  MicroRNA (miRNA) are small non-coding RNAs that regulates the gene expression
at the post-transcriptional level. Determining whether a sequence segment is
miRNA is experimentally challenging. Also, experimental results are sensitive
to the experimental environment. These limitations inspire the development of
computational methods for predicting the miRNAs. We propose a deep learning
based classification model, called DP-miRNA, for predicting precursor miRNA
sequence that contains the miRNA sequence. The feature set based Restricted
Boltzmann Machine method, which we call DP-miRNA, uses 58 features that are
categorized into four groups: sequence features, folding measures, stem-loop
features and statistical feature. We evaluate the performance of the DP-miRNA
on eleven twelve data sets of varying species, including the human. The deep
neural network based classification outperformed support vector machine, neural
network, naive Baye's classifiers, k-nearest neighbors, random forests, and a
hybrid system combining support vector machine and genetic algorithm.
