Classification of Noncoding RNA Families using Deep Convolutional Neural Networks

Abstract

In the last decade, the discovery of noncoding RNA (ncRNA) has exploded. Classifying thesencRNA is critical to determining their function. This thesis proposes a new method employing deep convolutional neural networks (CNNs) to classify ncRNA sequences. To this end, this thesis first proposes 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. This thesis 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 and three Siamese network 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.