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dc.contributorGavaldà Mestre, Ricard
dc.contributorPalou, Xavier Rafael
dc.contributor.authorValdés Graterol, María Gabriela
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.date.accessioned2018-05-18T09:26:04Z
dc.date.available2018-05-18T09:26:04Z
dc.date.issued2017-04
dc.identifier.urihttp://hdl.handle.net/2117/117335
dc.description.abstractWe propose a state-of-the-art, scalable and flexible alternative to the classical GWAS approach, based on machine learning techniques, to analyze large-scale data and discover epistatic and non-epistatic polygenic variants in complex diseases.
dc.language.isoeng
dc.publisherUniversitat Politècnica de Catalunya
dc.subjectÀrees temàtiques de la UPC::Informàtica
dc.subject.lcshMachine learning
dc.subject.lcshData analysis
dc.subject.othermissing heritability
dc.subject.otherGWAS
dc.subject.otherSNP
dc.subject.otherfeature selection
dc.subject.othersampling
dc.subject.otherclassification.
dc.titlePipeline design to identify key features and perform classification on response/predisposition large-scale genetic data
dc.typeMaster thesis
dc.subject.lemacAprenentatge automàtic
dc.subject.lemacAnàlisi de dades
dc.identifier.slug124451
dc.rights.accessOpen Access
dc.date.updated2017-05-11T04:00:17Z
dc.audience.educationlevelMàster
dc.audience.mediatorFacultat d'Informàtica de Barcelona


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