Abstract
This paper presents the use of neural networks for the prediction of protein Secondary Structure. We propose a pre-processing stage based on the method of Cascaded Nonlinear Components Analysis (C-NLPCA), in order to get a dimensional reduction of the data which may consider its nonlinearity. Then, the reduced data are placed in predictor networks and its results are combined. For the verification of possible improvements brought by the use of C-NLPCA, a set of tests was done and the results will be demonstrated in this paper. The C-NLPCA revealed to be efficient, propelling a new field of research.
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Botelho, S., Simas, G., Silveira, P. (2006). Prediction of Protein Secondary Structure Using Nonlinear Method. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893295_5
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DOI: https://doi.org/10.1007/11893295_5
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