Predicting enzyme class from protein structure using Bayesian classification.

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dc.contributorLUIZ 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.
dc.creatorBORRO, L. C.
dc.creatorOLIVEIRA, S. R. M.
dc.creatorYAMAGISHI, M. E. B.
dc.creatorMANCINI, A. L.
dc.creatorJARDINE, J. G.
dc.creatorMAZONI, I.
dc.creatorSANTOS, E. H. dos
dc.creatorHIGA, R. H.
dc.creatorKUSER, P. R.
dc.creatorNESHICH, G.
dc.date2011-04-10T11:11:11Z
dc.date2011-04-10T11:11:11Z
dc.date2007-03-07
dc.date2006
dc.date2017-05-17T11:11:11Z
dc.date.accessioned2026-07-07T04:15:07Z
dc.descriptionABSTRACT. 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.
dc.identifierGenetics and Molecular Research, v. 5, n. 1, p. 193-202, 2006.
dc.identifierhttp://www.alice.cnptia.embrapa.br/alice/handle/doc/9196
dc.identifier.urihttp://hdl.handle.net/123456789/455577
dc.languageeng
dc.rightsopenAccess
dc.subjectBioinformática
dc.subjectEstrutura de proteína
dc.subjectClasse de enzima
dc.subjectBayesian classification
dc.subjectProtein function prediction
dc.subjectNaive Bayes
dc.subjectEnzyme classification number
dc.subjectBayesian classifier
dc.subjectData classification
dc.subjectBioinformatics
dc.subjectProtein structure
dc.titlePredicting enzyme class from protein structure using Bayesian classification.
dc.typeArtigo de periódico

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