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10.1007/s12539-016-0194-3 http://scihub22266oqcxt.onion/10.1007/s12539-016-0194-3 C5403631!5403631!27778232     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Interdiscip+Sci 2018 ; 10 (2): 449-54 Nephropedia Template TP {{tp|p=27778232|t=2018. A computational systems biology approach for identifying candidate drugs for repositioning for cardiovascular disease |pdf=|usr=}}{{27778232}} gab.com Text A computational systems biology approach for identifying candidate drugs for repositioning for cardiovascular disease JO: Interdiscip Sci http://www.kidney.de/pget.php?&tw=1&pmid=27778232 #FOAvip We report an in silico method to screen for receptors or pathways that could be targeted to elicit beneficial transcriptional changes in a cellular model of a disease of interest. In our method we integrate: (i) a dataset of transcriptome responses of a cell line to a panel of drugs; (ii) two sets of genes for the disease; and (iii) mappings between drugs and the receptors or pathways that they target. We carried out a Gene Set Enrichment Analysis (GSEA) test for each of the two gene sets against a list of genes ordered by fold-change in response to a drug in a relevant cell line (HL60), with the overall score for a drug being the difference of the two enrichment scores. Next, we applied GSEA for drug targets based on drugs that have been ranked by their differential enrichment scores. The method ranks drugs by the degree of anticorrelation of their gene-level transcriptional effects on the cell line with the genes in the disease gene sets. We applied the method to data from (i) CMap 2.0; (ii) gene sets from two transcriptome profiling studies of atherosclerosis; and (iii) a combined dataset of drug/target information. Our analysis recapitulated known targets related to CVD (e.g., PPAR?; HMG-CoA reductase, HDACs) and novel targets (e.g., amine oxidase A, ?-opioid receptor). We conclude that combining disease-associated gene sets, drug-transcriptome-responses datasets and drug-target annotations can potentially be useful as a screening tool for diseases that lack an accepted cellular model for in vitro screening. Twit Text FOAVip A computational systems biology approach for identifying candidate drugs for repositioning for cardiovascular disease ????Interdiscip Sci http://www.kidney.de/pget.php?&tw=1&pmid=27778232 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27778232 #FOAvip English Wikipedia <ref>{{Cite pmid|27778232}}</ref> A computational systems biology approach for identifying candidate drugs for repositioning for cardiovascular disease #MMPMID27778232 Yu AZ; Ramsey SA Interdiscip Sci 2018[Jun]; 10 (2): 449-54 PMID27778232show ga We report an in silico method to screen for receptors or pathways that could be targeted to elicit beneficial transcriptional changes in a cellular model of a disease of interest. In our method we integrate: (i) a dataset of transcriptome responses of a cell line to a panel of drugs; (ii) two sets of genes for the disease; and (iii) mappings between drugs and the receptors or pathways that they target. We carried out a Gene Set Enrichment Analysis (GSEA) test for each of the two gene sets against a list of genes ordered by fold-change in response to a drug in a relevant cell line (HL60), with the overall score for a drug being the difference of the two enrichment scores. Next, we applied GSEA for drug targets based on drugs that have been ranked by their differential enrichment scores. The method ranks drugs by the degree of anticorrelation of their gene-level transcriptional effects on the cell line with the genes in the disease gene sets. We applied the method to data from (i) CMap 2.0; (ii) gene sets from two transcriptome profiling studies of atherosclerosis; and (iii) a combined dataset of drug/target information. Our analysis recapitulated known targets related to CVD (e.g., PPAR?; HMG-CoA reductase, HDACs) and novel targets (e.g., amine oxidase A, ?-opioid receptor). We conclude that combining disease-associated gene sets, drug-transcriptome-responses datasets and drug-target annotations can potentially be useful as a screening tool for diseases that lack an accepted cellular model for in vitro screening. äA computational systems biology approach for identifying candidate dru(epmc).pdf DeepDyve Pubget Overpricing