Difference between revisions of "Journal Club"
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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] | [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] | [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] | [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] | [https://www.biorxiv.org/content/10.1101/2022.03.16.484513v1 Integrated single-cell profiling dissects cell-state-specific enhancer landscapes of human tumor-infiltrating T cells] | ||
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[https://www.nature.com/articles/s41591-022-01799-y A T cell resilience model associated with response to immunotherapy in multiple tumor types] | [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] | [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] | [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] | [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] | [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://www.biorxiv.org/content/10.1101/2022.05.04.490536v1 Modeling fragment counts improves single-cell ATAC-seq analysis] | [https://www.biorxiv.org/content/10.1101/2022.05.04.490536v1 Modeling fragment counts improves single-cell ATAC-seq analysis] | ||
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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] | [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/10.1101/2022.08.19.504505v1 SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks] | [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;" rowspan=1|Microbiome | |style="padding:.4em;" rowspan=1|Microbiome | ||
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[https://www.nature.com/articles/s41564-022-01157-1 Phage–host coevolution in natural populations] | [https://www.nature.com/articles/s41564-022-01157-1 Phage–host coevolution in natural populations] | ||
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|style="padding:.4em;" rowspan=1|Microbiome | |style="padding:.4em;" rowspan=1|Microbiome | ||
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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] | [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] | [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] | [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] | [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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[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] | [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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[https://www.biorxiv.org/content/10.1101/2021.09.13.460160v3 Inference of disease-associated microbial biomarkers based on metagenomic and metatranscriptomic data] | [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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[https://www.sciencedirect.com/science/article/pii/S0092867422009199?via%3Dihub Personalized microbiome-driven effects of non-nutritive sweeteners on human glucose tolerance] | [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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Revision as of 15:43, 28 September 2022
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 |