Heterogeneous and Similarity Network Data

Precise prediction of potential drug-disease associations (DDAs) is essential for enhancing treatment strategies and expediting drug development. However, current methods often rely on single-modal data and fail to effectively integrate multimodal information when representing node attributes. Furthermore, many feature extraction processes neglect the integration of node attribute features with topological features.To address these limitations, we propose MedPathEx, a method that integrates multimodal data fusion with metapath feature extraction techniques. First, we constructed a biomedical heterogeneous network comprising three entities—drugs, genes, and diseases—along with their interrelationships. By incorporating multimodal data, we generated similarity networks for the nodes within this heterogeneous network. We then used a graph convolutional network to extract the node attribute features from these similarity networks. Simultaneously, meta-paths enhanced with a multi-head attention mechanism capture local topological features from the heterogeneous network, whereas a global attention mechanism further refines global topological features, enabling a seamless fusion of local and global features. Finally, MedPathEx effectively combined these node attribute features with network structural features to create a comprehensive feature representation that was used to calculate the potential association probabilities between drugs and diseases.The experimental results indicate that MedPathEx surpasses current methods in terms of critical metrics, including AUC, AP, and F1 scores. MedPathEx effectively identified novel candidate drugs in case studies of coronary artery disease and hypertension, underscoring its substantial potential for practical applications.