Abstract
There are so many existing classification methods from diverse fields including statistics, machine learning and pattern recognition. New methods have been invented constantly that claim superior performance over classical methods. It has become increasingly difficult for practitioners to choose the right kind of the methods for their applications. So this paper is not about the suggestion of another classification algorithm, but rather about conveying the message that some existing algorithms, if properly used, can lead to better solutions to some of the challenging real-world problems. This paper will look at some important problems in bioinformatics for which the best solutions were known and shows that improvement over those solutions can be achieved with a form of feed-forward neural networks by applying more advanced schemes for network supervised learning. The results are evaluated against those from other commonly used classifiers, such as the K nearest neighbours using cross validation, and their statistical significance is assessed using the nonparametric Wilcoxon test.
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References
Boland, M.V., Murphy, R.F.: After sequencing: quantitative analysis of protein localization. IEEE Engineering in Medicine and Biology, 115–119 (1999)
Cairns, P., Huyck, C., Mitchell, I., Wu, W.: Comparison of Categorisation Algorithms for Predicting the Cellular Localization Sites of Proteins. IEEE Engineering in Medicine and Biology, 296–300 (2001)
Duda, R.O., Hart, P.E.: Pattern Classification and Scene Analysis. John Wiley and Sons, Chichester (1973)
Horton, P., Nakai, K.: A probabilistic classification system for predicting the cellular localization sites of proteins. In: Proceedings of the Fourth International Conference on Intelligent Systems for Molecular Biology, pp. 109–115 (1996)
Horton, P., Nakai, K.: Better Prediction of Protein Cellular Localization Sites with the k Nearest Neighbors Classifier. In: Proceedings of Intelligent Systems in Molecular Biology, pp. 368–383 (1997)
Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: International Joint Conference on Artificial Intelligence, pp. 2230–228 (1995)
Murphy, P.M., Aha, D.W.: UCI repository of machine learning databases. (1996), http://www.ics.uci.edu/mlearn
Nakai, K., Kanehisa, M.: Expert system for predicting protein localization sites in gram-negative bacteria. PROTEINS 11, 95–110 (1991)
Nakai, K., Kanehisa, M.: A knowledge base for predicting protein localization sites in eukaryotic cells. Genomics 14, 897–911 (1992)
Opitz, D., Maclin, R.: Popular Ensemble Methods: An Empirical Study. Journal of Articial Intelligence Research 11, 169–198 (1999)
Riedmiller, M., Braun, H.: A direct adaptive method for faster backpropagation learning: The RPROP algorithm. In: Proceedings International Conference on Neural Networks, pp. 586–591 (1993)
Sharkey, A.J.C., Sharkey, N.E.: Combining diverse neural nets. The Knowledge Engineering Review 12, 231–247 (1997)
Snedecor, G., Cochran, W.: Statistical Methods, 8th edn. Iowa State University Press, Iowa (1989)
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© 2003 Springer-Verlag Berlin Heidelberg
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Anastasiadis, A.D., Magoulas, G.D., Liu, X. (2003). Classification of Protein Localisation Patterns via Supervised Neural Network Learning. In: R. Berthold, M., Lenz, HJ., Bradley, E., Kruse, R., Borgelt, C. (eds) Advances in Intelligent Data Analysis V. IDA 2003. Lecture Notes in Computer Science, vol 2810. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45231-7_40
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DOI: https://doi.org/10.1007/978-3-540-45231-7_40
Publisher Name: Springer, Berlin, Heidelberg
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