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10.21203/rs.3.rs-25643/v1 http://scihub22266oqcxt.onion/10.21203/rs.3.rs-25643/v1 32702077!7336712!32702077     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=32702077&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215       Res+Sq 2020 ; ? (?): ? Nephropedia Template TP {{tp|p=32702077|t=2020. Identification of new drug treatments to combat COVID19: A signature-based approach using iLINCS |pdf=|usr=}}{{32702077}} gab.com Text Identification of new drug treatments to combat COVID19: A signature-based approach using iLINCS JO: Res Sq http://www.kidney.de/pget.php?&tw=1&pmid=32702077 #FOAvip The COVID-19 pandemic caused by the novel SARS-CoV-2 is more contagious than other coronaviruses and has higher rates of mortality than influenza. As no vaccine or drugs are currently approved to specifically treat COVID-19, identification of effective therapeutics is crucial to treat the afflicted and limit disease spread. We deployed a bioinformatics workflow to identify candidate drugs for the treatment of COVID-19. Using an "omics" repository, the Library of Integrated Network-Based Cellular Signatures (LINCS), we simultaneously probed transcriptomic signatures of putative COVID-19 drugs and signatures of coronavirus-infected cell lines to identify therapeutics with concordant signatures and discordant signatures, respectively. Our findings include three FDA approved drugs that have established antiviral activity, including protein kinase inhibitors, providing a promising new category of candidates for COVID-19 interventions. Twit Text FOAVip Identification of new drug treatments to combat COVID19: A signature-based approach using iLINCS ????Res Sq http://www.kidney.de/pget.php?&tw=1&pmid=32702077 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32702077 #FOAvip English Wikipedia <ref>{{Cite pmid|32702077}}</ref> Identification of new drug treatments to combat COVID19: A signature-based approach using iLINCS #MMPMID32702077 O'Donovan SM; Eby H; Henkel ND; Creeden J; Imami A; Asah S; Zhang X; Wu X; Alnafisah R; Taylor RT; Reigle J; Thorman A; Shamsaei B; Meller J; McCullumsmith RE Res Sq 2020[Apr]; ? (?): ? PMID32702077show ga The COVID-19 pandemic caused by the novel SARS-CoV-2 is more contagious than other coronaviruses and has higher rates of mortality than influenza. As no vaccine or drugs are currently approved to specifically treat COVID-19, identification of effective therapeutics is crucial to treat the afflicted and limit disease spread. We deployed a bioinformatics workflow to identify candidate drugs for the treatment of COVID-19. Using an "omics" repository, the Library of Integrated Network-Based Cellular Signatures (LINCS), we simultaneously probed transcriptomic signatures of putative COVID-19 drugs and signatures of coronavirus-infected cell lines to identify therapeutics with concordant signatures and discordant signatures, respectively. Our findings include three FDA approved drugs that have established antiviral activity, including protein kinase inhibitors, providing a promising new category of candidates for COVID-19 interventions. ?Identification of new drug treatments to combat COVID19: A signature-b(epmc).pdf DeepDyve Pubget Overpricing