dc.creator | Gutiérrez Avilés, David | es |
dc.creator | Rubio Escudero, Cristina | es |
dc.creator | Martínez Álvarez, Francisco | es |
dc.creator | Riquelme Santos, José Cristóbal | es |
dc.date.accessioned | 2016-07-13T09:13:42Z | |
dc.date.available | 2016-07-13T09:13:42Z | |
dc.date.issued | 2014 | |
dc.identifier.citation | Gutiérrez Avilés, D., Rubio Escudero, C., Martínez Álvarez, F. y Riquelme Santos, J.C. (2014). TriGen: A genetic algorithm to mine triclusters in temporal gene expression data. Neurocomputing, 132, 42-53. | |
dc.identifier.issn | 0925-2312 | es |
dc.identifier.uri | http://hdl.handle.net/11441/43541 | |
dc.description.abstract | Analyzing microarray data represents a computational challenge due to the characteristics of these data. Clustering
techniques are widely applied to create groups of genes that exhibit a similar behavior under the conditions tested.
Biclustering emerges as an improvement of classical clustering since it relaxes the constraints for grouping genes to
be evaluated only under a subset of the conditions and not under all of them. However, this technique is not
appropriate for the analysis of longitudinal experiments in which the genes are evaluated under certain conditions at
several time points. We present the TriGen algorithm, a genetic algorithm that finds triclusters of gene expression that
take into account the experimental conditions and the time points simultaneously. We have used TriGen to mine
datasets related to synthetic data, yeast (Saccharomyces cerevisiae) cell cycle and human inflammation and host
response to injury experiments. TriGen has proved to be capable of extracting groups of genes with similar patterns in
subsets of conditions and times, and these groups have shown to be related in terms of their functional annotations
extracted from the Gene Ontology. | es |
dc.description.sponsorship | Ministerio de Ciencia y Tecnología TIN2011-28956-C00 | es |
dc.description.sponsorship | Ministerio de Ciencia y Tecnología TIN2009-13950 | es |
dc.description.sponsorship | Junta de Andalucía TIC-7528 | es |
dc.format | application/pdf | es |
dc.language.iso | eng | es |
dc.publisher | Elsevier | es |
dc.relation.ispartof | Neurocomputing, 132, 42-53. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Tricluster | es |
dc.subject | genetic algorithms | es |
dc.subject | Microarray data | es |
dc.subject | time series | es |
dc.title | TriGen: A genetic algorithm to mine triclusters in temporal gene expression data | 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-C00 | es |
dc.relation.projectID | TIN2009-13950 | es |
dc.relation.projectID | TIC-7528 | es |
dc.identifier.doi | http://dx.doi.org/10.1016/j.neucom.2013.03.061 | es |
idus.format.extent | 12 | es |
dc.journaltitle | Neurocomputing | es |
dc.publication.volumen | 132 | es |
dc.publication.initialPage | 42 | es |
dc.publication.endPage | 53 | es |
dc.identifier.idus | https://idus.us.es/xmlui/handle/11441/43541 | |