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dc.contributorCarrera Pérez, David
dc.contributorBerral García, Josep Ll.
dc.contributor.authorGómez Sánchez, Gonzalo
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.date.accessioned2018-08-27T10:12:53Z
dc.date.available2018-08-27T10:12:53Z
dc.date.issued2018-04-13
dc.identifier.urihttp://hdl.handle.net/2117/120585
dc.description.abstractWe propose an architecture for detecting patterns in genomic rearrangements that may cause cancer. We use an unsupervised clustering method based on Kernel Density Estimation function and Graph Mining techniques for pattern detection. The architecture has been tested in a real data set.
dc.language.isoeng
dc.publisherUniversitat Politècnica de Catalunya
dc.subjectÀrees temàtiques de la UPC::Informàtica
dc.subject.lcshMachine learning
dc.subject.lcshGraph theory
dc.subject.lcshGenomics
dc.subject.othergenòmica
dc.subject.otheragrupament
dc.subject.otherKernel Density Estimation
dc.subject.othermineria gràfica
dc.subject.othercromosoma
dc.subject.otherpunts de trencament
dc.subject.otherreordenaments
dc.subject.othercàncer
dc.subject.otherpatrons
dc.subject.otherclustering
dc.subject.othergraph mining
dc.subject.otherchromosome
dc.subject.otherbreakpoints
dc.subject.otherrearrangements
dc.subject.othercancer
dc.subject.otherpatterns
dc.titleUnsupervised clustering for mining patterns in genomic dataset
dc.title.alternativeFinding patterns and clusters in genomic data
dc.typeMaster thesis
dc.subject.lemacAprenentatge automàtic
dc.subject.lemacGrafs, Teoria de
dc.subject.lemacGenòmica
dc.identifier.slug131675
dc.rights.accessOpen Access
dc.date.updated2018-04-30T04:01:14Z
dc.audience.educationlevelMàster
dc.contributor.covenanteeUniversitat de Barcelona
dc.contributor.covenanteeUniversitat Rovira i Virgili
dc.contributor.covenanteeBarcelona Supercomputing Center


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