Comparison of discrimination methods for the classification of tumors using gene expression data

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Comparison of discrimination methods for the classification of tumors using gene expression data (2002)

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by Sandrine Dudoit , Jane Fridlyand , Terence P. Speed

Venue:	JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
Citations:	768 - 6 self

BibTeX

@ARTICLE{Dudoit02comparisonof,
    author = {Sandrine Dudoit and Jane Fridlyand and Terence P. Speed},
    title = {Comparison of discrimination methods for the classification of tumors using gene expression data},
    journal = {JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION},
    year = {2002},
    volume = {97},
    number = {457},
    pages = {77--87}
}

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Abstract

A reliable and precise classification of tumors is essential for successful diagnosis and treatment of cancer. cDNA microarrays and high-density oligonucleotide chips are novel biotechnologies increasingly used in cancer research. By allowing the monitoring of expression levels in cells for thousands of genes simultaneously, microarray experiments may lead to a more complete understanding of the molecular variations among tumors and hence to a finer and more informative classification. The ability to successfully distinguish between tumor classes (already known or yet to be discovered) using gene expression data is an important aspect of this novel approach to cancer classification. This article compares the performance of different discrimination methods for the classification of tumors based on gene expression data. The methods include nearest-neighbor classifiers, linear discriminant analysis, and classification trees. Recent machine learning approaches, such as bagging and boosting, are also considered. The discrimination methods are applied to datasets from three recently published cancer gene expression studies.

Keyphrases

gene expression data discrimination method cdna microarrays linear discriminant analysis nearest-neighbor classifier novel approach microarray experiment tumor class precise classification complete understanding recent machine learning approach classification tree novel biotechnology different discrimination method cancer classification cancer research expression level successful diagnosis molecular variation important aspect informative classification cancer gene expression study

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