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 |
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