An Efficient Functional Analysis Method for Micro-array Data Using Gene Ontology

Dong-wan Hong, Jong-keun Lee, Sung-soo Park, Sang-kyoon Hong and Jee-hee Yoon
Volume: 3, No: 1, Page: 38 ~ 42, Year: 2007

Keywords: Micro-array data, Functional analysis, Gene Ontology, Informative genes.
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Abstract
Microarray data includes tens of thousands of gene expressions simultaneously, so it can be effectively used in identifying the phenotypes of diseases. However, the retrieval of functional information from a large corpus of gene expression data is still a time-consuming task. In this paper, we propose an efficient method for identifying functional categories of differentially expressed genes from a micro-array experiment by using Gene Ontology (GO). Our method is as follows: (1) The expression data set is first filtered to include only genes with mean expression values that differ by at least 3-fold between the two groups. (2) The genes are then ranked based on the t-statistics. The 100 most highly ranked genes are selected as informative genes. (3) The t-value of each informative gene is imposed as a score on the associated GO terms. High-scoring GO terms are then listed with their associated genes and represent the functional category information of the micro-array experiment. A system called HMDA (Hallym Micro-array Data analysis) is implemented on publicly available microarray data sets and validated. Our results were also compared with the original analysis.

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Cite this article
IEEE Style
D. Hong, J. Lee, S. Park, S. Hong and J. Yoon, "An Efficient Functional Analysis Method for Micro-array Data Using Gene Ontology," Journal of Information Processing Systems, vol. 3, no. 1, pp. 38~42, 2007. DOI: .

ACM Style
Dong-wan Hong, Jong-keun Lee, Sung-soo Park, Sang-kyoon Hong, and Jee-hee Yoon. 2007. An Efficient Functional Analysis Method for Micro-array Data Using Gene Ontology, Journal of Information Processing Systems, 3, 1, (2007), 38~42. DOI: .