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 .
Highlights
MicroRNA is one type of non-coding RNA that regulates gene expression posttranscriptionally [1]
This study aims to improve the predictive performance for miRNA target prediction at both the site and UTR level by considering an extensive list of over 700 predictive features and using the latest collection of experimentally verified miRNA target data
Structure of mirMark Most of the identified locations of miRNA targets in the miRNA target database miRecords [23] are in the 3′ UTR region of the messenger RNAs (mRNAs) due to historical reason
Summary
MicroRNA (miRNA or miR) is one type of non-coding RNA (ncRNA) that regulates gene expression posttranscriptionally [1]. Using the miRNA sequence as a guide, this miRISC binds to messenger RNAs (mRNAs) to degrade targeted mRNAs or inhibit translation from mRNAs to proteins [2]. There have been over 1,000 annotated miRNAs in humans, and due to the potential to target multiple mRNAs by each miRNA, it is speculated that as much as 60% of mammalian genes are affected by miRNAs [3,4]. Abnormal changes in miRNA expression can cause dysregulation of important biological pathways, and are involved in many diseases such as cancers and cardiovascular disease [5,6,7]. Determination of the target mRNAs of the variety of miRNAs will help understand the development of these diseases
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