Ant colony optimization with multi-agent evolution for detecting functional modules in protein-protein interaction networks

Authors:
Junzhong Ji;Zhijun Liu;Aidong Zhang;Lang Jiao;Chunnian Liu
Affiliations:
College of Computer Science and Technology, Beijing University of Technology, Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, Beijing, China;College of Computer Science and Technology, Beijing University of Technology, Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, Beijing, China;Department of Computer Science and Engineering, University at Buffalo, The State University of New York, Buffalo;College of Computer Science and Technology, Beijing University of Technology, Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, Beijing, China;College of Computer Science and Technology, Beijing University of Technology, Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, Beijing, China
Venue:
ICICA'12 Proceedings of the Third international conference on Information Computing and Applications
Year:
2012

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Abstract

Functional module identification in a Protein-Protein Interaction (PPI) network is one of the most important and challenging tasks in computational biology. For detecting functional modules, it is difficult to solve the problem directly and always results in a low accuracy and a large discard rate. In this paper, we present a novel algorithm of ant colony optimization with multi-agent evolution for detecting functional modules. The proposed ACO-MAE algorithm enhances the performance of ant colony optimization (ACO) by incorporating multi-agent evolution (MAE). First, the ant colony optimization for solving Traveling Salesman Problems (TSP) is conducted to construct primary clustering results. Then, the multi-agent evolutionary process is performed to move out of local optima. From simulation results, it is shown that the proposed ACO-MAE algorithm has superior performance when compared to other existing algorithms.