Predicting enzyme class from protein structural parameters and bagging predictors.

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dc.contributorMICHEL EDUARDO BELEZA YAMAGISHI, CNPTIA; STANLEY ROBSON DE MEDEIROS OLIVEIRA, CNPTIA; LUIZ C. BORRO; EDGARD HENRIQUE DOS SANTOS, CNPTIA; JOSÉ GILBERTO JARDINE, CNPTIA; FÁBIO DANILO VIEIRA, CNPTIA; IVAN MAZONI, CNPTIA; MARCELO GONCALVES NARCISO, CNPTIA; PAULA REGINA KUSER FALCÃO, CNPTIA; GORAN NESHICH, CNPTIA.
dc.creatorYAMAGISHI, M. E. B.
dc.creatorOLIVEIRA, S. R. M.
dc.creatorBORRO, L. C.
dc.creatorSANTOS, E. H.
dc.creatorJARDINE, J. G.
dc.creatorVIEIRA, F. D.
dc.creatorMAZONI, I.
dc.creatorNARCISO, M. G.
dc.creatorKUSER-FALCÃO, P. R.
dc.creatorNESHICH, G.
dc.date2022-05-17T18:13:45Z
dc.date2022-05-17T18:13:45Z
dc.date2006-08-17
dc.date2006
dc.date.accessioned2026-07-07T04:16:59Z
dc.descriptionShort Abstract: In this work we present a new method to classify enzymes that uses the STING_DB physical-chemical parameters and Bagging predictors. By building models based on "decision tree" and "neural network", we obtained an accuracy of 74% on average. These results outperform the similar models proposed in literature.
dc.descriptionISMB, X-MEETING 2006. Poster I-13.
dc.formatNão paginado.
dc.identifierIn: ANNUAL INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS FOR MOLECULAR BIOLOGY, 14.; ANNUAL AB3C CONFERENCE, 2., 2006, Fortaleza. Conference Program... Fortaleza: ISCB, 2006.
dc.identifierhttp://www.alice.cnptia.embrapa.br/alice/handle/doc/9310
dc.identifier.urihttp://hdl.handle.net/123456789/456360
dc.languageeng
dc.rightsopenAccess
dc.subjectParâmetros estruturais da proteína
dc.subjectParâmetros
dc.subjectBioinformática
dc.subjectSting_DB
dc.subjectProteina
dc.subjectEnzima
dc.subjectProteins
dc.subjectEnzymes
dc.subjectBioinformatics
dc.titlePredicting enzyme class from protein structural parameters and bagging predictors.
dc.typeResumo em anais e proceedings

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