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Predictive Models for Breast Cancer Susceptibility from Multiple Single Nucleotide Polymorphisms (2004)
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| Venue: | mphClinical Cancer Research |
| Citations: | 27 - 2 self |
BibTeX
@ARTICLE{Listgarten04predictivemodels,
author = {Jennifer Listgarten and Sambasivarao Damaraju and Brett Poulin and Lillian Cook and Jennifer Dufour and Adrian Driga and John Mackey and David Wishart and Russ Greiner and Brent Zanke},
title = {Predictive Models for Breast Cancer Susceptibility from Multiple Single Nucleotide Polymorphisms},
journal = {mphClinical Cancer Research},
year = {2004},
volume = {10},
pages = {2004}
}
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Abstract
Hereditary predisposition and causative environmental exposures have long been recognized in human malignancies. In most instances, cancer cases occur sporadically, suggesting that environmental influences are critical in determining cancer risk. To test the influence of genetic polymorphisms on breast cancer risk, we have measured 98 single nucleotide polymorphisms (SNPs) distributed over 45 genes of potential relevance to breast cancer etiology in 174 patients and have compared these with matched normal controls. Using machine learning techniques such as support vector machines (SVMs), decision trees, and naïve Bayes, we identified a subset of three SNPs as key discriminators between breast cancer and controls. The SVMs performed maximally among predictive models, achieving 69 % predictive
Keyphrases
predictive model multiple single nucleotide polymorphism breast cancer susceptibility support vector machine hereditary predisposition decision tree matched normal control potential relevance causative environmental exposure ve bayes cancer case key discriminator human malignancy environmental influence genetic polymorphism cancer etiology breast cancer risk breast cancer cancer risk
