Incorporating multi-scale module kernel for disease-gene identification in biological networks

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 biomolecule 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 structure 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 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-art network-based baselines. The study demonstrates the utility of multi-scale module kernel in discovering disease genes, which could provide insights for the research of relevant issues.