Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites
Memorial Sloan Kettering Cancer Center · Columbia University
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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.
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Keywords
- Biology
- Computational biology
- Human genetics
- microRNA
- Genome Biology
- Evolutionary biology
- Genetics
- Conserved sequence
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