Difference between revisions of "Journal Club"
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|style="padding:.4em;" rowspan=1|Microbiome | |style="padding:.4em;" rowspan=1|Microbiome | ||
|style="padding:.4em;"|24-18 | |style="padding:.4em;"|24-18 | ||
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[https://doi.org/10.1101/2024.02.02.578701 Metagenomic estimation of dietary intake from human stool] | [https://doi.org/10.1101/2024.02.02.578701 Metagenomic estimation of dietary intake from human stool] | ||
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|style="padding:.4em;" rowspan=1|Microbiome | |style="padding:.4em;" rowspan=1|Microbiome | ||
|style="padding:.4em;"|24-17 | |style="padding:.4em;"|24-17 | ||
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[https://doi.org/10.1038/s41591-023-02685-x The infant gut virome is associated with preschool asthma risk independently of bacteria] | [https://doi.org/10.1038/s41591-023-02685-x The infant gut virome is associated with preschool asthma risk independently of bacteria] | ||
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|style="padding:.4em;" rowspan=1|Microbiome | |style="padding:.4em;" rowspan=1|Microbiome | ||
|style="padding:.4em;"|24-16 | |style="padding:.4em;"|24-16 | ||
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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] | [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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|style="padding:.4em;" rowspan=1|Microbiome | |style="padding:.4em;" rowspan=1|Microbiome | ||
|style="padding:.4em;"|24-15 | |style="padding:.4em;"|24-15 | ||
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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’] | [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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|style="padding:.4em;" rowspan=1|Microbiome | |style="padding:.4em;" rowspan=1|Microbiome | ||
|style="padding:.4em;"|24-14 | |style="padding:.4em;"|24-14 | ||
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[https://doi.org/10.1038/s41467-023-42997-7 Gut microbial structural variation associates with immune checkpoint inhibitor response] | [https://doi.org/10.1038/s41467-023-42997-7 Gut microbial structural variation associates with immune checkpoint inhibitor response] | ||
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|style="padding:.4em;" rowspan=1|Microbiome | |style="padding:.4em;" rowspan=1|Microbiome | ||
|style="padding:.4em;"|24-13 | |style="padding:.4em;"|24-13 | ||
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[https://doi.org/10.1080/19490976.2024.2307586 Fungal signature differentiates alcohol-associated liver disease from nonalcoholic fatty liver disease] | [https://doi.org/10.1080/19490976.2024.2307586 Fungal signature differentiates alcohol-associated liver disease from nonalcoholic fatty liver disease] | ||
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|style="padding:.4em;" rowspan=1|Microbiome | |style="padding:.4em;" rowspan=1|Microbiome | ||
|style="padding:.4em;"|24-12 | |style="padding:.4em;"|24-12 | ||
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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] | [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;" rowspan=1|Microbiome | |style="padding:.4em;" rowspan=1|Microbiome | ||
|style="padding:.4em;"|24-11 | |style="padding:.4em;"|24-11 | ||
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[https://doi.org/10.1038/s41467-023-42112-w Disease-specific loss of microbial cross feeding interactions in the human gut] | [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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|style="padding:.4em;" rowspan=1|Microbiome | |style="padding:.4em;" rowspan=1|Microbiome | ||
|style="padding:.4em;"|24-10 | |style="padding:.4em;"|24-10 | ||
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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] | [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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− | |style="padding:.4em;" rowspan=1|2024// | + | |style="padding:.4em;" rowspan=1|2024/03/27 |
|style="padding:.4em;" rowspan=1|Microbiome | |style="padding:.4em;" rowspan=1|Microbiome | ||
|style="padding:.4em;"|24-9 | |style="padding:.4em;"|24-9 | ||
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[https://doi.org/10.1038/s41467-023-40719-7 Microdiversity of the vaginal microbiome is associated with preterm birth] | [https://doi.org/10.1038/s41467-023-40719-7 Microdiversity of the vaginal microbiome is associated with preterm birth] | ||
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− | |style="padding:.4em;" rowspan=1|2024// | + | |style="padding:.4em;" rowspan=1|2024/03/20 |
|style="padding:.4em;" rowspan=1|Microbiome | |style="padding:.4em;" rowspan=1|Microbiome | ||
|style="padding:.4em;"|24-8 | |style="padding:.4em;"|24-8 | ||
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[https://doi.org/10.1038/s41564-023-01584-8 Large language models improve annotation of prokaryotic viral proteins] | [https://doi.org/10.1038/s41564-023-01584-8 Large language models improve annotation of prokaryotic viral proteins] | ||
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|style="padding:.4em;" rowspan=1|Microbiome | |style="padding:.4em;" rowspan=1|Microbiome | ||
|style="padding:.4em;"|24-7 | |style="padding:.4em;"|24-7 | ||
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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] | [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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|style="padding:.4em;" rowspan=1|Microbiome | |style="padding:.4em;" rowspan=1|Microbiome | ||
|style="padding:.4em;"|24-6-2 | |style="padding:.4em;"|24-6-2 | ||
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[https://academic.oup.com/nargab/article/2/2/lqaa023/5826153 Visualizing ’omic feature rankings and log-ratios using Qurro] | [https://academic.oup.com/nargab/article/2/2/lqaa023/5826153 Visualizing ’omic feature rankings and log-ratios using Qurro] | ||
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|style="padding:.4em;" rowspan=1|Microbiome | |style="padding:.4em;" rowspan=1|Microbiome | ||
|style="padding:.4em;"|24-6-1 | |style="padding:.4em;"|24-6-1 | ||
|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/10.1038/s41467-019-10656-5 Establishing microbial composition measurement standards with reference frames | + | [https://doi.org/10.1038/s41467-019-10656-5 Establishing microbial composition measurement standards with reference frames] |
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− | |style="padding:.4em;" rowspan=1|2024// | + | |style="padding:.4em;" rowspan=1|2024/03/13 |
|style="padding:.4em;" rowspan=1|Microbiome | |style="padding:.4em;" rowspan=1|Microbiome | ||
|style="padding:.4em;"|24-5 | |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] | [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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|style="padding:.4em;" rowspan=1|Microbiome | |style="padding:.4em;" rowspan=1|Microbiome | ||
|style="padding:.4em;"|24-4 | |style="padding:.4em;"|24-4 | ||
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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] | [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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|style="padding:.4em;" rowspan=1|Microbiome | |style="padding:.4em;" rowspan=1|Microbiome | ||
|style="padding:.4em;"|24-3 | |style="padding:.4em;"|24-3 | ||
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[https://doi.org/10.1038/s41467-023-44290-z Effective binning of metagenomic contigs using contrastive multi-view representation learning] | [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;" rowspan=1|Microbiome | |style="padding:.4em;" rowspan=1|Microbiome | ||
|style="padding:.4em;"|24-2 | |style="padding:.4em;"|24-2 | ||
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[https://doi.org/10.1038/s41559-020-01353-4 Polarization of microbial communities between competitive and cooperative metabolism] | [https://doi.org/10.1038/s41559-020-01353-4 Polarization of microbial communities between competitive and cooperative metabolism] | ||
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|style="padding:.4em;" rowspan=1|Microbiome | |style="padding:.4em;" rowspan=1|Microbiome | ||
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[https://onlinelibrary.wiley.com/doi/full/10.1002/advs.202303925 Metagenomic Insight into The Global Dissemination of The Antibiotic Resistome] | [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;" rowspan=1|Microbiome | |style="padding:.4em;" rowspan=1|Microbiome | ||
|style="padding:.4em;"|24-1-1 | |style="padding:.4em;"|24-1-1 |
Revision as of 21:41, 11 February 2024
Date | Team | Paper index |
Presenter | Paper title |
---|---|---|---|---|
2024/04/09 | Single-cell | 24-6 | SB Baek | |
2024/04/02 | Single-cell | 24-5 | JH Cha | |
2024/03/26 | Single-cell | 24-4 | EJ Sung |
Pathway centric analysis for single-cell RNA-seq and spatial transcriptomics data with GSDensity |
2024/03/19 | Single-cell | 24-3 | IS Choi |
Automatic cell-type harmonization and integration across Human Cell Atlas datasets |
2024/03/12 | Single-cell | 24-2 | SB Baek |
Deep generative modeling of transcriptional dynamics for RNA velocity analysis in single cells |
2024/03/05 | Single-cell | 24-1 | JH Cha |
Gene signature extraction and cell identity recognition at the single-cell level with Cell-ID |
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 |