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中文摘要：
由于PPI网络数据的无尺度和小世界特性,使得目前对此类数据的聚类算法效果不理想.根据PPI网络的拓扑结构特性,本文提出了一种基于连接强度的蚁群优化（Joint Strength based Ant Colony Optimization,JSACO）聚类算法,该算法引入了连接强度的概念对蚁群聚类算法中的拾起/放下规则加以改进,以连接强度作为拾起规则,对结点进行聚类,并根据放下规则放弃部分不良数据,产生最终聚类结果.最后采用了MIPS数据库中的PPI数据进行实验,将JSACO算法与PPI网络数据的其他聚类算法进行比较,聚类结果表明JSACO算法正确率高,时间开销低.
英文摘要：
Due to the sale-free and small-world characters of Protein-Protein Interaction（PPI） network data,current clustering algorithms did not perform well.According to the topological structural characters of PPI networks,this paper proposed an ant colony optimization clustering algorithm based on joint strength（JSACO）.This method modified the pickup/drop rules of ACO algorithm by means of introducing the concept of joint strength,which regarded the joint strength as pickup rule to cluster the protein nodes.In addition,the protein nodes which had the low joint strength were abandoned in accordance with drop rule and the final clustering result was obtained.Finally the PPI data in MIPS database was used to test the algorithm and the clustering result was compared with other PPI clustering methods.The simulation results show that JSACO algorithm performs better in terms of precision value and consumes less time.
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Protein complex identification through Markov clustering with firefly algorithm on dynamic protein-p
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