title: 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.
authors.affiliation: Division of Production and Information Systems Engineering, Muroran Institute of Technology, Mizumoto, Muroran, Japan.
authors.forename: Hikaru
authors.initials: H
authors.lastname: Mutsubayashi
authors.forename: Seiichiro
authors.initials: S
authors.lastname: Aso
authors.forename: Tomomasa
authors.initials: T
authors.lastname: Nagashima
authors.forename: Yoshifumi
authors.initials: Y
authors.lastname: Okada