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dc.contributor.authorRamos González, Juan 
dc.contributor.authorCastellanos Garzón, José Antonio 
dc.contributor.authorDe Paz, Juan F. 
dc.contributor.authorCorchado Rodríguez, Juan Manuel 
dc.date.accessioned2021-05-14T08:45:33Z
dc.date.available2021-05-14T08:45:33Z
dc.date.issued2018-01-20
dc.identifier.citationRamos, J., Castellanos Garzón, J., de Paz, J. and Corchado, J., 2018. A data mining framework based on boundary-points for gene selection from DNA-microarrays: Pancreatic Ductal Adenocarcinoma as a case study. Engineering Applications of Artificial Intelligence, 70, pp.92-108. https://doi.org/10.1016/j.engappai.2018.01.007es_ES
dc.identifier.issn0952-1976
dc.identifier.urihttp://hdl.handle.net/10366/145834
dc.description.abstract[EN] Gene selection (or feature selection) from DNA-microarray data can be focused on different techniques, which generally involve statistical tests, data mining and machine learning. In recent years there has been an increasing interest in using hybrid-technique sets to face the problem of meaningful gene selection; nevertheless, this issue remains a challenge. In an effort to address the situation, this paper proposes a novel hybrid framework based on data mining techniques and tuned to select gene subsets, which are meaningfully related to the target disease conducted in DNA-microarray experiments. For this purpose, the framework above deals with approaches such as statistical significance tests, cluster analysis, evolutionary computation, visual analytics and boundary points. The latter is the core technique of our proposal, allowing the framework to define two methods of gene selection. Another novelty of this work is the inclusion of the age of patients as an additional factor in our analysis, which can leading to gaining more insight into the disease. In fact, the results reached in this research have been very promising and have shown their biological validity. Hence, our proposal has resulted in a methodology that can be followed in the gene selection process from DNA-microarray data.es_ES
dc.language.isoenges_ES
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectFeature selectiones_ES
dc.subjectGene selectiones_ES
dc.subjectData mininges_ES
dc.subjectCluster analysises_ES
dc.subjectEvolutionary computationes_ES
dc.subjectBoundary pointes_ES
dc.subjectVisual analyticses_ES
dc.subjectFilter methodes_ES
dc.subjectBoundary genees_ES
dc.titleA data mining framework based on boundary-points for gene selection from DNA-microarrays: Pancreatic Ductal Adenocarcinoma as a case studyes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.subject.unesco1203.17 Informáticaes_ES
dc.identifier.doi10.1016/j.engappai.2018.01.007
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.journal.titleEngineering Applications of Artificial Intelligencees_ES
dc.volume.number70es_ES
dc.page.initial92es_ES
dc.page.final108es_ES
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_ES


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