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10.1093/bioinformatics/btu277 http://scihub22266oqcxt.onion/10.1093/bioinformatics/btu277 C4058935!4058935!24931975     free     free     free Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\24931975.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Bioinformatics 2014 ; 30 (12): i121-9 Nephropedia Template TP {{tp|p=24931975|t=2014. Deep learning of the tissue-regulated splicing code |pdf=|usr=}}{{24931975}} gab.com Text Deep learning of the tissue-regulated splicing code JO: Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=24931975 #FOAvip Motivation: Alternative splicing (AS) is a regulated process that directs the generation of different transcripts from single genes. A computational model that can accurately predict splicing patterns based on genomic features and cellular context is highly desirable, both in understanding this widespread phenomenon, and in exploring the effects of genetic variations on AS.Methods: Using a deep neural network, we developed a model inferred from mouse RNA-Seq data that can predict splicing patterns in individual tissues and differences in splicing patterns across tissues. Our architecture uses hidden variables that jointly represent features in genomic sequences and tissue types when making predictions. A graphics processing unit was used to greatly reduce the training time of our models with millions of parameters.Results: We show that the deep architecture surpasses the performance of the previous Bayesian method for predicting AS patterns. With the proper optimization procedure and selection of hyperparameters, we demonstrate that deep architectures can be beneficial, even with a moderately sparse dataset. An analysis of what the model has learned in terms of the genomic features is presented.Contact:frey@psi.toronto.eduSupplementary information:Supplementary data are available at Bioinformatics online. Twit Text FOAVip Deep learning of the tissue-regulated splicing code ????Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=24931975 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=24931975 #FOAvip English Wikipedia <ref>{{Cite pmid|24931975}}</ref> Deep learning of the tissue-regulated splicing code #MMPMID24931975 Leung MKK; Xiong HY; Lee LJ; Frey BJ Bioinformatics 2014[Jun]; 30 (12): i121-9 PMID24931975show ga Motivation: Alternative splicing (AS) is a regulated process that directs the generation of different transcripts from single genes. A computational model that can accurately predict splicing patterns based on genomic features and cellular context is highly desirable, both in understanding this widespread phenomenon, and in exploring the effects of genetic variations on AS.Methods: Using a deep neural network, we developed a model inferred from mouse RNA-Seq data that can predict splicing patterns in individual tissues and differences in splicing patterns across tissues. Our architecture uses hidden variables that jointly represent features in genomic sequences and tissue types when making predictions. A graphics processing unit was used to greatly reduce the training time of our models with millions of parameters.Results: We show that the deep architecture surpasses the performance of the previous Bayesian method for predicting AS patterns. With the proper optimization procedure and selection of hyperparameters, we demonstrate that deep architectures can be beneficial, even with a moderately sparse dataset. An analysis of what the model has learned in terms of the genomic features is presented.Contact:frey@psi.toronto.eduSupplementary information:Supplementary data are available at Bioinformatics online. äDeep learning of the tissue-regulated splicing code (epmc).pdf DeepDyve Pubget Overpricing