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10.1016/j.patter.2021.100242 http://scihub22266oqcxt.onion/10.1016/j.patter.2021.100242 33817672!8006187!33817672     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Patterns+(N+Y) 2021 ; 2 (5): 100242 Nephropedia Template TP {{tp|p=33817672|t=2021. Network-based virus-host interaction prediction with application to SARS-CoV-2 |pdf=|usr=}}{{33817672}} gab.com Text Network-based virus-host interaction prediction with application to SARS-CoV-2 JO: Patterns (N Y) http://www.kidney.de/pget.php?&tw=1&pmid=33817672 #FOAvip COVID-19, caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), has quickly become a global health crisis since the first report of infection in December of 2019. However, the infection spectrum of SARS-CoV-2 and its comprehensive protein-level interactions with hosts remain unclear. There is a massive amount of underutilized data and knowledge about RNA viruses highly relevant to SARS-CoV-2 and proteins of their hosts. More in-depth and more comprehensive analyses of that knowledge and data can shed new light on the molecular mechanisms underlying the COVID-19 pandemic and reveal potential risks. In this work, we constructed a multi-layer virus-host interaction network to incorporate these data and knowledge. We developed a machine-learning-based method to predict virus-host interactions at both protein and organism levels. Our approach revealed five potential infection targets of SARS-CoV-2 and 19 highly possible interactions between SARS-CoV-2 proteins and human proteins in the innate immune pathway. Twit Text FOAVip Network-based virus-host interaction prediction with application to SARS-CoV-2 ????Patterns (N Y) http://www.kidney.de/pget.php?&tw=1&pmid=33817672 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33817672 #FOAvip English Wikipedia <ref>{{Cite pmid|33817672}}</ref> Network-based virus-host interaction prediction with application to SARS-CoV-2 #MMPMID33817672 Du H; Chen F; Liu H; Hong P Patterns (N Y) 2021[May]; 2 (5): 100242 PMID33817672show ga COVID-19, caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), has quickly become a global health crisis since the first report of infection in December of 2019. However, the infection spectrum of SARS-CoV-2 and its comprehensive protein-level interactions with hosts remain unclear. There is a massive amount of underutilized data and knowledge about RNA viruses highly relevant to SARS-CoV-2 and proteins of their hosts. More in-depth and more comprehensive analyses of that knowledge and data can shed new light on the molecular mechanisms underlying the COVID-19 pandemic and reveal potential risks. In this work, we constructed a multi-layer virus-host interaction network to incorporate these data and knowledge. We developed a machine-learning-based method to predict virus-host interactions at both protein and organism levels. Our approach revealed five potential infection targets of SARS-CoV-2 and 19 highly possible interactions between SARS-CoV-2 proteins and human proteins in the innate immune pathway. äNetwork-based virus-host interaction prediction with application to SA(epmc).pdf DeepDyve Pubget Overpricing