Deep Neural Network Based Precursor microRNA Prediction on Eleven Species

TL;DR

This paper introduces DP-miRNA, a deep learning model that predicts precursor microRNA sequences across eleven species, outperforming traditional classifiers and aiding in understanding miRNA regulation.

Contribution

The study develops a novel deep neural network model using 58 features for precursor miRNA prediction across multiple species, demonstrating superior performance over existing classifiers.

Findings

DP-miRNA outperforms SVM, neural network, naive Bayes, k-NN, random forests, and hybrid models.

The model effectively predicts precursor miRNAs in eleven species, including humans.

Deep learning enhances miRNA prediction accuracy over traditional methods.

Abstract

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.