Title

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

Authors

Hikaru Mitsubayashi¹, Seiichiro Aso ², Tomomasa Nagashima² and Yoshifumi Okada ²

Affiliation

¹Division of Production and Information Systems Engineering, Muroran Institute of Technology; ²2 Department of Computer Science and Systems Engineering, Muroran Institute of Technology

Article Type

Prediction Model

Date

received September 05, 2008, accepted September 21, 2008; published October 24, 2008

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.

Keywords

gene expression; disease classification; Forward gene selection; F-value; Maharanobis distance

Citation

Mitsubayashi et al., Bioinformation 3(1): 68-71 (2008)

Edited by

P. Kangueane

ISSN

0973-2063

Publisher

Biomedical Informatics

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.