HyMM: Hybrid method for disease-gene prediction by integrating multiscale module structures

AbstractMotivationIdentifying disease-related genes is important for the study of human complex diseases. Module structures or community structures are ubiquitous in biological networks. Although the modular nature of human diseases can provide useful insights, the mining of information hidden in multiscale module structures has received less attention in disease-gene prediction.ResultsWe propose a hybrid method, HyMM, to predict disease-related genes more effectively by integrating the information from multiscale module structures. HyMM consists of three key steps: extraction of multiscale modules, gene rankings based on multiscale modules and integration of multiple gene rankings. The statistical analysis of multiscale modules extracted by three multiscale-module-decomposition algorithms (MO, AS and HC) shows that the functional consistency of the modules gradually improves as the resolution increases. This suggests the existence of different levels of functional relationships in the multiscale modules, which may help reveal disease-gene associations. We display the effectiveness of multiscale module information in the disease-gene prediction and confirm the excellent performance of HyMM by 5-fold cross-validation and independent test. Specifically, HyMM with MO can more effectively enhance the ability of disease-gene prediction; HyMM (MO, RWR) and HyMM (MO, RWRH) are especially preferred due to their excellent comprehensive performance, and HyMM (AS, RWRH) is also good choice due to its local performance. We anticipate that this work could provide useful insights for disease-module analysis and disease-gene prediction based on multi-scale module structures.Availabilityhttps://github.com/xiangiu0208/HvMMContactlimin@mail.csu.edu.cnSupplementary informationSupplementary data are available atBioinformaticsonline.