Abstract
Gene microarray experiment can monitor the expression of thousands of genes simultaneously. Using the promising technology, accurate classification of tumor subtypes becomes possible, allowing for specific treatment that maximizes efficacy and minimizes toxicity. Meanwhile, optimal genes selected from microarray data will contribute to diagnostic and prognostic of tumors in low cost. In this paper, we propose an improved FMM (fuzzy Min-Max) neural network classifier which provides higher performance than the original one. The improved one can automatically reduce redundant hyperboxes thus it can solve difficulty of setting the parameter θ value and is able to select discriminating genes. Finally we apply our improved classifier on the small, round blue-cell tumors dataset and get good results.
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Sorlie, T., Perou, C.M., Tibshirani, R., et al.: Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc. Natl. Acad. Sci. USA 98, 10869–10874 (2001)
van de Vijver, M.J., He, Y.D., van’t Veer, L.J.: Gene-Expression Signature as a Predictor of Survival in Breast Cancer. N. Engl. J. Med. 347(25), 1999–2009 (2002)
Luo, F., Khan, L., Bastani, F., et al.: A Dynamical Growing Self-Organizing Tree (DGSOT) for Hierarchical Clustering Gene Expression Profiles. Bioinformatics 20(16), 2605–2617 (2004)
Dembele, D., Kastner, P.: Fuzzy c-means method for clustering microarray data. Bioinformatics 19, 973–980 (2003)
Golub, T.R., et al.: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286, 531–537 (1999)
Alizadeh, A.A., et al.: Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403, 503–511 (2000)
Ramaswamy, S., et al.: Multiclass cancer diagnosis using tumor gene expression signatures. Proc. Natl. Acad. Sci. USA 98, 15149–15154 (2001)
Model, F., Adorjan, P.: Feature selection for DNA methylation based cancer classification. Bioinformatics 17(Suppl. 1), S157–S164 (2001)
Park, P.J., Pagano, M.: A nonparametric scoring algorithm for identifying informative genes from microarray data. In: Pac. Symp. Biocomput., pp. 52–63 (2001)
Guoyon, I., et al.: Gene selection for cancer classification using support vector machines. Machine Learning 46, 389–422 (2003)
Zadeh, L.: Fuzzy Set. Inform, and Control 8, 338–353 (1965)
Ho, S.-Y., Hsieh, C.-H., Chen, K.-W.: Scoring Method for Tumor Prediction from Microarray Data Using an Evolutionary Fuzzy Classifier. In: Ng, W.-K., et al. (eds.) PAKDD 2006. LNCS (LNAI), vol. 3918, pp. 520–529. Springer, Heidelberg (2006)
Chakraborty, D., Pal, N.R.: A neuro-fuzzy scheme for simultaneous feature selection and fuzzy rule-based classification. IEEE Transactions on Neural Networks 15(1), 110–123 (2004)
Simpson, P.K.: Fuzzy min-max neural networks: 1. Classification. IEEE Transactions on Neural Networks 3, 776–786 (1992)
Khan, J., Wei, J.S., Ringner, M., et al.: Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nat. Med. 7, 673–679 (2001)
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Juan, L., Fei, L., Yongqiong, Z. (2007). An Improved FMM Neural Network for Classification of Gene Expression Data. In: Cao, BY. (eds) Fuzzy Information and Engineering. Advances in Soft Computing, vol 40. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71441-5_8
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DOI: https://doi.org/10.1007/978-3-540-71441-5_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71440-8
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