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

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dc.contributor.authorLara, James
dc.contributor.authorKhudyakov, Yury
dc.contributor.authorRossi, Livia [UNESP]
dc.contributor.authorVaughan, Gilberta
dc.contributor.authorIEEE
dc.contributor.institutionCtr Dis Control
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2018-11-26T16:40:40Z
dc.date.available2018-11-26T16:40:40Z
dc.date.issued2015-01-01
dc.description.affiliationCtr Dis Control, Div Viral Hepatitis, Atlanta, GA 30333 USA
dc.description.affiliationUNESP, IBILCE, Dept Biol, Sao Jose Do Rio Preto, Brazil
dc.description.affiliationUnespUNESP, IBILCE, Dept Biol, Sao Jose Do Rio Preto, Brazil
dc.format.extent1
dc.identifier.citation2015 Ieee 5th International Conference On Computational Advances In Bio And Medical Sciences (iccabs). New York: Ieee, 1 p., 2015.
dc.identifier.issn2164-229X
dc.identifier.urihttp://hdl.handle.net/11449/161611
dc.identifier.wosWOS:000377899500030
dc.language.isoeng
dc.publisherIeee
dc.relation.ispartof2015 Ieee 5th International Conference On Computational Advances In Bio And Medical Sciences (iccabs)
dc.rights.accessRightsAcesso 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-reactivityen
dc.typeTrabalho apresentado em evento
dcterms.licensehttp://www.ieee.org/publications_standards/publications/rights/rights_policies.html
dcterms.rightsHolderIeee

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