Classification of Noncoding RNA Elements Using Deep Convolutional Neural Networks

• URL: http://arxiv.org/abs/2008.10580v1
• Date: Mon, 24 Aug 2020 17:43:50 GMT
• Title: Classification of Noncoding RNA Elements Using Deep Convolutional Neural Networks
• Authors: Brian McClannahan, Krushi Patel, Usman Sajid, Cuncong Zhong, Guanghui Wang
• Abstract summary: The paper proposes to employ deep convolutional neural networks (CNNs) to classify noncoding RNA (ncRNA) sequences. 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.
• Score: 16.650383406063956
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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