Small, fuzzy and interpretable gene expression based classifiers | Zendy

Staal A. Vinterbo | Zendy; Eun Young Kim | Zendy; Lucila OhnoMachado | Zendy

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Small, fuzzy and interpretable gene expression based classifiers

Author(s) -

Staal A. Vinterbo,

Eun Young Kim,

Lucila OhnoMachado

Publication year - 2005

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/bti287

Subject(s) - fuzzy logic , artificial intelligence , computer science , machine learning , domain (mathematical analysis) , logistic regression , data mining , expression (computer science) , process (computing) , interpretation (philosophy) , pattern recognition (psychology) , mathematics , mathematical analysis , programming language , operating system

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.

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