Predicting enzyme class from protein structure using Bayesian classification.

Predicting enzyme class from protein structure using Bayesian classification.

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.

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Autores principales: 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.
Otros Autores: 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.
Formato: Artigo de periódico biblioteca
Idioma:English
eng
Publicado: 2007-03-07
Materias:Bioinformática, Estrutura de proteína, Classe de enzima, Bayesian classification, Protein function prediction, Naive Bayes, Enzyme classification number, Bayesian classifier, Data classification, Bioinformatics, Protein structure,
Acceso en línea:http://www.alice.cnptia.embrapa.br/alice/handle/doc/9196
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http://www.alice.cnptia.embrapa.br/alice/handle/doc/9196

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