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An effective non-parametric method for globally clustering genes from expression profiles

Hou, Jingyu, Shi, Wei, Li, Gang and Zhou, Wanlei 2007, An effective non-parametric method for globally clustering genes from expression profiles, Medical and biological engineering and computing, vol. 45, no. 12, pp. 1175-1185.

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Title An effective non-parametric method for globally clustering genes from expression profiles
Author(s) Hou, Jingyu
Shi, Wei
Li, Gang
Zhou, Wanlei
Journal name Medical and biological engineering and computing
Volume number 45
Issue number 12
Start page 1175
End page 1185
Publisher Springer
Place of publication Berlin, Germany
Publication date 2007-12
ISSN 0140-0118
1741-0444
1741-0800
Keyword(s) bioinformatics
microarray
gene expression
clustering
data mining
Summary 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.

Notes Published online: 18 October 2007. The original publication can be found at www.springerlink.com
Language eng
Field of Research 080301 Bioinformatics Software
080109 Pattern Recognition and Data Mining
Socio Economic Objective 970106 Expanding Knowledge in the Biological Sciences
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2007, International Federation for Medical and Biological Engineering
Persistent URL http://hdl.handle.net/10536/DRO/DU:30007586

Document type: Journal Article
Collections: School of Engineering and Information Technology
Open Access Collection
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