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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References
Alizadeh, A.A., Eisen, M.B., Davis, E.E., Ma, C., Lossos, I.S., Rosenwald, A., Boldrick, J.C., Sabet, H., Tran, T., Yu, X., Powell, J.I., Yang, L., Marti, G.E., Moore, T., Hudson, J., Lu, L., Lewis, D.B., Tibshirani, R., Sherlock, G., Chan, W.C., Greiner, T.C., Weisenburger, D.D., Armitage, J.O., Warnke, R., Levy, R., Wilson, W., Grever, M.R., Byrd, J.C., Botstein, D., Brown, P.O., Staudt, L.M.: Different types of diffuse large B-cell lymphoma identified by gene expression profiles. Nature 403, 503–511 (2000)
Alon, U., Barkai, N., Notterman, D.A., Gish, K., Ybarra, S., Mack, D., Levine, A.J.: Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc. Natnl. Acad. Sci. USA 96, 6745–6750 (1999)
Breiman, L., Friedman, J.H., Olshen, R., Stone, R.: Classification and regression trees. Wadsworth (1984)
Dudoit, S., Fridlyand, J., Speed, T.P.: Comparison of discrimination methods for the classification of tumors using gene expression data. Journal of the American Statistical Association 97(457), 77–87 (2002)
Fort, G., Lambert-Lacroix, S.: Classification using partial least squares with penalized logistic regression. Bioinformatics 21(7), 1104–1111 (2005)
Furey, T.S., Cristianini, N., Duffy, N., Bednarski, D.W., Schummer, M., Haussler, D.: Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 16(10), 906–914 (2000)
Golub, T.R., Slonim, D.K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J.P., Coller, H., Loh, M.L., Downing, J.R., Caligiuri, M.A., Bloomfield, C.D., Lander, E.S.: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286, 531–537 (1999)
Grabisch, M., Dispot, F.: A comparison of some methods of fuzzy classification on real data. In: 2nd Int. Conference on Fuzzy Logic and Neural Networks, pp. 659–662 (1992)
Holland, J.H.: Adaptation in natural and artificial systems. University of Mitchigan Press (1975)
Ishibuchi, H., Nakashima, T.: Improving the performance of fuzzy classifier systems for pattern classification problems with continuous attributes. IEEE Trans. on Industrial Electronics 46(6), 1057–1068 (1999)
Ishibuchi, H., Nakashima, T.: Performance evaluation of fuzzy classifier systems for multi-dimensional pattern classification problems. IEEE Trans. Systems, Man and Cybernetics - Part B: Cybernetics 29, 601–618 (1999)
Ishibuchi, H., Nakashima, T.: Effect of rule weights in fuzzy rule-based classification systems. IEEE Trans. Fuzzy Systems 9(4), 506–515 (2001)
Ishibuchi, H., Nozaki, K., Tanaka, H.: Distributed representation of fuzzy rules and its application to pattern classification. Fuzzy Sets and Systems 52(1), 21–32 (1992)
Liu, H., Li, J., Wong, L.: A comparative study on feature selection and classification methods using gene expression profiles and proteomic patterns. Gene Informatics 13, 51–60 (2002)
Statnikov, A., Aliferis, C., Tsamardinos, I., Hardin, D., Levy, S.: A comprehensive evaluation of multicategory classification methods for microarray expression cancer diagnosis. Bioinformatics 21(5), 631–643 (2005)
Sugeno, M.: An introductory survey of fuzzy control. Information Science 30(1/2), 59–83 (1985)
Vinterbo, S.A., Kim, E.-Y., Ohno-Machado, L.: Small, fuzzy and interpretable gene expression based classifiers. Bioinformatics 21(9), 1964–1970 (2005)
Woolf, P.J., Wang, Y.: A fuzzy logic approach to analyzing gene expression data. Physiological Genomics 3, 9–15 (2000)
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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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