Leukemia prediction from gene expression data—a rough set approach

Authors:
Jianwen Fang;Jerzy W. Grzymala-Busse
Affiliations:
Bioinformatics Core Facility, and Information and Telecommunication Technology Center, University of Kansas, Lawrence, KS;Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS
Venue:
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
Year:
2006

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Abstract

We present our results on the prediction of leukemia from microarray data. Our methodology was based on data mining (rule induction) using rough set theory. We used a novel methodology based on rule generations and cumulative rule sets. The final rule set contained only eight rules, using some combinations of eight genes. All cases from the training data set and all but one cases from the testing data set were correctly classified. Moreover, six out of eight genes found by us are well known in the literature as relevant to leukemia.