Artificial immune system for classification of gene expression data

Authors:
Shin Ando;Hitoshi Iba
Affiliations:
Dept. of Electronics, School of Engineering, University of Tokyo;Dept. of Frontier Informatics, School of Frontier Science, University of Tokyo
Venue:
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Year:
2003

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Abstract

DNA microarray experiments generate thousands of gene expression measurement simultaneously. Analyzing the difference of gene expression in cell and tissue samples is useful in diagnosis of disease. This paper presents an Artificial Immune System for classifying microarray-monitored data. The system evolutionarily selects important features and optimizes their weights to derive classification rules. This system was applied to two datasets of cancerous cells and tissues. The primary result found few classification rules which correctly classified all the test samples and gave some interesting implications for feature selection.