Approaches to working in high-dimensional data spaces: Gene expression microarrays

Y. Wang, D. J. Miller, R. Clarke

Research output: Contribution to journalShort surveypeer-review

45 Scopus citations

Abstract

This review provides a focused summary of the implications of high-dimensional data spaces produced by gene expression microarrays for building better models of cancer diagnosis, prognosis, and therapeutics. We identify the unique challenges posed by high dimensionality to highlight methodological problems and discuss recent methods in predictive classification, unsupervised subclass discovery, and marker identification.

Original languageEnglish (US)
Pages (from-to)1023-1028
Number of pages6
JournalBritish Journal of Cancer
Volume98
Issue number6
DOIs
StatePublished - Mar 25 2008
Externally publishedYes

Bibliographical note

Funding Information:
This work was supported in part by the US National Institutes of Health under Grants CA109872, CA096483 and EB000830, and the US Department of Defense award BC030280.

Keywords

  • Data modelling and analysis
  • Gene expression profiling
  • High dimensionality
  • Microarray

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