Please use this identifier to cite or link to this item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/9196
Research center of Embrapa/Collection: Embrapa Informática Agropecuária - Artigo em periódico indexado (ALICE)
Date Issued: 2006
Type of Material: Artigo em periódico indexado (ALICE)
Authors: 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.
Additional Information: LUIZ C. BORRO, CNPTIA; STANLEY ROBSON DE MEDEIROS OLIVEIRA, CNPTIA; MICHEL EDUARDO BELEZA YAMAGISHI, CNPTIA; ADAUTO LUIZ MANCINI, CNPTIA; JOSE GILBERTO JARDINE, CNPTIA; IVAN MAZONI, CNPTIA; EDGARD HENRIQUE DOS SANTOS, CNPTIA; ROBERTO HIROSHI HIGA, CNPTIA; PAULA REGINA KUSER FALCAO, CNPTIA; GORAN NESHICH, CNPTIA.
Title: Predicting enzyme class from protein structure using Bayesian classification.
Publisher: Genetics and Molecular Research, v. 5, n. 1, p. 193-202, 2006.
Language: en
Keywords: Bioinformática
Estrutura de proteína
Classe de enzima
Bayesian classification
Protein function prediction
Naive Bayes
Enzyme classification number
Bayesian classifier
Data classification
Description: 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.
NAL Thesaurus: Bioinformatics
Protein structure
Data Created: 2007-03-07
Appears in Collections:Artigo em periódico indexado (CNPTIA)

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