Abstract：The clustering of protein-protein interaction (PPI) network is one of the principal methods to reveal and research the protein function.The traditional clustering methods are inefficient for PPI network due to its special characters. Therefore, a clustering method is proposed based on the optimal search of artificial bee colony (ABC) algorithm and the breadth first traverse (BFT) clustering algorithm. To avoid noisy interference on experimental results, the distance-density algorithm is used to roughly determine the number of clustering in the preprocessing stage. Then, the initial clustering center is determined based on the comprehensive feature value of nodes in the network. The BFT algorithm is used in the clustering process and the improved ABC algorithm is employed to automatically search the optimal merging threshold. Finally, the performance of the proposed algorithm is estimated by precision and recall and some key parameters of the algorithm is analyzed. The experimental results show that the proposed algorithm improves the clustering effect of the PPI network efficiently.
田建芳，雷秀娟. 基于蜂群和广度优先遍历的PPI网络聚类[J]. 模式识别与人工智能, 2012, 25(3): 481-490.
TIAN Jian-Fang, LEI Xiu-Juan. PPI Network Clustering Based on Artificial Bee Colony and Breadth First Traverse Algorithm. , 2012, 25(3): 481-490.
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2012, Vol. 25 
