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Machine learning methods applied to DNA microarray data can improve the diagnosis of cancer

Published:01 December 2003Publication History
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Abstract

The morbidity rate of cancer victims varies greatly for similar patients who receive similar treatments. It is hypothesized that this variation can be explained by the genetic heterogeneity of the disease. DNA Microarrays, which can simultaneously measure the expression level of thousands of different genes, have been successfully used to identify such genetic differences. However, microarray data typically has a large number of features and relatively few observations, meaning that conventional machine learning tools can fail when applied to such data. We describe a novel procedure called "nearest shrunken centroids" that has successfully detected clinically relevant genetic differences in cancer patients. This procedure has the potential to become a powerful tool for diagnosing and treating cancer.

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  • Published in

    cover image ACM SIGKDD Explorations Newsletter
    ACM SIGKDD Explorations Newsletter  Volume 5, Issue 2
    December 2003
    202 pages
    ISSN:1931-0145
    EISSN:1931-0153
    DOI:10.1145/980972
    Issue’s Table of Contents

    Copyright © 2003 Authors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 1 December 2003

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