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

• Published in: Briefings in bioinformatics Vol. 6; no. 4; pp. 331 - 343
• Main Authors: Boutros, P. C., Okey, A. B.
• Format: Journal Article
• Language: English
• Published: Oxford Oxford University Press 01.12.2005; Oxford Publishing Limited (England)
• Subjects: Algorithms; Artificial Intelligence; Bayes Theorem; Bioinformatics; Biological and medical sciences; Cluster Analysis; Data analysis; Fundamental and applied biological sciences. Psychology; Gene expression; Gene Expression Profiling - methods; General aspects; Genomics; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects); Medical research; Models, Genetic; Models, Statistical; Nitric oxide; Oligonucleotide Array Sequence Analysis - methods; Pattern recognition; Pattern Recognition, Automated - methods; Proteins; Studies; systems biology; Gene cluster; Coexpression; DNA chip; Biology; Pattern recognition; Microarray; System; co-regulation; Original document; Regulation(control); Classification; Network; clustering; Transcription factor; Bioinformatics; Learning algorithm; data integration
• ISSN: 1467-5463; 1477-4054; 1477-4054
• DOI: 10.1093/bib/6.4.331

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.