Small, fuzzy and interpretable gene expression based classifiers.
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.
Vinterbo
Staal A
SA
Decision Systems Group, Brigham and Women's Hospital, Harvard Medical School/Massachusetts Institute of Technology, Boston, USA. staal@dsg.harvard.edu
Kim
Eun-Young
EY
Ohno-Machado
Lucila
L
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
D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins