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A Method for Microarray Data Analysis based on Bayesian Networks using an Efficient Structural learning Algorithm and Data Dimensionality Reduction  

황규백 (서울대학교 컴퓨터공학부)
장정호 (서울대학교 컴퓨터공학부)
장병탁 (서울대학교 컴퓨터공학부)
Publication Information
Journal of KIISE:Software and Applications / v.29, no.11, 2002 , pp. 775-784 More about this Journal
Abstract
Microarray data, obtained from DNA chip technologies, is the measurement of the expression level of thousands of genes in cells or tissues. It is used for gene function prediction or cancer diagnosis based on gene expression patterns. Among diverse methods for data analysis, the Bayesian network represents the relationships among data attributes in the form of a graph structure. This property enables us to discover various relations among genes and the characteristics of the tissue (e.g., the cancer type) through microarray data analysis. However, most of the present microarray data sets are so sparse that it is difficult to apply general analysis methods, including Bayesian networks, directly. In this paper, we harness an efficient structural learning algorithm and data dimensionality reduction in order to analyze microarray data using Bayesian networks. The proposed method was applied to the analysis of real microarray data, i.e., the NC160 data set. And its usefulness was evaluated based on the accuracy of the teamed Bayesian networks on representing the known biological facts.
Keywords
microarray data analysis; Bayesian networks; data dimensionality reduction;
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