Skip Navigation


Bioinformatics Advance Access originally published online on January 20, 2005
Bioinformatics 2005 21(9):1964-1970; doi:10.1093/bioinformatics/bti287
This Article
Right arrow Full Text Freely available
Right arrow FREE Full Text (Print PDF) Freely available
Right arrow All Versions of this Article:
21/9/1964    most recent
bti287v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrow Search for citing articles in:
ISI Web of Science (6)
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Vinterbo, S. A.
Right arrow Articles by Ohno-Machado, L.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Vinterbo, S. A.
Right arrow Articles by Ohno-Machado, L.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

Published by Oxford University Press 2005.

Small, fuzzy and interpretable gene expression based classifiers

Staal A. Vinterbo *, Eun-Young Kim and Lucila Ohno-Machado

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

*To whom correspondence should be addressed.

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{at}dsg.harvard.edu


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?




Disclaimer:
Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.