TargetNet: Functional microRNA Target Prediction with Deep Neural Networks

TL;DR

TargetNet is a deep learning model that improves microRNA target prediction by using relaxed criteria, novel encoding, and residual networks, outperforming previous methods in identifying functional targets.

Contribution

The paper introduces TargetNet, a deep neural network with new encoding and relaxed criteria, advancing miRNA target prediction accuracy over existing algorithms.

Findings

Outperforms previous miRNA target prediction algorithms.

Effectively distinguishes high-functional miRNA targets.

Uses relaxed criteria to accommodate irregular seed regions.

Abstract

Motivation: MicroRNAs (miRNAs) play pivotal roles in gene expression regulation by binding to target sites of messenger RNAs (mRNAs). While identifying functional targets of miRNAs is of utmost importance, their prediction remains a great challenge. Previous computational algorithms have major limitations. They use conservative candidate target site (CTS) selection criteria mainly focusing on canonical site types, rely on laborious and time-consuming manual feature extraction, and do not fully capitalize on the information underlying miRNA-CTS interactions. Results: In this paper, we introduce TargetNet, a novel deep learning-based algorithm for functional miRNA target prediction. To address the limitations of previous approaches, TargetNet has three key components: (1) relaxed CTS selection criteria accommodating irregularities in the seed region, (2) a novel miRNA-CTS sequence encoding scheme incorporating extended seed region alignments, and (3) a deep residual network-based prediction model. The proposed model was trained with miRNA-CTS pair datasets and evaluated with miRNA-mRNA pair datasets. TargetNet advances the previous state-of-the-art algorithms used in functional miRNA target classification. Furthermore, it demonstrates great potential for distinguishing high-functional miRNA targets.