Improved Ant Colony Optimization for Detecting Functional Modules in Protein-Protein Interaction Networks

Mining functional modules in a Protein-Protein Interaction (PPI) network contributes greatly to the understanding of biological mechanism, where how to effectively detect functional modules in a PPI network has a significant application. As a meta-heuristic and stochastic search technology, the Ant Colony Optimization (ACO) algorithm has been one of the effective tools for solving discrete optimization problems. In this paper, we propose a new method based on the ACO algorithm for detecting functional modules in a PPI network, which combines topological characteristics with functional information. First, a new heuristic function is introduced to conduct ants searching effectively in constructing solutions. Second, a set of new strategies of partitioning, merging and filtering are adopted to form the final functional modules. Finally, we present experimental results on the benchmark testing set of yeast networks. Our experiments show that our approach is more effective compared to several other existing detection techniques.