title: Unsupervised pattern recognition: an introduction to the whys and wherefores of clustering microarray data.
abstract: 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.
authors.affiliation: Department of Medical Biophysics, University of Toronto, Ontario, Canada M5S 1A8. Paul.Boutros@utoronto.ca
authors.forename: Paul C
authors.initials: PC
authors.lastname: Boutros
authors.forename: Allan B
authors.initials: AB
authors.lastname: Okey
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D016000:Cluster Analysis; D020869:Gene Expression Profiling / Q000379:methods*; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*