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dc.contributor.authorCárdenas Domínguez, Martha Ivón
dc.contributor.authorVellido Alcacena, Alfredo
dc.contributor.authorGiraldo Arjonilla, Jesús
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.date.accessioned2016-02-15T14:19:35Z
dc.date.available2016-02-15T14:19:35Z
dc.date.issued2014
dc.identifier.citationCárdenas, M.I., Vellido, A., Giraldo, J. Manifold learning visualization of metabotropic glutamate receptors. A: International Conference of the Catalan Association for Artificial Intelligence. "Artificial Intelligence Research and Development: Proceedings of the 17th International Conference of the Catalan Association for Artificial Intelligence, Barcelona, Catalonia, Spain, October 22-24, 2014". Barcelona: IOS Press, 2014, p. 269-272.
dc.identifier.isbn978-1-61499-451-0
dc.identifier.urihttp://hdl.handle.net/2117/82949
dc.description.abstractG-Protein-Coupled Receptors (GPCRs) are cell membrane proteins with a key role in biological processes. GPCRs of class C, in particular, are of great interest in pharmacology. The lack of knowledge about their 3-D structures means they must be investigated through their primary amino acid sequences. Sequence visualization can help to explore the existing receptor sub-groupings at different partition levels. In this paper, we focus on Metabotropic Glutamate Receptors (mGluR), a subtype of class C GPCRs. Different versions of a probabilistic manifold learning model are employed to comparatively sub-group and visualize them through different transformations of their sequences.
dc.format.extent4 p.
dc.language.isoeng
dc.publisherIOS Press
dc.subjectÀrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Bioinformàtica
dc.subject.lcshProtein research
dc.subject.otherG-protein-coupled receptors
dc.subject.otherMetabotropic glutamate receptors
dc.subject.otherData visualization
dc.subject.otherGenerative topographic mapping
dc.titleManifold learning visualization of metabotropic glutamate receptors
dc.typeConference report
dc.subject.lemacProteïnes -- Investigació
dc.contributor.groupUniversitat Politècnica de Catalunya. SOCO - Soft Computing
dc.identifier.doi10.3233/978-1-61499-452-7-269
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://ebooks.iospress.nl/publication/38078
dc.rights.accessOpen Access
drac.iddocument17499552
dc.description.versionPostprint (author's final draft)
upcommons.citation.authorCárdenas, M.I.; Vellido, A.; Giraldo, J.
upcommons.citation.contributorInternational Conference of the Catalan Association for Artificial Intelligence
upcommons.citation.pubplaceBarcelona
upcommons.citation.publishedtrue
upcommons.citation.publicationNameArtificial Intelligence Research and Development: Proceedings of the 17th International Conference of the Catalan Association for Artificial Intelligence, Barcelona, Catalonia, Spain, October 22-24, 2014
upcommons.citation.startingPage269
upcommons.citation.endingPage272


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