title: 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.
authors.affiliation: Embrapa Information Technology, André Tosello, 209, Caixa Postal 6041, 13083-886 Campinas, SP, Brazil.
authors.forename: Luiz C
authors.initials: LC
authors.lastname: Borro
authors.forename: Stanley R M
authors.initials: SR
authors.lastname: Oliveira
authors.forename: Michel E B
authors.initials: ME
authors.lastname: Yamagishi
authors.forename: Adaulto L
authors.initials: AL
authors.lastname: Mancini
authors.forename: José G
authors.initials: JG
authors.lastname: Jardine
authors.forename: Ivan
authors.initials: I
authors.lastname: Mazoni
authors.forename: Edgard H dos
authors.initials: EH
authors.lastname: Santos
authors.forename: Roberto H
authors.initials: RH
authors.lastname: Higa
authors.forename: Paula R
authors.initials: PR
authors.lastname: Kuser
authors.forename: Goran
authors.initials: G
authors.lastname: Neshich
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D004798:Enzymes / Q000737:chemistry* / Q000145:classification*; D006801:Humans; D011487:Protein Conformation; D016415:Sequence Alignment
chemical_list: D004798:Enzymes