mirMark: a site-level and UTR-level classifier for miRNA target prediction |
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| Authors: | Mark Menor Travers Ching Xun Zhu David Garmire Lana X Garmire |
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| Affiliation: | .Department of Information and Computer Sciences, University of Hawaii at Manoa, Honolulu, HI 96822 USA ;.Molecular Biosciences and Bioengineering Graduate Program, University of Hawaii at Manoa, Honolulu, HI 96822 USA ;.Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI 96813 USA ;.Department of Electrical Engineering, University of Hawaii at Manoa, Honolulu, HI 96822 USA |
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| 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-014-0500-5) contains supplementary material, which is available to authorized users. |
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