  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.
