Pipeline design to identify key features and perform classification on response/predisposition large-scale genetic data

dc.contributor	Gavaldà Mestre, Ricard
dc.contributor	Palou, Xavier Rafael
dc.contributor.author	Valdés Graterol, María Gabriela
dc.contributor.other	Universitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.date.accessioned	2018-05-18T09:26:04Z
dc.date.available	2018-05-18T09:26:04Z
dc.date.issued	2017-04
dc.identifier.uri	http://hdl.handle.net/2117/117335
dc.description.abstract	We 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.iso	eng
dc.publisher	Universitat Politècnica de Catalunya
dc.subject	Àrees temàtiques de la UPC::Informàtica
dc.subject.lcsh	Machine learning
dc.subject.lcsh	Data analysis
dc.subject.other	missing heritability
dc.subject.other	GWAS
dc.subject.other	SNP
dc.subject.other	feature selection
dc.subject.other	sampling
dc.subject.other	classification.
dc.title	Pipeline design to identify key features and perform classification on response/predisposition large-scale genetic data
dc.type	Master thesis
dc.subject.lemac	Aprenentatge automàtic
dc.subject.lemac	Anàlisi de dades
dc.identifier.slug	124451
dc.rights.access	Open Access
dc.date.updated	2017-05-11T04:00:17Z
dc.audience.educationlevel	Màster
dc.audience.mediator	Facultat d'Informàtica de Barcelona
dc.audience.degree	MÀSTER UNIVERSITARI EN INNOVACIÓ I RECERCA EN INFORMÀTICA (Pla 2012)

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