MiRNA-Disease Association Prediction via Cosine Annealing and Multi-Head Self-Attention in HyperGCN

Biological studies have demonstrated that understanding the association between miRNAs and disease is critical for disease prevention, assessment, and therapy. However, traditional experimental methods for inferring these connections are not only costly but also inefficient. Hence, there is a pressing need to develop novel methods to improve the accuracy and efficiency of forecasting. Currently, graph convolutional networks (GCNs) techniques are one of the mainstream methods for predicting disease correlations. Nevertheless, traditional GCNs suffer from gradient vanishing and gradient explosion problems when dealing with long-range dependencies. To overcome these problems, we suggest a new approach called HGCMMDA, which relies on HyperGCN and combines a cosine annealing algorithm and a multi-head self-attention mechanism. In HGCMMDA, similarity networks for miRNAs and diseases are constructed, and GCN is used for feature extraction. A heterogeneity hypergraph is then built via HyperGCN for improved information propagation. Multi-head self-attention captures diverse node relations, while cosine annealing adjusts the learning rate. A combined BCE-Dice loss ensures accurate prediction. To evaluate the effectiveness of the proposed method, a comprehensive set of experiments was conducted using the Human microRNA Disease Database (HMDD v3.2). The method achieved a peak area under the receiver operating characteristic curve (AUC) of 0.9515, along with competitive performance in other evaluation metrics. The experimental findings indicate that HGCMMDA achieves notable enhancements over previously established approaches. These results strongly support the assertion that HGCMMDA serves as a dependable and effective framework for comprehensively exploring the intricate associations between microRNAs and human diseases.