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Briefings in Bioinformatics 2005 6(4):331-343; doi:10.1093/bib/6.4.331
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© Henry Stewart Publications

Unsupervised pattern recognition: An introduction to the whys and wherefores of clustering microarray data

Paul C. Boutros
A PhD student in the Department of Medical Biophysics, at the University of Toronto, Canada.

Allan B. Okey
A professor in the Department of Pharmacology, at the University of Toronto, Canada.


Paul C. Boutros, Department of Pharmacology, University of Toronto, 4302 Medical Sciences Building, 1 King's College Circle, Toronto, Ontario, Canada M5S 1A8 Tel: +1 416 978 2207 Fax: +1 416 978 6395 E-mail: Paul.Boutros{at}utoronto.ca

Clustering has become an integral part of microarray data analysis and interpretation. The algorithmic basis of clustering – the application of unsupervised machine-learning techniques to identify the patterns inherent in a data set – is well established. This review discusses the biological motivations for and applications of these techniques to integrating gene expression data with other biological information, such as functional annotation, promoter data and proteomic data.

Keywords: pattern recognition, clustering, microarray, systems biology, data integration, co-regulation


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