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Open AccessArticle

Identifying Cancer-Specific circRNA–RBP Binding Sites Based on Deep Learning

School of Computer Science, Shaanxi Normal University, Xi’an 710119, China
College of Information Science and Engineering, Guilin University of Technology, Guilin 541004, China
Department of Mechanical Engineering and Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada
Author to whom correspondence should be addressed.
Academic Editors: Leonidas A. Phylactou, Andrie Koutsoulidou and Ramon Eritja
Molecules 2019, 24(22), 4035;
Received: 29 September 2019 / Revised: 25 October 2019 / Accepted: 6 November 2019 / Published: 7 November 2019
(This article belongs to the Special Issue Circulating RNA Molecules: A New Class of Potential Biomarkers)
Circular RNAs (circRNAs) are extensively expressed in cells and tissues, and play crucial roles in human diseases and biological processes. Recent studies have reported that circRNAs could function as RNA binding protein (RBP) sponges, meanwhile RBPs can also be involved in back-splicing. The interaction with RBPs is also considered an important factor for investigating the function of circRNAs. Hence, it is necessary to understand the interaction mechanisms of circRNAs and RBPs, especially in human cancers. Here, we present a novel method based on deep learning to identify cancer-specific circRNA–RBP binding sites (CSCRSites), only using the nucleotide sequences as the input. In CSCRSites, an architecture with multiple convolution layers is utilized to detect the features of the raw circRNA sequence fragments, and further identify the binding sites through a fully connected layer with the softmax output. The experimental results show that CSCRSites outperform the conventional machine learning classifiers and some representative deep learning methods on the benchmark data. In addition, the features learnt by CSCRSites are converted to sequence motifs, some of which can match to human known RNA motifs involved in human diseases, especially cancer. Therefore, as a deep learning-based tool, CSCRSites could significantly contribute to the function analysis of cancer-associated circRNAs. View Full-Text
Keywords: circRNA; RNA binding protein; cancer-specific; convolutional neural network circRNA; RNA binding protein; cancer-specific; convolutional neural network
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Wang, Z.; Lei, X.; Wu, F.-X. Identifying Cancer-Specific circRNA–RBP Binding Sites Based on Deep Learning. Molecules 2019, 24, 4035.

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