deepTarget: End-to-end Learning Framework for microRNA Target Prediction using Deep Recurrent Neural Networks

TL;DR

deepTarget introduces an end-to-end deep learning framework utilizing recurrent neural networks for miRNA target prediction, significantly improving accuracy and eliminating manual feature extraction.

Contribution

It presents a novel deep RNN-based auto-encoding approach for miRNA target prediction, surpassing existing methods by over 25% in F-measure.

Findings

Over 25% increase in F-measure compared to existing tools

Eliminates manual feature extraction in miRNA target prediction

Achieves high accuracy with end-to-end deep learning

Abstract

MicroRNAs (miRNAs) are short sequences of ribonucleic acids that control the expression of target messenger RNAs (mRNAs) by binding them. Robust prediction of miRNA-mRNA pairs is of utmost importance in deciphering gene regulations but has been challenging because of high false positive rates, despite a deluge of computational tools that normally require laborious manual feature extraction. This paper presents an end-to-end machine learning framework for miRNA target prediction. Leveraged by deep recurrent neural networks-based auto-encoding and sequence-sequence interaction learning, our approach not only delivers an unprecedented level of accuracy but also eliminates the need for manual feature extraction. The performance gap between the proposed method and existing alternatives is substantial (over 25% increase in F-measure), and deepTarget delivers a quantum leap in the long-standing challenge of robust miRNA target prediction.