deepMiRGene: Deep Neural Network based Precursor microRNA Prediction

TL;DR

deepMiRGene is a novel deep learning approach using LSTM networks for automatic precursor microRNA prediction, outperforming traditional methods that rely on manual feature engineering.

Contribution

It introduces a deep neural network model that automatically learns features for miRNA precursor prediction, eliminating the need for manual feature engineering.

Findings

Achieved comparable performance to state-of-the-art tools on benchmark datasets.

Automatically learns structural features of precursor miRNAs.

Demonstrates robustness across multiple evaluation metrics.

Abstract

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.