Abstract
This paper investigates the use of the method of dimensional reduction Cascaded Nonlinear Components Analysis (C-NLPCA) in the protein secondary structure prediction problem. The use of the C-NLPCA is justified by the fact that this method manage to obtain a dimensional reduction that considers the nonlinearity of the data. In order to prove the effectiveness of the C-NLPCA, this paper presents comparisons of methods of components extraction, as well as, of existing predictors. The C-NLPCA revealed to be efficient, propelling a new field of research.
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Simas, G.M., Botelho, S.S.C., Grando, N., Colares, R.G. (2007). Dimensional Reduction in the Protein Secondary Structure Prediction — Nonlinear Method Improvements. In: Corchado, E., Corchado, J.M., Abraham, A. (eds) Innovations in Hybrid Intelligent Systems. Advances in Soft Computing, vol 44. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74972-1_55
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DOI: https://doi.org/10.1007/978-3-540-74972-1_55
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