Fusion of multiple heterogeneous networks for predicting circRNA-disease associations

Circular RNAs (circRNAs) are a newly identified type of non-coding RNA (ncRNA) that plays crucial roles in many cellular processes and human diseases, and are potential disease biomarkers and therapeutic targets in human diseases. However, experimentally verified circRNA-disease associations are very rare. Hence, developing an accurate and efficient method to predict the association between circRNA and disease may be beneficial to disease prevention, diagnosis, and treatment. Here, we propose a computational method named KATZCPDA, which is based on the KATZ method and the integrations among circRNAs, proteins, and diseases to predict circRNA-disease associations. KATZCPDA not only verifies existing circRNA-disease associations but also predicts unknown associations. As demonstrated by leave-one-out and 10-fold cross-validation, KATZCPDA achieves AUC values of 0.959 and 0.958, respectively. The performance of KATZCPDA was substantially higher than those of previously developed network-based methods. To further demonstrate the effectiveness of KATZCPDA, we apply KATZCPDA to predict the associated circRNAs of Colorectal cancer, glioma, breast cancer, and Tuberculosis. The results illustrated that the predicted circRNA-disease associations could rank the top 10 of the experimentally verified associations.