Summary
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. (2009). Gene Expression Analysis by Fuzzy and Hybrid Fuzzy Classification. In: Jin, Y., Wang, L. (eds) Fuzzy Systems in Bioinformatics and Computational Biology. Studies in Fuzziness and Soft Computing, vol 242. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89968-6_7
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DOI: https://doi.org/10.1007/978-3-540-89968-6_7
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