HomoTarget: A new algorithm for prediction of microRNA targets in Homo sapiens

MiRNAs play an essential role in the networks of gene regulation by inhibiting the translation of target mRNAs. Several computational approaches have been proposed for the prediction of miRNA target-genes. Reports reveal a large fraction of under-predicted or falsely predicted target genes. Thus, there is an imperative need to develop a computational method by which the target mRNAs of existing miRNAs can be correctly identified. In this study, combined pattern recognition neural network (PRNN) and principle component analysis (PCA) architecture has been proposed in order to model the complicated relationship between miRNAs and their target mRNAs in humans. The results of several types of intelligent classifiers and our proposed model were compared, showing that our algorithm outperformed them with higher sensitivity and specificity. Using the recent release of the mirBase database to find potential targets of miRNAs, this model incorporated twelve structural, thermodynamic and positional features of miRNA:mRNA binding sites to select target candidates.

► We need to develop a computational method to detect target mRNAs of existing miRNAs. ► We used a combined PRNN + PCA architecture to identify target mRNAs in humans. ► Our method outperformed other algorithm with 97.4% sensitivity and 99.6% specificity. ► Model merges structural, thermodynamic and positional features of miRNA:mRNA binding ► HomoTarget predicts the targets by combining biological and computational features.