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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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+ | [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 | |style="padding:.4em;"|HB Lee | ||
|style="padding:.4em;text-align:left"| | |style="padding:.4em;text-align:left"| | ||
− | [https://doi.org/10. | + | [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 | |style="padding:.4em;"|YL Jung | ||
|style="padding:.4em;text-align:left"| | |style="padding:.4em;text-align:left"| | ||
− | [https://doi.org/10. | + | [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 | |style="padding:.4em;"|EJ Sung | ||
|style="padding:.4em;text-align:left"| | |style="padding:.4em;text-align:left"| | ||
− | [https://doi.org/10. | + | [https://doi.org/10.17632/wdxwy8gmrz.1 Systematic Functional Annotation and Visualization of Biological Networks] |
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− | |style="padding:.4em;" rowspan=1|2024/ | + | |style="padding:.4em;" rowspan=1|2024/11/26 |
|style="padding:.4em;" rowspan=1|Single-cell | |style="padding:.4em;" rowspan=1|Single-cell | ||
− | |style="padding:.4em;"|24- | + | |style="padding:.4em;"|24-28 |
|style="padding:.4em;"|IS Choi | |style="padding:.4em;"|IS Choi | ||
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+ | [https://doi.org/10.1038/s41592-024-02303-9 CellRank 2: unified fate mapping in multiview single-cell data] | ||
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+ | [https://doi.org/10.1101/2024.07.29.605556 scPRINT: pre-training on 50 million cells allows robust gene network predictions] | ||
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+ | [https://doi.org/10.1038/s41467-024-46440-3 Bidirectional generation of structure and properties through a single molecular foundation model] | ||
+ | |} | ||
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+ | [https://doi.org/10.1080/19490976.2024.2418984 Fecal microbial marker panel for aiding diagnosis of autism spectrum disorders] | ||
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+ | [https://doi.org/10.1038/s41564-024-01739-1 Multikingdom and functional gut microbiota markers for autism spectrum disorder] | ||
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+ | [https://doi.org/10.1186/s13059-024-03390-9 A realistic benchmark for differential abundance testing and confounder adjustment in human microbiome studies] | ||
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+ | [https://doi.org/10.1038/s41564-024-01728-4 Microbial community-scale metabolic modelling predicts personalized short-chain fatty acid production profiles in the human gut] | ||
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+ | |style="padding:.4em;"|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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+ | [https://doi.org/10.1101/2024.08.14.607850 The OMG dataset: An Open MetaGenomic corpus for mixed-modality genomic language modeling] | ||
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+ | [https://doi.org/10.1038/s41467-024-46947-9 Genomic language model predicts protein co-regulation and function] | ||
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+ | [https://doi.org/10.1101/2024.07.26.605391 Protein Set 1 Transformer: A protein-based genome language model to power high diversity viromics] | ||
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+ | [https://doi.org/10.1101/2024.07.11.603044 Prophage-DB: A comprehensive database to explore diversity,distribution, and ecology of prophages] | ||
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+ | |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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+ | [https://doi.org/10.1101/2024.04.17.589959 Pangenomes of Human Gut Microbiota Uncover Links Between Genetic Diversity and Stress Response] | ||
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+ | [https://doi.org/10.1101/2024.05.28.596318 vClassifier: a toolkit for species-level classification of prokaryotic viruses] | ||
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+ | [https://doi.org/10.1101/2024.07.26.605250 GRAViTy-V2: a grounded viral taxonomy application] | ||
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+ | [https://doi.org/10.1038/s41467-024-52533-w Accurately predicting enzyme functions through geometric graph learning on ESMFold-predicted structures] | ||
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+ | [https://doi.org/10.1101/2024.06.27.600934 Improved detection of microbiome-disease associations via population structure-aware generalized linear mixed effects models (microSLAM)] | ||
+ | |} | ||
+ | {|class=wikitable style="text-align:center;" | ||
+ | |+style="text-align:left;font-size:12pt" | 2024-1 scOmics | ||
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+ | !scope="col" style="padding:.4em" | Date | ||
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+ | |style="padding:.4em;"|HB Lee | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [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] | [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;" rowspan=1|2024/09/ | + | |style="padding:.4em;" rowspan=1|2024/10/08 |
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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] | [https://doi.org/10.1038/s41592-024-02175-z SLIDE: Significant Latent Factor Interaction Discovery and Exploration across biological domains] | ||
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− | |style="padding:.4em;"| | + | |style="padding:.4em;"|HB Lee |
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− | [https://doi.org/10. | + | [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. | + | [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/10.1038/s41591-024-03067-7 Strain-specific gut microbial signatures in type 2 diabetes identified in a cross-cohort analysis of 8,117 metagenomes] | [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] | [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] | [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] | [https://doi.org/10.48550/arXiv.1707.07250 Tensor Fusion Network for Multimodal Sentiment Analysis] | ||
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+ | |style="padding:.4em;text-align:left"| | ||
+ | [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] | [https://doi.org/10.1186/s13059-024-03325-4 Gut microbiota DPP4-like enzymes are increased in type-2 diabetes and contribute to incretin inactivation] | ||
|- | |- | ||
− | |style="padding:.4em;" rowspan=1|2024/09/ | + | |style="padding:.4em;" rowspan=1|2024/09/11 |
|style="padding:.4em;" rowspan=1|Microbiome | |style="padding:.4em;" rowspan=1|Microbiome | ||
|style="padding:.4em;"|24-46 | |style="padding:.4em;"|24-46 | ||
|style="padding:.4em;"|SH Ahn | |style="padding:.4em;"|SH Ahn | ||
|style="padding:.4em;text-align:left"| | |style="padding:.4em;text-align:left"| | ||
− | [ | + | [https://pubmed.ncbi.nlm.nih.gov/31510656 Deep learning with multimodal representation for pancancer prognosis prediction] |
|- | |- | ||
|style="padding:.4em;" rowspan=1|2024/09/04 | |style="padding:.4em;" rowspan=1|2024/09/04 | ||
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|style="padding:.4em;"|HJ Kim | |style="padding:.4em;"|HJ Kim | ||
|style="padding:.4em;text-align:left"| | |style="padding:.4em;text-align:left"| | ||
− | [ | + | [https://pubmed.ncbi.nlm.nih.gov/32881682 Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosis] |
|- | |- | ||
|style="padding:.4em;" rowspan=1|2024/09/04 | |style="padding:.4em;" rowspan=1|2024/09/04 | ||
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[https://doi.org/10.1016/j.ccell.2022.07.004 Pan-cancer integrative histology-genomic analysis via multimodal deep learning] | [https://doi.org/10.1016/j.ccell.2022.07.004 Pan-cancer integrative histology-genomic analysis via multimodal deep learning] | ||
|- | |- | ||
− | |style="padding:.4em;" rowspan=1|2024/ | + | |style="padding:.4em;" rowspan=1|2024/09/04 |
|style="padding:.4em;" rowspan=1|Microbiome | |style="padding:.4em;" rowspan=1|Microbiome | ||
|style="padding:.4em;"|24-44 | |style="padding:.4em;"|24-44 | ||
− | |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/10.1016/j.chom.2024.03.005 A metagenomics pipeline reveals insertion sequence-driven evolution of the microbiota] |
|- | |- | ||
− | |style="padding:.4em;" rowspan=1|2024/08/ | + | |style="padding:.4em;" rowspan=1|2024/08/21 |
|style="padding:.4em;" rowspan=1|Microbiome | |style="padding:.4em;" rowspan=1|Microbiome | ||
− | |style="padding:.4em;"|24-43 | + | |style="padding:.4em;"|24-43-2 |
|style="padding:.4em;"|JH Cha | |style="padding:.4em;"|JH Cha | ||
|style="padding:.4em;text-align:left"| | |style="padding:.4em;text-align:left"| | ||
− | [https://doi.org/10.48550/arXiv.2103. | + | [https://doi.org/10.48550/arXiv.2303.00915 BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs] |
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2024/08/21 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|24-43-1 | ||
+ | |style="padding:.4em;"|JH Cha | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://arxiv.org/abs/2103.00020 Learning Transferable Visual Models From Natural Language Supervision] | ||
|- | |- | ||
|style="padding:.4em;" rowspan=1|2024/08/21 | |style="padding:.4em;" rowspan=1|2024/08/21 | ||
|style="padding:.4em;" rowspan=1|Microbiome | |style="padding:.4em;" rowspan=1|Microbiome | ||
|style="padding:.4em;"|24-42 | |style="padding:.4em;"|24-42 | ||
− | |style="padding:.4em;"| | + | |style="padding:.4em;"|JY Ma |
|style="padding:.4em;text-align:left"| | |style="padding:.4em;text-align:left"| | ||
− | [https://doi.org/10. | + | [https://doi.org/10.1038/s41592-022-01616-x BIONIC: biological network integration using convolutions] |
|- | |- | ||
|style="padding:.4em;" rowspan=1|2024/08/14 | |style="padding:.4em;" rowspan=1|2024/08/14 | ||
|style="padding:.4em;" rowspan=1|Microbiome | |style="padding:.4em;" rowspan=1|Microbiome | ||
|style="padding:.4em;"|24-41 | |style="padding:.4em;"|24-41 | ||
− | |style="padding:.4em;"| | + | |style="padding:.4em;"|G Koh |
|style="padding:.4em;text-align:left"| | |style="padding:.4em;text-align:left"| | ||
− | [https://doi.org/10.1038/ | + | [https://doi.org/10.1038/s41587-023-01917-2 Protein remote homology detection and structural alignment using deep learning] |
|- | |- | ||
|style="padding:.4em;" rowspan=1|2024/08/14 | |style="padding:.4em;" rowspan=1|2024/08/14 | ||
|style="padding:.4em;" rowspan=1|Microbiome | |style="padding:.4em;" rowspan=1|Microbiome | ||
− | |style="padding:.4em;"|24- | + | |style="padding:.4em;"|24-41 |
− | |style="padding:.4em;"| | + | |style="padding:.4em;"|YR Kim |
|style="padding:.4em;text-align:left"| | |style="padding:.4em;text-align:left"| | ||
− | [https://doi.org/10.1038/ | + | [https://doi.org/10.1038/s41586-024-07487-w Accurate structure prediction of biomolecular interactions with AlphaFold 3] |
|- | |- | ||
|style="padding:.4em;" rowspan=1|2024/08/07 | |style="padding:.4em;" rowspan=1|2024/08/07 | ||
|style="padding:.4em;" rowspan=1|Microbiome | |style="padding:.4em;" rowspan=1|Microbiome | ||
− | |style="padding:.4em;"|24- | + | |style="padding:.4em;"|24-39 |
|style="padding:.4em;"|WJ Kim | |style="padding:.4em;"|WJ Kim | ||
|style="padding:.4em;text-align:left"| | |style="padding:.4em;text-align:left"| | ||
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|style="padding:.4em;" rowspan=1|Microbiome | |style="padding:.4em;" rowspan=1|Microbiome | ||
|style="padding:.4em;"|24-38 | |style="padding:.4em;"|24-38 | ||
− | |style="padding:.4em;"| | + | |style="padding:.4em;"|JY kim |
|style="padding:.4em;text-align:left"| | |style="padding:.4em;text-align:left"| | ||
− | [https://doi.org/10.1038/ | + | [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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|style="padding:.4em;" rowspan=1|2024/07/31 | |style="padding:.4em;" rowspan=1|2024/07/31 |
Latest revision as of 11:29, 14 November 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 |