Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites

Genome Biol. 2010;11(8):R90. doi: 10.1186/gb-2010-11-8-r90. Epub 2010 Aug 27.

Abstract

mirSVR is a new machine learning method for ranking microRNA target sites by a down-regulation score. The algorithm trains a regression model on sequence and contextual features extracted from miRanda-predicted target sites. In a large-scale evaluation, miRanda-mirSVR is competitive with other target prediction methods in identifying target genes and predicting the extent of their downregulation at the mRNA or protein levels. Importantly, the method identifies a significant number of experimentally determined non-canonical and non-conserved sites.

Publication types

  • Evaluation Study
  • Research Support, N.I.H., Extramural

MeSH terms

  • Algorithms
  • Artificial Intelligence*
  • Binding Sites
  • Down-Regulation / genetics
  • MicroRNAs / genetics*
  • MicroRNAs / pharmacology
  • Models, Molecular
  • Proteins / genetics
  • RNA, Messenger / genetics
  • Regression Analysis

Substances

  • MicroRNAs
  • Proteins
  • RNA, Messenger