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

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

Author information

1
Computational Biology Program, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, 10065, NY, USA. betel@cbio.mskcc.org

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.

PMID:
20799968
PMCID:
PMC2945792
DOI:
10.1186/gb-2010-11-8-r90
[Indexed for MEDLINE]
Free PMC Article

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