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.
CITATION STYLE
Okada, Y., & Inoue, T. (2009). Identification of differentially expressed gene modules between two-class DNA microarray data. Bioinformation, 4(4), 134–137. https://doi.org/10.6026/97320630004134
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