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dc.contributor.authorUrda Muñoz, Daniel 
dc.contributor.authorAragón, Francisco
dc.contributor.authorBautista, Rocío
dc.contributor.authorFranco, Leonardo
dc.contributor.authorVeredas, Francisco J.
dc.contributor.authorClaros, Manuel Gonzalo
dc.contributor.authorJerez, José Manuel
dc.date.accessioned2023-01-18T08:00:34Z
dc.date.available2023-01-18T08:00:34Z
dc.date.issued2018
dc.identifier.issn1752-0509
dc.identifier.urihttp://hdl.handle.net/10259/7263
dc.description.abstractIn RNA-Seq gene expression analysis, a genetic signature or biomarker is defined as a subset of genes that is probably involved in a given complex human trait and usually provide predictive capabilities for that trait. The discovery of new genetic signatures is challenging, as it entails the analysis of complex-nature information encoded at gene level. Moreover, biomarkers selection becomes unstable, since high correlation among the thousands of genes included in each sample usually exists, thus obtaining very low overlapping rates between the genetic signatures proposed by different authors. In this sense, this paper proposes BLASSO, a simple and highly interpretable linear model with l1-regularization that incorporates prior biological knowledge to the prediction of breast cancer outcomes. Two different approaches to integrate biological knowledge in BLASSO, Gene-specific and Gene-disease, are proposed to test their predictive performance and biomarker stability on a public RNA-Seq gene expression dataset for breast cancer. The relevance of the genetic signature for the model is inspected by a functional analysis.en
dc.description.abstractTrabajo presentado en: V International Work-Conference on Bioinformatics and Biomedical Engineering
dc.description.sponsorshipPublication of this manuscript was sponsored by TIN2017-88728-C2 grant.en
dc.format.mimetypeapplication/pdf
dc.language.isoenges
dc.publisherSpringer Natureen
dc.relation.ispartofBMC Systems Biology. 2018, V. 12, n. 5, p. 14-26es
dc.rightsAtribución 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectRNA-Seqen
dc.subjectBiomarkers selectionen
dc.subjectBiological knowledgeen
dc.subjectMachine learningen
dc.subjectPrecision medicineen
dc.subject.otherInformáticaes
dc.subject.otherComputer scienceen
dc.subject.otherBiología moleculares
dc.subject.otherMolecular biologyen
dc.titleBLASSO: integration of biological knowledge into a regularized linear modelen
dc.typeinfo:eu-repo/semantics/conferenceObjectes
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.relation.publisherversionhttps://doi.org/10.1186/s12918-018-0612-8es
dc.identifier.dois12918-018-0612-8
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2017-88728-C2-1-R/ES/AVANCES EN EL DISEÑO Y ADAPTACION DE ALGORITMOS DE APRENDIZAJE PROFUNDO PARA SU APLICACION A PROBLEMAS EN LAS AREAS DE BIOMEDICINA Y CONTAMINACION ATMOSFERICA/es
dc.journal.titleBMC Systems Biologyen
dc.volume.number12es
dc.issue.number5es
dc.page.initial14es
dc.page.final26es
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones


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