Small, fuzzy and interpretable gene expression based classifiers.

Staal A Vinterbo; Eun-Young Kim; Lucila Ohno-Machado

doi:10.1093/bioinformatics/bti287

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Small, fuzzy and interpretable gene expression based classifiers.

Staal A Vinterbo·Eun-Young Kim·Lucila Ohno-Machado

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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.

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Algorithms Artificial Intelligence Biomarkers, Tumor Cluster Analysis Fuzzy Logic Gene Expression Profiling Humans Neoplasm Proteins Neoplasms Oligonucleotide Array Sequence Analysis Pattern Recognition, Automated Software Evaluation Study Journal Article Research Support, N.I.H., Extramural Research Support, U.S. Gov't, P.H.S.Validation Study

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Staal A Vinterbo, Eun-Young Kim, Lucila Ohno-Machado. 2005-01-20. Small, fuzzy and interpretable gene expression based classifiers.. https://doi.org/10.1093/bioinformatics%2Fbti287

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