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dc.contributorCremers, Daniel
dc.contributorGolkov, Vladimir
dc.contributor.authorAlba Avilés, Manuel
dc.date.accessioned2018-06-01T10:25:31Z
dc.date.available2018-06-01T10:25:31Z
dc.date.issued2017-09-26
dc.identifier.urihttp://hdl.handle.net/2117/117716
dc.description.abstractThe assignment of functions to proteins is a bottleneck due to the need of costly and time-consuming molecular experiments. This is the reason why more often data analysis methods are used for protein an- notation. In this thesis I consider an approach based on Deep Learning architectures.
dc.language.isoeng
dc.publisherUniversitat Politècnica de Catalunya
dc.subjectÀrees temàtiques de la UPC::Informàtica
dc.subject.lcshMachine learning
dc.subject.lcshProteins
dc.subject.otherBiology
dc.subject.otherData Science
dc.subject.otherDeep Learning
dc.subject.otherMachine Learning
dc.subject.otherBiologia
dc.titleDeep Learning for Protein Function Prediction
dc.title.alternativeTiefe künstliche neuronal Netze für Vorhersage von Proteinfunktion
dc.typeMaster thesis
dc.subject.lemacAprenentatge automàtic
dc.subject.lemacProteïnes
dc.identifier.slug126454
dc.rights.accessOpen Access
dc.date.updated2017-12-23T05:00:19Z
dc.audience.educationlevelMàster
dc.audience.mediatorFacultat d'Informàtica de Barcelona
dc.audience.degreeMÀSTER UNIVERSITARI EN INNOVACIÓ I RECERCA EN INFORMÀTICA (Pla 2012)
dc.contributor.covenanteeTechnische Universität München


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