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.
Bibliographical noteFunding 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.
- Data modelling and analysis
- Gene expression profiling
- High dimensionality