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

被引：56

作者：

Wang, Y. ^{[1
]}

Miller, D. J. ^{[2
]}

Clarke, R. ^{[3
,4
]}

机构：

[1] Virginia Polytech Inst & State Univ, Dept Elect Comp & Biomed Engn, Arlington, VA 22203 USA

[2] Penn State Univ, Dept Elect Engn, University Pk, PA 16802 USA

[3] Georgetown Univ, Lombardi Comprehens Canc Ctr, Dept Physiol & Biophys, Washington, DC 20057 USA

[4] Georgetown Univ, Lombardi Comprehens Canc Ctr, Dept Oncol, Washington, DC 20057 USA

来源：

BRITISH JOURNAL OF CANCER | 2008年 / 98卷 / 06期

关键词：

microarray; gene expression profiling; high dimensionality; data modelling and analysis;

D O I：

10.1038/sj.bjc.6604207

中图分类号：

R73 [肿瘤学];

学科分类号：

100214 ;

摘要：

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.

引用

页码：1023 / 1028

页数：6

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