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+ | [https://doi.org/10.1126/science.adj4857 A blueprint for tumor-infiltrating B cells across human cancers] | ||
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+ | [https://doi.org/10.1038/s41467-024-48310-4 Systematic dissection of tumor-normal single-cell ecosystems across a thousand tumors of 30 cancer types] | ||
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+ | [https://genomemedicine.biomedcentral.com/articles/10.1186/s13073-024-01314-7 scDrugPrio: a framework for the analysis of single‑cell transcriptomics to address multiple problems in precision medicine in immune‑mediated inflammatory diseases] | ||
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− | [https://doi.org/10. | + | [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. | + | [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:// | + | [https://doi.org/10.1101/2024.06.04.597354 Cell-Graph Compass: Modeling Single Cells with Graph Structure Foundation Model] |
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+ | [https://doi.org/10.1016/j.xgen.2023.100473 Single-cell transcriptome landscape of circulating CD4+ T cell populations in autoimmune diseases] | ||
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+ | [https://doi.org/10.1038/s43588-024-00597-5 Population-level comparisons of gene regulatory networks modeled on highthroughput single-cell transcriptomics data] | ||
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+ | [https://doi.org/10.1101/2024.06.16.599201 node2vec2rank: Large Scale and Stable Graph Differential Analysis via Multi-Layer Node Embeddings and Ranking] | ||
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+ | [https://doi.org/10.1016/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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+ | [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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+ | [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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+ | [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/10.1016/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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+ | [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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+ | [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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+ | [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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+ | [https://arxiv.org/abs/2103.00020 Learning Transferable Visual Models From Natural Language Supervision] | ||
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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/10.1038/s41591-024-02963-2 Microbiome confounders and quantitative profiling challenge predicted microbial targets in colorectal cancer development] | ||
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+ | [https://doi.org/10.1038/s41564-024-01751-5 A multi-kingdom collection of 33,804 reference genomes for the human vaginal microbiome] | ||
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+ | [https://doi.org/10.1101/2023.12.11.571168 Efficient and accurate detection of viral sequences at single-cell resolution reveals putative novel viruses perturbing host gene expression] | ||
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+ | [https://doi.org/10.1101/2024.06.04.596112 Compositional Differential Abundance Testing: Defining and Finding a New Type of Health-Microbiome Associations] | ||
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+ | [https://doi.org/10.1016/j.cell.2024.05.013 Discovery of antimicrobial peptides in the global microbiome with machine learning] | ||
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+ | [https://doi.org/10.1016/j.cell.2024.05.029 Custom scoring based on ecological topology of gut microbiota associated with cancer immunotherapy outcome] | ||
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+ | [https://doi.org/10.1038/s41586-024-07336-w Paternal microbiome perturbations impact offspring fitness] | ||
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+ | [https://doi.org/10.1016/j.crmeth.2024.100775 Interactions-based classification of a single microbial sample] | ||
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+ | [https://doi.org/10.1101/2024.04.10.588779 Accurate estimation of intraspecificmicrobial gene content variation inmetagenomic data with MIDAS v3 andStrainPGC] | ||
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+ | [https://doi.org/10.1016/j.cell.2024.03.021 A pan-cancer analysis of the microbiome inmetastatic cancer] | ||
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+ | [https://doi.org/10.1016/j.chom.2024.03.002 A specific enterotype derived from gut microbiomeof older individuals enables favorable responses toimmune checkpoint blockade therapy] | ||
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+ | [https://doi.org/10.1016/j.chom.2024.02.010 Stratification of Fusobacterium nucleatum by localhealth status in the oral cavity defines its subspeciesdisease association] | ||
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+ | [https://doi.org/10.1080/19490976.2024.2309684 A universe of human gut-derived bacterialprophages: unveiling the hidden viral players inintestinal microecology] | ||
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+ | [https://doi.org/10.1038/s41388-024-02974-w Robustness of cancer microbiome signals over a broad range of methodological variation] | ||
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+ | [https://doi.org/10.1038/s41586-024-07182-w A distinct Fusobacterium nucleatum clade dominates the colorectal cancer niche] | ||
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+ | [https://doi.org/10.1016/j.cell.2024.01.039 A cryptic plasmid is among the most numerous genetic elements in the human gut] | ||
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+ | [https://doi.org/10.1016/j.cell.2024.03.014 Gut microbiome and metabolome profiling in Framingham heart study reveals cholesterol-metabolizing bacteria] | ||
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+ | [https://doi.org/10.1101/2024.03.18.584290 Fecal microbial load is a major determinant of gut microbiome variation and aconfounder for disease associations] | ||
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+ | [https://doi.org/10.1038/s41586-024-07162-0 A host-microbiota interactome reveals extensive transkingdom connectivity] | ||
+ | |- | ||
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+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1101/2024.02.02.578701 Metagenomic estimation of dietary intake from human stool] | ||
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+ | [https://doi.org/10.1038/s41467-024-45793-z A metagenomic catalog of the early-life human gut virome] | ||
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+ | [https://doi.org/10.1101/2024.01.08.574624 Large-scale computational analyses of gut microbial CAZyme repertoires enabled by Cayman] | ||
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+ | [https://doi.org/10.1038/s41467-024-44720-6 Defining the biogeographical map and potential bacterial translocation of microbiome in human ‘surface organs’] | ||
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+ | [https://doi.org/10.1038/s41467-023-42997-7 Gut microbial structural variation associates with immune checkpoint inhibitor response] | ||
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+ | [https://doi.org/10.1080/19490976.2024.2307586 Fungal signature differentiates alcohol-associated liver disease from nonalcoholic fatty liver disease] | ||
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+ | [https://doi.org/10.1080/19490976.2024.2302076 Incorporating metabolic activity, taxonomy and community structure to improve microbiome based predictive models for host phenotype prediction] | ||
+ | |- | ||
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+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1038/s41467-023-42112-w Disease-specific loss of microbial cross feeding interactions in the human gut] | ||
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+ | [https://doi.org/10.1038/s41592-023-02092-7 Multigroup analysis of compositions of microbiomes with covariate adjustments and repeated measures] | ||
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+ | [https://doi.org/10.1038/s41467-023-40719-7 Microdiversity of the vaginal microbiome is associated with preterm birth] | ||
+ | |- | ||
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+ | [https://doi.org/10.1038/s41564-023-01584-8 Large language models improve annotation of prokaryotic viral proteins] | ||
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+ | [https://doi.org/10.1038/s41467-023-42998-6 Clinically relevant antibiotic resistance genes are linked to a limited set of taxa within gut microbiome worldwide] | ||
+ | |- | ||
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+ | [https://academic.oup.com/nargab/article/2/2/lqaa023/5826153 Visualizing ’omic feature rankings and log-ratios using Qurro] | ||
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+ | [https://doi.org/10.1038/s41467-019-10656-5 Establishing microbial composition measurement standards with reference frames] | ||
+ | |- | ||
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+ | |style="padding:.4em;"|24-5 | ||
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+ | [https://doi.org/10.1186/s13059-024-03166-1 AnnoPRO: a strategy for protein function annotation based on multi-scale protein representation and a hybrid deep learning of dual-path encoding] | ||
+ | |- | ||
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+ | [https://doi.org/10.1038/s41467-023-44289-6 Differential responses of the gut microbiome and resistome to antibiotic exposures in infants and adults] | ||
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+ | [https://doi.org/10.1038/s41467-023-44290-z Effective binning of metagenomic contigs using contrastive multi-view representation learning] | ||
+ | |- | ||
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+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1038/s41559-020-01353-4 Polarization of microbial communities between competitive and cooperative metabolism] | ||
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+ | [https://onlinelibrary.wiley.com/doi/full/10.1002/advs.202303925 Metagenomic Insight into The Global Dissemination of The Antibiotic Resistome] | ||
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+ | |style="padding:.4em;"|G Koh | ||
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+ | [https://doi.org/10.1073/pnas.2008731118 Conjugative plasmids interact with insertion sequences to shape the horizontal transfer of antimicrobial resistance genes] | ||
+ | |} | ||
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+ | {|class=wikitable style="text-align:center;" | ||
+ | |+style="text-align:left;font-size:12pt" | 2024-1 Advanced scOmics Data Analysis | ||
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+ | |- | ||
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+ | |style="padding:.4em;"|24-32 | ||
+ | |style="padding:.4em;"|EB Hong | ||
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+ | [https://doi.org/10.1038/s41586-023-07011-6 Spatial transcriptomics reveal neuron–astrocyte synergy in long-term memory] | ||
+ | |- | ||
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+ | |style="padding:.4em;"|JJ Heo | ||
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+ | [https://doi.org/10.1038/s41467-021-22197-x scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses] | ||
+ | |- | ||
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+ | |style="padding:.4em;"|SM Han | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [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] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2024/06/11 | ||
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+ | |style="padding:.4em;"|SA Choi | ||
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+ | [https://doi.org/10.1038/s41467-021-27464-5 Single-cell transcriptomics captures features of human midbrain development and dopamine neuron diversity in brain organoids] | ||
+ | |- | ||
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+ | |style="padding:.4em;"|24-27 | ||
+ | |style="padding:.4em;"|HJ Cha | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1016/j.chom.2023.08.019 Cell-type-specific responses to fungal infection in plants revealed by single-cell transcriptomics] | ||
+ | |- | ||
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+ | |style="padding:.4em;"|YK Jung | ||
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+ | [https://www.sciencedirect.com/science/article/pii/S1534580722002519?via%3Dihub The single-cell stereo-seq reveals region-specific cell subtypes and transcriptome profiling in Arabidopsis leaves] | ||
+ | |- | ||
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+ | |style="padding:.4em;"|HJ Lee | ||
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+ | [https://doi.org/10.1038/s41588-022-01100-4 Single-cell and bulk transcriptome sequencing identifies two epithelial tumor cell states and refines the consensus molecular classification of colorectal cancer] | ||
+ | |- | ||
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+ | |style="padding:.4em;"|HK Lee | ||
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+ | [https://doi.org/10.1038/s42255-023-00876-x Delineating mouse β-cell identity during lifetime and in diabetes with a single cell atlas] | ||
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+ | [https://doi.org/10.1038/s41587-023-01747-2 Multimodal spatiotemporal phenotyping of human retinal organoid development] | ||
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+ | [https://doi.org/10.1038/s41586-024-07251-0 Immune microniches shape intestinal Treg function] | ||
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+ | [https://doi.org/10.1016/j.devcel.2021.02.021 A single-cell analysis of the Arabidopsis vegetative shoot apex] | ||
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+ | [https://doi.org/10.1038/s41467-023-40137-9 Droplet-based high-throughput single microbe RNA sequencing by smRandom-seq] | ||
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+ | [https://doi.org/10.1038/s41564-023-01462-3 Single-cell massively-parallel multiplexed microbial sequencing (M3-seq) identifies rare bacterial populations and profiles phage infection] | ||
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+ | [https://doi.org/10.1016/j.celrep.2022.111736 Spatial transcriptomics demonstrates the role of CD4 T cells in effector CD8 T cell differentiation during chronic viral infection] | ||
+ | |- | ||
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+ | |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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+ | !scope="col" style="padding:.4em" | Paper<br/>index | ||
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+ | |style="padding:.4em;"|23-40 | ||
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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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+ | |style="padding:.4em;"|23-38 | ||
+ | |style="padding:.4em;"|JH Cha | ||
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+ | [https://doi.org/10.1038/s41587-022-01467-z Modeling intercellular communication in tissues using spatial graphs of cells] | ||
+ | |- | ||
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+ | |style="padding:.4em;"|23-37 | ||
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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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+ | |style="padding:.4em;"|23-35 | ||
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+ | [https://doi.org/10.1038/s41592-023-01971-3 Dictys: dynamic gene regulatory network dissects developmental continuum with single-cell multiomics] | ||
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+ | [https://www.science.org/doi/10.1126/sciimmunol.adf4968 Preexisting tumor-resident T cells with cytotoxic potential associate with response to neoadjuvant anti–PD-1 in head and neck cancer] | ||
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+ | [https://doi.org/10.1038/s41588-022-01273-y MHC II immunogenicity shapes the neoepitope landscape in human tumors] | ||
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+ | |style="padding:.4em;"|23-32 | ||
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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:// | + | [https://doi.org/10.1038/s41587-023-01782-z Comparative analysis of cell–cell communication at single-cell resolution] |
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− | [https:// | + | [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/10.1186/s40168-023-01607-w Phages are unrecognized players in the ecology of the oral pathogen Porphyromonas gingivalis] | ||
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+ | [https://doi.org/10.1038/s41564-023-01439-2 A predicted CRISPR-mediated symbiosis between uncultivated archaea] | ||
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+ | [https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-023-01692-x Integrating compositional and functional content to describe vaginal microbiomes in health and disease] | ||
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+ | [https://doi.org/10.1038/s41587-023-01696-w Contamination source modeling with SCRuB improves cancer phenotype prediction from microbiome data] | ||
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+ | [https://doi.org/10.1038/s41586-023-06431-8 Mapping the T cell repertoire to a complex gut bacterial community] | ||
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+ | [https://doi.org/10.1101/2023.07.03.547607 Multi-view integration of microbiome data for identifying disease-associated modules] | ||
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+ | [https://doi.org/10.1101/2023.09.28.559994 Phage-bacteria dynamics during the first years of life revealed by trans-kingdom marker gene analysis] | ||
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+ | [https://doi.org/10.1038/s41593-023-01361-0 Multi-level analysis of the gut–brain axis shows autism spectrum disorder-associated molecular and microbial profiles] | ||
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+ | [https://doi.org/10.1101/2023.11.21.568153 Metagenomic Immunoglobulin Sequencing (MIG-Seq) Exposes Patterns of IgA Antibody Binding in the Healthy Human Gut Microbiome] | ||
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+ | [https://doi.org/10.1038/s41467-023-41042-x Impact of dietary interventions on pre-diabetic oral and gut microbiome, metabolites and cytokines] | ||
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+ | [https://doi.org/10.1038/s41592-023-02018-3 Fast and robust metagenomic sequence comparison through sparse chaining with skani] | ||
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+ | [https://doi.org/10.1038/s41591-023-02599-8 Bacterial SNPs in the human gut microbiome associate with host BMI] | ||
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+ | [https://doi.org/10.1080/19490976.2023.2245562 Multimodal metagenomic analysis reveals microbial single nucleotide variants as superior biomarkers for early detection of colorectal cancer] | ||
+ | |- | ||
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+ | [https://doi.org/10.1016/j.chom.2023.10.005 Multi-kingdom gut microbiota analyses define bacterial-fungal interplay and microbial markers of pan-cancer immunotherapy across cohorts] | ||
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+ | [https://doi.org/10.1016/j.xcrm.2023.101251 Prior antibiotic administration disrupts anti-PD-1 responses in advanced gastric cancer by altering the gut microbiome and systemic immune response] | ||
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+ | [https://doi.org/10.1016/j.cell.2023.05.046 Ultra-deep sequencing of Hadza hunter-gatherers recovers vanishing gut microbes] | ||
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+ | [https://doi.org/10.1186/s40168-023-01472-7 Altered infective competence of the human gut microbiome in COVID-19] | ||
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+ | |style="padding:.4em;"|G Koh | ||
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+ | [https://onlinelibrary.wiley.com/doi/full/10.1002/aisy.202300342 Host-Variable-Embedding Augmented Microbiome-Based Simultaneous Detection of Multiple Diseases by Deep Learning] | ||
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+ | |style="padding:.4em;"|SH Ahn | ||
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+ | [https://doi.org/10.1038/s41467-023-39264-0 A data-driven approach for predicting the impact of drugs on the human microbiome] | ||
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+ | [https://doi.org/10.1101/2023.04.06.535777 Activation of programmed cell death and counter-defense functions of phage accessory genes] | ||
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+ | [https://doi.org/10.1038/s41467-023-39459-5 Top-down identification of keystone taxa in the microbiome] | ||
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+ | [https://doi.org/10.1016/j.cels.2022.12.007 Pitfalls of genotyping microbial communities with rapidly growing genome collections] | ||
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+ | [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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+ | [https://doi.org/10.1038/s41587-023-01868-8 Generation of accurate, expandable phylogenomic trees with uDance] | ||
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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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+ | [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 | ||
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+ | [https://doi.org/10.15252/msb.202311525 Consistency across multi-omics layers in a drug-perturbed gut microbial community] | ||
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+ | [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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+ | [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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+ | [https://doi.org/10.1080/19490976.2023.2224474 Ordering taxa in image convolution networks improves microbiome-based machine learning accuracy] | ||
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+ | [https://doi.org/10.1101/2023.08.12.553040 The defensome of complex bacterial communities] | ||
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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 | ||
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+ | [https://doi.org/10.1186/s40168-023-01614-x Statistical modeling of gut microbiota for personalized health status monitoring] | ||
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+ | [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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+ | |style="padding:.4em;"|JH Cha | ||
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+ | [https://doi.org/10.1016/j.chom.2023.01.003 Longitudinal comparison of the developing gut virome in infants and their mothers] | ||
+ | |} | ||
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+ | |style="padding:.4em;"|23-24 | ||
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+ | [https://doi.org/10.1016/j.chom.2020.03.005 Structure of the Mucosal and Stool Microbiome in Lynch Syndrome] | ||
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+ | |style="padding:.4em;"|23-23 | ||
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+ | [https://doi.org/10.1038/s41586-019-1237-9 Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases] | ||
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+ | [https://doi.org/10.1016/j.jare.2022.03.007 Roles of oral microbiota and oral-gut microbial transmission in hypertension] | ||
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+ | [https://doi.org/10.1016/j.chom.2022.08.009 Human gut microbiota stimulate defined innate immune responses that vary from phylum to strain] | ||
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+ | [https://doi.org/10.1038/s41591-023-02217-7 Gut microbial metabolism of 5-ASA diminishes its clinical efficacy in inflammatory bowel disease] | ||
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+ | [https://doi.org/10.1016/j.chom.2023.01.004 Deficient butyrate-producing capacity in the gut microbiome is associated with bacterial network disturbances and fatigue symptoms in ME/CFS] | ||
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+ | [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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+ | [https://doi.org/10.1016/j.chom.2021.06.019 Multi-omics reveal microbial determinants impacting responses to biologic therapies in inflammatory bowel disease] | ||
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+ | [https://doi.org/10.1038/s41586-022-05181-3 Identification of trypsin-degrading commensals in the large intestine] | ||
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+ | [https://doi.org/10.1038/s41586-022-05546-8 Questioning the fetal microbiome illustrates pitfalls of low-biomass microbial studies] | ||
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+ | [https://doi.org/10.1016/j.chom.2023.01.013 Tissue-resident Lachnospiraceae family bacteria protect against colorectal carcinogenesis by promoting tumor immune surveillance] | ||
+ | |- | ||
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+ | [https://doi.org/10.1016/j.cell.2022.09.005 Pan-cancer analyses reveal cancer-type-specific fungal ecologies and bacteriome interactions] | ||
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+ | [https://doi.org/10.1016/j.cell.2022.09.015 A pan-cancer mycobiome analysis reveals fungal involvement in gastrointestinal and lung tumors] | ||
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+ | |style="padding:.4em;"|HR Shin | ||
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+ | [https://doi.org/10.1038/s41564-021-01030-7 Multi-kingdom microbiota analyses identify bacterial–fungal interactions and biomarkers of colorectal cancer across cohorts] | ||
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+ | |style="padding:.4em;"|23-10 | ||
+ | |style="padding:.4em;"|SG Oh | ||
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+ | [https://doi.org/10.1038/s42255-022-00716-4 The antitumour effects of caloric restriction are mediated by the gut microbiome] | ||
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+ | |style="padding:.4em;"|23-9 | ||
+ | |style="padding:.4em;"|WJ Kim | ||
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+ | [https://doi.org/10.1038/s41591-022-01964-3 Variability of strain engraftment and predictability of microbiome composition after fecal microbiota transplantation across different diseases] | ||
+ | |- | ||
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+ | |style="padding:.4em;"|23-8 | ||
+ | |style="padding:.4em;"|SM Han | ||
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+ | [https://doi.org/10.1038/s41591-022-01913-0 Drivers and determinants of strain dynamics following fecal microbiota transplantation] | ||
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+ | [https://doi.org/10.1016/j.cell.2022.11.023 Mobile genetic elements from the maternal microbiome shape infant gut microbial assembly and metabolism] | ||
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+ | [https://doi.org/10.1016/j.chom.2023.01.018 Mother-to-infant microbiota transmission and infant microbiota development across multiple body sites] | ||
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+ | |style="padding:.4em;"|SY Yang | ||
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+ | [https://doi.org/10.1038/s41467-023-36633-7 Population-level impacts of antibiotic usage on the human gut microbiome] | ||
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+ | |style="padding:.4em;"|YH Yoon | ||
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+ | [https://doi.org/10.1038/s41586-022-05438-x Enterococci enhance Clostridioides difficile pathogenesis] | ||
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+ | [https://doi.org/10.1002/imt2.61 Targeting keystone species helps restore the dysbiosis of butyrate‐producing bacteria in nonalcoholic fatty liver disease] | ||
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+ | [https://doi.org/10.1002/advs.202203115 Differential Oral Microbial Input Determines Two Microbiota Pneumo-Types Associated with Health Status] | ||
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+ | [https://doi.org/10.1016/j.cell.2022.08.021 Gut microbiome of multiple sclerosis patients and paired household healthy controls reveal associations with disease risk and course] | ||
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− | [https:// | + | [https://doi.org/10.1038/s43018-022-00433-7 Single-cell meta-analyses reveal responses of tumor-reactive CXCL13+ T cells to immune-checkpoint blockade] |
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− | [https://pubmed.ncbi.nlm.nih.gov/ | + | [https://doi.org/10.1038/s41587-022-01342-x Estimation of tumor cell total mRNA expression in 15 cancer types predicts disease progression] |
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+ | [https://doi.org/10.1038/s41587-022-01288-0 DIALOGUE maps multicellular programs in tissue from single-cell or spatial transcriptomics data] | ||
+ | |- | ||
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+ | [https://doi.org/10.1038/s41591-023-02371-y Pan-cancer T cell atlas links a cellular stress response state to immunotherapy resistance] | ||
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+ | [https://doi.org/10.1016/j.patter.2022.100651 Self-supervised graph representation learning integrates multiple molecular networks and decodes gene-disease relationships] | ||
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+ | [https://doi.org/10.1016/j.ccell.2022.10.008 High-resolution single-cell atlas reveals diversity and plasticity of tissue-resident neutrophils in non-small cell lung cancer] | ||
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+ | [https://doi.org/10.1186/s13059-022-02828-2 Parallel single-cell and bulk transcriptome analyses reveal key features of the gastric tumor microenvironment] | ||
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+ | [https://doi.org/10.1038/s41467-022-32838-4 Mutated processes predict immune checkpoint inhibitor therapy benefit in metastatic melanoma] | ||
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+ | [https://doi.org/10.1038/s43018-021-00292-8 Temporal single-cell tracing reveals clonal revival and expansion of precursor exhausted T cells during anti-PD-1 therapy in lung cancer] | ||
+ | |- | ||
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+ | [https://doi.org/10.1016/j.cell.2022.11.028 Integrative single-cell analysis of cardiogenesis indentifies developmental trajectories and non-conding mutations in congenital heart disease] | ||
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+ | [https://doi.org/10.1101/2022.12.20.521311 Supervised discovery of interpretable gene programs from single-cell data] | ||
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+ | [https://doi.org/10.1038/s41586-022-05435-0 Effect of the intratumoral microbiota on spatial and cellular heterogeneity in cancer] | ||
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+ | [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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+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.nature.com/articles/s41591-022-01799-y A T cell resilience model associated with response to immunotherapy in multiple tumor types] | ||
+ | |- | ||
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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://www.sciencedirect.com/science/article/pii/S1535610822003178 Pan-cancer integrative histology-genomic analysis via multimodal deep learning] | ||
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+ | [https://pubmed.ncbi.nlm.nih.gov/35803260/ Tissue-resident memory and circulating T cells are early responders to pre-surgical cancer immunotherapy] | ||
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+ | [https://doi.org/10.1186/s13059-023-02902-3 PhyloMed: a phylogeny-based test of mediation effect in microbiome] | ||
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− | [https:// | + | [https://doi.org/10.1016/j.chom.2023.05.027 The TaxUMAP atlas: Efficient display of large clinical microbiome data reveals ecological competition in protection against bacteremia] |
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+ | [https://doi.org/10.1186/s40168-023-01564-4 Skin microbiome diferentiates into distinct cutotypes with unique metabolic functions upon exposure to polycyclic aromatic hydrocarbons] | ||
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+ | [https://doi.org/10.1016/j.xcrm.2023.100920 Enrichment of oral-derived bacteria in inflamed colorectal tumors and distinct associations of Fusobacterium in the mesenchymal subtype] | ||
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− | [https:// | + | [https://doi.org/10.1186/s40168-022-01435-4 Alterations of oral microbiota and impact on the gut microbiome in type 1 diabetes mellitus revealed by integrated multi‑omic analyses] |
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− | [https:// | + | [https://doi.org/10.1038/s41467-022-33397-4 Deciphering microbial gene function using natural language processing] |
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− | [https:// | + | [https://doi.org/10.1101/2022.11.28.518265 Rethinking bacterial relationships in light of their molecular abilities] |
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− | [https:// | + | [https://doi.org/10.1038/s41591-018-0203-7 Antigen discovery and specification of immunodominance hierarchies for MHCIIrestricted epitopes] |
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+ | [https://doi.org/10.1101/2022.10.11.511790 Single Cell Transcriptomics Reveals the Hidden Microbiomes of Human Tissues] | ||
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+ | [https://doi.org/10.1038/s41564-017-0096-0 Stability of the human faecal microbiome in a cohort of adult men] | ||
+ | |- | ||
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+ | [https://doi.org/10.1038/s41564-017-0084-4 Metatranscriptome of human faecal microbial communities in a cohort of adult men] | ||
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+ | [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;"|23-8 | ||
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+ | [https://doi.org/10.1038/s41467-022-32832-w Extensive gut virome variation and its associations with host and environmental factors in a population-level cohort] | ||
+ | |- | ||
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+ | |style="padding:.4em;"|23-7 | ||
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+ | |style="padding:.4em;text-align:left"| | ||
+ | [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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+ | [https://doi.org/10.1101/2023.01.30.526328 BIRDMAn: A Bayesian differential abundance framework that enables robust inference of host-microbe associations] | ||
+ | |- | ||
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+ | [https://www.nature.com/articles/s41564-022-01157-1 Phage–host coevolution in natural populations] | ||
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+ | [https://www.nature.com/articles/s41467-022-29968-0 A randomized controlled trial for response of microbiome network to exercise and diet intervention in patients with nonalcoholic fatty liver disease] | ||
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+ | [https://www.biorxiv.org/content/10.1101/2022.05.19.492684v1 Scalable power analysis and effect size exploration of microbiome community differences with Evident] | ||
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+ | [https://www.biorxiv.org/content/10.1101/2022.08.05.502982v1 Phanta: Phage-inclusive profiling of human gut metagenomes] | ||
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+ | [https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1010373 Computational approach to modeling microbiome landscapes associated with chronic human disease progression] | ||
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− | |+style="text-align:left;font-size:12pt" | 2022 | + | |+style="text-align:left;font-size:12pt" | 2022 scOmics |
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+ | |style="padding:.4em;"|EJ Sung | ||
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+ | [https://www.biorxiv.org/content/10.1101/2022.08.19.504505v1 SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks] | ||
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+ | |style="padding:.4em;"|JH Cha, SB Baek, IS Choi | ||
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+ | [https://www.pnas.org/doi/10.1073/pnas.2105859118 Representation learning of RNA velocity reveals robust cell transitions] | ||
+ | [https://www.nature.com/articles/s41467-022-34188-7 UniTVelo: temporally unified RNA velocity reinforces single-cell trajectory inference] | ||
+ | [https://www.sciencedirect.com/science/article/pii/S0092867421015774 Mapping transcriptomic vector fields of single cells] | ||
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+ | [https://www.nature.com/articles/s41467-022-31535-6 Network-based machine learning approach to predict immunotherapy response in cancer patients] | ||
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+ | [https://www.biorxiv.org/content/10.1101/2022.05.04.490536v1 Modeling fragment counts improves single-cell ATAC-seq analysis] | ||
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+ | |style="padding:.4em;"|G Koh | ||
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+ | [https://www.nature.com/articles/s41586-022-04718-w Extricating human tumour immune alterations from tissue inflammation] | ||
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+ | [https://www.biorxiv.org/content/10.1101/2022.06.15.495325v1 T cell receptor convergence is an indicator of antigen-specific T cell response in cancer immunotherapies] | ||
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+ | |style="padding:.4em;"|JH Cha | ||
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+ | [https://www.nature.com/articles/s41587-021-01091-3 Identifying phenotype-associated subpopulations by integrating bulk and single-cell sequencing data] | ||
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+ | [https://www.biorxiv.org/content/10.1101/2021.12.06.471401v1 MIRA: Joint regulatory modeling of multimodal expression and chromatin accessibility in single cells] | ||
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+ | [https://www.sciencedirect.com/science/article/pii/S1535610822000654 Immune phenotypic linkage between colorectal cancer and liver metastasis] | ||
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+ | [https://doi.org/10.1101/2022.02.05.479217 Biologically informed deep learning to infer gene program activity in single cells] | ||
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+ | [https://www.nature.com/articles/s43018-022-00356-3 Cell type and gene expression deconvolution with BayesPrism enables Bayesian integrative analysis across bulk and single-cell RNA sequencing in oncology] | ||
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+ | [https://pubmed.ncbi.nlm.nih.gov/34462589/ Mapping single-cell data to reference atlases by transfer learning] | ||
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+ | [https://doi.org/10.1101/2021.10.31.466532 Pan-cancer mapping of single T cell profiles reveals a TCF1:CXCR6-CXCL16 regulatory axis essential for effective anti-tumor immunity] | ||
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+ | [https://pubmed.ncbi.nlm.nih.gov/34845454/ Identifying disease-critical cell types and cellular processes across the human body by integration of single-cell profiles and human genetics] | ||
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+ | [https://www.biorxiv.org/content/10.1101/2021.06.07.447430v2 Metacells untangle large and complex single-cell transcriptome networks] | ||
+ | [https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1812-2 MetaCell: analysis of single-cell RNA-seq data using K-nn graph partitions] | ||
+ | [https://genomebiology.biomedcentral.com/articles/10.1186/s13059-022-02667-1 Metacell‑2: a divide‑and‑conquer metacell algorithm for scalable scRNA‑seq analysis] | ||
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+ | [https://pubmed.ncbi.nlm.nih.gov/34675423/ Co-varying neighborhood analysis identifies cell populations associated with phenotypes of interest from single-cell transcriptomics] | ||
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+ | [https://pubmed.ncbi.nlm.nih.gov/34426704/ Integrating T cell receptor sequences and transcriptional profiles by clonotype neighbor graph analysis (CoNGA)] | ||
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+ | [https://pubmed.ncbi.nlm.nih.gov/34986867/ Hepatocellular carcinoma patients with high circulating cytotoxic T cells and intra-tumoral immune signature benefit from pembrolizumab: results from a single-arm phase 2 trial] | ||
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+ | [https://pubmed.ncbi.nlm.nih.gov/35199064/ Effect of imputation on gene network reconstruction from single-cell RNA-seq data] | ||
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+ | [https://pubmed.ncbi.nlm.nih.gov/35105355/ Diagnostic Evidence GAuge of Single cells (DEGAS): a flexible deep transfer learning framework for prioritizing cells in relation to disease] | ||
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+ | [https://pubmed.ncbi.nlm.nih.gov/35172892/ Integrating single-cell sequencing data with GWAS summary statistics reveals CD16+monocytes and memory CD8+T cells involved in severe COVID-19] | ||
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+ | |style="padding:.4em;"|JH Cha | ||
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+ | [https://pubmed.ncbi.nlm.nih.gov/34663807/ Single cell T cell landscape and T cell receptor repertoire profiling of AML in context of PD-1 blockade therapy] | ||
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+ | [https://pubmed.ncbi.nlm.nih.gov/34594031/ Systematic investigation of cytokine signaling activity at the tissue and single-cell levels] | ||
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+ | [https://pubmed.ncbi.nlm.nih.gov/34852236/ Neoantigen-driven B cell and CD4 T follicular helper cell collaboration promotes anti-tumor CD8 T cell responses] | ||
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[https://pubmed.ncbi.nlm.nih.gov/34489465/ Single-cell ATAC and RNA sequencing reveal pre-existing and persistent cells associated with prostate cancer relapse] | [https://pubmed.ncbi.nlm.nih.gov/34489465/ Single-cell ATAC and RNA sequencing reveal pre-existing and persistent cells associated with prostate cancer relapse] | ||
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{|class=wikitable style="text-align:center;" | {|class=wikitable style="text-align:center;" | ||
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+ | |style="padding:.4em;"|22-34 | ||
+ | |style="padding:.4em;"|JY Ma | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.biorxiv.org/content/10.1101/2022.08.02.502504v1 A novel in silico method employs chemical and protein similarity algorithms to accurately identify chemical transformations in the human gut microbiome] | ||
+ | |- | ||
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+ | |style="padding:.4em;"|22-33 | ||
+ | |style="padding:.4em;"|NY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.biorxiv.org/content/10.1101/2021.09.13.460160v3 Inference of disease-associated microbial biomarkers based on metagenomic and metatranscriptomic data] | ||
+ | |- | ||
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+ | |style="padding:.4em;"|SH Lee | ||
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+ | [https://www.sciencedirect.com/science/article/pii/S0092867422009199?via%3Dihub Personalized microbiome-driven effects of non-nutritive sweeteners on human glucose tolerance] | ||
+ | |- | ||
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+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|22-31 | ||
+ | |style="padding:.4em;"|SH Ahn | ||
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+ | [https://www.nature.com/articles/s41564-022-01121-z Identification of shared and disease-specific host gene–microbiome associations across human diseases using multi-omic integration] | ||
+ | |- | ||
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+ | [https://www.sciencedirect.com/science/article/pii/S193131282200049X Caudovirales bacteriophages are associated with improved executive function and memory in flies, mice, and humans] | ||
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+ | [https://www.biorxiv.org/content/10.1101/2021.10.06.463341v2.full SynTracker: a synteny based tool for tracking microbial strains] | ||
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+ | [https://www.nature.com/articles/s41587-022-01226-0 Identification of antimicrobial peptides from the human gut microbiome using deep learning] | ||
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+ | [https://www.nature.com/articles/s41586-022-04648-7 Discovery of bioactive microbial gene products in inflammatory bowel disease] | ||
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+ | [https://www.nature.com/articles/s43588-022-00247-8 Large-scale microbiome data integration enables robust biomarker identification] | ||
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+ | |style="padding:.4em;" rowspan=1|2022/08/18 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|22-25 | ||
+ | |style="padding:.4em;"|SH Ahn | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.nature.com/articles/s41467-022-30512-3 Predicting cancer prognosis and drug response from the tumor microbiome] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2022/08/11 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|22-24 | ||
+ | |style="padding:.4em;"|HJ Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.cell.com/cell-reports/pdf/S2211-1247(22)00770-7.pdf Thousands of small, novel genes predicted in global phage genomes] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2022/08/04 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|22-23 | ||
+ | |style="padding:.4em;"|JH Cha | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-022-01231-0 MetaPop: a pipeline for macro- and microdiversity analyses and visualization of microbial and viral metagenome-derived populations] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2022/07/28 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|22-22 | ||
+ | |style="padding:.4em;"|NY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.nature.com/articles/s41586-022-04862-3 Biosynthetic potential of the global ocean microbiome] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2022/07/14 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|22-21 | ||
+ | |style="padding:.4em;"|JY Ma | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://journals.asm.org/doi/10.1128/msystems.00050-22 Compositionally Aware Phylogenetic Beta-Diversity Measures Better Resolve Microbiomes Associated with Phenotype] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2022/07/07 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|22-20 | ||
+ | |style="padding:.4em;"|SH Lee | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.nature.com/articles/s41596-020-00480-3 Tutorial: assessing metagenomics software with the CAMI benchmarking toolkit disease] | ||
+ | [https://www.nature.com/articles/s41592-022-01431-4 Critical Assessment of Metagenome Interpretation: the second round of challenges] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2022/06/24 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|22-19 | ||
+ | |style="padding:.4em;"|SH Ann | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.sciencedirect.com/science/article/pii/S2666379121002561 Identification of Faecalibacterium prausnitzii strains for gut microbiome-based intervention in Alzheimer’s-type dementia] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2022/06/10 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|22-18 | ||
+ | |style="padding:.4em;"|HJ Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.nature.com/articles/s41591-022-01688-4 Microbiome and metabolome features of the cardiometabolic disease spectrum] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2022/06/03 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|22-17 | ||
+ | |style="padding:.4em;"|JH Cha | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02195-w Functional and genetic markers of niche partitioning among enigmatic members of the human oral microbiome] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2022/05/27 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|22-16 | ||
+ | |style="padding:.4em;"|NY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.biorxiv.org/content/10.1101/2022.02.21.480893v1 Integrating phylogenetic and functional data in microbiome studies] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2022/05/20 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|22-15 | ||
+ | |style="padding:.4em;"|MY Ma | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://genomebiology.biomedcentral.com/articles/10.1186/s13059-021-02473-1 Pandora: nucleotide-resolution bacterial pan-genomics with reference graphs] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2022/05/13 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|22-14 | ||
+ | |style="padding:.4em;"|SH Lee | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009442 Multivariable association discovery in population-scale meta-omics studies] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2022/04/08 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|22-13 | ||
+ | |style="padding:.4em;"|SH Ahn | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://journals.asm.org/doi/10.1128/mSystems.00252-19 Comprehensive Analysis Reveals the Evolution and Pathogenicity of Aeromonas, Viewed from Both Single Isolated Species and Microbial Communities] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2022/04/01 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|22-12 | ||
+ | |style="padding:.4em;"|HJ Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://genomebiology.biomedcentral.com/articles/10.1186/s13059-022-02610-4 AGAMEMNON: an Accurate metaGenomics And MEtatranscriptoMics quaNtificatiON analysis suite] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2022/03/18 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|22-11 | ||
+ | |style="padding:.4em;"|JH Cha | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02200-2 Metapangenomics of the oral microbiome provides insights into habitat adaptation and cultivar diversity] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2022/03/04 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|22-10 | ||
+ | |style="padding:.4em;"|JY Ma | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://genomemedicine.biomedcentral.com/articles/10.1186/s13073-022-01011-3 Microbiota of the prostate tumor environment investigated by whole-transcriptome profiling] | ||
|- | |- | ||
|style="padding:.4em;" rowspan=1|2022/02/25 | |style="padding:.4em;" rowspan=1|2022/02/25 | ||
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[https://pubmed.ncbi.nlm.nih.gov/29347966/ ReprDB and panDB: minimalist databases with maximal microbial representation] | [https://pubmed.ncbi.nlm.nih.gov/29347966/ ReprDB and panDB: minimalist databases with maximal microbial representation] | ||
|} | |} | ||
+ | |||
+ | |||
+ | {|class=wikitable style="text-align:center;" | ||
+ | |+style="text-align:left;font-size:12pt" | 2022 Microbiome Special JC | ||
+ | |- | ||
+ | !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|2022/08/30 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|22-15 | ||
+ | |style="padding:.4em;"|HY Kang | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.biorxiv.org/content/10.1101/2022.07.06.499075v1 Maast: genotyping thousands of microbial strains efficiently] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2022/08/30 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|22-14 | ||
+ | |style="padding:.4em;"|YJ Roh | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.biorxiv.org/content/10.1101/2022.06.16.496510v2 MIDAS2: Metagenomic Intra-species Diversity Analysis System] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2022/08/30 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|22-13 | ||
+ | |style="padding:.4em;"|SC Yang | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.biorxiv.org/content/10.1101/2022.02.01.478746v2 Scalable microbial strain inference in metagenomic data using StrainFacts] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2022/08/26 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|22-12 | ||
+ | |style="padding:.4em;"|SH Ahn | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.biorxiv.org/content/10.1101/2022.02.15.480535v1 StrainPanDA: linked reconstruction of strain composition and gene content profiles via pangenome-based decomposition of metagenomic data] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2022/08/26 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|22-11 | ||
+ | |style="padding:.4em;"|HJ Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-022-01251-w Metagenomic strain detection with SameStr: identification of a persisting core gut microbiota transferable by fecal transplantation] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2022/08/26 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|22-10 | ||
+ | |style="padding:.4em;"|JY Ma | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.nature.com/articles/s41587-021-01102-3 Fast and accurate metagenotyping of the human gut microbiome with GT-Pro] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2022/08/19 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|22-9 | ||
+ | |style="padding:.4em;"|JH Cha | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.nature.com/articles/s41587-020-00797-0 inStrain profiles population microdiversity from metagenomic data and sensitively detects shared microbial strains] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2022/08/19 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|22-8 | ||
+ | |style="padding:.4em;"|NY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://genome.cshlp.org/content/early/2021/07/22/gr.265058.120 Longitudinal linked-read sequencing reveals ecological and evolutionary responses of a human gut microbiome during antibiotic treatment] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2022/08/19 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|22-7 | ||
+ | |style="padding:.4em;"|SH Lee | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.sciencedirect.com/science/article/pii/S1931312821002365 Dispersal strategies shape persistence and evolution of human gut bacteria] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2022/08/09 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|22-6 | ||
+ | |style="padding:.4em;"|SH Ahn | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.sciencedirect.com/science/article/pii/S0092867421003524 The long-term genetic stability and individual specificity of the human gut microbiome] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2022/08/09 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|22-5 | ||
+ | |style="padding:.4em;"|HJ Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [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] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2022/08/09 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|22-4 | ||
+ | |style="padding:.4em;"|JY Ma | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3000102 Evolutionary dynamics of bacteria in the gut microbiome within and across hosts] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2022/07/29 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|22-3 | ||
+ | |style="padding:.4em;"|JH Cha | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://elifesciences.org/articles/42693 Extensive transmission of microbes along the gastrointestinal tract] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2022/07/29 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|22-2 | ||
+ | |style="padding:.4em;"|NY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [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] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2022/07/29 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|22-1 | ||
+ | |style="padding:.4em;"|SH Lee | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://www.nature.com/articles/nmeth.3802 Strain-level microbial epidemiology and population genomics from shotgun metagenomics] | ||
+ | |} | ||
+ | |||
+ | |||
{|class=wikitable style="text-align:center;" | {|class=wikitable style="text-align:center;" |
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 |