dc.creator | Romero Zaliz, Rocío | es |
dc.creator | Rubio Escudero, Cristina | es |
dc.creator | Cordón, Óscar | es |
dc.creator | Harari, Óscar | es |
dc.creator | Val, Coral del | es |
dc.creator | Zwir, Igor | es |
dc.date.accessioned | 2022-12-01T09:48:53Z | |
dc.date.available | 2022-12-01T09:48:53Z | |
dc.date.issued | 2006 | |
dc.identifier.citation | Romero Zaliz, R., Rubio Escudero, C., Cordón, Ó., Harari, Ó., Val, C.d. y Zwir, I. (2006). Mining Structural Databases: An Evolutionary Multi-Objetive Conceptual Clustering Methodology. En EvoWorkshops 2006: Workshops on Applications of Evolutionary Computation (159-171), Budapest, Hungary: Springer. | |
dc.identifier.isbn | 978-3-540-33237-4 | es |
dc.identifier.issn | 0302-9743 | es |
dc.identifier.uri | https://hdl.handle.net/11441/139993 | |
dc.description.abstract | The increased availability of biological databases contain ing representations of complex objects permits access to vast amounts of
data. In spite of the recent renewed interest in knowledge-discovery tech niques (or data mining), there is a dearth of data analysis methods in tended to facilitate understanding of the represented objects and related
systems by their most representative features and those relationship de rived from these features (i.e., structural data). In this paper we propose
a conceptual clustering methodology termed EMO-CC for Evolution ary Multi-Objective Conceptual Clustering that uses multi-objective and
multi-modal optimization techniques based on Evolutionary Algorithms
that uncover representative substructures from structural databases. Be sides, EMO-CC provides annotations of the uncovered substructures,
and based on them, applies an unsupervised classification approach to
retrieve new members of previously discovered substructures. We apply
EMO-CC to the Gene Ontology database to recover interesting sub structures that describes problems from different points of view and use
them to explain inmuno-inflammatory responses measured in terms of
gene expression profiles derived from the analysis of longitudinal blood
expression profiles of human volunteers treated with intravenous endo toxin compared to placebo. | es |
dc.format | application/pdf | es |
dc.format.extent | 13 | es |
dc.language.iso | eng | es |
dc.publisher | Springer | es |
dc.relation.ispartof | EvoWorkshops 2006: Workshops on Applications of Evolutionary Computation (2006), pp. 159-171. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.title | Mining Structural Databases: An Evolutionary Multi-Objetive Conceptual Clustering Methodology | es |
dc.type | info:eu-repo/semantics/conferenceObject | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/submittedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos | es |
dc.relation.publisherversion | https://link.springer.com/chapter/10.1007/11732242_15 | es |
dc.identifier.doi | 10.1007/11732242_15 | es |
dc.contributor.group | Universidad de Sevilla. TIC-254: Data Science and Big Data Lab | es |
dc.publication.initialPage | 159 | es |
dc.publication.endPage | 171 | es |
dc.eventtitle | EvoWorkshops 2006: Workshops on Applications of Evolutionary Computation | es |
dc.eventinstitution | Budapest, Hungary | es |
dc.relation.publicationplace | Berlin, Germany | es |