Classification of Normal and Tumor Tissues Using Geometric Representation of Gene Expression Microarray Data

Authors:
Saejoon Kim;Donghyuk Shin
Affiliations:
Department of Computer Science, Sogang University, Seoul 121-742, Korea;Department of Computer Science, Sogang University, Seoul 121-742, Korea
Venue:
MDAI '07 Proceedings of the 4th international conference on Modeling Decisions for Artificial Intelligence
Year:
2007

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Abstract

Microarray is a fascinating technology that provides us with accurate predictions of the state of biological tissue samples simply based on the expression levels of genes available from it. Of particular interest in the use of microarray technology is the classification of normal and tumor tissues which is vital for accurate diagnosis of the disease of interest. In this paper, we shall make use of geometric representationfrom graph theory for the classification of normal and tumor tissues of colon and ovary. The accuracy of our geometric representation-based classification algorithm will be shown to be comparable to that of the currently known best classification algorithms for the two datasets. In particular, the presented algorithm will be shown to have the highest classification accuracy when the number of genes used for classification is small.