Enhanced framework for miRNA target prediction

MicroRNAs are small non-coding RNA molecules that play an important role in post-transcriptional gene regulation. They bind with messenger RNAs, affecting the regulation of gene expression causing diseases such as heart diseases and cancer. The two pioneer strategies of miRNA target prediction are either experimentally or computationally. Since experimental techniques are very expensive and slow, several computational tools have been proposed to defeat some of the experimental technical difficulties. In this paper, we propose an enhanced computational framework that predicts miRNA targets depending on structural, positional, and thermodynamic features extracted from the alignment of miRNA and its targets. Then, we select the linearly separable features using the eigenvalue analysis of the covariance matrix of the features. Finally, the selected features are applied to random forest classifier. The obtained results prove that our framework significantly excels other existing tools.