dc.creator | Martínez Ballesteros, María del Mar | es |
dc.creator | Rubio Escudero, Cristina | es |
dc.creator | Riquelme Santos, José Cristóbal | es |
dc.creator | Martínez Álvarez, Francisco | es |
dc.date.accessioned | 2022-04-27T08:05:51Z | |
dc.date.available | 2022-04-27T08:05:51Z | |
dc.date.issued | 2011 | |
dc.identifier.citation | Martínez Ballesteros, M.d.M., Rubio Escudero, C., Riquelme Santos, J.C. y Martínez Álvarez, F. (2011). Mining Quantitative Association Rules in Microarray Data Using Evolutive Algorithms. En ICAART 2011: 3rd International Conference on Agents and Artificial Intelligence (574-577), Rome, Italy: SciTePress. | |
dc.identifier.isbn | 978-989-8425-40-9 | es |
dc.identifier.issn | 2184-433X | es |
dc.identifier.uri | https://hdl.handle.net/11441/132716 | |
dc.description.abstract | The microarray technique is able to monitor the change in concentration of RNA in thousands of genes simultaneously. The interest in this technique has grown exponentially in recent years and the difficulties in analyzing data from such experiments, which are characterized by the high number of genes to be analyzed in relation to the low number of experiments or samples available. In this paper we show the result of applying a data mining method based on quantitative association rules for microarray data. These rules work with intervals on the attributes, without discretizing the data before. The rules are generated by an evolutionary algorithm. | es |
dc.description.sponsorship | Ministerio de Ciencia y Tecnología TIN2007-68084-C-00 | es |
dc.description.sponsorship | Junta de Andalucía P07-TIC-02611 | es |
dc.format | application/pdf | es |
dc.format.extent | 4 | es |
dc.language.iso | eng | es |
dc.publisher | SciTePress | es |
dc.relation.ispartof | ICAART 2011: 3rd International Conference on Agents and Artificial Intelligence (2011), pp. 574-577. | |
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 | Evolutionary algorithms | es |
dc.subject | Quantitative association rules | es |
dc.subject | Microarray | es |
dc.title | Mining Quantitative Association Rules in Microarray Data Using Evolutive Algorithms | es |
dc.type | info:eu-repo/semantics/conferenceObject | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | 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.projectID | TIN2007-68084-C-00 | es |
dc.relation.projectID | P07-TIC-02611 | es |
dc.relation.publisherversion | https://www.scitepress.org/PublicationsDetail.aspx?ID=6VTEylG1spc=&t=1 | es |
dc.identifier.doi | 10.5220/0003152705740577 | es |
dc.contributor.group | Universidad de Sevilla. TIC-254: Data Science and Big Data Lab | es |
dc.publication.initialPage | 574 | es |
dc.publication.endPage | 577 | es |
dc.eventtitle | ICAART 2011: 3rd International Conference on Agents and Artificial Intelligence | es |
dc.eventinstitution | Rome, Italy | es |
dc.relation.publicationplace | Setúbal, Portugal | es |
dc.contributor.funder | Ministerio de Ciencia Y Tecnología (MCYT). España | es |
dc.contributor.funder | Junta de Andalucía | es |