Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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An Efficient Functional Analysis Method for Micro-array Data Using Gene Ontology

  • Hong, Dong-Wan (Department of Computer Engineering, Hallym University) ;
  • Lee, Jong-Keun (Department of Computer Engineering, Hallym University) ;
  • Park, Sung-Soo (Department of Computer Engineering, Hallym University) ;
  • Hong, Sang-Kyoon (Department of Computer Engineering, Hallym University) ;
  • Yoon, Jee-Hee (Department of Computer Engineering, Hallym University)
  • Published : 2007.06.30
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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 micro-array data sets and validated. Our results were also compared with the original analysis.

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References

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