title: mirMark: a site-level and UTR-level classifier for  miRNA target prediction.
abstract: MiRNAs play important roles in many diseases including cancers. However computational prediction of miRNA target genes is challenging and the accuracies of existing methods remain poor. We report mirMark, a new machine learning-based method of miRNA target prediction at the site and UTR levels. This method uses experimentally verified miRNA targets from miRecords and mirTarBase as training sets and considers over 700 features. By combining Correlation-based Feature Selection with a variety of statistical or machine learning methods for the site- and UTR-level classifiers, mirMark significantly improves the overall predictive performance compared to existing publicly available methods. MirMark is available from https://github.com/lanagarmire/MirMark.
authors.affiliation: Department of Information and Computer Sciences, University of Hawaii at Manoa, Honolulu, HI 96822, USA.
authors.forename: Mark
authors.initials: M
authors.lastname: Menor
authors.forename: Travers
authors.initials: T
authors.lastname: Ching
authors.forename: Xun
authors.initials: X
authors.lastname: Zhu
authors.forename: David
authors.initials: D
authors.lastname: Garmire
authors.forename: Lana X
authors.initials: LX
authors.lastname: Garmire
mesh_terms: D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods; D035683:MicroRNAs / Q000502:physiology*; D012984:Software; D020506:Untranslated Regions
chemical_list: D035683:MicroRNAs; D020506:Untranslated Regions