BFO-FMD: bacterial foraging optimization for functional module detection in protein-protein interaction networks

Identifying functional modules in PPI networks contributes greatly to the understanding of cellular functions and mechanisms. Recently, the swarm intelligence-based approaches have become effective ways for detecting functional modules in PPI networks. This paper presents a new computational approach based on bacterial foraging optimization for functional module detection in PPI networks (called BFO-FMD). In BFO-FMD, each bacterium represents a candidate module partition encoded as a directed graph, which is first initialized by a random-walk behavior according to the topological and functional information between protein nodes. Then, BFO-FMD utilizes four principal biological mechanisms, chemotaxis, conjugation, reproduction, and elimination and dispersal to search for better protein module partitions. To verify the performance of BFO-FMD, we compared it with several other typical methods on three common yeast datasets. The experimental results demonstrate the excellent performances of BFO-FMD in terms of various evaluation metrics. BFO-FMD achieves outstanding Recall, F-measure, and PPV while performing very well in terms of other metrics. Thus, it can accurately predict protein modules and help biologists to find some novel biological insights.