Heterogeneous network impulsive dynamics for identifying disease-associated genes

Identifying disease-associated genes (DAGs) is important for the research of complex diseases, and network-based methods have been a powerful and elegant strategy for this topic. Genes and their products perform biological functions through synergy in biological networks, but mining useful information from the networks remains an open issue. Therefore, we propose a novel heterogeneous network impulsive dynamics model to identify DAGs more effectively. It inspires a heterogeneous network impulsive dynamical process by imposing impulsive signals at specific nodes, in an enhanced dual-layer heterogeneous network. Then, it extracts the impulsive dynamical signatures of responses of nodes to the impulsive signals so as to infer DAGs. A series of experiments confirm that this model has good performance of inferring DAGs under different conditions, and case studies further demonstrate its effectiveness. Furthermore, a user-friendly web platform is provided to facilitate prioritization and analysis of DAGs. It may become a useful tool for studying complex diseases and relevant genes.