Dimensional reduction in the protein secondary structure prediction: non-linear method improvements

Authors:
Gisele M. Simas;Silvia S. C. Botelho;Rafael G. Colares;Renan R. Almeida
Affiliations:
Fundacao Universidade Federal do Rio Grande do Sul (FURG), Av. Italia Km 8 – 96.200-090 – Rio Grande – RS – Brazil.;Fundacao Universidade Federal do Rio Grande do Sul (FURG), Av. Italia Km 8 – 96.200-090 – Rio Grande – RS – Brazil.;Fundacao Universidade Federal do Rio Grande do Sul (FURG), Av. Italia Km 8 – 96.200-090 – Rio Grande – RS – Brazil.;Fundacao Universidade Federal do Rio Grande do Sul (FURG), Av. Italia Km 8 – 96.200-090 – Rio Grande – RS – Brazil
Venue:
International Journal of Computational Intelligence in Bioinformatics and Systems Biology
Year:
2010

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Abstract

This paper investigates the use of a dimensional reduction method, called cascaded non-linear components analysis (C-NLPCA), in the protein secondary structure prediction problem. C-NLPCA treats dimensional reductions considering the non-linearity of the data. In order to prove the effectiveness of the C-NLPCA, a set of tests are presented, comparing our approach with other existing predictors. The C-NLPCA is revealed to be efficient, propelling a new field of research.