title: Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites.
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.
authors.affiliation: Computational Biology Program, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, 10065, NY, USA. betel@cbio.mskcc.org
authors.forename: Doron
authors.initials: D
authors.lastname: Betel
authors.forename: Anjali
authors.initials: A
authors.lastname: Koppal
authors.forename: Phaedra
authors.initials: P
authors.lastname: Agius
authors.forename: Chris
authors.initials: C
authors.lastname: Sander
authors.forename: Christina
authors.initials: C
authors.lastname: Leslie
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001665:Binding Sites; D015536:Down-Regulation / Q000235:genetics; D035683:MicroRNAs / Q000235:genetics* / Q000494:pharmacology; D008958:Models, Molecular; D011506:Proteins / Q000235:genetics; D012333:RNA, Messenger / Q000235:genetics; D012044:Regression Analysis
chemical_list: D035683:MicroRNAs; D011506:Proteins; D012333:RNA, Messenger
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