RPMVCDA: Random Perturbation and Multi-View Graph Convolutional Networks for CircRNA-Disease Association Prediction

Numerous studies have demonstrated the regulatory role of circular RNA (circRNA) in various diseases, emphasizing the importance of identifying disease-related circRNAs. Although several computational models have been developed to predict circRNA-disease associations, the limited number of experimentally validated associations has resulted in the sparse association network. Therefore, there is a need for continuously improving circRNA-disease prediction models. In this study, we propose RPMVCDA, a computational model based on random perturbation and multi-view graph convolutional networks (GCNs), to predict circRNA-disease associations. Specifically, RPMVCDA first constructs multiple similarity networks of circRNAs and diseases, applying multi-view GCNs to obtain embedding representations. Second, to enable message passing between circRNA-disease samples, RPMVCDA constructs the feature similarity association network. Third, RPMVCDA introduces a random perturbation association network to further explore the potential associations, which is the highlight of the RPMVCDA. Finally, based on these three association networks, RPMVCDA utilizes the self-attention mechanism to generate high-quality features for circRNAs and diseases, which are used to calculate association scores. To evaluate the performance of RPMVCDA, five-fold cross-validation and case studies on the CircR2Disease dataset are performed, results of which shows that RPMVCDA outperforms the compared models, implying that it might be an alternative for predicting circRNA-disease associations.