4.4 Article

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

期刊

GENOME BIOLOGY
卷 11, 期 8, 页码 -

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BMC
DOI: 10.1186/gb-2010-11-8-r90

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  1. NIH [PO1GM073047, 1U24CA143840]
  2. NATIONAL CANCER INSTITUTE [U24CA143840] Funding Source: NIH RePORTER
  3. NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES [P01GM073047] Funding Source: NIH RePORTER

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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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