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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Main 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. |
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Other Authors: | 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. |
Format: | Artigo de periódico biblioteca |
Language: | English eng |
Published: |
2007-03-07
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Subjects: | Bioinformática, Estrutura de proteína, Classe de enzima, Bayesian classification, Protein function prediction, Naive Bayes, Enzyme classification number, Bayesian classifier, Data classification, Bioinformatics, Protein structure, |
Online Access: | http://www.alice.cnptia.embrapa.br/alice/handle/doc/9196 |
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