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10.1093/bioinformatics/btv392 http://scihub22266oqcxt.onion/10.1093/bioinformatics/btv392 C4804770!4804770!26130578     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Bioinformatics 2015 ; 31 (21): 3529-36 Nephropedia Template TP {{tp|p=26130578|t=2015. Integrating full spectrum of sequence features into predicting functional microRNA?mRNA interactions |pdf=|usr=}}{{26130578}} gab.com Text Integrating full spectrum of sequence features into predicting functional microRNA mRNA interactions JO: Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=26130578 #FOAvip Motivation: MicroRNAs (miRNAs) play important roles in general biological processes and diseases pathogenesis. Identifying miRNA target genes is an essential step to fully understand the regulatory effects of miRNAs. Many computational methods based on the sequence complementary rules and the miRNA and mRNA expression profiles have been developed for this purpose. It is noted that there have been many sequence features of miRNA targets available, including the context features of the target sites, the thermodynamic stability and the accessibility energy for miRNA-mRNA interaction. However, most of current computational methods that combine sequence and expression information do not effectively integrate full spectrum of these features; instead, they perceive putative miRNA?mRNA interactions from sequence-based prediction as equally meaningful. Therefore, these sequence features have not been fully utilized for improving miRNA target prediction.Results: We propose a novel regularized regression approach that is based on the adaptive Lasso procedure for detecting functional miRNA?mRNA interactions. Our method fully takes into account the gene sequence features and the miRNA and mRNA expression profiles. Given a set of sequence features for each putative miRNA?mRNA interaction and their expression values, our model quantifies the down-regulation effect of each miRNA on its targets while simultaneously estimating the contribution of each sequence feature to predicting functional miRNA?mRNA interactions. By applying our model to the expression datasets from two cancer studies, we have demonstrated our prediction results have achieved better sensitivity and specificity and are more biologically meaningful compared with those based on other methods.Availability and implementation: The source code is available at: http://nba.uth.tmc.edu/homepage/liu/miRNALasso.Supplementary information:Supplementary data are available at Bioinformatics online.Contact:Yin.Liu@uth.tmc.edu Twit Text FOAVip Integrating full spectrum of sequence features into predicting functional microRNA mRNA interactions ????Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=26130578 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26130578 #FOAvip English Wikipedia <ref>{{Cite pmid|26130578}}</ref> Integrating full spectrum of sequence features into predicting functional microRNA?mRNA interactions #MMPMID26130578 Wang Z; Xu W; Liu Y Bioinformatics 2015[Nov]; 31 (21): 3529-36 PMID26130578show ga Motivation: MicroRNAs (miRNAs) play important roles in general biological processes and diseases pathogenesis. Identifying miRNA target genes is an essential step to fully understand the regulatory effects of miRNAs. Many computational methods based on the sequence complementary rules and the miRNA and mRNA expression profiles have been developed for this purpose. It is noted that there have been many sequence features of miRNA targets available, including the context features of the target sites, the thermodynamic stability and the accessibility energy for miRNA-mRNA interaction. However, most of current computational methods that combine sequence and expression information do not effectively integrate full spectrum of these features; instead, they perceive putative miRNA?mRNA interactions from sequence-based prediction as equally meaningful. Therefore, these sequence features have not been fully utilized for improving miRNA target prediction.Results: We propose a novel regularized regression approach that is based on the adaptive Lasso procedure for detecting functional miRNA?mRNA interactions. Our method fully takes into account the gene sequence features and the miRNA and mRNA expression profiles. Given a set of sequence features for each putative miRNA?mRNA interaction and their expression values, our model quantifies the down-regulation effect of each miRNA on its targets while simultaneously estimating the contribution of each sequence feature to predicting functional miRNA?mRNA interactions. By applying our model to the expression datasets from two cancer studies, we have demonstrated our prediction results have achieved better sensitivity and specificity and are more biologically meaningful compared with those based on other methods.Availability and implementation: The source code is available at: http://nba.uth.tmc.edu/homepage/liu/miRNALasso.Supplementary information:Supplementary data are available at Bioinformatics online.Contact:Yin.Liu@uth.tmc.edu äIntegrating full spectrum of sequence features into predicting functio(epmc).pdf DeepDyve Pubget Overpricing