Fireworks algorithm for functional module detection in protein-protein interaction networks

To solve the problem that the swarm intelligence clustering methods are time-consuming in detecting functional modules in protein-protein interaction networks, this paper proposes a method based on fireworks algorithm for functional module detection in protein-protein interaction networks (FWA-FMD). First, each firework individual was initialized as a candidate solution based on the label propagation idea by combining the topological and functional information. Then in each generation of evolution, each firework individual was optimized by using explosion operation with local search and global search self-adjustment capabilities, and the next generation of fireworks individuals were selected by using elite retention and roulette strategy. Finally, the nodes with the same label in the optimal firework were divided into the same function module to obtain the final function module detection result. Functional module detection results on the four protein-protein interaction network datasets of Saccharomyces cerevisiae and Homo sapiens were evaluated by using two standard functional module datasets as benchmarks, which shows that the FWA-FMD algorithm not only costs less time than GA-PPI, ACC-FMD, and BFO-FMD, but also has obvious advantages in many evaluation indicators compared with some representative algorithms, which can better identify functional modules. © 2019, Editorial Board of Journal of Harbin Institute of Technology. All right reserved.