CircRNA-Disease Association Prediction based on Heterogeneous Graph Representation

Circular RNAs (circRNAs) have important effects on various biological processes, and their dysfunction is closely related to the emergence and development of diseases. Identifying the associations between circRNAs and diseases is helpful in analyzing the pathogenesis of diseases. Therefore, it is necessary to develop effective computational methods for predicting circRNA-disease associations. Here, we present a computational model called HRCDA to predict associations between circRNA and disease based on heterogeneous graph representation. Firstly, an integrated network of circRNA functional similarity is built by Random Walk with Restart in the view of biological functions of circRNA. Then, a heterogeneous graph of circRNAs and diseases is constructed with known circRNA-disease associations. Finally, we design a heterogeneous graph representation learn model based on Graph Auto-Encoder (GAE) to predict circRNA-disease associations. Experiments have shown that the proposed method perform better than existing state-of-the-art methods and can be an effective tool to predict potential disease-related circRNAs.