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10.1177/1176935117690778 http://scihub22266oqcxt.onion/10.1177/1176935117690778 C5392014!5392014!28469387     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Cancer+Inform 2017 ; 16 (ä): ä Nephropedia Template TP {{tp|p=28469387|t=2017. Integrative Analysis of Gene Networks and Their Application to Lung Adenocarcinoma Studies |pdf=|usr=}}{{28469387}} gab.com Text Integrative Analysis of Gene Networks and Their Application to Lung Adenocarcinoma Studies JO: Cancer Inform http://www.kidney.de/pget.php?&tw=1&pmid=28469387 #FOAvip The construction of gene regulatory networks (GRNs) is an essential component of biomedical research to determine disease mechanisms and identify treatment targets. Gaussian graphical models (GGMs) have been widely used for constructing GRNs by inferring conditional dependence among a set of gene expressions. In practice, GRNs obtained by the analysis of a single data set may not be reliable due to sample limitations. Therefore, it is important to integrate multiple data sets from comparable studies to improve the construction of a GRN. In this article, we introduce an equivalent measure of partial correlation coefficients in GGMs and then extend the method to construct a GRN by combining the equivalent measures from different sources. Furthermore, we develop a method for multiple data sets with a natural missing mechanism to accommodate the differences among different platforms in multiple sources of data. Simulation results show that this integrative analysis outperforms the standard methods and can detect hub genes in the true network. The proposed integrative method was applied to 12 lung adenocarcinoma data sets collected from different studies. The constructed network is consistent with the current biological knowledge and reveals new insights about lung adenocarcinoma. Twit Text FOAVip Integrative Analysis of Gene Networks and Their Application to Lung Adenocarcinoma Studies ????Cancer Inform http://www.kidney.de/pget.php?&tw=1&pmid=28469387 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28469387 #FOAvip English Wikipedia <ref>{{Cite pmid|28469387}}</ref> Integrative Analysis of Gene Networks and Their Application to Lung Adenocarcinoma Studies #MMPMID28469387 Lee S; Liang F; Cai L; Xiao G Cancer Inform 2017[]; 16 (ä): ä PMID28469387show ga The construction of gene regulatory networks (GRNs) is an essential component of biomedical research to determine disease mechanisms and identify treatment targets. Gaussian graphical models (GGMs) have been widely used for constructing GRNs by inferring conditional dependence among a set of gene expressions. In practice, GRNs obtained by the analysis of a single data set may not be reliable due to sample limitations. Therefore, it is important to integrate multiple data sets from comparable studies to improve the construction of a GRN. In this article, we introduce an equivalent measure of partial correlation coefficients in GGMs and then extend the method to construct a GRN by combining the equivalent measures from different sources. Furthermore, we develop a method for multiple data sets with a natural missing mechanism to accommodate the differences among different platforms in multiple sources of data. Simulation results show that this integrative analysis outperforms the standard methods and can detect hub genes in the true network. The proposed integrative method was applied to 12 lung adenocarcinoma data sets collected from different studies. The constructed network is consistent with the current biological knowledge and reveals new insights about lung adenocarcinoma. äIntegrative Analysis of Gene Networks and Their Application to Lung Ad(epmc).pdf DeepDyve Pubget Overpricing