Use my Search Websuite to scan PubMed, PMCentral, Journal Hosts and Journal Archives, FullText. Kick-your-searchterm to multiple Engines kick-your-query now !>

A dictionary by aggregated review articles of nephrology, medicine and the life sciences Your one-stop-run pathway from word to the immediate pdf of peer-reviewed on-topic knowledge.

10.1021/acs.jproteome.0c00316 http://scihub22266oqcxt.onion/10.1021/acs.jproteome.0c00316 32654489!ä!32654489 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Proteome+Res 2020 ; 19 (11): 4624-4636 Nephropedia Template TP {{tp|p=32654489|t=2020. Repurpose Open Data to Discover Therapeutics for COVID-19 Using Deep Learning |pdf=|usr=}}{{32654489}} gab.com Text Repurpose Open Data to Discover Therapeutics for COVID-19 Using Deep Learning JO: J Proteome Res http://www.kidney.de/pget.php?&tw=1&pmid=32654489 #FOAvip There have been more than 2.2 million confirmed cases and over 120?000 deaths from the human coronavirus disease 2019 (COVID-19) pandemic, caused by the novel severe acute respiratory syndrome coronavirus (SARS-CoV-2), in the United States alone. However, there is currently a lack of proven effective medications against COVID-19. Drug repurposing offers a promising route for the development of prevention and treatment strategies for COVID-19. This study reports an integrative, network-based deep-learning methodology to identify repurposable drugs for COVID-19 (termed CoV-KGE). Specifically, we built a comprehensive knowledge graph that includes 15 million edges across 39 types of relationships connecting drugs, diseases, proteins/genes, pathways, and expression from a large scientific corpus of 24 million PubMed publications. Using Amazon's AWS computing resources and a network-based, deep-learning framework, we identified 41 repurposable drugs (including dexamethasone, indomethacin, niclosamide, and toremifene) whose therapeutic associations with COVID-19 were validated by transcriptomic and proteomics data in SARS-CoV-2-infected human cells and data from ongoing clinical trials. Whereas this study by no means recommends specific drugs, it demonstrates a powerful deep-learning methodology to prioritize existing drugs for further investigation, which holds the potential to accelerate therapeutic development for COVID-19. Twit Text FOAVip Repurpose Open Data to Discover Therapeutics for COVID-19 Using Deep Learning ????J Proteome Res http://www.kidney.de/pget.php?&tw=1&pmid=32654489 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32654489 #FOAvip English Wikipedia <ref>{{Cite pmid|32654489}}</ref> Repurpose Open Data to Discover Therapeutics for COVID-19 Using Deep Learning #MMPMID32654489 Zeng X; Song X; Ma T; Pan X; Zhou Y; Hou Y; Zhang Z; Li K; Karypis G; Cheng F J Proteome Res 2020[Nov]; 19 (11): 4624-4636 PMID32654489show ga There have been more than 2.2 million confirmed cases and over 120?000 deaths from the human coronavirus disease 2019 (COVID-19) pandemic, caused by the novel severe acute respiratory syndrome coronavirus (SARS-CoV-2), in the United States alone. However, there is currently a lack of proven effective medications against COVID-19. Drug repurposing offers a promising route for the development of prevention and treatment strategies for COVID-19. This study reports an integrative, network-based deep-learning methodology to identify repurposable drugs for COVID-19 (termed CoV-KGE). Specifically, we built a comprehensive knowledge graph that includes 15 million edges across 39 types of relationships connecting drugs, diseases, proteins/genes, pathways, and expression from a large scientific corpus of 24 million PubMed publications. Using Amazon's AWS computing resources and a network-based, deep-learning framework, we identified 41 repurposable drugs (including dexamethasone, indomethacin, niclosamide, and toremifene) whose therapeutic associations with COVID-19 were validated by transcriptomic and proteomics data in SARS-CoV-2-infected human cells and data from ongoing clinical trials. Whereas this study by no means recommends specific drugs, it demonstrates a powerful deep-learning methodology to prioritize existing drugs for further investigation, which holds the potential to accelerate therapeutic development for COVID-19. |*Betacoronavirus[MESH] |*Coronavirus Infections/drug therapy/virology[MESH] |*Deep Learning[MESH] |*Pandemics[MESH] |*Pneumonia, Viral/drug therapy/virology[MESH] |Antiviral Agents[MESH] |COVID-19[MESH] |Drug Repositioning/*methods[MESH] |Humans[MESH] |Proteome[MESH] |SARS-CoV-2[MESH]Repurpose Open Data to Discover Therapeutics for COVID-19 Using Deep L(epmc).pdf DeepDyve Pubget Overpricing