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10.1186/s12859-018-2063-z http://scihub22266oqcxt.onion/10.1186/s12859-018-2063-z C5872517!5872517!29589555     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       BMC+Bioinformatics 2018 ; 19 (Suppl 3): ä Nephropedia Template TP {{tp|p=29589555|t=2018. Bayesian graphical models for computational network biology |pdf=|usr=}}{{29589555}} gab.com Text Bayesian graphical models for computational network biology JO: BMC Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=29589555 #FOAvip Background: Computational network biology is an emerging interdisciplinary research area. Among many other network approaches, probabilistic graphical models provide a comprehensive probabilistic characterization of interaction patterns between molecules and the associated uncertainties. Results: In this article, we first review graphical models, including directed, undirected, and reciprocal graphs (RG), with an emphasis on the RG models that are curiously under-utilized in biostatistics and bioinformatics literature. RG?s strictly contain chain graphs as a special case and are suitable to model reciprocal causality such as feedback mechanism in molecular networks. We then extend the RG approach to modeling molecular networks by integrating DNA-, RNA- and protein-level data. We apply the extended RG method to The Cancer Genome Atlas multi-platform ovarian cancer data and reveal several interesting findings. Conclusions: This study aims to review the basics of different probabilistic graphical models as well as recent development in RG approaches for network modeling. The extension presented in this paper provides a principled and efficient way of integrating DNA copy number, DNA methylation, mRNA gene expression and protein expression. Twit Text FOAVip Bayesian graphical models for computational network biology ????BMC Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=29589555 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=29589555 #FOAvip English Wikipedia <ref>{{Cite pmid|29589555}}</ref> Bayesian graphical models for computational network biology #MMPMID29589555 Ni Y; Müller P; Wei L; Ji Y BMC Bioinformatics 2018[]; 19 (Suppl 3): ä PMID29589555show ga Background: Computational network biology is an emerging interdisciplinary research area. Among many other network approaches, probabilistic graphical models provide a comprehensive probabilistic characterization of interaction patterns between molecules and the associated uncertainties. Results: In this article, we first review graphical models, including directed, undirected, and reciprocal graphs (RG), with an emphasis on the RG models that are curiously under-utilized in biostatistics and bioinformatics literature. RG?s strictly contain chain graphs as a special case and are suitable to model reciprocal causality such as feedback mechanism in molecular networks. We then extend the RG approach to modeling molecular networks by integrating DNA-, RNA- and protein-level data. We apply the extended RG method to The Cancer Genome Atlas multi-platform ovarian cancer data and reveal several interesting findings. Conclusions: This study aims to review the basics of different probabilistic graphical models as well as recent development in RG approaches for network modeling. The extension presented in this paper provides a principled and efficient way of integrating DNA copy number, DNA methylation, mRNA gene expression and protein expression. äBayesian graphical models for computational network biology (epmc).pdf DeepDyve Pubget Overpricing