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10.1016/j.patter.2021.100247 http://scihub22266oqcxt.onion/10.1016/j.patter.2021.100247 33842903!8020120!33842903     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Patterns+(N+Y) 2021 ; 2 (5): 100247 Nephropedia Template TP {{tp|p=33842903|t=2021. Secondary analysis of transcriptomes of SARS-CoV-2 infection models to characterize COVID-19 |pdf=|usr=}}{{33842903}} gab.com Text Secondary analysis of transcriptomes of SARS-CoV-2 infection models to characterize COVID-19 JO: Patterns (N Y) http://www.kidney.de/pget.php?&tw=1&pmid=33842903 #FOAvip Standard transcriptomic analyses alone have limited power in capturing the molecular mechanisms driving disease pathophysiology and outcomes. To overcome this, unsupervised network analyses are used to identify clusters of genes that can be associated with distinct molecular mechanisms and outcomes for a disease. In this study, we developed an integrated network analysis framework that integrates transcriptional signatures from multiple model systems with protein-protein interaction data to find gene modules. Through a meta-analysis of different enriched features from these gene modules, we extract communities of highly interconnected features. These clusters of higher-order features, working as a multifeatured machine, enable collective assessment of their contribution for disease or phenotype characterization. We show the utility of this workflow using transcriptomics data from three different models of SARS-CoV-2 infection and identify several pathways and biological processes that could enable understanding or hypothesizing molecular signatures inducing pathophysiological changes, risks, or sequelae of COVID-19. Twit Text FOAVip Secondary analysis of transcriptomes of SARS-CoV-2 infection models to characterize COVID-19 ????Patterns (N Y) http://www.kidney.de/pget.php?&tw=1&pmid=33842903 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33842903 #FOAvip English Wikipedia <ref>{{Cite pmid|33842903}}</ref> Secondary analysis of transcriptomes of SARS-CoV-2 infection models to characterize COVID-19 #MMPMID33842903 Ghandikota S; Sharma M; Jegga AG Patterns (N Y) 2021[May]; 2 (5): 100247 PMID33842903show ga Standard transcriptomic analyses alone have limited power in capturing the molecular mechanisms driving disease pathophysiology and outcomes. To overcome this, unsupervised network analyses are used to identify clusters of genes that can be associated with distinct molecular mechanisms and outcomes for a disease. In this study, we developed an integrated network analysis framework that integrates transcriptional signatures from multiple model systems with protein-protein interaction data to find gene modules. Through a meta-analysis of different enriched features from these gene modules, we extract communities of highly interconnected features. These clusters of higher-order features, working as a multifeatured machine, enable collective assessment of their contribution for disease or phenotype characterization. We show the utility of this workflow using transcriptomics data from three different models of SARS-CoV-2 infection and identify several pathways and biological processes that could enable understanding or hypothesizing molecular signatures inducing pathophysiological changes, risks, or sequelae of COVID-19. äSecondary analysis of transcriptomes of SARS-CoV-2 infection models to(epmc).pdf DeepDyve Pubget Overpricing