dc.creator | Martínez Ballesteros, María del Mar | es |
dc.creator | Nepomuceno Chamorro, Isabel de los Ángeles | es |
dc.creator | Riquelme Santos, José Cristóbal | es |
dc.date.accessioned | 2016-06-13T10:47:49Z | |
dc.date.available | 2016-06-13T10:47:49Z | |
dc.date.issued | 2011 | |
dc.identifier.isbn | 978-1-4577-1676-8 | es |
dc.identifier.uri | http://hdl.handle.net/11441/42186 | |
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. Microarray experiments
are generating datasets that can help in reconstructing gene
networks. One of the most important problems in network
reconstruction is finding, for each gene in the network, which
genes can affect it and how. Association Rules are an approach
of unsupervised learning to relate attributes to each other.
In this work we use Quantitative Association Rules in order
to define interrelations between genes. These rules work with
intervals on the attributes, without discretizing the data before
and they are generated by a multi-objective evolutionary
algorithm. In most cases the extracted rules confirm the existing
knowledge about cell-cycle gene expression, while hitherto
unknown relationships can be treated as new hypotheses. | es |
dc.description.sponsorship | Ministerio de Ciencia y Tecnología TIN2007-68084-C-00 | |
dc.description.sponsorship | Junta de Andalucía P07-TIC-02611 | |
dc.format | application/pdf | es |
dc.language.iso | eng | es |
dc.publisher | IEEE | es |
dc.relation.ispartof | Proceedings of the 2011 11th International Conference on Intelligent Systems Design and Applications 22 – 24 November 2011 Córdoba, Spain | es |
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 | gene networks | es |
dc.title | Inferring Gene-Gene Associations from Quantitative Association Rules | es |
dc.type | info:eu-repo/semantics/bookPart | 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.identifier.doi | http://dx.doi.org/10.1109/ISDA.2011.6121829 | es |
idus.format.extent | 6 | es |
dc.publication.initialPage | 1241 | es |
dc.publication.endPage | 1246 | es |
dc.identifier.idus | https://idus.us.es/xmlui/handle/11441/42186 | |
dc.contributor.funder | Ministerio de Ciencia y Tecnología (MCYT). España | |
dc.contributor.funder | Junta de Andalucía | |