A network clustering algorithm for detection of protein families.
Detection of protein families in large scale database is a difficult but important biological problem. Computational clustering methods can effectively address the problem. Although there exist many clustering algorithms, most of them are just based on the threshold. Their computational performances are affected by the weight distribution greatly, and they are only valid for some special networks. A new network clustering algorithm, Markov Finding and Clustering (MFC), is proposed to cluster the proteins into their functionally specific families accurately in this paper. The MFC algorithm makes an improvement in the random walk process and reduces the affection of the noise on the clustering result. It has a good performance on these networks which are not well addressed by existing algorithms sensitive to the noise. Finally, experiments on the protein sequence datasets demonstrate that the algorithm is effective in the detection of protein families and has a better performance than the current algorithms.
School of Computer Engineering and Science, Shanghai University, Shanghai 200072, China.
Jiang
J
Xie
Minchao
M
Wang
Dongbo
D
Dai
Huiran
H
Zhang
Wu
W
Zhang
D000465:Algorithms; D016000:Cluster Analysis; D008390:Markov Chains; D011506:Proteins / Q000032:analysis* / Q000737:chemistry
D011506:Proteins