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10.1155/2017/8520480 http://scihub22266oqcxt.onion/10.1155/2017/8520480 C5405575!5405575!28487748     free     free     free Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Comput+Math+Methods+Med 2017 ; 2017 (ä): ä Nephropedia Template TP {{tp|p=28487748|t=2017. Node-Structured Integrative Gaussian Graphical Model Guided by Pathway Information |pdf=|usr=}}{{28487748}} gab.com Text Node-Structured Integrative Gaussian Graphical Model Guided by Pathway Information JO: Comput Math Methods Med http://www.kidney.de/pget.php?&tw=1&pmid=28487748 #FOAvip Up to date, many biological pathways related to cancer have been extensively applied thanks to outputs of burgeoning biomedical research. This leads to a new technical challenge of exploring and validating biological pathways that can characterize transcriptomic mechanisms across different disease subtypes. In pursuit of accommodating multiple studies, the joint Gaussian graphical model was previously proposed to incorporate nonzero edge effects. However, this model is inevitably dependent on post hoc analysis in order to confirm biological significance. To circumvent this drawback, we attempt not only to combine transcriptomic data but also to embed pathway information, well-ascertained biological evidence as such, into the model. To this end, we propose a novel statistical framework for fitting joint Gaussian graphical model simultaneously with informative pathways consistently expressed across multiple studies. In theory, structured nodes can be prespecified with multiple genes. The optimization rule employs the structured input-output lasso model, in order to estimate a sparse precision matrix constructed by simultaneous effects of multiple studies and structured nodes. With an application to breast cancer data sets, we found that the proposed model is superior in efficiently capturing structures of biological evidence (e.g., pathways). An R software package nsiGGM is publicly available at author's webpage. Twit Text FOAVip Node-Structured Integrative Gaussian Graphical Model Guided by Pathway Information ????Comput Math Methods Med http://www.kidney.de/pget.php?&tw=1&pmid=28487748 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28487748 #FOAvip English Wikipedia <ref>{{Cite pmid|28487748}}</ref> Node-Structured Integrative Gaussian Graphical Model Guided by Pathway Information #MMPMID28487748 Kim S; Jhong JH; Lee J; Koo JY; Lee B; Han S Comput Math Methods Med 2017[]; 2017 (ä): ä PMID28487748show ga Up to date, many biological pathways related to cancer have been extensively applied thanks to outputs of burgeoning biomedical research. This leads to a new technical challenge of exploring and validating biological pathways that can characterize transcriptomic mechanisms across different disease subtypes. In pursuit of accommodating multiple studies, the joint Gaussian graphical model was previously proposed to incorporate nonzero edge effects. However, this model is inevitably dependent on post hoc analysis in order to confirm biological significance. To circumvent this drawback, we attempt not only to combine transcriptomic data but also to embed pathway information, well-ascertained biological evidence as such, into the model. To this end, we propose a novel statistical framework for fitting joint Gaussian graphical model simultaneously with informative pathways consistently expressed across multiple studies. In theory, structured nodes can be prespecified with multiple genes. The optimization rule employs the structured input-output lasso model, in order to estimate a sparse precision matrix constructed by simultaneous effects of multiple studies and structured nodes. With an application to breast cancer data sets, we found that the proposed model is superior in efficiently capturing structures of biological evidence (e.g., pathways). An R software package nsiGGM is publicly available at author's webpage. äNode-Structured Integrative Gaussian Graphical Model Guided by Pathway(epmc).pdf DeepDyve Pubget Overpricing