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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.

SUBMITTER: Betel D 

PROVIDER: S-EPMC2945792 | biostudies-literature | 2010

REPOSITORIES: biostudies-literature

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

Betel Doron D   Koppal Anjali A   Agius Phaedra P   Sander Chris C   Leslie Christina C  

Genome biology 20100827 8


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 experimenta  ...[more]

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