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

Abstract

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.

Fig 1. The overall workflow of MVHGCN.

Fig 2. The ROC curves and results of MVHGCN obtained through 5-fold cross-validation on the three datasets.

(a) ROC curves, (b) PR curves, (c) Box plots.

Fig 3. The ROC curves of MVHGCN compared with six benchmark methods on the three datasets.

Fig 4. Performance comparison of different view construction methods on the three datasets.

Fig 5. Performance comparison of different loss functions on the three datasets.

Fig 6. Performance comparison of different GNNs on the three datasets.

Fig 7. The ROC curves of MVHGCN with different GNNs on the three datasets.

Fig 8. The impact of temperature and dimensionality on AUC values across the three datasets.