Summary
Cancer diagnosis from huge microarray gene expression data is an important and challenging bioinformatics research topic. We used a fuzzy neural network (FNN) proposed earlier for cancer classification. This FNN contains three valuable aspects i.e., automatically generating fuzzy membership functions, parameter optimization, and rule-base simplification. One major obstacle in microarray data set classifier is that the number of features (genes) is much larger than the number of objects. We therefore used a feature selection method based on t-test to select more significant genes before applying the FNN. In this work we used three well-known microarray databases, i.e., the lymphoma data set, the small round blue cell tumor (SRBCT) data set, and the ovarian cancer data set. In all cases we obtained 100% accuracy with fewer genes in comparison with previously published results. Our result shows the FNN classifier not only improves the accuracy of cancer classification problem but also helps biologists to find a better relationship between important genes and development of cancers.
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References
Schena, M., Shalon, D., Davis, R.W., Brown, P.O.: Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270, 467–470 (1995)
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., et al.: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286, 531–537 (1999)
Khan, J.M., Wei, J.S., Ringner, M., Saal, L.H., Ladanyi, M., Westermann, F., Berthold, F., Schwab, M., Antonescu, C.R., Peterson, C., et al.: Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nature Medicine 7, 673–679 (2001)
Brown, M.P., Grundy, W.N., Lin, D., Cristianini, N., Sugnet, C.W., Furey, T.S., Jr. M., Haussler, D.: Knowledge-based analysis of microarray gene expression data by using support vector machines. In: Proc. Natl Acad. Sci., USA, vol. 97, pp. 262–267 (2000)
Tibshirani, R., Hastie, T., Narashiman, B., Chu, G.: Diagnosis of multiple cancer types by shrunken centroids of gene expression. In: Proc. Natl. Acad. Sci., USA, vol. 99, pp. 6567–6572 (2002)
Tibshirani, R., Hastie, T., Narasimhan, B., Chu, G.: Class predicition by nearest shrunken centroids with applications to DNA microarrays. Statistical Science 18, 104–117 (2003)
Alizadeh, A.A., Eisen, M.B., Davis, R.E., Ma, C., Lossos, I.S., Rosenwald, A., Boldrick, J.C., Sabet, H., Tran, T., Yu, X., et al.: Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403, 503–511 (2000)
Deutsch, J.M.: Evolutionary algorithms for finding optimal gene sets in microarray prediction. Bioinformatics 19, 45–52 (2003)
Lee, Y., Lee, C.K.: Classification of multiple cancer types by mulitcategory support vector machines using gene expression data. Bioinformatics 19, 1132–1139 (2003)
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, 77–87 (2002)
Schaner, M.E., Ross, D.T., Ciaravino, G., Sorlie, T., Troyanskaya, O., Diehn, M., Wang, Y.C., Duran, G.E., Sikic, T.L., Caldeira, S.: Gene expression patterns in ovarian carcinomas. Molecular Biology of the Cell 14, 4376–4386 (2003)
Frayman, Y., Wang, L.: Data mining using dynamically constructed fuzzy neural networks. In: Wu, X., Kotagiri, R., Korb, K.B. (eds.) PAKDD 1998. LNCS, vol. 1394, pp. 122–131. Springer, Heidelberg (1998)
Frayman, Y., Wang, L.: A Dynamically-constructed fuzzy neural controller for direct model reference adaptive control of multi-input-multi-output nonlinear processes. Soft Computing 6, 244–253 (2002)
Chu, F., Xie, W., Wang, L.: Gene selection and cancer classification using a fuzzy neural network. In: Proceedings of the North-American Fuzzy Information Processing Conference (NAFIPS 2004), vol. 2, pp. 555–559 (2004)
Xie, W., Chu, F., Wang, L., Lim, E.T.: A fuzzy neural network for intelligent data processing. In: Proceedings of SPIE, Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security 2005, vol. 5812, pp. 283–290 (2005)
Devore, J., Peck, R.: Statistics: the Exploration and Analysis of Data, 3rd edn. Duxbury Press, Pacific Grove, CA (1997)
Tusher, V.G., Tibshirani, R., Chu, G.: Significance analysis of microarrays applied to the ionizing radiation response. In: Proc. Natl. Acad. Sci., USA, vol. 98, pp. 5116–5121 (2001)
Higgins, C.M., Goodman, R.M.: Fuzzy rule-based networks for control. IEEE Transactions on Fuzzy Systems 2, 82–88 (1994)
Wang, L.X., Mendel, J.M.: Generating fuzzy rules by learning from examples. IEEE Trans. Syst., Man, Cybern 22, 1414–1427 (1992)
Sugeno, M., Kang, G.T.: Structure identification of fuzzy model. Fuzzy Sets Syst. 28, 15–33 (1988)
Werbos, P.: Beyond regression: new tools for prediction and analysis in the behavioral sciences. Ph.D. thesis, Harvard University, Cambridge, MA (1974)
Troyanskaya, O., Cantor, M., Sherlock, G., Brown, P., Hastie, T., Tibshirani, R., Botstein, D., Altman, R.B.: Missing value estimation methods for DNA microarrays. Bioinformatics 17, 520–525 (2001)
Dubios, D., Prade, H.: A unifying view of comparison indices in a fuzzy set theoretic framework. In: Yager, R.R. (ed.) Fuzzy sets and possibility theory: Recent Developments, Pergamon, NY (1982)
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Chu, F., Xie, W., Fazayeli, F., Wang, L. (2008). Assisting Cancer Diagnosis with Fuzzy Neural Networks. In: Smolinski, T.G., Milanova, M.G., Hassanien, AE. (eds) Computational Intelligence in Biomedicine and Bioinformatics. Studies in Computational Intelligence, vol 151. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70778-3_9
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DOI: https://doi.org/10.1007/978-3-540-70778-3_9
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