The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Predicting the clinical status of human breast cancer by using gene expression profiles
Open accessAbstract
Prognostic and predictive factors are indispensable tools in the treatment of patients with neoplastic disease. For the most part, such factors rely on a few specific cell surface, histological, or gross pathologic features. Gene expression assays have the potential to supplement what were previously a few distinct features with many thousands of features. We have developed Bayesian regression models that provide predictive capability based on gene expression data derived from DNA microarray analysis of a series of primary breast cancer samples. These patterns have the capacity to discriminate breast tumors on the basis of estrogen receptor status and also on the categorized lymph node status. Importantly, we assess the utility and validity of such models in predicting the status of tumors in crossvalidation determinations. The practical value of such approaches relies on the ability not only to assess relative probabilities of clinical outcomes for future samples but also to provide an honest assessment of the uncertainties associated with such predictive classifications on the basis of the selection of gene subsets for each validation analysis. This latter point is of critical importance in the ability to apply these methodologies to clinical assessment of tumor phenotype.
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Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2003) · doi:10.1201/9780429258480 · Cited by 27,276
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Metadata
- Authors
- West, M., Blanchette, C., Dressman, H., Huang, E., Ishida, S., Spang, R., Zuzan, H., Olson, J. A., Marks, J. R., & Nevins, J. R.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2001-09-18
- Volume
- 98
- Issue
- 20
- Pages
- 11462-11467
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2168561598
- Cited by
- 1,318
- References
- 1
- FWCI
- 22.80
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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