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Accurate and robust gene selection for disease classification using a simple statistic.


ABSTRACT: Discrimination of disease patients based on gene expression data is a crucial problem in clinical area. An important issue to solve this problem is to find a discriminative subset of genes from thousands of genes on a microarray or DNA chip. Aiming at finding informative genes for disease classification on microarray, we present a gene selection method based on the forward variable (gene) selection method (FSM) and show, using typical public microarray datasets, that our method can extract a small set of genes being crucial for discriminating different classes with a very high accuracy almost closed to perfect classification.

SUBMITTER: Mutsubayashi H 

PROVIDER: S-EPMC2637954 | biostudies-literature | 2008

REPOSITORIES: biostudies-literature

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Accurate and robust gene selection for disease classification using a simple statistic.

Mutsubayashi Hikaru H   Aso Seiichiro S   Nagashima Tomomasa T   Okada Yoshifumi Y  

Bioinformation 20081024 2


Discrimination of disease patients based on gene expression data is a crucial problem in clinical area. An important issue to solve this problem is to find a discriminative subset of genes from thousands of genes on a microarray or DNA chip. Aiming at finding informative genes for disease classification on microarray, we present a gene selection method based on the forward variable (gene) selection method (FSM) and show, using typical public microarray datasets, that our method can extract a sma  ...[more]

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