关键词: 人工鱼群算法，蛋白质交互作用网络，加权聚集系数

Abstract: Predicting function of unknown proteins in the protein-protein interaction networks is a hot topic in the bioinformatics. Recently functional flow method has effectively solved the problem of clustering PPI networks. However, the accuracy is relatively low and the time complexity is high. So the PPI networks clustering algorithm combining with the principle of artificial fish school was proposed, which considered an artificial fish as a set of cluster centers. The foragrog behavior was regarded as searching the neighbor nodes of initial cluster centers and adding the nodes into cluster module. Afterwards the set of cluster modules having the highest fitness value was selected as the initial cluster result,which was corresponding to the following behavior of artificial fish school. Then other artificial fish began to execute swarming behavior and judged the similarities between the corresponding cluster modules and initial cluster result. If the similarity was lower than given threshold, the cluster module was added into the initial cluster result The simulation experiment on PPI datascts shows that this algorithm can automatically determine cluster number. In addition, both the accuracy of cluster result and efficiency of algorithm are superior to functional flow algorithm

Key words: Artificial fish school algorithm,Protein-protein interaction networks,Weighted clustering coefficient