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

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.