Classification of gene expression data using fuzzy logic

Authors:
Lucila Ohno-Machado;Staal Vinterbo;Griffin Weber
Affiliations:
(Correspd. Dec. Sys. Grp., Tel.: +1 617 732 8543/ Fax: +1 617 732 9260/ machado@dsg.bwh.harvard.edu) Decision Sys. Grp., Brigham and Women's Hospital, Boston, MA, USA and Div. of Health Sci. and T ...;Decision Systems Group, Brigham and Women's Hospital, Boston, MA, USA and Div. of Health Sci. and Technol., Harvard and MIT, Boston, MA, USA;Decision Systems Group, Brigham and Women's Hospital, Boston, MA, USA and Div. of Health Sci. and Technol., Harvard and MIT, Boston, MA, USA
Venue:
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Challenges for future intelligent systems in biomedicine
Year:
2002

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Abstract

Microarray technologies have allowed the measurement of expression of multiple genes simultaneously. Gene expression levels can be used to classify tissues into diagnostic or prognostic categories. As measurements from different microarray technologies are made in different scales (which are not guaranteed to be linear and not easily re-scalable), it is helpful to develop an easy-to-interpret technology-independent classification scheme. To capture the essentials of the problem of classification using gene expression data, we show how fuzzy logic can be applied using two examples. Using information from genes previously shown to be important, the classification performance of the fuzzy inference is similar to that of other classifiers, but simpler and easier to interpret. The fuzzy inference system has the theoretical advantage that it does not need to be retrained when using measurements obtained from a different type of microarray. Although the data sets for gene expression analysis utilized in this paper are relatively small, they are among the largest available in this domain.