Dimensional reduction in the protein secondary structure prediction - Nonlinear method improvements

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. © 2007 Springer-Verlag Berlin Heidelberg.