Menor Mark, Ching Travers, Zhu Xun, Garmire David, Garmire Lana X
Department of Information and Computer Sciences, University of Hawaii at Manoa, Honolulu, HI 96822, USA.
Genome Biol. 2014;15(10):500. doi: 10.1186/s13059-014-0500-5.
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.
微小RNA（miRNAs）在包括癌症在内的许多疾病中发挥着重要作用。然而，对miRNA靶基因进行计算预测具有挑战性，现有方法的准确性仍然很低。我们报告了mirMark，这是一种基于机器学习的新方法，用于在位点和非翻译区（UTR）水平上预测miRNA靶标。该方法使用来自miRecords和mirTarBase的经过实验验证的miRNA靶标作为训练集，并考虑了700多个特征。通过将基于相关性的特征选择与用于位点和UTR水平分类器的各种统计或机器学习方法相结合，与现有的公开可用方法相比，mirMark显著提高了整体预测性能。可从https://github.com/lanagarmire/MirMark获取MirMark。
