title: Small, fuzzy and interpretable gene expression based classifiers.
abstract: BACKGROUND
  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.
  
  
  BACKGROUND
  Prototype available upon request.
authors.affiliation: Decision Systems Group, Brigham and Women's Hospital, Harvard Medical School/Massachusetts Institute of Technology, Boston, USA. staal@dsg.harvard.edu
authors.forename: Staal A
authors.initials: SA
authors.lastname: Vinterbo
authors.forename: Eun-Young
authors.initials: EY
authors.lastname: Kim
authors.forename: Lucila
authors.initials: L
authors.lastname: Ohno-Machado
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D014408:Biomarkers, Tumor / Q000235:genetics / Q000378:metabolism*; D016000:Cluster Analysis; D017143:Fuzzy Logic; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D009363:Neoplasm Proteins / Q000235:genetics / Q000378:metabolism*; D009369:Neoplasms / Q000235:genetics / Q000378:metabolism*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D012984:Software
chemical_list: D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins