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

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

doi:10.1038/sj.bjc.6604207

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

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

Research output: Contribution to journal › Short survey › peer-review

55 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 language	English (US)
Pages (from-to)	1023-1028
Number of pages	6
Journal	British Journal of Cancer
Volume	98
Issue number	6
DOIs	https://doi.org/10.1038/sj.bjc.6604207
State	Published - Mar 25 2008
Externally published	Yes

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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

Abstract

Bibliographical note

Keywords

UN SDGs

Access

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