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+ | |+style="text-align:left;font-size:12pt" | 2024-2 scOmics | ||
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+ | |style="padding:.4em;"|YL Jung | ||
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+ | [https://doi.org/10.1101/2024.04.04.24305313 Single-cell RNA sequencing of human tissue supports successful drug targets] | ||
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+ | [https://doi.org/10.1101/2023.11.21.568145 ANDES: a novel best-match approach for enhancing gene set analysis in embedding spaces] | ||
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+ | [https://doi.org/10.1101/2024.09.24.614685 Evaluating the Utilities of Foundation Models in Single-cell Data Analysis] | ||
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+ | [https://doi.org/10.1101/2024.09.24.614685 Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation] | ||
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+ | |style="padding:.4em;"|JH Cha | ||
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+ | [https://doi.org/10.1101/2024.09.24.614685 scEMB: Learning context representation of genes based on large-scale single-cell transcriptomics] | ||
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+ | |style="padding:.4em;"|HB Lee | ||
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+ | [https://doi.org/10.1101/2024.09.09.611960 Mouse-Geneformer: A Deep Learning Model for Mouse Single-Cell Transcriptome and Its Cross-Species Utility] | ||
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+ | |style="padding:.4em;"|YL Jung | ||
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+ | [https://doi.org/10.1101/2024.08.16.608180 Quantized multi-task learning for context-specific representations of gene network dynamics] | ||
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+ | |style="padding:.4em;"|EJ Sung | ||
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+ | [https://doi.org/10.17632/wdxwy8gmrz.1 Systematic Functional Annotation and Visualization of Biological Networks] | ||
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+ | [https://doi.org/10.1038/s41592-024-02303-9 CellRank 2: unified fate mapping in multiview single-cell data] | ||
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+ | [https://doi.org/10.1101/2024.07.29.605556 scPRINT: pre-training on 50 million cells allows robust gene network predictions] | ||
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+ | |style="padding:.4em;"|JH Cha | ||
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+ | [https://doi.org/10.1038/s41467-024-46440-3 Bidirectional generation of structure and properties through a single molecular foundation model] | ||
+ | |} | ||
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+ | |+style="text-align:left;font-size:12pt" | 2024-2 Microbiome | ||
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+ | |style="padding:.4em;"|YR Kim | ||
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+ | [https://doi.org/10.1038/s41564-024-01739-1 Multikingdom and functional gut microbiota markers for autism spectrum disorder] | ||
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+ | |style="padding:.4em;"|JY Kim | ||
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+ | [https://doi.org/10.1186/s13059-024-03390-9 A realistic benchmark for differential abundance testing and confounder adjustment in human microbiome studies] | ||
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+ | [https://doi.org/10.1038/s41564-024-01728-4 Microbial community-scale metabolic modelling predicts personalized short-chain fatty acid production profiles in the human gut] | ||
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+ | |style="padding:.4em;"|G Koh | ||
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+ | [https://doi.org/10.1038/s41467-024-52561-6 Gut metagenomes of Asian octogenarians reveal metabolic potential expansion and distinct microbial species associated with aging phenotypes] | ||
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+ | |style="padding:.4em;"|SH Ahn | ||
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+ | [https://doi.org/10.1038/s41467-024-52561-6 Gut microbiota wellbeing index predicts overall health in a cohort of 1000 infants] | ||
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+ | [https://doi.org/10.1186/s13059-024-03320-9 VirRep: a hybrid language representation learning framework for identifying viruses from human gut metagenomes] | ||
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+ | [https://doi.org/10.1101/2024.06.27.601020 Ultrafast and accurate sequence alignment and clustering of viral genomes] | ||
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+ | |style="padding:.4em;"|JY Ma | ||
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+ | [https://doi.org/10.1038/s41467-024-46947-9 The OMG dataset: An Open MetaGenomic corpus for mixed-modality genomic language modeling] | ||
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+ | |style="padding:.4em;"|JH Cha | ||
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+ | [https://doi.org/10.1038/s41467-024-46947-9 Genomic language model predicts protein co-regulation and function] | ||
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+ | |style="padding:.4em;"|NY Kim | ||
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+ | [https://doi.org/10.1101/2024.07.26.605391 Protein Set 1 Transformer: A protein-based genome language model to power high diversity viromics] | ||
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+ | [https://doi.org/10.1101/2024.07.11.603044 Prophage-DB: A comprehensive database to explore diversity,distribution, and ecology of prophages] | ||
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+ | |style="padding:.4em;"|JY Kim | ||
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+ | [https://doi.org/10.1186/s40168-024-01904-y Strain‑resolved de‑novo metagenomic assembly of viral genomes and microbial 16S rRNAs] | ||
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+ | [https://doi.org/10.1186/s40168-024-01876-z Prokaryotic‑virus‑encoded auxiliary metabolic genes throughout the global oceans] | ||
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+ | |style="padding:.4em;"|G Koh | ||
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+ | [https://doi.org/10.1016/j.cell.2024.07.039 Unexplored microbial diversity from 2,500 food metagenomes and links with the human microbiome] | ||
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+ | |style="padding:.4em;"|SH Ahn | ||
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+ | [https://doi.org/10.1101/2024.04.17.589959 Pangenomes of Human Gut Microbiota Uncover Links Between Genetic Diversity and Stress Response] | ||
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+ | [https://doi.org/10.1101/2024.05.28.596318 vClassifier: a toolkit for species-level classification of prokaryotic viruses] | ||
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+ | [https://doi.org/10.1101/2024.07.26.605250 GRAViTy-V2: a grounded viral taxonomy application] | ||
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+ | |style="padding:.4em;"|JY Ma | ||
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+ | [https://doi.org/10.1038/s41467-024-52533-w Accurately predicting enzyme functions through geometric graph learning on ESMFold-predicted structures] | ||
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+ | |style="padding:.4em;"|JH Cha | ||
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+ | [https://doi.org/10.1101/2024.06.27.600934 Improved detection of microbiome-disease associations via population structure-aware generalized linear mixed effects models (microSLAM)] | ||
+ | |} | ||
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+ | |+style="text-align:left;font-size:12pt" | 2024-1 scOmics | ||
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+ | !scope="col" style="padding:.4em" | Date | ||
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+ | !scope="col" style="padding:.4em" | Paper<br/>index | ||
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+ | !scope="col" style="padding:.4em" | Paper title | ||
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+ | |style="padding:.4em;"|HB Lee | ||
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+ | [https://doi.org/10.1126/science.adj4857 A blueprint for tumor-infiltrating B cells across human cancers] | ||
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+ | |style="padding:.4em;"|YL Jung | ||
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+ | [https://doi.org/10.1038/s41467-024-48310-4 Systematic dissection of tumor-normal single-cell ecosystems across a thousand tumors of 30 cancer types] | ||
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+ | |style="padding:.4em;"|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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+ | [https://doi.org/10.1038/s41591-024-02856-4 A visual-language foundation model for computational pathology] | ||
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+ | [https://doi.org/10.1038/s41592-024-02175-z SLIDE: Significant Latent Factor Interaction Discovery and Exploration across biological domains] | ||
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+ | [https://doi.org/10.1016/j.ccell.2023.12.013 Clinical and molecular features of acquired resistance to immunotherapy in non-small cell lungcancer] | ||
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+ | [https://doi.org/10.1101/2024.06.04.597354 Cell-Graph Compass: Modeling Single Cells with Graph Structure Foundation Model] | ||
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+ | |style="padding:.4em;"|YL Jung | ||
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+ | [https://doi.org/10.1016/j.xgen.2023.100473 Single-cell transcriptome landscape of circulating CD4+ T cell populations in autoimmune diseases] | ||
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+ | |style="padding:.4em;"|EJ Sung | ||
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+ | [https://doi.org/10.1038/s43588-024-00597-5 Population-level comparisons of gene regulatory networks modeled on highthroughput single-cell transcriptomics data] | ||
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+ | [https://doi.org/10.1101/2024.06.16.599201 node2vec2rank: Large Scale and Stable Graph Differential Analysis via Multi-Layer Node Embeddings and Ranking] | ||
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+ | [https://doi.org/10.1016/j.xgen.2024.100553 Unified cross-modality integration and analysis of T cell receptors and T cell transcriptomes by low-resource-aware representation learning] | ||
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+ | |style="padding:.4em;"|JH Cha | ||
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+ | [https://doi.org/10.1101/2023.07.18.549602 Contextual AI models for single-cell protein biology] | ||
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+ | |style="padding:.4em;"|EJ Sung | ||
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+ | [https://doi.org/10.1101/2024.04.15.589472 Nicheformer: a foundation model for single-cell and spatial omics] | ||
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+ | [https://doi.org/10.1101/2023.05.29.542705 Large Scale Foundation Model on Single-cell Transcriptomics] | ||
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+ | [https://doi.org/10.1038/s41592-024-02201-0 scGPT: toward building a foundation modelfor single-cell multi-omics using generative AI] | ||
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+ | |style="padding:.4em;"|JH Cha | ||
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+ | [https://doi.org/10.1038/s41586-023-06139-9 Transfer learning enables predictions in network biology] | ||
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+ | [https://doi.org/10.1158/0008-5472.CAN-23-2650 The Web-Based Portal SpatialTME Integrates Histological Images with Single-Cell and Spatial Transcriptomics to Explore the Tumor Microenvironment] | ||
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+ | [https://doi.org/10.1038/s41592-023-02117-1 SEVtras delineates small extracellular vesicles at droplet resolution from single-cell transcriptomes] | ||
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+ | [https://doi.org/10.1038/s41587-023-01728-5 A relay velocity model infers cell-dependent RNA velocity] | ||
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+ | |style="padding:.4em;"|EJ Sung | ||
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+ | [https://doi.org/10.1038/s41467-023-44206-x Pathway centric analysis for single-cell RNA-seq and spatial transcriptomics data with GSDensity] | ||
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+ | [https://doi.org/10.1038/s41587-023-01734-7 Dandelion uses the single-cell adaptive immune receptor repertoire to explore lymphocyte developmental origins] | ||
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+ | [https://doi.org/10.1016/j.cell.2023.11.026 Automatic cell-type harmonization and integration across Human Cell Atlas datasets] | ||
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+ | [https://doi.org/10.1038/s41592-023-01994-w Deep generative modeling of transcriptional dynamics for RNA velocity analysis in single cells] | ||
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+ | [https://doi.org/10.1038/s41587-021-00896-6 Gene signature extraction and cell identity recognition at the single-cell level with Cell-ID] | ||
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+ | [https://doi.org/10.1038/s41591-024-03067-7 Strain-specific gut microbial signatures in type 2 diabetes identified in a cross-cohort analysis of 8,117 metagenomes] | ||
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+ | [https://doi.org/10.1186/s40168-024-01832-x Gut virome-wide association analysis identifes cross-population viral signatures for infammatory bowel disease] | ||
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+ | [https://doi.org/10.48550/arXiv.1806.00064 Efficient Low-rank Multimodal Fusion with Modality-Specific Factors] | ||
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+ | [https://doi.org/10.48550/arXiv.1707.07250 Tensor Fusion Network for Multimodal Sentiment Analysis] | ||
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+ | [https://doi.org/10.1016/j.cell.2024.03.034 Gut symbionts alleviate MASH through a secondary bile acid biosynthetic pathway] | ||
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+ | |style="padding:.4em;"|G Koh | ||
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+ | [https://doi.org/10.1186/s13059-024-03325-4 Gut microbiota DPP4-like enzymes are increased in type-2 diabetes and contribute to incretin inactivation] | ||
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+ | [https://pubmed.ncbi.nlm.nih.gov/31510656 Deep learning with multimodal representation for pancancer prognosis prediction] | ||
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+ | [https://pubmed.ncbi.nlm.nih.gov/32881682 Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosis] | ||
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+ | [https://doi.org/10.1016/j.ccell.2022.07.004 Pan-cancer integrative histology-genomic analysis via multimodal deep learning] | ||
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+ | [https://doi.org/10.1016/j.chom.2024.03.005 A metagenomics pipeline reveals insertion sequence-driven evolution of the microbiota] | ||
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+ | |style="padding:.4em;"|JH Cha | ||
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+ | |style="padding:.4em;"|JH Cha | ||
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+ | [https://arxiv.org/abs/2103.00020 Learning Transferable Visual Models From Natural Language Supervision] | ||
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+ | [https://doi.org/10.1038/s41592-022-01616-x BIONIC: biological network integration using convolutions] | ||
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+ | [https://doi.org/10.1038/s41587-023-01917-2 Protein remote homology detection and structural alignment using deep learning] | ||
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+ | [https://doi.org/10.1038/s41586-024-07487-w Accurate structure prediction of biomolecular interactions with AlphaFold 3] | ||
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+ | [https://doi.org/10.1186/s40168-023-01737-1 Gut microbiome-metabolome interactions predict host condition] | ||
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+ | |style="padding:.4em;"|SH Ahn | ||
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+ | [https://doi.org/10.1038/s41586-024-07336-w Paternal microbiome perturbations impact offspring fitness] | ||
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+ | [https://doi.org/10.1016/j.cell.2024.01.039 A cryptic plasmid is among the most numerous genetic elements in the human gut] | ||
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+ | [https://doi.org/10.1016/j.cell.2024.03.014 Gut microbiome and metabolome profiling in Framingham heart study reveals cholesterol-metabolizing bacteria] | ||
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+ | |style="padding:.4em;"|JY Ma | ||
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+ | |style="padding:.4em;"|G Koh | ||
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+ | [https://doi.org/10.1038/s41467-023-44290-z Effective binning of metagenomic contigs using contrastive multi-view representation learning] | ||
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+ | [https://doi.org/10.1038/s41559-020-01353-4 Polarization of microbial communities between competitive and cooperative metabolism] | ||
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+ | |style="padding:.4em;"|G Koh | ||
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+ | [https://onlinelibrary.wiley.com/doi/full/10.1002/advs.202303925 Metagenomic Insight into The Global Dissemination of The Antibiotic Resistome] | ||
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+ | [https://doi.org/10.1073/pnas.2008731118 Conjugative plasmids interact with insertion sequences to shape the horizontal transfer of antimicrobial resistance genes] | ||
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+ | [https://doi.org/10.1038/s41586-023-07011-6 Spatial transcriptomics reveal neuron–astrocyte synergy in long-term memory] | ||
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+ | [https://doi.org/10.1038/s41467-021-22197-x scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses] | ||
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+ | |style="padding:.4em;"|SM Han | ||
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+ | [https://doi.org/10.1126/science.abi4882 Spatial transcriptomics of planktonic and sessile bacterial populations at single-cell resolution] | ||
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+ | |style="padding:.4em;"|HJ Choi | ||
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+ | [https://doi.org/10.1038/s41590-024-01792-2 Human lung cancer harbors spatially organized stem-immunity hubs associated with response to immunotherapy] | ||
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+ | [https://doi.org/10.1038/s41467-021-27464-5 Single-cell transcriptomics captures features of human midbrain development and dopamine neuron diversity in brain organoids] | ||
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+ | |style="padding:.4em;"|HJ Cha | ||
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+ | [https://doi.org/10.1016/j.chom.2023.08.019 Cell-type-specific responses to fungal infection in plants revealed by single-cell transcriptomics] | ||
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+ | [https://www.sciencedirect.com/science/article/pii/S1534580722002519?via%3Dihub The single-cell stereo-seq reveals region-specific cell subtypes and transcriptome profiling in Arabidopsis leaves] | ||
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+ | [https://doi.org/10.1038/s41588-022-01100-4 Single-cell and bulk transcriptome sequencing identifies two epithelial tumor cell states and refines the consensus molecular classification of colorectal cancer] | ||
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+ | [https://doi.org/10.1038/s42255-023-00876-x Delineating mouse β-cell identity during lifetime and in diabetes with a single cell atlas] | ||
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+ | [https://doi.org/10.1038/s41587-023-01747-2 Multimodal spatiotemporal phenotyping of human retinal organoid development] | ||
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+ | [https://doi.org/10.1038/s41586-024-07251-0 Immune microniches shape intestinal Treg function] | ||
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+ | [https://doi.org/10.1016/j.devcel.2021.02.021 A single-cell analysis of the Arabidopsis vegetative shoot apex] | ||
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+ | [https://doi.org/10.1038/s41467-023-40137-9 Droplet-based high-throughput single microbe RNA sequencing by smRandom-seq] | ||
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+ | [https://doi.org/10.1038/s41564-023-01462-3 Single-cell massively-parallel multiplexed microbial sequencing (M3-seq) identifies rare bacterial populations and profiles phage infection] | ||
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+ | |style="padding:.4em;"|EB Yu | ||
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+ | [https://doi.org/10.1016/j.celrep.2022.111736 Spatial transcriptomics demonstrates the role of CD4 T cells in effector CD8 T cell differentiation during chronic viral infection] | ||
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+ | |style="padding:.4em;"|DY Won | ||
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+ | [https://doi.org/10.1038/s41587-023-01979-2 Spatial metatranscriptomics resolves host–bacteria–fungi interactomes] | ||
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+ | [https://doi.org/10.1038/s41467-023-36325-2 Dissecting the immune suppressive human prostate tumor microenvironment via integrated single-cell and spatial transcriptomic analyses] | ||
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+ | [https://doi.org/10.1038/s41593-023-01452-y Single-nucleus genomics in outbred rats with divergent cocaine addiction-like behaviors reveals changes in amygdala GABAergic inhibition] | ||
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+ | [https://doi.org/10.1038/s41593-023-01455-9 Spatial transcriptomics reveals the distinct organization of mouse prefrontal cortex and neuronal subtypes regulating chronic pain] | ||
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+ | [https://doi.org/10.1038/s41467-023-39933-0 Spatial cellular architecture predicts prognosis in glioblastoma] | ||
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+ | [https://doi.org/10.1016/j.celrep.2024.113784 Single-cell spatial transcriptomic and translatomic profiling of dopaminergic neurons in health, aging, and disease] | ||
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+ | [https://doi.org/10.1038/s41467-022-30511-4 Transcriptional adaptation of olfactory sensory neurons to GPCR identity and activity] | ||
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+ | [https://doi.org/10.1038/s41467-021-26271-2 Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions] | ||
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+ | [https://doi.org/10.1021/acscentsci.3c01169 Single-Cell Analysis Reveals Cxcl14+ Fibroblast Accumulation in Regenerating Diabetic Wounds Treated by Hydrogel-Delivering Carbon Monoxide] | ||
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+ | [https://doi.org/10.1038/s41477-022-01291-y Single-cell RNA sequencing provides a high-resolution roadmap for understanding the multicellular compartmentation of specialized metabolism] | ||
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+ | [https://doi.org/10.1038/s41556-023-01316-4 Single-cell spatial multi-omics and deep learning dissect enhancer-driven gene regulatory networks in liver zonation] | ||
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+ | [https://doi.org/10.1038/s41467-022-35319-w Spatial transcriptomics landscape of lesions from non-communicable inflammatory skin diseases] | ||
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+ | [https://doi.org/10.1016/j.cmet.2022.07.010 Neuregulin 4 suppresses NASH-HCC development by restraining tumor-prone liver microenvironment] | ||
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+ | [https://doi.org/10.1038/s41593-023-01334-3 Single-nucleus multiregion transcriptomic analysis of brain vasculature in Alzheimer’s disease] | ||
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+ | [https://doi.org/10.1136/gutjnl-2023-330243 Single-cell transcriptomic analysis deciphers heterogenous cancer stem-like cells in colorectal cancer and their organ-specific metastasis] | ||
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+ | [https://doi.org/10.1038/s41467-022-31519-6 Single cell sequencing identifies clonally expanded synovial CD4+ TPH cells expressing GPR56 in rheumatoid arthritis] | ||
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+ | [https://doi.org/10.1016/j.ccell.2023.09.011 Progenitor-like exhausted SPRY1+CD8+ T cells potentiate responsiveness to neoadjuvant PD-1 blockade in esophageal squamous cell carcinoma] | ||
+ | |} | ||
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+ | [https://doi.org/10.1038/s41592-023-02035-2 Population-level integration of single-cell datasets enables multi-scale analysis across samples] | ||
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+ | [https://doi.org/10.1038/s43587-023-00514-x scDiffCom: a tool for differential analysis of cell–cell interactions provides a mouse atlas of aging changes in intercellular communication] | ||
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+ | [https://doi.org/10.1038/s41587-022-01467-z Modeling intercellular communication in tissues using spatial graphs of cells] | ||
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+ | [https://doi.org/10.1038/s41588-023-01523-7 Precise identification of cell states altered in disease using healthy single-cell references] | ||
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+ | [https://aacrjournals.org/clincancerres/article/29/19/3924/729105/Learning-Individual-Survival-Models-from-PanCancer Learning Individual Survival Models from PanCancer Whole Transcriptome Data] | ||
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+ | [https://www.science.org/doi/10.1126/sciimmunol.adf4968 Preexisting tumor-resident T cells with cytotoxic potential associate with response to neoadjuvant anti–PD-1 in head and neck cancer] | ||
+ | |- | ||
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+ | [https://doi.org/10.1038/s41588-022-01273-y MHC II immunogenicity shapes the neoepitope landscape in human tumors] | ||
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+ | [https://doi.org/10.1038/s41586-023-06130-4 Hallmarks of transcriptional intratumour heterogeneity across a thousand tumours] | ||
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+ | [https://doi.org/10.1038/s41467-023-37353-8 Pan-cancer classification of single cells in the tumour microenvironment] | ||
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+ | |style="padding:.4em;"|JH Cha | ||
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+ | [https://doi.org/10.1038/s41587-023-01686-y Single-cell mapping of combinatorial target antigens for CAR switches using logic gates] | ||
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+ | |style="padding:.4em;"|SB Baek | ||
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+ | [https://doi.org/10.1038/s41587-023-01782-z Comparative analysis of cell–cell communication at single-cell resolution] | ||
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+ | |style="padding:.4em;"|EJ Sung | ||
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+ | [https://doi.org/10.1038/s43018-023-00566-3 Phenotypic diversity of T cells in human primary and metastatic brain tumors revealed by multiomic interrogation] | ||
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+ | [https://doi.org/10.1038/s41591-023-02324-5 An integrated tumor, immune and microbiome atlas of colon cancer] | ||
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+ | [https://doi.org/10.1038/s41587-022-01476-y Multi-omic single-cell velocity models epigenome–transcriptome interactions and improves cell fate prediction] | ||
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+ | [https://doi.org/10.1016/j.immuni.2023.04.010 Recruitment of epitope-specific T cell clones with a low-avidity threshold supports efficacy against mutational escape upon re-infection] | ||
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+ | |style="padding:.4em;"|HJ Kim | ||
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+ | [https://doi.org/10.1186/s40168-023-01607-w Phages are unrecognized players in the ecology of the oral pathogen Porphyromonas gingivalis] | ||
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+ | |style="padding:.4em;"|JH Cha | ||
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+ | [https://doi.org/10.1038/s41564-023-01439-2 A predicted CRISPR-mediated symbiosis between uncultivated archaea] | ||
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+ | |style="padding:.4em;"|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/10.1038/s41587-023-01696-w Contamination source modeling with SCRuB improves cancer phenotype prediction from microbiome data] | ||
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+ | |style="padding:.4em;"|NY Kim | ||
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+ | [https://doi.org/10.1038/s41586-023-06431-8 Mapping the T cell repertoire to a complex gut bacterial community] | ||
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+ | |style="padding:.4em;"|YR Kim | ||
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+ | [https://doi.org/10.1101/2023.07.03.547607 Multi-view integration of microbiome data for identifying disease-associated modules] | ||
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+ | |style="padding:.4em;"|JY Kim | ||
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+ | [https://doi.org/10.1101/2023.09.28.559994 Phage-bacteria dynamics during the first years of life revealed by trans-kingdom marker gene analysis] | ||
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+ | |style="padding:.4em;"|23-59 | ||
+ | |style="padding:.4em;"|WJ Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1038/s41593-023-01361-0 Multi-level analysis of the gut–brain axis shows autism spectrum disorder-associated molecular and microbial profiles] | ||
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+ | |style="padding:.4em;"|23-58 | ||
+ | |style="padding:.4em;"|G Koh | ||
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+ | [https://doi.org/10.1101/2023.11.21.568153 Metagenomic Immunoglobulin Sequencing (MIG-Seq) Exposes Patterns of IgA Antibody Binding in the Healthy Human Gut Microbiome] | ||
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+ | |style="padding:.4em;"|23-57 | ||
+ | |style="padding:.4em;"|SH Ahn | ||
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+ | [https://doi.org/10.1038/s41467-023-41042-x Impact of dietary interventions on pre-diabetic oral and gut microbiome, metabolites and cytokines] | ||
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+ | |style="padding:.4em;"|HJ Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1038/s41592-023-02018-3 Fast and robust metagenomic sequence comparison through sparse chaining with skani] | ||
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+ | |style="padding:.4em;"|JY Ma | ||
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+ | [https://doi.org/10.1038/s41591-023-02599-8 Bacterial SNPs in the human gut microbiome associate with host BMI] | ||
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+ | |style="padding:.4em;"|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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+ | |style="padding:.4em;"|NY Kim | ||
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+ | [https://doi.org/10.1016/j.chom.2023.10.005 Multi-kingdom gut microbiota analyses define bacterial-fungal interplay and microbial markers of pan-cancer immunotherapy across cohorts] | ||
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+ | |style="padding:.4em;"|YR Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1016/j.xcrm.2023.101251 Prior antibiotic administration disrupts anti-PD-1 responses in advanced gastric cancer by altering the gut microbiome and systemic immune response] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/12/27 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|23-51 | ||
+ | |style="padding:.4em;"|JY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1016/j.cell.2023.05.046 Ultra-deep sequencing of Hadza hunter-gatherers recovers vanishing gut microbes] | ||
+ | |- | ||
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+ | |style="padding:.4em;"|23-50 | ||
+ | |style="padding:.4em;"|WJ Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1186/s40168-023-01472-7 Altered infective competence of the human gut microbiome in COVID-19] | ||
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+ | |style="padding:.4em;"|23-49 | ||
+ | |style="padding:.4em;"|G Koh | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://onlinelibrary.wiley.com/doi/full/10.1002/aisy.202300342 Host-Variable-Embedding Augmented Microbiome-Based Simultaneous Detection of Multiple Diseases by Deep Learning] | ||
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+ | |style="padding:.4em;"|23-48 | ||
+ | |style="padding:.4em;"|SH Ahn | ||
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+ | [https://doi.org/10.1038/s41467-023-39264-0 A data-driven approach for predicting the impact of drugs on the human microbiome] | ||
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+ | |style="padding:.4em;"|HJ Kim | ||
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+ | [https://doi.org/10.1101/2023.04.06.535777 Activation of programmed cell death and counter-defense functions of phage accessory genes] | ||
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+ | |style="padding:.4em;"|JH Cha | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1038/s41467-023-39459-5 Top-down identification of keystone taxa in the microbiome] | ||
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+ | |style="padding:.4em;"|JY Ma | ||
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+ | [https://doi.org/10.1016/j.cels.2022.12.007 Pitfalls of genotyping microbial communities with rapidly growing genome collections] | ||
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+ | |style="padding:.4em;"|NY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1186/s13059-023-03028-2 Reconstruction of the last bacterial common ancestor from 183 pangenomes reveals a versatile ancient core genome] | ||
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+ | |style="padding:.4em;"|SH Lee | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1038/s41587-023-01868-8 Generation of accurate, expandable phylogenomic trees with uDance] | ||
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+ | |style="padding:.4em;"|WJ Kim | ||
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+ | [https://doi.org/10.1016/j.immuni.2023.04.003 Phage display sequencing reveals that genetic, environmental, and intrinsic factors influence variation of human antibody epitope repertoire] | ||
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+ | |style="padding:.4em;"|JY Kim | ||
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+ | [https://doi.org/10.1016/j.immuni.2023.04.017 Phage-display immunoprecipitation sequencing of the antibody epitope repertoire in inflammatory bowel disease reveals distinct antibody signatures] | ||
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+ | |style="padding:.4em;"|G Koh | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.15252/msb.202311525 Consistency across multi-omics layers in a drug-perturbed gut microbial community] | ||
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+ | |style="padding:.4em;"|HJ Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1038/s41587-023-01953-y Identification of mobile genetic elements with geNomad] | ||
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+ | |style="padding:.4em;"|SH Ahn | ||
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+ | [https://doi.org/10.1038/s41591-023-02407-3 Microbiome-derived cobalamin and succinyl-CoA as biomarkers for improved screening of anal cancer] | ||
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+ | |style="padding:.4em;"|JH Cha | ||
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+ | [https://doi.org/10.1038/s41591-023-02424-2 The airway microbiome mediates the interaction between environmental exposure and respiratory health in humans] | ||
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+ | |style="padding:.4em;"|JY Ma | ||
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+ | [https://doi.org/10.1080/19490976.2023.2224474 Ordering taxa in image convolution networks improves microbiome-based machine learning accuracy] | ||
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+ | |style="padding:.4em;"|NY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1101/2023.08.12.553040 The defensome of complex bacterial communities] | ||
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+ | |style="padding:.4em;"|SH Lee | ||
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+ | [https://doi.org/10.1016/j.cell.2023.03.011 Dietary tryptophan metabolite released by intratumoral Lactobacillus reuteri facilitates immune checkpoint inhibitor treatment] | ||
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+ | [https://doi.org/10.1038/s43587-022-00306-9 Toward an improved definition of a healthy microbiome for healthy aging] | ||
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+ | [https://doi.org/10.1038/s43587-022-00287-9 Associations of the skin, oral and gut microbiome with aging, frailty and infection risk reservoirs in older adults] | ||
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+ | |style="padding:.4em;"|G Koh | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1186/s40168-023-01614-x Statistical modeling of gut microbiota for personalized health status monitoring] | ||
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+ | |style="padding:.4em;"|SH Ahn | ||
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+ | [https://doi.org/10.7554/eLife.50240 Adjusting for age improves identification of gut microbiome alterations in multiple diseases] | ||
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+ | [https://doi.org/10.1038/s41564-023-01370-6 Centenarians have a diverse gut virome with the potential to modulate metabolism and promote healthy lifespan] | ||
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+ | [https://doi.org/10.1016/j.chom.2023.01.003 Longitudinal comparison of the developing gut virome in infants and their mothers] | ||
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|+style="text-align:left;font-size:12pt" | 2023-1 ADVANCED MICROBIOME DATA ANALYSIS | |+style="text-align:left;font-size:12pt" | 2023-1 ADVANCED MICROBIOME DATA ANALYSIS | ||
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!scope="col" style="padding:.4em" | Presenter | !scope="col" style="padding:.4em" | Presenter | ||
!scope="col" style="padding:.4em" | Paper title | !scope="col" style="padding:.4em" | Paper title | ||
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+ | [https://doi.org/10.1016/j.chom.2020.03.005 Structure of the Mucosal and Stool Microbiome in Lynch Syndrome] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/06/13 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|23-23 | ||
+ | |style="padding:.4em;"|WJ Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1038/s41586-019-1237-9 Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/06/13 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|23-22 | ||
+ | |style="padding:.4em;"|SH Ahn | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1016/j.jare.2022.03.007 Roles of oral microbiota and oral-gut microbial transmission in hypertension] | ||
|- | |- | ||
|style="padding:.4em;" rowspan=1|2023/05/30 | |style="padding:.4em;" rowspan=1|2023/05/30 | ||
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{|class=wikitable style="text-align:center;" | {|class=wikitable style="text-align:center;" | ||
− | |+style="text-align:left;font-size:12pt" | 2023 scOmics | + | |+style="text-align:left;font-size:12pt" | 2023-1 scOmics |
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|- | |- | ||
− | |style="padding:.4em;" rowspan=1|2023/07/ | + | |style="padding:.4em;" rowspan=1|2023/08/30 |
+ | |style="padding:.4em;" rowspan=1|Single-cell | ||
+ | |style="padding:.4em;"|23-24 | ||
+ | |style="padding:.4em;"|JW Yu | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1101/2023.01.17.524482 Decoupling the correlation between cytotoxic and exhausted T lymphocyte transcriptomic signatures enhances melanoma immunotherapy response prediction from tumor expression] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/08/09 | ||
+ | |style="padding:.4em;" rowspan=1|Single-cell | ||
+ | |style="padding:.4em;"|23-23 | ||
+ | |style="padding:.4em;"|IS Choi | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1101/2023.07.28.550993 Major data analysis errors invalidate cancer microbiome findings] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/08/02 | ||
+ | |style="padding:.4em;" rowspan=1|Single-cell | ||
+ | |style="padding:.4em;"|23-22 | ||
+ | |style="padding:.4em;"|EJ Sung | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1038/s43018-022-00475-x A single-cell atlas of glioblastoma evolution under therapy reveals cell-intrinsic and cell-extrinsic therapeutic targets] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/07/26 | ||
|style="padding:.4em;" rowspan=1|Single-cell | |style="padding:.4em;" rowspan=1|Single-cell | ||
|style="padding:.4em;"|23-21 | |style="padding:.4em;"|23-21 | ||
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[https://doi.org/10.1038/s43018-022-00433-7 Single-cell meta-analyses reveal responses of tumor-reactive CXCL13+ T cells to immune-checkpoint blockade] | [https://doi.org/10.1038/s43018-022-00433-7 Single-cell meta-analyses reveal responses of tumor-reactive CXCL13+ T cells to immune-checkpoint blockade] | ||
|- | |- | ||
− | |style="padding:.4em;" rowspan=1|2023/07/ | + | |style="padding:.4em;" rowspan=1|2023/07/19 |
|style="padding:.4em;" rowspan=1|Single-cell | |style="padding:.4em;" rowspan=1|Single-cell | ||
|style="padding:.4em;"|23-20 | |style="padding:.4em;"|23-20 | ||
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[https://doi.org/10.1038/s41587-022-01342-x Estimation of tumor cell total mRNA expression in 15 cancer types predicts disease progression] | [https://doi.org/10.1038/s41587-022-01342-x Estimation of tumor cell total mRNA expression in 15 cancer types predicts disease progression] | ||
|- | |- | ||
− | |style="padding:.4em;" rowspan=1|2023/07/ | + | |style="padding:.4em;" rowspan=1|2023/07/12 |
|style="padding:.4em;" rowspan=1|Single-cell | |style="padding:.4em;" rowspan=1|Single-cell | ||
|style="padding:.4em;"|23-19 | |style="padding:.4em;"|23-19 | ||
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[https://doi.org/10.1038/s41587-022-01288-0 DIALOGUE maps multicellular programs in tissue from single-cell or spatial transcriptomics data] | [https://doi.org/10.1038/s41587-022-01288-0 DIALOGUE maps multicellular programs in tissue from single-cell or spatial transcriptomics data] | ||
|- | |- | ||
− | |style="padding:.4em;" rowspan=1|2023/ | + | |style="padding:.4em;" rowspan=1|2023/07/05 |
|style="padding:.4em;" rowspan=1|Single-cell | |style="padding:.4em;" rowspan=1|Single-cell | ||
|style="padding:.4em;"|23-18 | |style="padding:.4em;"|23-18 | ||
|style="padding:.4em;"|SB Baek | |style="padding:.4em;"|SB Baek | ||
|style="padding:.4em;text-align:left"| | |style="padding:.4em;text-align:left"| | ||
− | [https://doi.org/10. | + | [https://doi.org/10.1038/s41591-023-02371-y Pan-cancer T cell atlas links a cellular stress response state to immunotherapy resistance] |
|- | |- | ||
− | |style="padding:.4em;" rowspan=1|2023/06/ | + | |style="padding:.4em;" rowspan=1|2023/06/28 |
|style="padding:.4em;" rowspan=1|Single-cell | |style="padding:.4em;" rowspan=1|Single-cell | ||
|style="padding:.4em;"|23-17 | |style="padding:.4em;"|23-17 | ||
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[https://doi.org/10.1016/j.patter.2022.100651 Self-supervised graph representation learning integrates multiple molecular networks and decodes gene-disease relationships] | [https://doi.org/10.1016/j.patter.2022.100651 Self-supervised graph representation learning integrates multiple molecular networks and decodes gene-disease relationships] | ||
|- | |- | ||
− | |style="padding:.4em;" rowspan=1|2023/06/ | + | |style="padding:.4em;" rowspan=1|2023/06/21 |
|style="padding:.4em;" rowspan=1|Single-cell | |style="padding:.4em;" rowspan=1|Single-cell | ||
|style="padding:.4em;"|23-16 | |style="padding:.4em;"|23-16 | ||
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[https://doi.org/10.1016/j.ccell.2022.10.008 High-resolution single-cell atlas reveals diversity and plasticity of tissue-resident neutrophils in non-small cell lung cancer] | [https://doi.org/10.1016/j.ccell.2022.10.008 High-resolution single-cell atlas reveals diversity and plasticity of tissue-resident neutrophils in non-small cell lung cancer] | ||
|- | |- | ||
− | |style="padding:.4em;" rowspan=1|2023/06/ | + | |style="padding:.4em;" rowspan=1|2023/06/14 |
|style="padding:.4em;" rowspan=1|Single-cell | |style="padding:.4em;" rowspan=1|Single-cell | ||
|style="padding:.4em;"|23-15 | |style="padding:.4em;"|23-15 | ||
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{|class=wikitable style="text-align:center;" | {|class=wikitable style="text-align:center;" | ||
− | |+style="text-align:left;font-size:12pt" | 2023 Microbiome | + | |+style="text-align:left;font-size:12pt" | 2023-1 Microbiome |
|- | |- | ||
!scope="col" style="padding:.4em" | Date | !scope="col" style="padding:.4em" | Date | ||
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− | |style="padding:.4em;" rowspan=1|2023/ | + | |style="padding:.4em;" rowspan=1|2023/08/25 |
|style="padding:.4em;" rowspan=1|Microbiome | |style="padding:.4em;" rowspan=1|Microbiome | ||
− | |style="padding:.4em;"|23- | + | |style="padding:.4em;"|23-27 |
+ | |style="padding:.4em;"|JY Ma | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1016/j.chom.2023.05.024 Enterosignatures define common bacterial guilds in the human gut microbiome] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/08/25 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|23-26 | ||
+ | |style="padding:.4em;"|NY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1186/s13059-023-02902-3 PhyloMed: a phylogeny-based test of mediation effect in microbiome] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/08/18 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|23-25 | ||
+ | |style="padding:.4em;"|SH Lee | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1016/j.chom.2023.05.027 The TaxUMAP atlas: Efficient display of large clinical microbiome data reveals ecological competition in protection against bacteremia] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/08/18 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|23-24 | ||
+ | |style="padding:.4em;"|WJ Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [http://dx.doi.org/10.1038/nbt.3704 Measurement of bacterial replication rates in microbial communities] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/08/11 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|23-23 | ||
|style="padding:.4em;"|JY Kim | |style="padding:.4em;"|JY Kim | ||
|style="padding:.4em;text-align:left"| | |style="padding:.4em;text-align:left"| | ||
− | [https://doi.org/10. | + | [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] |
|- | |- | ||
− | |style="padding:.4em;" rowspan=1|2023/ | + | |style="padding:.4em;" rowspan=1|2023/08/11 |
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|23-22 | ||
+ | |style="padding:.4em;"|SH Ahn | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1016/j.xcrm.2023.100920 Enrichment of oral-derived bacteria in inflamed colorectal tumors and distinct associations of Fusobacterium in the mesenchymal subtype] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/08/04 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|23-21 | ||
+ | |style="padding:.4em;"|HJ Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1038/s41586-023-05989-7 Profiling the human intestinal environment under physiological conditions] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/07/28 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|23-20 | ||
+ | |style="padding:.4em;"|JH Cha | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1186/s40168-023-01494-1 Genome-centric metagenomics reveals the host-driven dynamics and ecological role of CPR bacteria in an activated sludge system] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/07/14 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|23-19 | ||
+ | |style="padding:.4em;"|JY Ma | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1016/j.patter.2022.100658 Enhanced metagenomic deep learning for disease prediction and consistent signature recognition by restructured microbiome 2D representations] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2023/07/07 | ||
|style="padding:.4em;" rowspan=1|Microbiome | |style="padding:.4em;" rowspan=1|Microbiome | ||
|style="padding:.4em;"|23-18 | |style="padding:.4em;"|23-18 | ||
− | |style="padding:.4em;"| | + | |style="padding:.4em;"|NY Kim |
|style="padding:.4em;text-align:left"| | |style="padding:.4em;text-align:left"| | ||
− | [https://doi.org/10. | + | [https://doi.org/10.1186/s13059-022-02809-5 Gene fow and introgression are pervasive forces shaping the evolution of bacterial species] |
|- | |- | ||
− | |style="padding:.4em;" rowspan=1|2023/06/ | + | |style="padding:.4em;" rowspan=1|2023/06/30 |
|style="padding:.4em;" rowspan=1|Microbiome | |style="padding:.4em;" rowspan=1|Microbiome | ||
|style="padding:.4em;"|23-17 | |style="padding:.4em;"|23-17 | ||
− | |style="padding:.4em;"| | + | |style="padding:.4em;"|JH Cha |
|style="padding:.4em;text-align:left"| | |style="padding:.4em;text-align:left"| | ||
− | [https://doi.org/10. | + | [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] |
|- | |- | ||
− | |style="padding:.4em;" rowspan=1|2023/06/ | + | |style="padding:.4em;" rowspan=1|2023/06/23 |
|style="padding:.4em;" rowspan=1|Microbiome | |style="padding:.4em;" rowspan=1|Microbiome | ||
|style="padding:.4em;"|23-16 | |style="padding:.4em;"|23-16 | ||
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[https://doi.org/10.1038/s41467-022-33397-4 Deciphering microbial gene function using natural language processing] | [https://doi.org/10.1038/s41467-022-33397-4 Deciphering microbial gene function using natural language processing] | ||
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− | |style="padding:.4em;" rowspan=1|2023/06/ | + | |style="padding:.4em;" rowspan=1|2023/06/16 |
|style="padding:.4em;" rowspan=1|Microbiome | |style="padding:.4em;" rowspan=1|Microbiome | ||
|style="padding:.4em;"|23-15 | |style="padding:.4em;"|23-15 | ||
− | |style="padding:.4em;"| | + | |style="padding:.4em;"|SH Lee |
|style="padding:.4em;text-align:left"| | |style="padding:.4em;text-align:left"| | ||
− | [https://doi.org/10. | + | [https://doi.org/10.1101/2022.11.28.518265 Rethinking bacterial relationships in light of their molecular abilities] |
|- | |- | ||
|style="padding:.4em;" rowspan=1|2023/06/02 | |style="padding:.4em;" rowspan=1|2023/06/02 |
Latest revision as of 16:15, 16 October 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 |