Fuzzy classification for gene expression data analysis

Gerald Schaefer*, Tomoharu Nakashima, Yasuyuki Yokota

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Microarray expression studies measure, through a hybridisation process, the levels of genes expressed in biological samples. Knowledge gained from these studies is deemed increasingly important due to its potential of contributing to the understanding of fundamental questions in biology and clinical medicine. One important aspect of microarray expression analysis is the classification of the recorded samples which poses many challenges due to the vast number of recorded expression levels compared to the relatively small numbers of analysed samples. In this chapter we show how fuzzy rule-based classification can be applied successfully to analyse gene expression data. The generated classifier consists of an ensemble of fuzzy if-then rules which together provide a reliable and accurate classification of the underlying data. Experimental results on several standard microarray datasets confirm the efficacy of the approach.

Original languageEnglish
Title of host publicationStudies in Computational Intelligence
Pages209-218
Number of pages10
Volume94
DOIs
Publication statusPublished - 15 Jan 2008

Publication series

NameStudies in Computational Intelligence
Volume94
ISSN (Print)1860949X

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    Schaefer, G., Nakashima, T., & Yokota, Y. (2008). Fuzzy classification for gene expression data analysis. In Studies in Computational Intelligence (Vol. 94, pp. 209-218). (Studies in Computational Intelligence; Vol. 94). https://doi.org/10.1007/978-3-540-76803-6_8