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10.1038/s41598-017-15235-6 http://scihub22266oqcxt.onion/10.1038/s41598-017-15235-6 C5670180!5670180!29101378     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       Sci+Rep 2017 ; 7 (ä): ä Nephropedia Template TP {{tp|p=29101378|t=2017. A deep ensemble model to predict miRNA-disease association |pdf=|usr=}}{{29101378}} gab.com Text A deep ensemble model to predict miRNA-disease association JO: Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=29101378 #FOAvip Cumulative evidence from biological experiments has confirmed that microRNAs (miRNAs) are related to many types of human diseases through different biological processes. It is anticipated that precise miRNA-disease association prediction could not only help infer potential disease-related miRNA but also boost human diagnosis and disease prevention. Considering the limitations of previous computational models, a more effective computational model needs to be implemented to predict miRNA-disease associations. In this work, we first constructed a human miRNA-miRNA similarity network utilizing miRNA-miRNA functional similarity data and heterogeneous miRNA Gaussian interaction profile kernel similarities based on the assumption that similar miRNAs with similar functions tend to be associated with similar diseases, and vice versa. Then, we constructed disease-disease similarity using disease semantic information and heterogeneous disease-related interaction data. We proposed a deep ensemble model called DeepMDA that extracts high-level features from similarity information using stacked autoencoders and then predicts miRNA-disease associations by adopting a 3-layer neural network. In addition to five-fold cross-validation, we also proposed another cross-validation method to evaluate the performance of the model. The results show that the proposed model is superior to previous methods with high robustness. Twit Text FOAVip A deep ensemble model to predict miRNA-disease association ????Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=29101378 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=29101378 #FOAvip English Wikipedia <ref>{{Cite pmid|29101378}}</ref> A deep ensemble model to predict miRNA-disease association #MMPMID29101378 Fu L; Peng Q Sci Rep 2017[]; 7 (ä): ä PMID29101378show ga Cumulative evidence from biological experiments has confirmed that microRNAs (miRNAs) are related to many types of human diseases through different biological processes. It is anticipated that precise miRNA-disease association prediction could not only help infer potential disease-related miRNA but also boost human diagnosis and disease prevention. Considering the limitations of previous computational models, a more effective computational model needs to be implemented to predict miRNA-disease associations. In this work, we first constructed a human miRNA-miRNA similarity network utilizing miRNA-miRNA functional similarity data and heterogeneous miRNA Gaussian interaction profile kernel similarities based on the assumption that similar miRNAs with similar functions tend to be associated with similar diseases, and vice versa. Then, we constructed disease-disease similarity using disease semantic information and heterogeneous disease-related interaction data. We proposed a deep ensemble model called DeepMDA that extracts high-level features from similarity information using stacked autoencoders and then predicts miRNA-disease associations by adopting a 3-layer neural network. In addition to five-fold cross-validation, we also proposed another cross-validation method to evaluate the performance of the model. The results show that the proposed model is superior to previous methods with high robustness. äA deep ensemble model to predict miRNA-disease association (epmc).pdf DeepDyve Pubget Overpricing