MVHGCN: Predicting circRNA-disease associations with multi-view heterogeneous graph convolutional neural networks

Circular RNA, a class of RNA molecules gaining widespread attentions, has been widely recognized as a potential biomarker for many diseases. In recent years, significant progress has been made in the study of the associations between circRNA and diseases. However, traditional experimental methods are often inefficient and costly, making computational models an effective alternative. Nevertheless, existing computational methods still face challenges such as data sparsity and the difficulty of confirming negative samples, which limits the accuracy of predictions. To address these challenges, a novel computational method, namely MVHGCN, is proposed based on multi-view and graph convolutional networks to predict potential associations between circRNA and diseases. MVHGCN first constructs a heterogeneous graph and generates feature descriptors by integrating multiple databases. Then it extracts different connection views of circRNA and diseases through meta-paths, maximizing the utilization of known association information, and aggregates deep feature information through graph convolutional networks. Finally, a MLP is used to predict the association scores. The experimental results show that MVHGCN significantly outperforms existing methods on benchmark datasets by 5-fold cross-validation. This research provides an effective new approach to studying the associations between circRNAs and diseases, capable of alleviating the problem of data sparsity and accurately identifying potential associations.Author summary: Circular RNA has garnered significant attention due to its unique structure and potential role in regulating gene expression. It can interact with microRNAs, preventing the degradation of messenger RNAs, and influencing a network of competing RNAs. However, traditional experimental methods are often inefficient and costly, making computational models an effective alternative. Nevertheless, existing computational methods still face challenges such as data sparsity and the difficulty of confirming negative samples, which limits the accuracy of predictions. To address these issues, I propose a novel method called MVHGCN, which leverages heterogeneous graphs and graph convolutional networks to predict associations between circRNAs and diseases. This approach integrates diverse data views and uses deep learning to provide accurate predictions. When compared to existing methods, MVHGCN significantly outperforms them, demonstrating its potential to advance disease research and the development of therapeutic targets. The results underscore the importance of improving prediction models in the study of circRNA-disease relationships, ultimately contributing to more effective disease diagnosis and treatment strategies.