A Dynamics-GCN Hybrid Framework for Feature Learning in Disease-Related Association Prediction

Disease-related association prediction is a crucial task in the biomedical field, aiming to identify relations between diseases and various biological entities such as RNAs (like circRNAs, lncRNAs), drugs and genes. Understanding these interactions not only deepens our understanding of pathological mechanisms, but also facilitates the development of novel diagnostic tools, therapeutic strategies, and preventive measures. Current challenges in disease-related association prediction primarily encompass data sparsity, data heterogeneity, limited generalization ability, and the absence of a unified analytical framework. To address the above issues, we propose a hybrid framework integrating dynamics mechanisms and graph convolutional networks in hyperbolic space for disease-related association prediction. Our approach begins by constructing a heterogeneous network using interaction information to represent multiple types of biological associations. This network is then processed through a game-guided dynamics mechanism that incorporates both individual node features and other influences. The hyperbolic graph convolutional network is then designed to model hierarchical and scale-free graph-structured data. Comprehensive experimental results on multiple types of associations demonstrate that our model achieves high predictive performance. The results of the case study validate the robust predictive capability of our proposed method in the prediction of disease-related associations.