Gene expression analysis by fuzzy and hybrid fuzzy classification

Gerald Schaefer*, Tomoharu Nakashima, Hisao Ishibuchi

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Microarray studies and gene expression analysis has received tremendous attention over the last few years and provide many promising avenues towards the understanding of fundamental questions in biology and medicine. In this chapter we show that the employment of a fuzzy rule-based classification system allows for effective analysis of gene expression data. The applied classifier consists of a set of fuzzy if-then rules that allows for accurate non-linear classification of input patterns. We further show that a hybrid fuzzy classification scheme in which a small number of fuzzy if-then rules are selected through means of a genetic algorithm is capable of providing a compact classifier for gene expression analysis. Extensive experimental results on various well-known gene expression databsets confirm the efficacy of the presented approaches.

Original languageEnglish
Title of host publicationFuzzy systems in bioinformatics and computational biology
EditorsYaochu Jin, Lipo Wang
Place of PublicationBerlin (DE)
PublisherSpringer
Pages127-140
Number of pages14
ISBN (Electronic)978-3-540-89968-6
ISBN (Print)978-3-540-89967-9
DOIs
Publication statusPublished - 15 Jan 2009

Publication series

NameStudies in Fuzziness and Soft Computing
PublisherSpringer
Volume242
ISSN (Print)1434-9922

Fingerprint

Fuzzy If-then Rules
Gene Expression Analysis
Fuzzy Classification
Gene expression
Classifier
Gene Expression Data
Fuzzy Rules
Microarray
Medicine
Gene Expression
Biology
Efficacy
Classifiers
Genetic Algorithm
Experimental Results
Fuzzy rules
Microarrays
Genetic algorithms

Cite this

Schaefer, G., Nakashima, T., & Ishibuchi, H. (2009). Gene expression analysis by fuzzy and hybrid fuzzy classification. In Y. Jin, & L. Wang (Eds.), Fuzzy systems in bioinformatics and computational biology (pp. 127-140). (Studies in Fuzziness and Soft Computing; Vol. 242). Berlin (DE): Springer. https://doi.org/10.1007/978-3-540-89968-6_7
Schaefer, Gerald ; Nakashima, Tomoharu ; Ishibuchi, Hisao. / Gene expression analysis by fuzzy and hybrid fuzzy classification. Fuzzy systems in bioinformatics and computational biology. editor / Yaochu Jin ; Lipo Wang. Berlin (DE) : Springer, 2009. pp. 127-140 (Studies in Fuzziness and Soft Computing).
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Schaefer, G, Nakashima, T & Ishibuchi, H 2009, Gene expression analysis by fuzzy and hybrid fuzzy classification. in Y Jin & L Wang (eds), Fuzzy systems in bioinformatics and computational biology. Studies in Fuzziness and Soft Computing, vol. 242, Springer, Berlin (DE), pp. 127-140. https://doi.org/10.1007/978-3-540-89968-6_7

Gene expression analysis by fuzzy and hybrid fuzzy classification. / Schaefer, Gerald; Nakashima, Tomoharu; Ishibuchi, Hisao.

Fuzzy systems in bioinformatics and computational biology. ed. / Yaochu Jin; Lipo Wang. Berlin (DE) : Springer, 2009. p. 127-140 (Studies in Fuzziness and Soft Computing; Vol. 242).

Research output: Chapter in Book/Report/Conference proceedingChapter

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N2 - Microarray studies and gene expression analysis has received tremendous attention over the last few years and provide many promising avenues towards the understanding of fundamental questions in biology and medicine. In this chapter we show that the employment of a fuzzy rule-based classification system allows for effective analysis of gene expression data. The applied classifier consists of a set of fuzzy if-then rules that allows for accurate non-linear classification of input patterns. We further show that a hybrid fuzzy classification scheme in which a small number of fuzzy if-then rules are selected through means of a genetic algorithm is capable of providing a compact classifier for gene expression analysis. Extensive experimental results on various well-known gene expression databsets confirm the efficacy of the presented approaches.

AB - Microarray studies and gene expression analysis has received tremendous attention over the last few years and provide many promising avenues towards the understanding of fundamental questions in biology and medicine. In this chapter we show that the employment of a fuzzy rule-based classification system allows for effective analysis of gene expression data. The applied classifier consists of a set of fuzzy if-then rules that allows for accurate non-linear classification of input patterns. We further show that a hybrid fuzzy classification scheme in which a small number of fuzzy if-then rules are selected through means of a genetic algorithm is capable of providing a compact classifier for gene expression analysis. Extensive experimental results on various well-known gene expression databsets confirm the efficacy of the presented approaches.

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Schaefer G, Nakashima T, Ishibuchi H. Gene expression analysis by fuzzy and hybrid fuzzy classification. In Jin Y, Wang L, editors, Fuzzy systems in bioinformatics and computational biology. Berlin (DE): Springer. 2009. p. 127-140. (Studies in Fuzziness and Soft Computing). https://doi.org/10.1007/978-3-540-89968-6_7