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10.1038/srep11970 http://scihub22266oqcxt.onion/10.1038/srep11970 C4496667!4496667!26155766     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=26155766&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 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       Sci+Rep 2015 ; 5 (ä): ä Nephropedia Template TP {{tp|p=26155766|t=2015. Large-scale Direct Targeting for Drug Repositioning and Discovery |pdf=|usr=}}{{26155766}} gab.com Text Large-scale Direct Targeting for Drug Repositioning and Discovery JO: Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=26155766 #FOAvip A system-level identification of drug-target direct interactions is vital to drug repositioning and discovery. However, the biological means on a large scale remains challenging and expensive even nowadays. The available computational models mainly focus on predicting indirect interactions or direct interactions on a small scale. To address these problems, in this work, a novel algorithm termed weighted ensemble similarity (WES) has been developed to identify drug direct targets based on a large-scale of 98,327 drug-target relationships. WES includes: (1) identifying the key ligand structural features that are highly-related to the pharmacological properties in a framework of ensemble; (2) determining a drug?s affiliation of a target by evaluation of the overall similarity (ensemble) rather than a single ligand judgment; and (3) integrating the standardized ensemble similarities (Z score) by Bayesian network and multi-variate kernel approach to make predictions. All these lead WES to predict drug direct targets with external and experimental test accuracies of 70% and 71%, respectively. This shows that the WES method provides a potential in silico model for drug repositioning and discovery. Twit Text FOAVip Large-scale Direct Targeting for Drug Repositioning and Discovery ????Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=26155766 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26155766 #FOAvip English Wikipedia <ref>{{Cite pmid|26155766}}</ref> Large-scale Direct Targeting for Drug Repositioning and Discovery #MMPMID26155766 Zheng C; Guo Z; Huang C; Wu Z; Li Y; Chen X; Fu Y; Ru J; Ali Shar P; Wang Y; Wang Y Sci Rep 2015[]; 5 (ä): ä PMID26155766show ga A system-level identification of drug-target direct interactions is vital to drug repositioning and discovery. However, the biological means on a large scale remains challenging and expensive even nowadays. The available computational models mainly focus on predicting indirect interactions or direct interactions on a small scale. To address these problems, in this work, a novel algorithm termed weighted ensemble similarity (WES) has been developed to identify drug direct targets based on a large-scale of 98,327 drug-target relationships. WES includes: (1) identifying the key ligand structural features that are highly-related to the pharmacological properties in a framework of ensemble; (2) determining a drug?s affiliation of a target by evaluation of the overall similarity (ensemble) rather than a single ligand judgment; and (3) integrating the standardized ensemble similarities (Z score) by Bayesian network and multi-variate kernel approach to make predictions. All these lead WES to predict drug direct targets with external and experimental test accuracies of 70% and 71%, respectively. This shows that the WES method provides a potential in silico model for drug repositioning and discovery. äLarge-scale Direct Targeting for Drug Repositioning and Discovery (epmc).pdf DeepDyve Pubget Overpricing