TY - JOUR
T1 - Approaches to working in high-dimensional data spaces
T2 - Gene expression microarrays
AU - Wang, Y.
AU - Miller, D. J.
AU - Clarke, R.
N1 - 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.
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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U2 - 10.1038/sj.bjc.6604207
DO - 10.1038/sj.bjc.6604207
M3 - Short survey
C2 - 18283324
AN - SCOPUS:40849105031
SN - 0007-0920
VL - 98
SP - 1023
EP - 1028
JO - British Journal of Cancer
JF - British Journal of Cancer
IS - 6
ER -