Small, fuzzy and interpretable gene expression based classifiers

Authors:
Staal A. Vinterbo;Eun-Young Kim;Lucila Ohno-Machado
Affiliations:
Decision Systems Group, Brigham and Women's Hospital, and Division of Health Sciences and Technology, Harvard Medical School/Massachusetts Institute of Technology Boston, MA, USA;Decision Systems Group, Brigham and Women's Hospital, and Division of Health Sciences and Technology, Harvard Medical School/Massachusetts Institute of Technology Boston, MA, USA;Decision Systems Group, Brigham and Women's Hospital, and Division of Health Sciences and Technology, Harvard Medical School/Massachusetts Institute of Technology Boston, MA, USA
Venue:
Bioinformatics
Year:
2005

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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 datasets 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 datasets. Availability: Prototype available upon request. Contact: staal@dsg.harvard.edu