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

Brief Bioinform. 2005 Dec;6(4):331-43. doi: 10.1093/bib/6.4.331.

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.

Publication types

  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Algorithms*
  • Artificial Intelligence*
  • Bayes Theorem
  • Cluster Analysis*
  • Gene Expression Profiling / methods*
  • Models, Genetic
  • Models, Statistical
  • Oligonucleotide Array Sequence Analysis / methods*
  • Pattern Recognition, Automated / methods*