Automatic Classification of Enzyme Family in Protein Annotation

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniv Evora-
Autor(es): dc.contributorUniv Fed Rio Grande do Sul-
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.creatorSantos, Cassia T. dos-
Autor(es): dc.creatorBazzan, Ana L. C.-
Autor(es): dc.creatorLemke, Ney [UNESP]-
Autor(es): dc.creatorGuimaraes, K. S.-
Autor(es): dc.creatorPanchenko, A.-
Autor(es): dc.creatorPrzytycka, T. M.-
Data de aceite: dc.date.accessioned2022-02-22T00:12:20Z-
Data de disponibilização: dc.date.available2022-02-22T00:12:20Z-
Data de envio: dc.date.issued2020-12-09-
Data de envio: dc.date.issued2020-12-09-
Data de envio: dc.date.issued2009-01-01-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/197391-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/197391-
Descrição: dc.descriptionMost of the tasks in genome annotation can be at least partially automated. Since this annotation is time-consuming, facilitating some parts of the process - thus freeing the specialist to carry out more valuable tasks - has been the motivation of many tools and annotation environments. In particular, annotation of protein function can benefit from knowledge about enzymatic processes. The use of sequence homology alone is not a good approach to derive this knowledge when there are only a few homologues of the sequence to be annotated. The alternative is to use motifs. This paper uses a symbolic machine learning approach to derive rules for the classification of enzymes according to the Enzyme Commission (EC). Our results show that, for the top class, the average global classification error is 3.13%. Our technique also produces a set of rules relating structural to functional information, which is important to understand the protein tridimensional structure and determine its biological function.-
Descrição: dc.descriptionUniv Evora, Dept Informat, Evora, Portugal-
Descrição: dc.descriptionUniv Fed Rio Grande do Sul, Inst Informat, BR-59072970 Natal, RN, Brazil-
Descrição: dc.descriptionUNESP, Inst Biociencias, Dept Fysica Biofysica, Botucatu, SP, Brazil-
Descrição: dc.descriptionUNESP, Inst Biociencias, Dept Fysica Biofysica, Botucatu, SP, Brazil-
Formato: dc.format86-+-
Idioma: dc.languageen-
Publicador: dc.publisherSpringer-
Relação: dc.relationAdvances In Bioinformatics And Computational Biology, Proceedings-
???dc.source???: dc.sourceWeb of Science-
Título: dc.titleAutomatic Classification of Enzyme Family in Protein Annotation-
Aparece nas coleções:Repositório Institucional - Unesp

Não existem arquivos associados a este item.