An ensemble approach for CircRNA-disease association prediction based on autoencoder and deep neural network

Circular RNAs (circRNA) are a special kind of covalently closed single-stranded RNA molecules. They have been shown to control and coordinate various biological processes. Recent researches show that circRNAs are closely associated with numerous chronic human diseases. Identification of circRNA-disease associations will contribute towards diagnosing the pathogenesis of diseases. Experimental methods for finding the relation between the diseases and their causal circRNAs are difficult and time-consuming. So computational methods are of critical need for predicting the associations between circRNAs and various human diseases. In this study, we propose an ensemble approach AE-DNN, which relies on autoencoder and deep neural networks to predict new circRNA-disease relationships. We utilized circRNA sequence similarity, disease semantic similarity, and Gaussian interaction profile kernel similarities of circRNAs and diseases for feature construction. The constructed features are fed to a deep autoencoder, and the extracted compact, high-level features are fed to the deep neural network for association prediction. We conducted 5-fold and 10-fold cross-validation experiments to assess the performance; AE-DNN could achieve AUC scores of 0.9392 and 0.9431, respectively. Experimental results and case studies indicate the robustness of our model in circRNA-disease association prediction.