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dc.contributor.authorUrda, Daniel
dc.contributor.authorMontes-Torres, Julio
dc.contributor.authorMoreno, Fernando
dc.contributor.authorFranco, Leonardo 
dc.contributor.authorJerez-Aragonés, José Manuel 
dc.date.accessioned2017-06-20T09:22:23Z
dc.date.available2017-06-20T09:22:23Z
dc.date.issued2017
dc.identifier.citationI. Rojas et al. (Eds.): IWANN 2017, Part II, LNCS 10306, pp. 50–59, 2017. DOI: 10.1007/978-3-319-59147-6_5es_ES
dc.identifier.urihttp://hdl.handle.net/10630/13942
dc.description.abstractDeep learning models are currently being applied in several areas with great success. However, their application for the analysis of high-throughput sequencing data remains a challenge for the research community due to the fact that this family of models are known to work very well in big datasets with lots of samples available, just the opposite scenario typically found in biomedical areas. In this work, a first approximation on the use of deep learning for the analysis of RNA-Seq gene expression profiles data is provided. Three public cancer-related databases are analyzed using a regularized linear model (standard LASSO) as baseline model, and two deep learning models that differ on the feature selection technique used prior to the application of a deep neural net model. The results indicate that a straightforward application of deep nets implementations available in public scientific tools and under the conditions described within this work is not enough to outperform simpler models like LASSO. Therefore, smarter and more complex ways that incorporate prior biological knowledge into the estimation procedure of deep learning models may be necessary in order to obtain better results in terms of predictive performance.es_ES
dc.description.sponsorshipUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.es_ES
dc.language.isoenges_ES
dc.publisherSpringeres_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjectExpresión génica - Proceso electrónico de datoses_ES
dc.subject.otherMachine learninges_ES
dc.subject.otherGenetic signatureses_ES
dc.titleDeep Learning to Analyze RNA-Seq Gene Expression Dataes_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.centroE.T.S.I. Informáticaes_ES
dc.relation.eventtitleInternational Work-Conference on Artificial Neural Networkses_ES
dc.relation.eventplaceCádiz (España)es_ES
dc.relation.eventdate14/06/2017es_ES
dc.identifier.orcidhttp://orcid.org/0000-0002-7858-2966es_ES
dc.cclicenseby-nc-ndes_ES


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