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10.1016/j.jbi.2016.01.012 http://scihub22266oqcxt.onion/10.1016/j.jbi.2016.01.012 C4886234!4886234 !26827624     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\26827624 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       J+Biomed+Inform 2016 ; 60 (ä): 114-9 Nephropedia Template TP {{tp|p=26827624 |t=2016. Generating a robust statistical causal structure over 13 cardiovascular disease risk factors using genomics data |pdf=|usr=}}{{26827624 }} gab.com Text Generating a robust statistical causal structure over 13 cardiovascular disease risk factors using genomics data JO: J Biomed Inform http://www.kidney.de/pget.php?&tw=1&pmid=26827624 #FOAvip Understanding causal relationships among large numbers of variables is a fundamental goal of biomedical sciences and can be facilitated by Directed Acyclic Graphs (DAGs) where directed edges between nodes represent the influence of components of the system on each other. In an observational setting, some of the directions are often unidentifiable because of Markov equivalency. Additional exogenous information, such as expert knowledge or genotype data can help establish directionality among the endogenous variables. In this study, we use the method of principle component analysis to extract information across the genome in order to generate a robust statistical causal network among phenotypes, the variables of primary interest. The method is applied to 590,020 SNP genotypes measured on 1596 individuals to generate the statistical causal network of 13 cardiovascular disease risk factor phenotypes. First, principal component analysis was used to capture information across the genome. The principal components were then used to identify a robust causal network structure, GDAG, among the phenotypes. Analyzing a robust causal network over risk factors reveals the flow of information in direct and alternative paths, as well as determining predictors and good targets for intervention. For example, the analysis identified BMI as influencing multiple other risk factor phenotypes and a good target for intervention to lower disease risk. Twit Text FOAVip Generating a robust statistical causal structure over 13 cardiovascular disease risk factors using genomics data ????J Biomed Inform http://www.kidney.de/pget.php?&tw=1&pmid=26827624 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26827624 #FOAvip English Wikipedia <ref>{{Cite pmid|26827624 }}</ref> Generating a robust statistical causal structure over 13 cardiovascular disease risk factors using genomics data #MMPMID26827624 Yazdani A ; Yazdani A ; Samiei A ; Boerwinkle E J Biomed Inform 2016[Apr]; 60 (ä): 114-9 PMID26827624 show ga Understanding causal relationships among large numbers of variables is a fundamental goal of biomedical sciences and can be facilitated by Directed Acyclic Graphs (DAGs) where directed edges between nodes represent the influence of components of the system on each other. In an observational setting, some of the directions are often unidentifiable because of Markov equivalency. Additional exogenous information, such as expert knowledge or genotype data can help establish directionality among the endogenous variables. In this study, we use the method of principle component analysis to extract information across the genome in order to generate a robust statistical causal network among phenotypes, the variables of primary interest. The method is applied to 590,020 SNP genotypes measured on 1596 individuals to generate the statistical causal network of 13 cardiovascular disease risk factor phenotypes. First, principal component analysis was used to capture information across the genome. The principal components were then used to identify a robust causal network structure, GDAG, among the phenotypes. Analyzing a robust causal network over risk factors reveals the flow of information in direct and alternative paths, as well as determining predictors and good targets for intervention. For example, the analysis identified BMI as influencing multiple other risk factor phenotypes and a good target for intervention to lower disease risk. |*Genomics [MESH] |*Medical Informatics [MESH] |*Models, Statistical [MESH] |Algorithms [MESH] |Cardiovascular Diseases/*genetics [MESH] |Genotype [MESH] |Humans [MESH] |Phenotype [MESH] |Polymorphism, Single Nucleotide [MESH] |Principal Component Analysis [MESH]Generating a robust statistical causal structure over 13 cardiovascula(epmc).pdf DeepDyve Pubget Overpricing