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10.1093/bioinformatics/btv315 http://scihub22266oqcxt.onion/10.1093/bioinformatics/btv315 C4668779!4668779!25995230     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       Bioinformatics 2015 ; 31 (18): 3008-15 Nephropedia Template TP {{tp|p=25995230|t=2015. Trans-species learning of cellular signaling systems with bimodal deep belief networks |pdf=|usr=}}{{25995230}} gab.com Text Trans-species learning of cellular signaling systems with bimodal deep belief networks JO: Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=25995230 #FOAvip Motivation: Model organisms play critical roles in biomedical research of human diseases and drug development. An imperative task is to translate information/knowledge acquired from model organisms to humans. In this study, we address a trans-species learning problem: predicting human cell responses to diverse stimuli, based on the responses of rat cells treated with the same stimuli.Results: We hypothesized that rat and human cells share a common signal-encoding mechanism but employ different proteins to transmit signals, and we developed a bimodal deep belief network and a semi-restricted bimodal deep belief network to represent the common encoding mechanism and perform trans-species learning. These ?deep learning? models include hierarchically organized latent variables capable of capturing the statistical structures in the observed proteomic data in a distributed fashion. The results show that the models significantly outperform two current state-of-the-art classification algorithms. Our study demonstrated the potential of using deep hierarchical models to simulate cellular signaling systems.Availability and implementation: The software is available at the following URL: http://pubreview.dbmi.pitt.edu/TransSpeciesDeepLearning/. The data are available through SBV IMPROVER website, https://www.sbvimprover.com/challenge-2/overview, upon publication of the report by the organizers.Contact: xinghua@pitt.eduSupplementary information:Supplementary data are available at Bioinformatics online. Twit Text FOAVip Trans-species learning of cellular signaling systems with bimodal deep belief networks ????Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=25995230 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=25995230 #FOAvip English Wikipedia <ref>{{Cite pmid|25995230}}</ref> Trans-species learning of cellular signaling systems with bimodal deep belief networks #MMPMID25995230 Chen L; Cai C; Chen V; Lu X Bioinformatics 2015[Sep]; 31 (18): 3008-15 PMID25995230show ga Motivation: Model organisms play critical roles in biomedical research of human diseases and drug development. An imperative task is to translate information/knowledge acquired from model organisms to humans. In this study, we address a trans-species learning problem: predicting human cell responses to diverse stimuli, based on the responses of rat cells treated with the same stimuli.Results: We hypothesized that rat and human cells share a common signal-encoding mechanism but employ different proteins to transmit signals, and we developed a bimodal deep belief network and a semi-restricted bimodal deep belief network to represent the common encoding mechanism and perform trans-species learning. These ?deep learning? models include hierarchically organized latent variables capable of capturing the statistical structures in the observed proteomic data in a distributed fashion. The results show that the models significantly outperform two current state-of-the-art classification algorithms. Our study demonstrated the potential of using deep hierarchical models to simulate cellular signaling systems.Availability and implementation: The software is available at the following URL: http://pubreview.dbmi.pitt.edu/TransSpeciesDeepLearning/. The data are available through SBV IMPROVER website, https://www.sbvimprover.com/challenge-2/overview, upon publication of the report by the organizers.Contact: xinghua@pitt.eduSupplementary information:Supplementary data are available at Bioinformatics online. äTrans-species learning of cellular signaling systems with bimodal deep(epmc).pdf DeepDyve Pubget Overpricing