Inferring Candidate CircRNA-Disease Associations by Bi-random Walk Based on CircRNA Regulatory Similarity

Identification of associations between circular RNAs (circRNA) and diseases has become a hot topic, which is beneficial for researchers to understand the disease mechanism. However, traditional biological experiments are expensive and time-consuming. In this study, we proposed a novel method named BWHCDA, which applied bi-random walk algorithm on the heterogeneous network for predicting circRNA-disease associations. First, circRNA regulatory similarity is measured based on circRNA-miRNA interactions, and circRNA similarity is calculated by the average of circRNA regulatory similarity and Gaussian interaction profiles (GIP) kernel similarity for circRNAs. Similarly, disease similarity is the mean of disease semantic similarity and GIP kernel similarity for diseases. Then, the heterogeneous network is constructed by integrating circRNA network, disease network via circRNA-disease associations. Subsequently, the bi-random walk algorithm is implemented on the heterogeneous network to predict circRNA-disease associations. Finally, we utilize leave-one-out cross validation and 10-fold cross validation frameworks to evaluate the prediction performance of BWHCDA method and obtain AUC of 0.9334 and 0.8764 ± 0.0038, respectively. Moreover, the predicted hsa_circ_0000519-gastric cancer association is analyzed. Results show that BWHCDA could be an effective resource for clinical experimental guidance.