A model of multi-view contrastive hypergraph learning for predicting circRNA-disease associations

In recent years, circRNAs have been found to be closely related to a variety of human diseases. In-depth exploration of their potential associations with diseases is of great value for understanding disease mechanisms and auxiliary diagnosis. To address the association between circRNAs and human diseases, this paper proposes a multi-view contrastive learning model MCHG that integrates attention mechanism and hypergraph structure modeling. The model first constructs a multi-view hypergraph structure based on the circRNA-disease heterogeneous network to capture high-order relationships between nodes. Subsequently, a hypergraph convolutional network is used to extract structured features from different perspectives, and an improved contrastive learning strategy is used to enhance the discriminative ability of feature representation. Furthermore, the model improves the convolutional block attention model, weighting features from two dimensions: channel and space, highlighting key information and suppressing redundant interference. The fused multi-view representations are input into the neural network projection module to achieve efficient prediction of potential associations by reconstructing the circRNA-disease association matrix. Validation experiments on the CircR2Disease dataset show that MCHG achieves an AUC of 0.9394 and an AUPR of 0.9602. Compared with previous models, MCHG can simultaneously model the high-order structural relationships and multi-perspective complementary information in the circRNA-disease heterogeneous network. Furthermore, it improves the quality of feature representation through improved contrastive learning and a two-dimensional attention weighting mechanism, thereby enhancing the accuracy and robustness of potential association prediction. In addition, among the top 25 unknown CDAs predicted by MCHG, 23 were confirmed by relevant literature, indicating that MCHG can effectively predict potential CDAs and provide assistance for further biological wet experiments.