  Since microRNAs (miRNAs) play a crucial role in post-transcriptional gene
regulation, miRNA identification is one of the most essential problems in
computational biology. miRNAs are usually short in length ranging between 20
and 23 base pairs. It is thus often difficult to distinguish miRNA-encoding
sequences from other non-coding RNAs and pseudo miRNAs that have a similar
length, and most previous studies have recommended using precursor miRNAs
instead of mature miRNAs for robust detection. A great number of conventional
machine-learning-based classification methods have been proposed, but they
often have the serious disadvantage of requiring manual feature engineering,
and their performance is limited as well. In this paper, we propose a novel
miRNA precursor prediction algorithm, deepMiRGene, based on recurrent neural
networks, specifically long short-term memory networks. deepMiRGene
automatically learns suitable features from the data themselves without manual
feature engineering and constructs a model that can successfully reflect
structural characteristics of precursor miRNAs. For the performance evaluation
of our approach, we have employed several widely used evaluation metrics on
three recent benchmark datasets and verified that deepMiRGene delivered
comparable performance among the current state-of-the-art tools.
