Title

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

Authors

Yoshifumi Okada^1*, 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

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.