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Highlights: Predicting the cross-immunoreactivity of hepatitis C virus hyper-variable region 1 peptides using polynomial neural networks PNN models of HCV-HVR1 cross-reactivity

dc.contributorCtr Dis Control
dc.contributorUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2018-11-26T16:40:40Z
dc.date.available2018-11-26T16:40:40Z
dc.date.created2018-11-26T16:40:40Z
dc.date.issued2015-01-01
dc.identifier2015 Ieee 5th International Conference On Computational Advances In Bio And Medical Sciences (iccabs). New York: Ieee, 1 p., 2015.
dc.identifier2164-229X
dc.identifierhttp://hdl.handle.net/11449/161611
dc.identifierWOS:000377899500030
dc.languageeng
dc.publisherIeee
dc.relation2015 Ieee 5th International Conference On Computational Advances In Bio And Medical Sciences (iccabs)
dc.rightsAcesso aberto
dc.sourceWeb of Science
dc.subjecthepatitis C virus
dc.subjectpolynomial neural networks (PNN)
dc.subjectprediction
dc.subjectvaccine
dc.subjectlinear projection
dc.subjectquantitative-structure-activity-relationship (QSAR)
dc.titleHighlights: Predicting the cross-immunoreactivity of hepatitis C virus hyper-variable region 1 peptides using polynomial neural networks PNN models of HCV-HVR1 cross-reactivity
dc.typeActas de congresos


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