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Computational prognostic indicators for breast cancer
Breast cancer remains the leading cause of cancer-related mortality in women. Comprehensive genomics, proteomics, and metabolomics studies are emerging that offer an opportunity to model disease biology, prognosis, and response to specific therapies. Although many biomarkers have been identified thr...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Dove Medical Press
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4103923/ https://www.ncbi.nlm.nih.gov/pubmed/25050076 http://dx.doi.org/10.2147/CMAR.S46483 |
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author | Yang, Xinan Ai, Xindi Cunningham, John M |
author_facet | Yang, Xinan Ai, Xindi Cunningham, John M |
author_sort | Yang, Xinan |
collection | PubMed |
description | Breast cancer remains the leading cause of cancer-related mortality in women. Comprehensive genomics, proteomics, and metabolomics studies are emerging that offer an opportunity to model disease biology, prognosis, and response to specific therapies. Although many biomarkers have been identified through advances in data mining techniques, few have been applied broadly to make patient-specific decisions. Here, we review a selection of breast cancer prognostic indicators and their implications. Our goal is to provide clinicians with a general evaluation of emerging computational methodologies for outcome prediction. |
format | Online Article Text |
id | pubmed-4103923 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Dove Medical Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-41039232014-07-21 Computational prognostic indicators for breast cancer Yang, Xinan Ai, Xindi Cunningham, John M Cancer Manag Res Review Breast cancer remains the leading cause of cancer-related mortality in women. Comprehensive genomics, proteomics, and metabolomics studies are emerging that offer an opportunity to model disease biology, prognosis, and response to specific therapies. Although many biomarkers have been identified through advances in data mining techniques, few have been applied broadly to make patient-specific decisions. Here, we review a selection of breast cancer prognostic indicators and their implications. Our goal is to provide clinicians with a general evaluation of emerging computational methodologies for outcome prediction. Dove Medical Press 2014-07-12 /pmc/articles/PMC4103923/ /pubmed/25050076 http://dx.doi.org/10.2147/CMAR.S46483 Text en © 2014 Yang et al. This work is published by Dove Medical Press Limited, and licensed under Creative Commons Attribution – Non Commercial (unported, v3.0) License The full terms of the License are available at http://creativecommons.org/licenses/by-nc/3.0/. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. |
spellingShingle | Review Yang, Xinan Ai, Xindi Cunningham, John M Computational prognostic indicators for breast cancer |
title | Computational prognostic indicators for breast cancer |
title_full | Computational prognostic indicators for breast cancer |
title_fullStr | Computational prognostic indicators for breast cancer |
title_full_unstemmed | Computational prognostic indicators for breast cancer |
title_short | Computational prognostic indicators for breast cancer |
title_sort | computational prognostic indicators for breast cancer |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4103923/ https://www.ncbi.nlm.nih.gov/pubmed/25050076 http://dx.doi.org/10.2147/CMAR.S46483 |
work_keys_str_mv | AT yangxinan computationalprognosticindicatorsforbreastcancer AT aixindi computationalprognosticindicatorsforbreastcancer AT cunninghamjohnm computationalprognosticindicatorsforbreastcancer |