Classification of Noncoding RNA Elements Using Deep Convolutional Neural Networks

TL;DR

This paper introduces a novel method that converts ncRNA sequences into images based on their base-pairing probabilities, enabling CNNs to classify ncRNAs effectively, demonstrating superior performance over classical models.

Contribution

The paper presents a new image-based representation of ncRNA sequences for CNN classification and provides a benchmark dataset for future research.

Findings

CNN models outperform classical methods in ncRNA classification

The image-based approach improves classification accuracy

Generated a benchmark dataset from RFAM database

Abstract

The paper proposes to employ deep convolutional neural networks (CNNs) to classify noncoding RNA (ncRNA) sequences. To this end, we first propose an efficient approach to convert the RNA sequences into images characterizing their base-pairing probability. As a result, classifying RNA sequences is converted to an image classification problem that can be efficiently solved by available CNN-based classification models. The paper also considers the folding potential of the ncRNAs in addition to their primary sequence. Based on the proposed approach, a benchmark image classification dataset is generated from the RFAM database of ncRNA sequences. In addition, three classical CNN models have been implemented and compared to demonstrate the superior performance and efficiency of the proposed approach. Extensive experimental results show the great potential of using deep learning approaches for RNA classification.