Format

Send to

Choose Destination
Genome Biol. 2014;15(10):500.

mirMark: a site-level and UTR-level classifier for miRNA target prediction.

Author information

1
Department of Information and Computer Sciences, University of Hawaii at Manoa, Honolulu, HI 96822, USA.

Abstract

MiRNAs play important roles in many diseases including cancers. However computational prediction of miRNA target genes is challenging and the accuracies of existing methods remain poor. We report mirMark, a new machine learning-based method of miRNA target prediction at the site and UTR levels. This method uses experimentally verified miRNA targets from miRecords and mirTarBase as training sets and considers over 700 features. By combining Correlation-based Feature Selection with a variety of statistical or machine learning methods for the site- and UTR-level classifiers, mirMark significantly improves the overall predictive performance compared to existing publicly available methods. MirMark is available from https://github.com/lanagarmire/MirMark.

PMID:
25344330
PMCID:
PMC4243195
DOI:
10.1186/s13059-014-0500-5
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for BioMed Central Icon for PubMed Central
Loading ...
Support Center