Solving large protein secondary structure classification problems by a nonlinear complementarity algorithm with {0, 1} variables

The aim of this paper is to present a nonlinear complementarity algorithm, limited to binary variables, to implement a classification algorithm, which will be applied to the determination of the secondary structure of proteins, and to present its statistical and mathematical convergence properties....

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Published inOptimization methods & software Vol. 22; no. 1; pp. 25 - 49
Main Authors Cifarelli, C., Patrizi, G.
Format Journal Article
LanguageEnglish
Published Taylor & Francis 01.02.2007
Subjects
Complementarity Problems
Protein secondary structure classification
Protein sequences
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Abstract The aim of this paper is to present a nonlinear complementarity algorithm, limited to binary variables, to implement a classification algorithm, which will be applied to the determination of the secondary structure of proteins, and to present its statistical and mathematical convergence properties. Extensive application results will be given on the available test data sets of proteins and the results will be compared with those of other implementations. The increase in the recognition accuracy, which will be in evidence, will be shown to be attributable to the adoption of a strict statistical methodology, which will permit to obtain unbiased results, without requiring extraneous assumptions.
AbstractList The aim of this paper is to present a nonlinear complementarity algorithm, limited to binary variables, to implement a classification algorithm, which will be applied to the determination of the secondary structure of proteins, and to present its statistical and mathematical convergence properties. Extensive application results will be given on the available test data sets of proteins and the results will be compared with those of other implementations. The increase in the recognition accuracy, which will be in evidence, will be shown to be attributable to the adoption of a strict statistical methodology, which will permit to obtain unbiased results, without requiring extraneous assumptions.
Author Patrizi, G.
Cifarelli, C.
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SubjectTerms Complementarity Problems
Protein secondary structure classification
Protein sequences
Title Solving large protein secondary structure classification problems by a nonlinear complementarity algorithm with {0, 1} variables
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