TY - GEN
T1 - Ant colony optimization with multi-agent evolution for detecting functional modules in protein-protein interaction networks
AU - Ji, Junzhong
AU - Liu, Zhijun
AU - Zhang, Aidong
AU - Jiao, Lang
AU - Liu, Chunnian
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
KW - Ant Colony Optimization
KW - Functional Module Detection
KW - Multi-agent Evolution
KW - Protein-Protein Interaction Network
UR - https://www.scopus.com/pages/publications/84867648030
U2 - 10.1007/978-3-642-34062-8_58
DO - 10.1007/978-3-642-34062-8_58
M3 - Conference contribution
AN - SCOPUS:84867648030
SN - 9783642340611
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 445
EP - 453
BT - Information Computing and Applications - Third International Conference, ICICA 2012, Proceedings
T2 - 3rd International Conference on Information Computing and Applications, ICICA 2012
Y2 - 14 September 2012 through 16 September 2012
ER -