/14 | Single-cell | 23-5 | EJ Sung |
A T cell resilience model associated with response to immunotherapy in multiple tumor types |
2022/01/31 | Single-cell | 23-4 | IS Choi | |
2023/01/25 | Single-cell | 23-3 | G Koh | |
2023/01/17 | Single-cell | 23-2 | JW Yu |
Pan-cancer integrative histology-genomic analysis via multimodal deep learning |
2023/01/11 | Single-cell | 23-1 | JH Cha |
Date | Team | Paper index |
Presenter | Paper title |
---|---|---|---|---|
2021/11/23 | Single-cell | 21-39 | IS Choi | |
2021/11/16 | Single-cell | 21-38 | SB Back | |
2021/11/09 | Single-cell | 21-37 | JH Cha | |
2021/11/02 | Single-cell | 21-36 | SB Baek |
Functional Inference of Gene Regulation using Single-Cell Multi-Omics |
2021/10/26 | Single-cell | 21-35 | IS Choi | |
2021/10/19 | Single-cell | 21-34 | JH Cha | |
2021/10/05 | Single-cell | 21-33 | JH Cha |
Tumor and immune reprogramming during immunotherapy in advanced renal cell carcinoma |
2021/09/28 | Single-cell | 21-32 | SB Baek | |
2021/09/14 | Single-cell | 21-31 | IS Choi | |
2021/09/07 | Single-cell | 21-30 | JH Cha |
A single-cell map of intratumoral changes during anti-PD1 treatment of patients with breast cancer |
2021/08/31 | Single-cell | 21-29 | IS Choi |
Single-cell landscape of the ecosystem in early-relapse hepatocellular carcinoma |
2021/08/24 | Single-cell | 21-28 | SB Baek |
Interpreting type 1 diabetes risk with genetics and single-cell epigenomics |
Date | Team | Paper index |
Presenter | Paper title |
---|---|---|---|---|
2021/02/22 | Single-cell | 21-8 | IS Choi |
Functional CRISPR dissection of gene networks controlling human regulatory T cell identity |
21-7 | JH Cha |
Molecular Pathways of Colon Inflammation Induced by Cancer Immunotherapy | ||
2021/02/15 | Single-cell | 21-6 | SB Baek | |
21-5 | IS Choi |
Trajectory-based differential expression analysis for single-cell sequencing data | ||
2021/02/08 | Single-cell | 21-4 | SB Baek |
Genetic determinants of co-accessible chromatin regions in activated T cells across humans |
21-3 | JH Cha |
Single-Cell Analyses Inform Mechanisms of Myeloid-Targeted Therapies in Colon Cancer | ||
2021/02/01 | Single-cell | 21-2 | JW Cho | |
21-1 | JW Cho |
2012
2011
2010