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Title

 

 

 

 

 

Identification of differentially expressed gene modules between two-class DNA microarray data

 

Authors

 

Yoshifumi Okada1*, Terufumi Inoue2

Affiliation

 

1College of Information and Systems, Muroran Institute of Technology, 27-1, Mizumoto-cho, Muroran 050-8585, Japan;  2Department of Information and Electronic Engineering, Muroran Institute of Technology, 27-1, Mizumoto-cho, Muroran 050-8585, Japan

 

Email

 

okada@csse.muroran-it.ac.jp

Article Type

 

Hypothesis

Date

 

Received August 20, 2009; Accepted September 11, 2009; Published October 11, 2009

Abstract

Identifying biologically useful genes from massive gene expression data is a critical issue in DNA microarray data analysis. Recent studies on gene module discovery have shown a substantial effect on identifying transcriptional regulatory networks involved in complex diseases for different sample subsets. These have targeted a single disease class, but discovering discriminative modules in different classes has remained to be addressed. In this paper, we propose a novel method that can discover differentially expressed gene modules from two-class DNA microarray data. The proposed method is applied to breast cancer and leukemia datasets, and the biological functions of the extracted modules are evaluated by functional enrichment analysis. As a result, we show that our method can extract genes well reflecting known biological functions compared to a traditional t-test-based approach.

 

Keywords

DNA, microarray, gene expression, two-class

Citation

 

Okada & Inoue, Bioinformation 4(4): 134-137 (2009)

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.