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40

GECCO
2004
Springer

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A Genetic Approach for Gene Selection on Microarray Expression Data

14 years 4 months ago

A Genetic Approach for Gene Selection on Microarray Expression Data

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Abstract. Microarrays allow simultaneous measurement of the expression levels of thousands of genes in cells under diﬀerent physiological or disease states. Because the number of genes exceeds the number of samples, class prediction on microarray expression data leads to an extreme “curse of dimensionality” problem. A principal goal of these studies is to identify a subset of informative genes for class prediction to reduce the curse of dimensionality. We propose a novel genetic approach that selects a subset of predictive genes for classiﬁcation on the basis of gene expression data. Our genetic algorithm maximizes correlation between genes and classes and minimizes intercorrelation among genes. We tested the genetic algorithm on leukemia data sets and obtained improved results over previous results.

Yong-Hyuk Kim, Su-Yeon Lee, Byung Ro Moon

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Class Prediction | Expression Data | GECCO 2004 | Microarray Expression Data |

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Added	01 Jul 2010
Updated	01 Jul 2010
Type	Conference
Year	2004
Where	GECCO
Authors	Yong-Hyuk Kim, Su-Yeon Lee, Byung Ro Moon

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