Leukemia Prediction from Gene Expression Data—A Rough Set Approach

  • Jianwen Fang
  • Jerzy W. Grzymala-Busse
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4029)


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.


Acute Myeloid Leukemia Rule Induction Matching Rule Bayesian Model Average Data Mining System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jianwen Fang
    • 1
  • Jerzy W. Grzymala-Busse
    • 2
    • 3
  1. 1.Bioinformatics Core Facility, and Information and Telecommunication Technology CenterUniversity of KansasLawrenceUSA
  2. 2.Department of Electrical Engineering and Computer ScienceUniversity of KansasLawrenceUSA
  3. 3.Institute of Computer Science Polish Academy of SciencesWarsawPoland

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