Title: GANCDA: a novel method for predicting circRNA-disease associations based on deep generative adversarial network

Authors: Xin Yan; Lei Wang; Zhu-Hong You; Li-Ping Li; Kai Zheng

Addresses: School of Foreign Languages, Zaozhuang University, Zaozhuang 277100, Shandong, China ' College of Information Science and Engineering, Zaozhuang University, Zaozhuang 277100, Shandong, China; Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, Beijing, China ' Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, Beijing, China ' Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, Beijing, China ' School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China

Abstract: Circular RNA (circRNA) plays a key regulatory role in life activities. Recognising the association between circRNA and disease is of great significance for the study of disease mechanism. However, traditional experimental methods for identifying the association between circular RNA and disease are usually extremely blind and time consuming. Therefore, the method based on intelligent computing is needed to effectively predict the potential circRNA-disease association and narrow the identification range for biological experiments. In this paper, we propose a model GANCDA based on multi-source similar information and deep Generative Adversarial Network (GAN) to predict disease associated circRNA. The fivefold cross-validation of GANCDA on the circR2Disease dataset achieved 90.6% AUC, 89.2% accuracy and 89.4% precision. Moreover, GANCDA prediction results are also supported by biological experiments. These excellent results show that GANCDA can accurately predict the potential circRNA-disease association and can be used as an effective assistant tool for biological experiments.

Keywords: circular RNA; diseases; CircRNA-disease association; generative adversarial network; logistic model tree.

DOI: 10.1504/IJDMB.2020.107880

International Journal of Data Mining and Bioinformatics, 2020 Vol.23 No.3, pp.265 - 283

Received: 12 Apr 2020
Accepted: 13 Apr 2020
Published online: 26 Jun 2020 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article