Predicting enzyme class from protein structure using Bayesian classification.

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Author(s): BORRO, L. C.; OLIVEIRA, S. R. M.; YAMAGISHI, M. E. B.; MANCINI, A. L.; JARDINE, J. G.; MAZONI, I.; SANTOS, E. H. dos; HIGA, R. H.; KUSER, P. R.; NESHICH, G.

Summary: ABSTRACT. Predicting enzyme class from protein structure parameters is a challenging problem in protein analysis. We developed a method to predict enzyme class that combines the strengths of statistical and data-mining methods. This method has a strong mathematical foundation and is simple to implement, achieving an accuracy of 45%. A comparison with the methods found in the literature designed to predict enzyme class showed that our method outperforms the existing methods.

Publication year: 2006

Types of publication: Journal article

Unit: Embrapa Digital Agriculture

Keywords: Bayesian classification, Bayesian classifier, Bioinformatics, Bioinformática, Classe de enzima, Data classification, Enzyme classification number, Estrutura de proteína, Naive Bayes, Protein function prediction, Protein structure

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