HAM-FMD: Mining functional modules in protein-protein interaction networks using ant colony optimization and multi-agent evolution

Mining functional modules in a protein-protein interaction (PPI) network contributes greatly to the understanding of biological mechanism, thus how to effectively detect functional modules in a PPI network has a significant application. In this paper, we present a hybrid approach using ant colony optimization and multi-agent evolution for detection functional modules in PPI networks. The proposed algorithm enhances the performance of ant colony optimization by incorporating multi-agent evolution for detecting functional modules. In the ant colony optimization process, a new heuristic, which merges topological characteristics with functional information function, is introduced to effectively conduct ants searching in finding optimal results. Thereafter, the multi-agent evolutionary process based on an energy function is performed to move out of local optima and obtain some enclosed connecting subgraphs which represent functional modules mined in a PPI network. Finally, systematic experiments have been conducted on four benchmark testing sets of yeast networks. Experimental results show that the hybrid approach is more effective compared to several other existing algorithms.