BioMed Research International

BioMed Research International / 2005 / Article
Special Issue

Data Mining in Genomics and Proteomics

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Research article | Open Access

Volume 2005 |Article ID 589157 | https://doi.org/10.1155/JBB.2005.132

Dechang Chen, Zhenqiu Liu, Xiaobin Ma, Dong Hua, "Selecting Genes by Test Statistics", BioMed Research International, vol. 2005, Article ID 589157, 7 pages, 2005. https://doi.org/10.1155/JBB.2005.132

Selecting Genes by Test Statistics

Received28 Apr 2004
Revised22 Nov 2004
Accepted23 Nov 2004

Abstract

Gene selection is an important issue in analyzing multiclass microarray data. Among many proposed selection methods, the traditional ANOVA F test statistic has been employed to identify informative genes for both class prediction (classification) and discovery problems. However, the F test statistic assumes an equal variance. This assumption may not be realistic for gene expression data. This paper explores other alternative test statistics which can handle heterogeneity of the variances. We study five such test statistics, which include Brown-Forsythe test statistic and Welch test statistic. Their performance is evaluated and compared with that of F statistic over different classification methods applied to publicly available microarray datasets.

Copyright © 2005 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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