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A multicriteria genetic algorithm to analyze microarray data

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3 Author(s)
Khabzaoui, M. ; Univ. des Sci. et Technol. de Lille, Villeneuve d''Ascq, France ; Dhaenens, C. ; Talbi, E.

Knowledge discovery from DNA microarray data has become an important research area for biologists. Association rules is an important task of knowledge discovery that can be applied to the analysis of gene expression in order to identify patterns of genes and regulatory network. Association rules discovery may be modeled as an optimization problem. We propose a multicriteria model for association rules problem and present a genetic algorithm designed to deal with association rules on DNA microarray data, in order to obtain associations between genes. Hence, we expose the main features of the proposed genetic algorithm. We emphasize on specificities for the association rule problem (encoding, mutation and crossover operators) and on its multicriteria aspects. Results are given for real datasets.

Published in:

Evolutionary Computation, 2004. CEC2004. Congress on  (Volume:2 )

Date of Conference:

19-23 June 2004

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