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10.1093/bioinformatics/btu272 http://scihub22266oqcxt.onion/10.1093/bioinformatics/btu272 C4058945!4058945!24932007     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=24932007&cmd=llinks): Failed to open stream: HTTP request failed! 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Robust clinical outcome prediction based on Bayesian analysis of transcriptional profiles and prior causal networks |pdf=|usr=}}{{24932007}} gab.com Text Robust clinical outcome prediction based on Bayesian analysis of transcriptional profiles and prior causal networks JO: Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=24932007 #FOAvip Motivation: Understanding and predicting an individual?s response in a clinical trial is the key to better treatments and cost-effective medicine. Over the coming years, more and more large-scale omics datasets will become available to characterize patients with complex and heterogeneous diseases at a molecular level. Unfortunately, genetic, phenotypical and environmental variation is much higher in a human trial population than currently modeled or measured in most animal studies. In our experience, this high variability can lead to failure of trained predictors in independent studies and undermines the credibility and utility of promising high-dimensional datasets.Methods: We propose a method that utilizes patient-level genome-wide expression data in conjunction with causal networks based on prior knowledge. Our approach determines a differential expression profile for each patient and uses a Bayesian approach to infer corresponding upstream regulators. These regulators and their corresponding posterior probabilities of activity are used in a regularized regression framework to predict response.Results: We validated our approach using two clinically relevant phenotypes, namely acute rejection in kidney transplantation and response to Infliximab in ulcerative colitis. To demonstrate pitfalls in translating trained predictors across independent trials, we analyze performance characteristics of our approach as well as alternative feature sets in the regression on two independent datasets for each phenotype. We show that the proposed approach is able to successfully incorporate causal prior knowledge to give robust performance estimates.Contact:daniel.ziemek@pfizer.comSupplementary information:Supplementary data are available at Bioinformatics online. Twit Text FOAVip Robust clinical outcome prediction based on Bayesian analysis of transcriptional profiles and prior causal networks ????Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=24932007 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=24932007 #FOAvip English Wikipedia <ref>{{Cite pmid|24932007}}</ref> Robust clinical outcome prediction based on Bayesian analysis of transcriptional profiles and prior causal networks #MMPMID24932007 Zarringhalam K; Enayetallah A; Reddy P; Ziemek D Bioinformatics 2014[Jun]; 30 (12): i69-77 PMID24932007show ga Motivation: Understanding and predicting an individual?s response in a clinical trial is the key to better treatments and cost-effective medicine. Over the coming years, more and more large-scale omics datasets will become available to characterize patients with complex and heterogeneous diseases at a molecular level. Unfortunately, genetic, phenotypical and environmental variation is much higher in a human trial population than currently modeled or measured in most animal studies. In our experience, this high variability can lead to failure of trained predictors in independent studies and undermines the credibility and utility of promising high-dimensional datasets.Methods: We propose a method that utilizes patient-level genome-wide expression data in conjunction with causal networks based on prior knowledge. Our approach determines a differential expression profile for each patient and uses a Bayesian approach to infer corresponding upstream regulators. These regulators and their corresponding posterior probabilities of activity are used in a regularized regression framework to predict response.Results: We validated our approach using two clinically relevant phenotypes, namely acute rejection in kidney transplantation and response to Infliximab in ulcerative colitis. To demonstrate pitfalls in translating trained predictors across independent trials, we analyze performance characteristics of our approach as well as alternative feature sets in the regression on two independent datasets for each phenotype. We show that the proposed approach is able to successfully incorporate causal prior knowledge to give robust performance estimates.Contact:daniel.ziemek@pfizer.comSupplementary information:Supplementary data are available at Bioinformatics online. äRobust clinical outcome prediction based on Bayesian analysis of trans(epmc).pdf DeepDyve Pubget Overpricing