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10.26508/lsa.202000904 http://scihub22266oqcxt.onion/10.26508/lsa.202000904 33593923!7893815!33593923     free     free     free Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Life+Sci+Alliance 2021 ; 4 (5): ä Nephropedia Template TP {{tp|p=33593923|t=2021. A systems-based method to repurpose marketed therapeutics for antiviral use: a SARS-CoV-2 case study |pdf=|usr=}}{{33593923}} gab.com Text A systems-based method to repurpose marketed therapeutics for antiviral use: a SARS-CoV-2 case study JO: Life Sci Alliance http://www.kidney.de/pget.php?&tw=1&pmid=33593923 #FOAvip This study describes two complementary methods that use network-based and sequence similarity tools to identify drug repurposing opportunities predicted to modulate viral proteins. This approach could be rapidly adapted to new and emerging viruses. The first method built and studied a virus-host-physical interaction network; a three-layer multimodal network of drug target proteins, human protein-protein interactions, and viral-host protein-protein interactions. The second method evaluated sequence similarity between viral proteins and other proteins, visualized by constructing a virus-host-similarity interaction network. Methods were validated on the human immunodeficiency virus, hepatitis B, hepatitis C, and human papillomavirus, then deployed on SARS-CoV-2. Comparison of virus-host-physical interaction predictions to known antiviral drugs had AUCs of 0.69, 0.59, 0.78, and 0.67, respectively, reflecting that the scores are predictive of effective drugs. For SARS-CoV-2, 569 candidate drugs were predicted, of which 37 had been included in clinical trials for SARS-CoV-2 (AUC = 0.75, P-value 3.21 x 10(-3)). As further validation, top-ranked candidate antiviral drugs were analyzed for binding to protein targets in silico; binding scores generated by BindScope indicated a 70% success rate. Twit Text FOAVip A systems-based method to repurpose marketed therapeutics for antiviral use: a SARS-CoV-2 case study ????Life Sci Alliance http://www.kidney.de/pget.php?&tw=1&pmid=33593923 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33593923 #FOAvip English Wikipedia <ref>{{Cite pmid|33593923}}</ref> A systems-based method to repurpose marketed therapeutics for antiviral use: a SARS-CoV-2 case study #MMPMID33593923 Wang M; Withers JB; Ricchiuto P; Voitalov I; McAnally M; Sanchez HN; Saleh A; Akmaev VR; Ghiassian SD Life Sci Alliance 2021[May]; 4 (5): ä PMID33593923show ga This study describes two complementary methods that use network-based and sequence similarity tools to identify drug repurposing opportunities predicted to modulate viral proteins. This approach could be rapidly adapted to new and emerging viruses. The first method built and studied a virus-host-physical interaction network; a three-layer multimodal network of drug target proteins, human protein-protein interactions, and viral-host protein-protein interactions. The second method evaluated sequence similarity between viral proteins and other proteins, visualized by constructing a virus-host-similarity interaction network. Methods were validated on the human immunodeficiency virus, hepatitis B, hepatitis C, and human papillomavirus, then deployed on SARS-CoV-2. Comparison of virus-host-physical interaction predictions to known antiviral drugs had AUCs of 0.69, 0.59, 0.78, and 0.67, respectively, reflecting that the scores are predictive of effective drugs. For SARS-CoV-2, 569 candidate drugs were predicted, of which 37 had been included in clinical trials for SARS-CoV-2 (AUC = 0.75, P-value 3.21 x 10(-3)). As further validation, top-ranked candidate antiviral drugs were analyzed for binding to protein targets in silico; binding scores generated by BindScope indicated a 70% success rate. |*COVID-19 Drug Treatment[MESH] |*Drug Repositioning[MESH] |*Systems Biology[MESH] |Antiviral Agents/pharmacology/*therapeutic use[MESH] |Clinical Trials as Topic[MESH] |Computer Simulation[MESH] |Gene Ontology[MESH] |Host-Pathogen Interactions/drug effects[MESH] |Humans[MESH] |ROC Curve[MESH] |SARS-CoV-2/drug effects/*physiology[MESH]A systems-based method to repurpose marketed therapeutics for antivira(epmc).pdf DeepDyve Pubget Overpricing