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.
authors.affiliation: Department of Electrical, Computer, and Biomedical Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA. yuewang@vt.edu
authors.forename: Y
authors.initials: Y
authors.lastname: Wang
authors.forename: D J
authors.initials: DJ
authors.lastname: Miller
authors.forename: R
authors.initials: R
authors.lastname: Clarke
mesh_terms: D014408:Biomarkers, Tumor / Q000032:analysis; D019295:Computational Biology; D020869:Gene Expression Profiling; D046228:Microarray Analysis / Q000379:methods*; D015233:Models, Statistical; D009369:Neoplasms / Q000175:diagnosis / Q000235:genetics*
chemical_list: D014408:Biomarkers, Tumor