Biomedical data mining plays a crucial role in studying diseases, with disease-gene identification being one of the most prominent areas of research in this field. Many biomolecular networks are known to have multi-scale module structures, which may be helpful for studying complex diseases, but the mining and utilization of multi-scale module structures is an open issue. Therefore, we present a kind of novel hybrid network-based method (HyMSMK) for disease-gene identification through incorporating multi-scale module kernel in biomolecule networks. We first apply exponential sampling to construct a multi-scale module profile containing local to global structural information, where modules at different scales are extracted from comprehensive interactome by multi-scale modularity optimization. Then, the multi-scale module profile is preprocessed by the relative information content, and is used to generate multi-scale module kernel, which is further preprocessed by kernel sparsification. We design multiple schemes for incorporating multi-scale module kernel to discover potential disease-related genes. We investigate the performance of these schemes by experimental evaluations, show the positive effect of kernel sparsification on reducing the requirement for space and time, and confirm the superior performance of our method compared to other state-of-the-art network-based baselines. The study demonstrates the utility of multi-scale module kernel in discovering disease genes, which can provide insights for the research of relevant issues.

Drug repositioning has been widely applied to explore new therapeutic applications for existing drugs, significantly reducing the transition time from laboratory research to clinical application. However, most existing models rely on static training over complex and large-scale networks, lacking detailed analysis and specificity for individual drug-disease pairs. To address this limitation, we propose the subgraph-aware graph attention network (SAGAN), which constructs subgraphs centered on target drug-disease pairs by extracting their surrounding interactions. Within each extracted subgraph, SAGAN employs an attention mechanism to focus on critical interactions among directly associated nodes within the subgraph, while simultaneously capturing cross-level relational patterns. A hierarchical pooling technique is then applied to aggregate the nodes and edges within the subgraph into more compact representations. Additionally, SAGAN integrates neighborhood features and interaction information from drug and disease similarity networks to enhance the expressive power of subgraph features. Finally, the method predicts drug-disease associations as a graph classification task. On three widely used benchmark datasets, SAGAN demonstrates outstanding performance, achieving an average area under the receiver operating characteristic curve (AUROC) of 0.9648 and an average area under the precision-recall curve (AUPR) of 0.9678, showcasing its robustness in handling sparse and imbalanced data. Furthermore, case studies validate the practical utility of SAGAN in predicting potential effective drugs for Alzheimer’s disease and breast cancer.