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 | 2016-07-13T08:47:59Z | |
dc.date.available | 2016-07-13T08:47:59Z | |
dc.date.issued | 2014 | |
dc.identifier.citation | Martínez Ballesteros, M.d.M., Nepomuceno Chamorro, I.d.l.Á. y Riquelme Santos, J.C. (2014). Discovering gene association networks by multi-objective evolutionary quantitative association rules. Journal of Computer and System Sciences, 80 (1), 118-136. | |
dc.identifier.issn | 0022-0000 | es |
dc.identifier.uri | http://hdl.handle.net/11441/43540 | |
dc.description.abstract | In the last decade, the interest in microarray technology has exponentially increased due to its
ability to monitor the expression of thousands of genes simultaneously. The reconstruction of gene
association networks from gene expression profiles is a relevant task and several statistical
techniques have been proposed to build them. The problem lies in the process to discover which
genes are more relevant and to identify the direct regulatory relationships among them. We
developed a multi-objective evolutionary algorithm for mining quantitative association rules to deal
with this problem. We applied our methodology named GarNet to a well-known microarray data of
yeast cell cycle. The performance analysis of GarNet was organized in three steps similarly to the
study performed by Gallo et al. GarNet outperformed the benchmark methods in most cases in terms
of quality metrics of the networks, such as accuracy and precision, which were measured using
YeastNet database as true network. Furthermore, the results were consistent with previous
biological knowledge. | es |
dc.description.sponsorship | Ministerio de Ciencia y Tecnología TIN2011-28956-C02-02 | es |
dc.description.sponsorship | Junta de Andalucía P11-TIC-7528 | es |
dc.format | application/pdf | es |
dc.language.iso | eng | es |
dc.publisher | Elsevier | es |
dc.relation.ispartof | Journal of Computer and System Sciences, 80 (1), 118-136. | |
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 | Multi-objective evolutionary algorithms | es |
dc.subject | quantitative association rules | es |
dc.subject | gene networks | es |
dc.subject | Microarray analysis | es |
dc.title | Discovering gene association networks by multi-objective evolutionary quantitative association rules | es |
dc.type | info:eu-repo/semantics/article | 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.projectID | TIN2011-28956-C02-02 | es |
dc.relation.projectID | P11-TIC-7528 | es |
dc.relation.publisherversion | http://dx.doi.org/10.1016/j.jcss.2013.03.010 | |
dc.identifier.doi | 10.1016/j.jcss.2013.03.010 | es |
idus.format.extent | 19 | es |
dc.journaltitle | Journal of Computer and System Sciences | es |
dc.publication.volumen | 80 | es |
dc.publication.issue | 1 | es |
dc.publication.initialPage | 118 | es |
dc.publication.endPage | 136 | es |
dc.identifier.idus | https://idus.us.es/xmlui/handle/11441/43540 | |
dc.contributor.funder | Ministerio de Ciencia y Tecnología (MCYT). España | |
dc.contributor.funder | Junta de Andalucía | |