GRPGAT: Predicting CircRNA-disease Associations Based on Graph Random Propagation Network and Graph Attention Network

CircRNA as a biomarker has been shown to have an essential effect on the occurrence and prognosis of a wide range of human diseases. Because of the high cost of wet experiments, computational methods are widely used to explore circRNA. However, the performance and robustness of the computational models still need to be further improved. To solve these problems, this paper proposes a novel method based on graph random propagation network and multi-head dynamic graph attention network (GRPGAT) to predict the potential associations between circRNAs and diseases. Firstly, GRPGAT uses centered kernel alignment method to fuse the circRNA similarity kernels and disease similarity kernels. Then the integrated vectors build a heterogeneous graph and are sent to a graph random propagation network. The remaining nodes are fed into a multi-head dynamic attention network for feature extraction. Finally, a four-layer Multilayer Perceptron is used to learn features and gain the prediction scores. Experiments are supported by cirR2Disease, and achieve Area Under Curve (AUC) scores of 0.9636 in 5-fold cross validation. In comparison with the state-of-the-art models, GRPGAT also shows superior performance.