Abstract
Predicting the three-dimensional (3D) structure of a protein is a key problem in molecular biology. It is also an interesting issue for statistical methods recognition. There are many approaches to this problem considering discriminative and generative classifiers. In this paper a classifier combining the well-known Support Vector Machine (SVM) classifier with Regularized Discriminant Analysis (RDA) classifier is presented. It is used on a real world data set. The obtained results improve previously published methods.
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References
Baldi, P., Brunak, S., Chauvin, Y., Andersen, C., Nielsen, H.: Assessing the accuracy of prediction algorithms for classification: an overview. Bioinformatics 16, 412–424 (2000)
Prevost, L., Qudot, L., Moises, A., Michel-Sendis, C., Milgram, M.: Hybrid generative/disciminative classifier for unconstrained character recognition. Pattern Recognition Letters 26, 1840–1848 (2005)
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2001), Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
Chothia, C.: One thousand families for the molecular biologist. Nature 357, 543–544 (1992)
Ding, C.H., Dubchak, I.: Multi-class protein fold recognition using support vector machines and neural networks. Bioinformatics 17, 349–358 (2001)
Dubchak, I., Muchnik, I., Holbrook, S.R., Kim, S.H.: Prediction of protein folding class using global description of amino acid sequence. Proc. Natl. Acad. Sci. USA 92, 8700–8704 (1995)
Dubchak, I., Muchnik, I., Kim, S.H.: Protein folding class predictor for SCOP: approach based on global descriptors. In: Proceedings ISMB (1997)
Lo Conte, L., Ailey, B., Hubbard, T.J.P., Brenner, S.E., Murzin, A.G., Chotchia, C.: SCOP: a structural classification of protein database. Nucleic Acids Res. 28, 257–259 (2000)
Shen, H.B., Chou, K.C.: Ensemble classifier for protein fold pattern recognition. Bioinformatics 22, 1717–1722 (2006)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Vural, V., Dy, J.G.: A hierarchical method for multi-class support vector machines. In: Proceedings of the twenty-first ICML, Banff, Alberta, Canada, July 4-8, p. 105 (2004)
Wang, L., Shen, X.: Multi-category support vector machines, feature selection and solution path. Statistica Sinica 16, 617–633 (2006)
Nanni, L.: A novel ensemble of classifiers for protein fold recognition. Neurocomputing 69, 2434–2437 (2006)
Okun, O.: Protein fold recognition with k-local hyperplane distance nearest neighbor algorithm. In: Proceedings of the Second European Workshop on Data Mining and Text Mining in Bioinformatics, Pisa, Italy, pp. 51–57, September 24 (2004)
Bologna, G., Appel, R.D.: A comparison study on protein fold recognition. In: Proceedings of the ninth ICONIP, Singapore, November 18-22, vol. 5, pp. 2492–2496 (2002)
Pal, N.R., Chakraborty, D.: Some new features for protein fold recognition. In: Kaynak, O., Alpaydın, E., Oja, E., Xu, L. (eds.) ICANN 2003 and ICONIP 2003. LNCS, vol. 2714, pp. 1176–1183. Springer, Heidelberg (2003)
Chung, I.F., Huang, C.D., Shen, Y.H., Lin, C.T.: Recognition of structure classification of protein folding by NN and SVM hierarchical learning architecture. In: Kaynak, O., Alpaydın, E., Oja, E., Xu, L. (eds.) ICANN 2003 and ICONIP 2003. LNCS, vol. 2714, pp. 1159–1167. Springer, Heidelberg (2003)
Hobohm, U., Sander, C.: Enlarged representative set of Proteins. Protein Sci. 3, 522–524 (1994)
Hobohm, U., Scharf, M., Schneider, R., Sander, C.: Selection of a representative set of structures from the Brookhaven. Protein Bank Protein Sci. 1, 409–417 (1992)
Platt, J.C., Cristianini, N., Shawe-Taylor, J.: Large Margin DAGs for Multiclass Classification. In: Proceedings of Neural Information Processing Systems, NIPS 1999, pp. 547–553 (2000)
Fei, B., Liu, J.: Binary Tree of SVM: A New Fast Multiclass Training and Classification Algorithm. IEEE Transaction on Neural Networks 17(3) (2006)
Kijsirikul, B., Ussivakul, N.: Multiclass support vector machines using adaptive directed acyclic graph. In: Proceedings of IJCNN, pp. 980–985 (2002)
Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn. Academic Press, New York (1990)
Hastie, T., Tibshirani, R.: Classification by pairwise coupling. Annals of Statistics 26(2), 451–471 (1998)
Dietterich, T.G., Bakiri, G.: Solving multiclass problems via error-correcting output codes. Journal of Artificial Intelligence Research 2, 263–286 (1995)
Friedman, J.H.: Regularized Discriminant Analysis. Journal of the American Statistical Association 84(405), 165–175 (1989)
Liu, C.L., Fujisawa, H.: Classification and Learning for Character Recognition: Comparison of Methods and Remaining Problems. In: Proc. Int. Workshop on Neural 21 Networks and Learning in Document Analysis and Recognition, Seoul, Korea (August 2005)
Gori, M., Scarselli, F.: Are multilayer perceptrons adequate for pattern recognition and verification? IEEE Trans. Pattern Anal. Mach. Intell. 20(11), 1121–1132 (1998)
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Chmielnicki, W., Sta̧por, K. (2010). Protein Fold Recognition with Combined SVM-RDA Classifier. In: Graña Romay, M., Corchado, E., Garcia Sebastian, M.T. (eds) Hybrid Artificial Intelligence Systems. HAIS 2010. Lecture Notes in Computer Science(), vol 6076. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13769-3_20
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DOI: https://doi.org/10.1007/978-3-642-13769-3_20
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