Discovery of Genes Implied in Cancer by Genetic Algorithms and Association Rules

Sánchez Medina, Alejandro; Gil Pichardo, Alberto; García Heredia, José Manuel; Martínez Ballesteros, María del Mar

doi:10.1007/978-3-319-32034-2_58

Presentation

dc.creator	Sánchez Medina, Alejandro	es
dc.creator	Gil Pichardo, Alberto	es
dc.creator	García Heredia, José Manuel	es
dc.creator	Martínez Ballesteros, María del Mar	es
dc.date.accessioned	2022-04-25T10:10:22Z
dc.date.available	2022-04-25T10:10:22Z
dc.date.issued	2016
dc.identifier.citation	Sánchez Medina, A., Gil Pichardo, A., García Heredia, J.M. y Martínez Ballesteros, M.d.M. (2016). Discovery of Genes Implied in Cancer by Genetic Algorithms and Association Rules. En HAIS 2016 : 11th International Conference on Hybrid Artificial Intelligence Systems (694-705), Sevilla, España: Springer.
dc.identifier.isbn	978-3-319-32033-5	es
dc.identifier.issn	0302-9743	es
dc.identifier.uri	https://hdl.handle.net/11441/132529
dc.description.abstract	This work proposes a methodology to identify genes highly related with cancer. In particular, a multi-objective evolutionary algo rithm named CANGAR is applied to obtain quantitative association rules. This kind of rules are used to identify dependencies between genes and their expression levels. Hierarchical cluster analysis, fold-change and review of the literature have been considered to validate the relevance of the results obtained. The results show that the reported genes are consistent with prior knowledge and able to characterize cancer colon patients.	es
dc.description.sponsorship	Ministerio de Ciencia y Tecnología TIN2011-28956-C02-02	es
dc.description.sponsorship	Ministerio de Economía y Competitividad TIN2014-55894-C2-1-R	es
dc.description.sponsorship	Junta de Andalucía P11-TIC-7528	es
dc.description.sponsorship	Junta de Andalucía P12-TIC-1728	es
dc.format	application/pdf	es
dc.format.extent	12	es
dc.language.iso	eng	es
dc.publisher	Springer	es
dc.relation.ispartof	HAIS 2016 : 11th International Conference on Hybrid Artificial Intelligence Systems (2016), pp. 694-705.
dc.rights	Attribution-NonCommercial-NoDerivatives 4.0 Internacional	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/4.0/	*
dc.subject	Data mining	es
dc.subject	Association Rules	es
dc.subject	Gene expression	es
dc.subject	Cancer	es
dc.title	Discovery of Genes Implied in Cancer by Genetic Algorithms and Association Rules	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.contributor.affiliation	Universidad de Sevilla. Departamento de Bioquímica Vegetal y Biología Molecular	es
dc.relation.projectID	TIN2011-28956-C02-02	es
dc.relation.projectID	TIN2014-55894-C2-1-R	es
dc.relation.projectID	P11-TIC-7528	es
dc.relation.projectID	P12-TIC-1728	es
dc.relation.publisherversion	https://link.springer.com/chapter/10.1007/978-3-319-32034-2_58	es
dc.identifier.doi	10.1007/978-3-319-32034-2_58	es
dc.contributor.group	Universidad de Sevilla. TIC-254: Data Science and Big Data Lab	es
dc.publication.initialPage	694	es
dc.publication.endPage	705	es
dc.eventtitle	HAIS 2016 : 11th International Conference on Hybrid Artificial Intelligence Systems	es
dc.eventinstitution	Sevilla, España	es
dc.relation.publicationplace	Cham, Switzerland	es
dc.identifier.sisius	21011061	es
dc.contributor.funder	Ministerio de Ciencia Y Tecnología (MCYT). España	es
dc.contributor.funder	Ministerio de Economía y Competitividad (MINECO). España	es
dc.contributor.funder	Junta de Andalucía	es

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