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

Doron Betel1, Anjali Koppal2, Phaedra Agius1, Chris Sander1 and Christina Leslie1*

Author affiliations

1 Computational Biology Program, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, 10065, NY, USA

2 Department of Computer Science, Columbia University, 1214 Amsterdam Avenue, New York, 10027, NY, USA

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Citation and License

Genome Biology 2010, 11:R90  doi:10.1186/gb-2010-11-8-r90

Published: 27 August 2010

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.