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

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 p...

Descripción completa

Detalles Bibliográficos
Autores principales: Wang, Y, Miller, D J, Clarke, R
Formato: Texto
Lenguaje:English
Publicado: Nature Publishing Group 2008
Materias:
Minireview
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2275474/
https://www.ncbi.nlm.nih.gov/pubmed/18283324
http://dx.doi.org/10.1038/sj.bjc.6604207
_version_ 1782151869744807936
author Wang, Y
Miller, D J
Clarke, R
author_facet Wang, Y
Miller, D J
Clarke, R
author_sort Wang, Y
collection PubMed
description 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.
format Text
id pubmed-2275474
institution National Center for Biotechnology Information
language English
publishDate 2008
publisher Nature Publishing Group
record_format MEDLINE/PubMed
spelling pubmed-22754742009-09-10 Approaches to working in high-dimensional data spaces: gene expression microarrays Wang, Y Miller, D J Clarke, R Br J Cancer Minireview 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. Nature Publishing Group 2008-03-25 2008-02-19 /pmc/articles/PMC2275474/ /pubmed/18283324 http://dx.doi.org/10.1038/sj.bjc.6604207 Text en Copyright © 2008 Cancer Research UK https://creativecommons.org/licenses/by/4.0/This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material.If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/.
spellingShingle Minireview
Wang, Y
Miller, D J
Clarke, R
Approaches to working in high-dimensional data spaces: gene expression microarrays
title Approaches to working in high-dimensional data spaces: gene expression microarrays
title_full Approaches to working in high-dimensional data spaces: gene expression microarrays
title_fullStr Approaches to working in high-dimensional data spaces: gene expression microarrays
title_full_unstemmed Approaches to working in high-dimensional data spaces: gene expression microarrays
title_short Approaches to working in high-dimensional data spaces: gene expression microarrays
title_sort approaches to working in high-dimensional data spaces: gene expression microarrays
topic Minireview
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2275474/
https://www.ncbi.nlm.nih.gov/pubmed/18283324
http://dx.doi.org/10.1038/sj.bjc.6604207
work_keys_str_mv AT wangy approachestoworkinginhighdimensionaldataspacesgeneexpressionmicroarrays
AT millerdj approachestoworkinginhighdimensionaldataspacesgeneexpressionmicroarrays
AT clarker approachestoworkinginhighdimensionaldataspacesgeneexpressionmicroarrays

Ejemplares similares