An effective non-parametric method for globally clustering genes from expression profiles.
Clustering is widely used in bioinformatics to find gene correlation patterns. Although many algorithms have been proposed, these are usually confronted with difficulties in meeting the requirements of both automation and high quality. In this paper, we propose a novel algorithm for clustering genes from their expression profiles. The unique features of the proposed algorithm are twofold: it takes into consideration global, rather than local, gene correlation information in clustering processes; and it incorporates clustering quality measurement into the clustering processes to implement non-parametric, automatic and global optimal gene clustering. The evaluation on simulated and real gene data sets demonstrates the effectiveness of the algorithm.
Hou
Jingyu
J
School of Engineering and Information Technology, Deakin University, 221 Burwood Highway, Burwood, VIC 3125, Australia. jingyu@deakin.edu.au
Shi
Wei
W
Li
Gang
G
Zhou
Wanlei
W
D000465:Algorithms; D000818:Animals; D016000:Cluster Analysis; D019295:Computational Biology; D020869:Gene Expression Profiling; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis