Title:	Predicting enzyme class from protein structure using Bayesian classification.
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.
Affiliation:	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.
Date Issued:	2006
Citation:	Genetics and Molecular Research, v. 5, n. 1, p. 193-202, 2006.
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
Keywords:	Bioinformática Estrutura de proteína Classe de enzima Bayesian classification Protein function prediction Naive Bayes Enzyme classification number Bayesian classifier Data classification
Type of Material:	Artigo de periódico
Access:	openAccess
Appears in Collections:	Artigo em periódico indexado (CNPTIA)