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+ | |+style="text-align:left;font-size:12pt" | 2024-1 scOmics | ||
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+ | !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|2024/10/22 | ||
+ | |style="padding:.4em;" rowspan=1|Single-cell | ||
+ | |style="padding:.4em;"|24-25 | ||
+ | |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] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2024/10/15 | ||
+ | |style="padding:.4em;" rowspan=1|Single-cell | ||
+ | |style="padding:.4em;"|24-24 | ||
+ | |style="padding:.4em;"|YL Jung | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [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] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2024/10/08 | ||
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+ | |style="padding:.4em;"|24-23 | ||
+ | |style="padding:.4em;"|EJ Sung | ||
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+ | [https://genomemedicine.biomedcentral.com/articles/10.1186/s13073-024-01314-7 scDrugPrio: a framework for the analysis of single‑cell transcriptomics to address multiple problems in precision medicine in immune‑mediated inflammatory diseases] | ||
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+ | |style="padding:.4em;"|24-22 | ||
+ | |style="padding:.4em;"|IS Choi | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1038/s41591-024-02856-4 A visual-language foundation model for computational pathology] | ||
+ | |- | ||
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+ | |style="padding:.4em;"|24-21 | ||
+ | |style="padding:.4em;"|SB Baek | ||
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+ | [https://doi.org/10.1038/s41592-024-02175-z SLIDE: Significant Latent Factor Interaction Discovery and Exploration across biological domains] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2024/09/03 | ||
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+ | |style="padding:.4em;"|24-20 | ||
+ | |style="padding:.4em;"|HB Lee | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1016/j.ccell.2023.12.013 Clinical and molecular features of acquired resistance to immunotherapy in non-small cell lungcancer] | ||
+ | |- | ||
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+ | |style="padding:.4em;"|24-19 | ||
+ | |style="padding:.4em;"|JH Cha | ||
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+ | [https://doi.org/10.1101/2024.06.04.597354 Cell-Graph Compass: Modeling Single Cells with Graph Structure Foundation Model] | ||
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+ | |style="padding:.4em;" rowspan=1|2024/08/16 | ||
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+ | |style="padding:.4em;"|24-18 | ||
+ | |style="padding:.4em;"|YL Jung | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1016/j.xgen.2023.100473 Single-cell transcriptome landscape of circulating CD4+ T cell populations in autoimmune diseases] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2024/08/09 | ||
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+ | |style="padding:.4em;"|24-17 | ||
+ | |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] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2024/08/02 | ||
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+ | |style="padding:.4em;"|24-16 | ||
+ | |style="padding:.4em;"|IS Choi | ||
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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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+ | |style="padding:.4em;"|SB Baek | ||
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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] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2024/07/19 | ||
+ | |style="padding:.4em;" rowspan=1|Single-cell | ||
+ | |style="padding:.4em;"|24-14 | ||
+ | |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;"|24-10 | ||
+ | |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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+ | |style="padding:.4em;"|24-9 | ||
+ | |style="padding:.4em;"|EJ Sung | ||
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+ | [https://doi.org/10.1158/0008-5472.CAN-23-2650 The Web-Based Portal SpatialTME Integrates Histological Images with Single-Cell and Spatial Transcriptomics to Explore the Tumor Microenvironment] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2024/05/17 | ||
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+ | |style="padding:.4em;"|24-8 | ||
+ | |style="padding:.4em;"|IS Choi | ||
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+ | [https://doi.org/10.1038/s41592-023-02117-1 SEVtras delineates small extracellular vesicles at droplet resolution from single-cell transcriptomes] | ||
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+ | |style="padding:.4em;"|24-7 | ||
+ | |style="padding:.4em;"|SB Baek | ||
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+ | [https://doi.org/10.1038/s41587-023-01728-5 A relay velocity model infers cell-dependent RNA velocity] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2024/05/03 | ||
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+ | |style="padding:.4em;"|24-5 | ||
+ | |style="padding:.4em;"|EJ Sung | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1038/s41467-023-44206-x Pathway centric analysis for single-cell RNA-seq and spatial transcriptomics data with GSDensity] | ||
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+ | |style="padding:.4em;"|24-6 | ||
+ | |style="padding:.4em;"|JH Cha | ||
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+ | [https://doi.org/10.1038/s41587-023-01734-7 Dandelion uses the single-cell adaptive immune receptor repertoire to explore lymphocyte developmental origins] | ||
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+ | |style="padding:.4em;"|IS Choi | ||
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+ | [https://doi.org/10.1016/j.cell.2023.11.026 Automatic cell-type harmonization and integration across Human Cell Atlas datasets] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2024/03/22 | ||
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+ | |style="padding:.4em;"|24-3 | ||
+ | |style="padding:.4em;"|SB Baek | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1038/s41592-023-01994-w Deep generative modeling of transcriptional dynamics for RNA velocity analysis in single cells] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2024/03/15 | ||
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+ | |style="padding:.4em;"|24-2 | ||
+ | |style="padding:.4em;"|JH Cha | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [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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+ | |style="padding:.4em;"|EJ Sung | ||
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+ | [https://doi.org/10.1016/j.xgen.2023.100383 Polygenic regression uncovers trait-relevant cellular contexts through pathway activation transformation of single-cell RNA sequencing data] | ||
+ | |} | ||
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+ | |+style="text-align:left;font-size:12pt" | 2024-1 Microbiome | ||
+ | |- | ||
+ | !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 | ||
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+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|24-51 | ||
+ | |style="padding:.4em;"|NY Kim | ||
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+ | [https://doi.org/10.1038/s41591-024-03067-7 Strain-specific gut microbial signatures in type 2 diabetes identified in a cross-cohort analysis of 8,117 metagenomes] | ||
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+ | |style="padding:.4em;"|YR Kim | ||
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+ | [https://doi.org/10.1186/s40168-024-01832-x Gut virome-wide association analysis identifes cross-population viral signatures for infammatory bowel disease] | ||
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+ | |style="padding:.4em;"|JY Kim | ||
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+ | [https://doi.org/10.48550/arXiv.1806.00064 Efficient Low-rank Multimodal Fusion with Modality-Specific Factors] | ||
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+ | |style="padding:.4em;"|JY Kim | ||
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+ | [https://doi.org/10.48550/arXiv.1707.07250 Tensor Fusion Network for Multimodal Sentiment Analysis] | ||
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+ | |style="padding:.4em;"|24-49 | ||
+ | |style="padding:.4em;"|WJ Kim | ||
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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] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2024/09/11 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
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+ | |style="padding:.4em;"|SH Ahn | ||
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+ | [https://pubmed.ncbi.nlm.nih.gov/31510656 Deep learning with multimodal representation for pancancer prognosis prediction] | ||
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+ | |style="padding:.4em;"|HJ Kim | ||
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+ | [https://pubmed.ncbi.nlm.nih.gov/32881682 Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosis] | ||
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+ | |style="padding:.4em;"|HJ Kim | ||
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+ | [https://doi.org/10.1016/j.ccell.2022.07.004 Pan-cancer integrative histology-genomic analysis via multimodal deep learning] | ||
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+ | |style="padding:.4em;"|24-44 | ||
+ | |style="padding:.4em;"|NY Kim | ||
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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;"|24-43-2 | ||
+ | |style="padding:.4em;"|JH Cha | ||
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+ | [https://doi.org/10.48550/arXiv.2303.00915 BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs] | ||
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+ | |style="padding:.4em;"|24-43-1 | ||
+ | |style="padding:.4em;"|JH Cha | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://arxiv.org/abs/2103.00020 Learning Transferable Visual Models From Natural Language Supervision] | ||
+ | |- | ||
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+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|24-42 | ||
+ | |style="padding:.4em;"|JY Ma | ||
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+ | [https://doi.org/10.1038/s41592-022-01616-x BIONIC: biological network integration using convolutions] | ||
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+ | |style="padding:.4em;"|G Koh | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1038/s41587-023-01917-2 Protein remote homology detection and structural alignment using deep learning] | ||
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+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|24-41 | ||
+ | |style="padding:.4em;"|YR Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1038/s41586-024-07487-w Accurate structure prediction of biomolecular interactions with AlphaFold 3] | ||
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+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|24-39 | ||
+ | |style="padding:.4em;"|WJ Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1186/s40168-023-01737-1 Gut microbiome-metabolome interactions predict host condition] | ||
+ | |- | ||
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+ | |style="padding:.4em;"|24-38 | ||
+ | |style="padding:.4em;"|JY kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1038/s41591-024-02963-2 Microbiome confounders and quantitative profiling challenge predicted microbial targets in colorectal cancer development] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2024/07/31 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|24-37 | ||
+ | |style="padding:.4em;"|SH Ahn | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1038/s41564-024-01751-5 A multi-kingdom collection of 33,804 reference genomes for the human vaginal microbiome] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2024/07/31 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|24-36 | ||
+ | |style="padding:.4em;"|HJ Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1101/2023.12.11.571168 Efficient and accurate detection of viral sequences at single-cell resolution reveals putative novel viruses perturbing host gene expression] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2024/07/24 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|24-35 | ||
+ | |style="padding:.4em;"|JY Ma | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1101/2024.06.04.596112 Compositional Differential Abundance Testing: Defining and Finding a New Type of Health-Microbiome Associations] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2024/07/24 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|24-34 | ||
+ | |style="padding:.4em;"|JH Cha | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1016/j.cell.2024.05.013 Discovery of antimicrobial peptides in the global microbiome with machine learning] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2024/07/17 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|24-33 | ||
+ | |style="padding:.4em;"|NY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1016/j.cell.2024.05.029 Custom scoring based on ecological topology of gut microbiota associated with cancer immunotherapy outcome] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2024/07/17 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|24-32 | ||
+ | |style="padding:.4em;"|YR Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1038/s41586-024-07336-w Paternal microbiome perturbations impact offspring fitness] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2024/07/10 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|24-31 | ||
+ | |style="padding:.4em;"|JY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1016/j.crmeth.2024.100775 Interactions-based classification of a single microbial sample] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2024/07/10 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|24-30 | ||
+ | |style="padding:.4em;"|NY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1101/2024.04.10.588779 Accurate estimation of intraspecificmicrobial gene content variation inmetagenomic data with MIDAS v3 andStrainPGC] | ||
+ | |- | ||
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+ | |style="padding:.4em;"|SA Choi | ||
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+ | |style="padding:.4em;"|HK Lee | ||
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+ | |style="padding:.4em;"|G Koh | ||
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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://www.science.org/doi/10.1126/sciimmunol.adf4968 Preexisting tumor-resident T cells with cytotoxic potential associate with response to neoadjuvant anti–PD-1 in head and neck cancer] | ||
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+ | [https://doi.org/10.1038/s41588-022-01273-y MHC II immunogenicity shapes the neoepitope landscape in human tumors] | ||
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+ | |style="padding:.4em;"|JW Yu | ||
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+ | |style="padding:.4em;"|JH Cha | ||
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+ | |} | ||
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+ | !scope="col" style="padding:.4em" | Paper<br/>index | ||
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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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+ | |style="padding:.4em;"|SH Ahn | ||
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+ | [https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-023-01692-x Integrating compositional and functional content to describe vaginal microbiomes in health and disease] | ||
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+ | |style="padding:.4em;"|JY Ma | ||
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+ | [https://doi.org/10.1101/2023.07.03.547607 Multi-view integration of microbiome data for identifying disease-associated modules] | ||
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+ | [https://doi.org/10.1101/2023.09.28.559994 Phage-bacteria dynamics during the first years of life revealed by trans-kingdom marker gene analysis] | ||
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+ | [https://doi.org/10.1038/s41593-023-01361-0 Multi-level analysis of the gut–brain axis shows autism spectrum disorder-associated molecular and microbial profiles] | ||
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+ | |style="padding:.4em;"|G Koh | ||
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+ | [https://doi.org/10.1101/2023.11.21.568153 Metagenomic Immunoglobulin Sequencing (MIG-Seq) Exposes Patterns of IgA Antibody Binding in the Healthy Human Gut Microbiome] | ||
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+ | [https://doi.org/10.1038/s41467-023-41042-x Impact of dietary interventions on pre-diabetic oral and gut microbiome, metabolites and cytokines] | ||
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+ | [https://doi.org/10.1038/s41592-023-02018-3 Fast and robust metagenomic sequence comparison through sparse chaining with skani] | ||
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+ | [https://doi.org/10.1038/s41591-023-02599-8 Bacterial SNPs in the human gut microbiome associate with host BMI] | ||
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+ | |style="padding:.4em;"|JH Cha | ||
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+ | [https://doi.org/10.1080/19490976.2023.2245562 Multimodal metagenomic analysis reveals microbial single nucleotide variants as superior biomarkers for early detection of colorectal cancer] | ||
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+ | [https://doi.org/10.1016/j.chom.2023.10.005 Multi-kingdom gut microbiota analyses define bacterial-fungal interplay and microbial markers of pan-cancer immunotherapy across cohorts] | ||
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+ | [https://doi.org/10.1016/j.xcrm.2023.101251 Prior antibiotic administration disrupts anti-PD-1 responses in advanced gastric cancer by altering the gut microbiome and systemic immune response] | ||
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+ | [https://doi.org/10.1016/j.cell.2023.05.046 Ultra-deep sequencing of Hadza hunter-gatherers recovers vanishing gut microbes] | ||
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+ | |style="padding:.4em;"|WJ Kim | ||
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+ | |style="padding:.4em;"|G Koh | ||
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+ | |style="padding:.4em;"|23-48 | ||
+ | |style="padding:.4em;"|SH Ahn | ||
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+ | [https://doi.org/10.1038/s41467-023-39264-0 A data-driven approach for predicting the impact of drugs on the human microbiome] | ||
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+ | [https://doi.org/10.1186/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;"|G Koh | ||
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+ | [https://doi.org/10.1038/s41587-023-01953-y Identification of mobile genetic elements with geNomad] | ||
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+ | [https://doi.org/10.1038/s41591-023-02407-3 Microbiome-derived cobalamin and succinyl-CoA as biomarkers for improved screening of anal cancer] | ||
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+ | [https://doi.org/10.1016/j.cell.2023.03.011 Dietary tryptophan metabolite released by intratumoral Lactobacillus reuteri facilitates immune checkpoint inhibitor treatment] | ||
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+ | |style="padding:.4em;"|G Koh | ||
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+ | |style="padding:.4em;"|SH Ahn | ||
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+ | |style="padding:.4em;"|SY Lim | ||
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+ | [https://doi.org/10.1016/j.ccell.2022.11.013 Gut microbiota-mediated nucleotide synthesis attenuates the response to neoadjuvant chemoradiotherapy in rectal cancer] | ||
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+ | |style="padding:.4em;"|SM Han | ||
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+ | [https://doi.org/10.1016/j.cell.2022.08.021 Gut microbiome of multiple sclerosis patients and paired household healthy controls reveal associations with disease risk and course] | ||
+ | |} | ||
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+ | |style="padding:.4em;"|G Koh | ||
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+ | [https://doi.org/10.1186/s13059-022-02828-2 Parallel single-cell and bulk transcriptome analyses reveal key features of the gastric tumor microenvironment] | ||
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+ | [https://doi.org/10.1038/s41467-022-32838-4 Mutated processes predict immune checkpoint inhibitor therapy benefit in metastatic melanoma] | ||
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+ | [https://doi.org/10.1038/s43018-021-00292-8 Temporal single-cell tracing reveals clonal revival and expansion of precursor exhausted T cells during anti-PD-1 therapy in lung cancer] | ||
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+ | [https://doi.org/10.1016/j.cell.2022.11.028 Integrative single-cell analysis of cardiogenesis indentifies developmental trajectories and non-conding mutations in congenital heart disease] | ||
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+ | [https://doi.org/10.1038/s41586-022-05435-0 Effect of the intratumoral microbiota on spatial and cellular heterogeneity in cancer] | ||
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+ | [https://www.nature.com/articles/s41588-022-01141-9 Cancer cell states recur across tumor types and form specific interactions with the tumor microenvironment] | ||
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+ | [https://www.biorxiv.org/content/10.1101/2022.08.05.502989v1 MetaTiME: Meta-components of the Tumor Immune Microenvironment] | ||
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+ | [https://www.nature.com/articles/s41590-022-01262-7 Pre-encoded responsiveness to type I interferon in the peripheral immune system defines outcome of PD1 blockade therapy] | ||
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+ | [https://www.biorxiv.org/content/10.1101/2022.03.16.484513v1 Integrated single-cell profiling dissects cell-state-specific enhancer landscapes of human tumor-infiltrating T cells] | ||
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+ | [https://www.nature.com/articles/s41591-022-01799-y A T cell resilience model associated with response to immunotherapy in multiple tumor types] | ||
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+ | [https://www.nature.com/articles/s41588-022-01134-8 Single-nucleus and spatial transcriptome profiling of pancreatic cancer identifies multicellular dynamics associated with neoadjuvant treatment] | ||
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+ | [https://pubmed.ncbi.nlm.nih.gov/35649411/ Cross-tissue, single-cell stromal atlas identifies shared pathological fibroblast phenotypes in four chronic inflammatory diseases] | ||
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+ | [https://pubmed.ncbi.nlm.nih.gov/35803260/ Tissue-resident memory and circulating T cells are early responders to pre-surgical cancer immunotherapy] | ||
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+ | [https://doi.org/10.1016/j.chom.2023.05.024 Enterosignatures define common bacterial guilds in the human gut microbiome] | ||
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+ | |style="padding:.4em;"|SH Lee | ||
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+ | [https://doi.org/10.1186/s40168-023-01564-4 Skin microbiome diferentiates into distinct cutotypes with unique metabolic functions upon exposure to polycyclic aromatic hydrocarbons] | ||
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+ | |style="padding:.4em;"|SH Ahn | ||
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+ | |style="padding:.4em;"|JH Cha | ||
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+ | |style="padding:.4em;"|NY Kim | ||
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+ | [https://doi.org/10.1186/s13059-022-02809-5 Gene fow and introgression are pervasive forces shaping the evolution of bacterial species] | ||
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+ | |style="padding:.4em;"|JH Cha | ||
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+ | [https://doi.org/10.1186/s40168-022-01435-4 Alterations of oral microbiota and impact on the gut microbiome in type 1 diabetes mellitus revealed by integrated multi‑omic analyses] | ||
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+ | |style="padding:.4em;"|SH Lee | ||
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+ | |style="padding:.4em;"|JY Ma | ||
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+ | [https://doi.org/10.1016/j.immuni.2022.08.016 The CD4+ T cell response to a commensal-derived epitope transitions from a tolerant to an inflammatory state in Crohn’s disease] | ||
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+ | |style="padding:.4em;"|NY Kim | ||
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+ | |style="padding:.4em;"|SH Lee | ||
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+ | |style="padding:.4em;"|JY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |style="padding:.4em;text-align:left"| | ||
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+ | |style="padding:.4em;"|SH Ahn | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1016/j.ccell.2022.09.009 Tumor microbiome links cellular programs and immunity in pancreatic cancer] | ||
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+ | |style="padding:.4em;"|JH Cha | ||
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+ | [https://doi.org/10.1038/s41586-022-05620-1 The person-to-person transmission landscape of the gut and oral microbiomes] | ||
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+ | |style="padding:.4em;"|JY Ma | ||
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+ | |style="padding:.4em;text-align:left"| | ||
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+ | |style="padding:.4em;"|SH Lee | ||
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+ | [https://www.nature.com/articles/s41467-022-29968-0 A randomized controlled trial for response of microbiome network to exercise and diet intervention in patients with nonalcoholic fatty liver disease] | ||
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+ | |style="padding:.4em;"|SH Ahn | ||
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+ | [https://www.biorxiv.org/content/10.1101/2022.05.19.492684v1 Scalable power analysis and effect size exploration of microbiome community differences with Evident] | ||
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+ | |} | ||
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+ | [https://www.biorxiv.org/content/10.1101/2022.08.19.504505v1 SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks] | ||
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+ | [https://www.pnas.org/doi/10.1073/pnas.2105859118 Representation learning of RNA velocity reveals robust cell transitions] | ||
+ | [https://www.nature.com/articles/s41467-022-34188-7 UniTVelo: temporally unified RNA velocity reinforces single-cell trajectory inference] | ||
+ | [https://www.sciencedirect.com/science/article/pii/S0092867421015774 Mapping transcriptomic vector fields of single cells] | ||
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+ | [https://www.nature.com/articles/s41467-022-31535-6 Network-based machine learning approach to predict immunotherapy response in cancer patients] | ||
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+ | [https://www.nature.com/articles/s41586-022-04718-w Extricating human tumour immune alterations from tissue inflammation] | ||
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+ | |style="padding:.4em;"|IS Choi | ||
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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;"|JH Cha | ||
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+ | |} | ||
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+ | |style="padding:.4em;"|SH Lee | ||
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+ | |style="padding:.4em;"|HJ Kim | ||
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+ | |style="padding:.4em;"|JH Cha | ||
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+ | |style="padding:.4em;"|JY Ma | ||
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+ | |style="padding:.4em;"|SH Lee | ||
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+ | |style="padding:.4em;"|SH Ahn | ||
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+ | |style="padding:.4em;"|SH Ann | ||
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+ | |style="padding:.4em;"|NY Kim | ||
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+ | |style="padding:.4em;"|MY Ma | ||
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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://pubmed.ncbi.nlm.nih.gov/34618582/ Commensal bacteria promote endocrine resistance in prostate cancer through androgen biosynthesis] | ||
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+ | [https://www.nature.com/articles/s41587-021-01102-3 Fast and accurate metagenotyping of the human gut microbiome with GT-Pro] | ||
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+ | [https://www.nature.com/articles/s41587-020-00797-0 inStrain profiles population microdiversity from metagenomic data and sensitively detects shared microbial strains] | ||
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+ | [https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02042-y Analysis of 1321 Eubacterium rectale genomes from metagenomes uncovers complex phylogeographic population structure and subspecies functional adaptations] | ||
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+ | [https://www.cell.com/cell-host-microbe/fulltext/S1931-3128(19)30041-1?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS1931312819300411%3Fshowall%3Dtrue Distinct Genetic and Functional Traits of Human Intestinal Prevotella copri Strains Are Associated with Different Habitual Diets] | ||
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+ | [https://www.nature.com/articles/nmeth.3802 Strain-level microbial epidemiology and population genomics from shotgun metagenomics] | ||
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+ | [https://www.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/10.1101/2021.07.28.453784v1 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/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.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://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/s41467-021-21475-y Gastrointestinal microbiota composition predicts peripheral inflammatory state during treatment of human tuberculosis] | ||
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+ | |style="padding:.4em;"|HH Eom | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://science.sciencemag.org/content/369/6506/936 Cross-reactivity between tumor MHC class 1-restricted antigens and an enterococcal bacteriophage] | ||
+ | |- | ||
+ | |style="padding:.4em;"|21-15 | ||
+ | |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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+ | |style="padding:.4em;"|MH Lee | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [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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+ | |style="padding:.4em;"|YY Jang | ||
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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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+ | |style="padding:.4em;"|JM Lee | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.cell.com/fulltext/S0092-8674(17)30107-1 Mining the Human Gut Microbiota for Immunomodulatory Organisms] | ||
+ | |- | ||
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+ | |style="padding:.4em;"|JH Cha | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.nature.com/articles/s41586-020-2095-1 Microbiome analyses of blood and tissues suggest cancer diagnostic approach] | ||
+ | |- | ||
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+ | |style="padding:.4em;"|JH Cha | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://science.sciencemag.org/content/368/6494/973 The human tumor microbiome is composed of tumor type-specific intracellular bacteria] | ||
+ | |} | ||
+ | |||
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+ | |style="padding:.4em;"|21-8 | ||
+ | |style="padding:.4em;"|IS Choi | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.nature.com/articles/s41590-020-0784-4 Functional CRISPR dissection of gene networks controlling human regulatory T cell identity] | ||
+ | |- | ||
+ | |style="padding:.4em;"|21-7 | ||
+ | |style="padding:.4em;"|JH Cha | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.sciencedirect.com/science/article/pii/S0092867420306887 Molecular Pathways of Colon Inflammation Induced by Cancer Immunotherapy] | ||
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+ | |style="padding:.4em;"|SB Baek | ||
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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] | ||
+ | |- | ||
+ | |style="padding:.4em;"|21-5 | ||
+ | |style="padding:.4em;"|IS Choi | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.nature.com/articles/s41467-020-14766-3 Trajectory-based differential expression analysis for single-cell sequencing data] | ||
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+ | |style="padding:.4em;" rowspan=2|Single-cell | ||
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+ | |style="padding:.4em;"|SB Baek | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.nature.com/articles/s41588-018-0156-2 Genetic determinants of co-accessible chromatin regions in activated T cells across humans] | ||
+ | |- | ||
+ | |style="padding:.4em;"|21-3 | ||
+ | |style="padding:.4em;"|JH Cha | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.sciencedirect.com/science/article/pii/S009286742030341X Single-Cell Analyses Inform Mechanisms of Myeloid-Targeted Therapies in Colon Cancer] | ||
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+ | |style="padding:.4em;"|JW Cho | ||
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+ | [https://www.sciencedirect.com/science/article/pii/S0092867419308967?via%3Dihub Single-Cell Analysis of Crohn's Disease Lesions Identifies a Pathogenic Cellular Module Associated with Resistance to Anti-TNF Therapy] | ||
+ | |- | ||
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+ | |style="padding:.4em;"|JW Cho | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.nature.com/articles/s41467-020-15956-9 Multi-omic single-cell snapshots reveal multiple independent trajectories to drug tolerance in a melanoma cell line] | ||
+ | |} | ||
+ | |||
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+ | |+style="text-align:left;font-size:12pt" | 2020-1st semester | ||
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+ | |style="padding:.4em;"|JW Cho | ||
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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"| | ||
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+ | |style="padding:.4em;" rowspan=2|2020/06/09 | ||
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+ | |style="padding:.4em;"|JY Lee | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;"|20-11 | ||
+ | |style="padding:.4em;"|JH Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |style="padding:.4em;"|HY Seo | ||
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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 | ||
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+ | |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 | ||
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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;" | ||
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+ | !scope="col" style="padding:.4em" |Date | ||
+ | !scope="col" stype="padding:.4em" | Team | ||
+ | !scope="col" style="padding:.4em" | Paper<br/>index | ||
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+ | |style="padding:.4em;" rowspan=2|2019/10/15 | ||
+ | |style="padding:.4em;" rowspan=2|Microbiome | ||
+ | |style="padding:.4em;"|19-51 | ||
+ | |style="padding:.4em;"|CY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;"|19-50 | ||
+ | |style="padding:.4em;"|SH Lee | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2019/10/08 | ||
+ | |style="padding:.4em;" rowspan=2|Single-cell | ||
+ | |style="padding:.4em;"|19-49 | ||
+ | |style="padding:.4em;"|SB Baek | ||
+ | |style="padding:.4em;text-align:left"| | ||
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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 | ||
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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"| | ||
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+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2019/09/10 | ||
+ | |style="padding:.4em;" rowspan=2|Single-cell | ||
+ | |style="padding:.4em;"|19-41 | ||
+ | |style="padding:.4em;"|KS Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
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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"| | ||
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+ | |- | ||
+ | |style="padding:.4em;"|19-38 | ||
+ | |style="padding:.4em;"|CY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |} | ||
+ | {|class=wikitable style="text-align:center;" | ||
+ | |+style="text-align:left;font-size:12pt" | 2019 | ||
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+ | !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 | ||
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+ | |style="padding:.4em;" rowspan=2|2019/08/20 | ||
+ | |style="padding:.4em;"|19-37 | ||
+ | |style="padding:.4em;"|JH Cha | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;"|19-36 | ||
+ | |style="padding:.4em;"|MY Lee | ||
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+ | |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 | ||
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+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2019/08/13 | ||
+ | |style="padding:.4em;"|19-32 | ||
+ | |style="padding:.4em;"|KS Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;"|19-31 | ||
+ | |style="padding:.4em;"|KS Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2019/08/08 | ||
+ | |style="padding:.4em;"|19-30 | ||
+ | |style="padding:.4em;"|JH Cha | ||
+ | |style="padding:.4em;text-align:left"| | ||
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+ | |- | ||
+ | |style="padding:.4em;"|19-29 | ||
+ | |style="padding:.4em;"|KS Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.sciencedirect.com/science/article/pii/S009286741831242X High-Dimensional Analysis Delineates Myeloid and Lymphoid Compartment Remodeling during Successful Immune-Checkpoint Cancer Therapy] | ||
+ | |- | ||
+ | |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"| | ||
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+ | |- | ||
+ | |style="padding:.4em;" rowspan=2|2019/07/30 | ||
+ | |style="padding:.4em;"|19-26 | ||
+ | |style="padding:.4em;"|IS Choi | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.nature.com/articles/nm.4466 High-dimensional single-cell analysis predicts response to anti-PD-1 immunotherapy] | ||
+ | |- | ||
+ | |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"| | ||
+ | [https://www.nature.com/articles/ncomms14825 Sensitive detection of rare disease-associated cell subsets via representation learning] | ||
+ | |- | ||
+ | |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"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/30936548 Metagenomic analysis of colorectal cancer datasets identifies cross-cohort microbial diagnostic signatures and a link with choline degradation] | ||
+ | |- | ||
+ | |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"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/29942094 Global characterization of T cells in non-small-cell lung cancer by single-cell sequencing] | ||
+ | |- | ||
+ | |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"| | ||
+ | [https://www.ncbi.nlm.nih.gov/pubmed/30787436 A single-cell molecular map of mouse gastrulation and early organogenesis] | ||
+ | |- | ||
+ | |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;" | {|class=wikitable style="text-align:center;" | ||
|+style="text-align:left;font-size:12pt" | 2015 | |+style="text-align:left;font-size:12pt" | 2015 | ||
Line 7: | Line 3,569: | ||
!scope="col" style="padding:.4em" | Paper title | !scope="col" style="padding:.4em" | Paper title | ||
|- | |- | ||
− | |style="padding:.4em;" rowspan=3|2015/ | + | |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;"|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;"|DS Bae | ||
|style="padding:.4em;text-align:left"| | |style="padding:.4em;text-align:left"| | ||
[http://www.sciencedirect.com/science/article/pii/S0092867414011787 Genome-Scale CRISPR-Mediated Control of Gene Repression and Activation.] | [http://www.sciencedirect.com/science/article/pii/S0092867414011787 Genome-Scale CRISPR-Mediated Control of Gene Repression and Activation.] | ||
|- | |- | ||
− | |style="padding:.4em;"|2015- | + | |style="padding:.4em;"|2015-22 |
|style="padding:.4em;"|CY Kim | |style="padding:.4em;"|CY Kim | ||
|style="padding:.4em;text-align:left"| | |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.] | [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- | + | |style="padding:.4em;"|2015-21 |
|style="padding:.4em;"|ER Kim | |style="padding:.4em;"|ER Kim | ||
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|style="padding:.4em;"|ER Kim | |style="padding:.4em;"|ER Kim | ||
|style="padding:.4em;text-align:left"| | |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.] | [http://www.sciencemag.org/content/346/6212/1007.long Mouse regulatory DNA landscapes reveal global principles of cis-regulatory evolution.] | ||
|- | |- | ||
− | |style="padding:.4em;"|2015- | + | |style="padding:.4em;"|2015-19 |
|style="padding:.4em;"|KS Kim | |style="padding:.4em;"|KS Kim | ||
|style="padding:.4em;text-align:left"| | |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.] | [http://www.nature.com/nature/journal/v515/n7527/full/nature13985.html Principles of regulatory information conservation between mouse and human.] | ||
|- | |- | ||
− | |style="padding:.4em;"|2015- | + | |style="padding:.4em;"|2015-18 |
− | |style="padding:.4em;"| | + | |style="padding:.4em;"|BH Kang |
|style="padding:.4em;text-align:left"| | |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.] | [http://www.nature.com/nature/journal/v515/n7527/full/nature13972.html Conservation of trans-acting circuitry during mammalian regulatory evolution.] | ||
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|style="padding:.4em;" rowspan=3|2015/02/16 | |style="padding:.4em;" rowspan=3|2015/02/16 | ||
− | |style="padding:.4em;"|2015- | + | |style="padding:.4em;"|2015-17 |
|style="padding:.4em;"|T Lee | |style="padding:.4em;"|T Lee | ||
|style="padding:.4em;text-align:left"| | |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.] | [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- | + | |style="padding:.4em;"|2015-16 |
|style="padding:.4em;"|CY Kim | |style="padding:.4em;"|CY Kim | ||
|style="padding:.4em;text-align:left"| | |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.] | [http://www.nature.com/ng/journal/v46/n3/full/ng.2892.html A general framework for estimating the relative pathogenicity of human genetic variants.] | ||
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|style="padding:.4em;"|CY Kim | |style="padding:.4em;"|CY Kim | ||
|style="padding:.4em;text-align:left"| | |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.] | [http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003825 A probabilistic model to predict clinical phenotypic traits from genome sequencing.] | ||
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Revision as of 19:06, 24 September 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 |