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 regulation 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.



Byunghan Lee, Junghwan Baek, Seunghyun Park, and Sungroh Yoon*, "deepTarget: End-to-end Learning Framework for microRNA Target Prediction using Deep Recurrent Neural Networks," in Proceedings of the 7th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB), Seattle, USA, October 2016.


deepTarget is currently compatible with: theano=0.7.0, keras=0.3.3, scikit-learn=0.17.