Prediction of Protein Secondary Structure Using Nonlinear Method

  • Silvia Botelho
  • Gisele Simas
  • Patricia Silveira
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4234)


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.


Neural Network Secondary Structure Dimensional Reduction Protein Secondary Structure Reduction Stage 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Silvia Botelho
    • 1
  • Gisele Simas
    • 1
  • Patricia Silveira
    • 1
  1. 1.FURGRio GrandeBrazil

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