Automatic classification of enzyme family in protein annotation

dc.contributorUniversidade Estadual Paulista (UNESP)
dc.creatorDos Santos, Cássia T.
dc.creatorBazzan, Ana L. C.
dc.creatorLemke, Ney
dc.date2014-05-27T11:23:58Z
dc.date2016-10-25T18:27:23Z
dc.date2014-05-27T11:23:58Z
dc.date2016-10-25T18:27:23Z
dc.date2009-09-14
dc.date.accessioned2017-04-06T01:37:03Z
dc.date.available2017-04-06T01:37:03Z
dc.identifierLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 5676 LNBI, p. 86-96.
dc.identifier0302-9743
dc.identifier1611-3349
dc.identifierhttp://hdl.handle.net/11449/71147
dc.identifierhttp://acervodigital.unesp.br/handle/11449/71147
dc.identifier10.1007/978-3-642-03223-3_8
dc.identifier2-s2.0-69949190117
dc.identifierhttp://dx.doi.org/10.1007/978-3-642-03223-3_8
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/892165
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. © 2009 Springer Berlin Heidelberg.
dc.languageeng
dc.relationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectAutomatic classification
dc.subjectBiological functions
dc.subjectClassification errors
dc.subjectEnzymatic process
dc.subjectEnzyme commissions
dc.subjectFunctional information
dc.subjectGenome annotation
dc.subjectProtein annotation
dc.subjectProtein functions
dc.subjectSequence homology
dc.subjectSet of rules
dc.subjectSymbolic machine learning
dc.subjectTri-dimensional structure
dc.subjectAutomatic indexing
dc.subjectBiology
dc.subjectEnzymes
dc.subjectBioinformatics
dc.titleAutomatic classification of enzyme family in protein annotation
dc.typeOtro


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