A network clustering algorithm for detection of protein families

• Published in: 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society Vol. 2012; pp. 6329 - 6332
• Main Authors: Xie, Jiang, Wang, Minchao, Dai, Dongbo, Zhang, Huiran, Zhang, Wu
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01.01.2012
• Subjects: Accuracy; Algorithms; Bioinformatics; Cluster Analysis; Clustering algorithms; Educational institutions; Legged locomotion; Markov Chains; Noise; Proteins; Proteins - analysis; Proteins - chemistry
• ISBN: 1424441196; 9781424441198
• ISSN: 1094-687X; 1557-170X
• DOI: 10.1109/EMBC.2012.6347441

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.