title: miTarget: microRNA target gene prediction using a support vector machine.
abstract: BACKGROUND
  MicroRNAs (miRNAs) are small noncoding RNAs, which play significant roles as posttranscriptional regulators. The functions of animal miRNAs are generally based on complementarity for their 5' components. Although several computational miRNA target-gene prediction methods have been proposed, they still have limitations in revealing actual target genes.
  
  
  RESULTS
  We implemented miTarget, a support vector machine (SVM) classifier for miRNA target gene prediction. It uses a radial basis function kernel as a similarity measure for SVM features, categorized by structural, thermodynamic, and position-based features. The latter features are introduced in this study for the first time and reflect the mechanism of miRNA binding. The SVM classifier produces high performance with a biologically relevant data set obtained from the literature, compared with previous tools. We predicted significant functions for human miR-1, miR-124a, and miR-373 using Gene Ontology (GO) analysis and revealed the importance of pairing at positions 4, 5, and 6 in the 5' region of a miRNA from a feature selection experiment. We also provide a web interface for the program.
  
  
  CONCLUSIONS
  miTarget is a reliable miRNA target gene prediction tool and is a successful application of an SVM classifier. Compared with previous tools, its predictions are meaningful by GO analysis and its performance can be improved given more training examples.
authors.affiliation: Graduate Program in Bioinformatics, Seoul National University, Seoul, Korea. skkim@bi.snu.ac.kr
authors.forename: Sung-Kyu
authors.initials: SK
authors.lastname: Kim
authors.forename: Jin-Wu
authors.initials: JW
authors.lastname: Nam
authors.forename: Je-Keun
authors.initials: JK
authors.lastname: Rhee
authors.forename: Wha-Jin
authors.initials: WJ
authors.lastname: Lee
authors.forename: Byoung-Tak
authors.initials: BT
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001483:Base Sequence; D020868:Gene Silencing; D018390:Gene Targeting / Q000379:methods*; D035683:MicroRNAs / Q000737:chemistry / Q000235:genetics*; D008969:Molecular Sequence Data; D010363:Pattern Recognition, Automated / Q000379:methods*; D016415:Sequence Alignment / Q000379:methods*; D017423:Sequence Analysis, RNA / Q000379:methods*
chemical_list: D035683:MicroRNAs