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

Electrical Engineering

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

65 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

All Science Journal Classification (ASJC) codes

Oncology
Cancer Research

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10.1038/sj.bjc.6604207

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title = "Approaches to working in high-dimensional data spaces: Gene expression microarrays",

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.",

author = "Y. Wang and Miller, \{D. J.\} and R. Clarke",

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.",

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AU - Clarke, R.

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PY - 2008/3/25

Y1 - 2008/3/25

N2 - 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.

AB - 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.

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JO - British Journal of Cancer

JF - British Journal of Cancer

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

Abstract

All Science Journal Classification (ASJC) codes

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