title: Integrating full spectrum of sequence features into predicting functional microRNA-mRNA interactions.
abstract: BACKGROUND
  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.
  
  
  METHODS
  The source code is available at: http://nba.uth.tmc.edu/homepage/liu/miRNALasso.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
  
  
  BACKGROUND
  Yin.Liu@uth.tmc.edu.
authors.affiliation: Department of Neurobiology and Anatomy, University of Texas Health Science Center at Houston and.
authors.forename: Zixing
authors.initials: Z
authors.lastname: Wang
authors.affiliation: Department of Neurobiology and Anatomy, University of Texas Health Science Center at Houston and.
authors.forename: Wenlong
authors.initials: W
authors.lastname: Xu
authors.affiliation: Department of Neurobiology and Anatomy, University of Texas Health Science Center at Houston and University of Texas Graduate School of Biomedical Sciences, Houston, Texas, USA.
authors.forename: Yin
authors.initials: Y
authors.lastname: Liu
mesh_terms: D015536:Down-Regulation; D020869:Gene Expression Profiling; D006801:Humans; D035683:MicroRNAs / Q000378:metabolism*; D008957:Models, Genetic; D009369:Neoplasms / Q000235:genetics / Q000378:metabolism; D012333:RNA, Messenger / Q000737:chemistry / Q000378:metabolism*; D012680:Sensitivity and Specificity; D017423:Sequence Analysis, RNA
chemical_list: D035683:MicroRNAs; D012333:RNA, Messenger