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/acsomega.0c00857 http://scihub22266oqcxt.onion/10.1021/acsomega.0c00857 C7315437!7315437!32632398     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=32632398&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       ACS+Omega 2020 ; 5 (25): 15039-51 Nephropedia Template TP {{tp|p=32632398|t=2020. Recommender Systems in Antiviral Drug Discovery |pdf=|usr=}}{{32632398}} gab.com Text Recommender Systems in Antiviral Drug Discovery JO: ACS Omega http://www.kidney.de/pget.php?&tw=1&pmid=32632398 #FOAvip Recommender systems (RSs), which underwent rapid development and had an enormous impact on e-commerce, have the potential to become useful tools for drug discovery. In this paper, we applied RS methods for the prediction of the antiviral activity class (active/inactive) for compounds extracted from ChEMBL. Two main RS approaches were applied: collaborative filtering (Surprise implementation) and content-based filtering (sparse-group inductive matrix completion (SGIMC) method). The effectiveness of RS approaches was investigated for prediction of antiviral activity classes (?interactions?) for compounds and viruses, for which some of their interactions with other viruses or compounds are known, and for prediction of interaction profiles for new compounds. Both approaches achieved relatively good prediction quality for binary classification of individual interactions and compound profiles, as quantified by cross-validation and external validation receiver operating characteristic (ROC) score >0.9. Thus, even simple recommender systems may serve as an effective tool in antiviral drug discovery. Twit Text FOAVip Recommender Systems in Antiviral Drug Discovery ????ACS Omega http://www.kidney.de/pget.php?&tw=1&pmid=32632398 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32632398 #FOAvip English Wikipedia <ref>{{Cite pmid|32632398}}</ref> Recommender Systems in Antiviral Drug Discovery #MMPMID32632398 Sosnina EA; Sosnin S; Nikitina AA; Nazarov I; Osolodkin DI; Fedorov MV ACS Omega 2020[Jun]; 5 (25): 15039-51 PMID32632398show ga Recommender systems (RSs), which underwent rapid development and had an enormous impact on e-commerce, have the potential to become useful tools for drug discovery. In this paper, we applied RS methods for the prediction of the antiviral activity class (active/inactive) for compounds extracted from ChEMBL. Two main RS approaches were applied: collaborative filtering (Surprise implementation) and content-based filtering (sparse-group inductive matrix completion (SGIMC) method). The effectiveness of RS approaches was investigated for prediction of antiviral activity classes (?interactions?) for compounds and viruses, for which some of their interactions with other viruses or compounds are known, and for prediction of interaction profiles for new compounds. Both approaches achieved relatively good prediction quality for binary classification of individual interactions and compound profiles, as quantified by cross-validation and external validation receiver operating characteristic (ROC) score >0.9. Thus, even simple recommender systems may serve as an effective tool in antiviral drug discovery. äRecommender Systems in Antiviral Drug Discovery (epmc).pdf DeepDyve Pubget Overpricing