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Bioinformatics Advance Access published online on January 20, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti287
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Bioinformatics © Oxford University Press 2005; all rights reserved.
Received August 13, 2004
Revised November 18, 2004
Accepted December 17, 2004

Article

Small, fuzzy and interpretable gene expression based classifiers

Staal A. Vinterbo 1*, Eun-Young Kim 1, and Lucila Ohno-Machado 1

1 Decision Systems Group, Brigham and Women's Hospital, and Division of Health Sciences and Technology, Harvard Medical School/Massachusetts Institute of Technology, Boston, Massachusetts

* To whom correspondence should be addressed.
Staal A. Vinterbo, E-mail: staal{at}dsg.harvard.edu


  Abstract

Motivation: Interpretation of classification models derived from gene expression data is usually not simple, yet it is an important aspect in the analytical process. We investigate the performance of small rule-based classifiers based on fuzzy logic in five data sets that are different in size, laboratory origin, and biomedical domain.

Results: The classifiers resulted in rules that can be readily examined by biomedical researchers. The fuzzy-logic-based classifiers compare favorably with logistic regression in all data sets.

Availability: Prototype available upon request.


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