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dc.contributorCerquides Bueno, Jesús
dc.contributor.authorBellón Molina, Víctor Manuel
dc.date.accessioned2013-02-11T14:02:25Z
dc.date.available2013-02-11T14:02:25Z
dc.date.issued2013-01-18
dc.identifier.urihttp://hdl.handle.net/2099.1/17173
dc.descriptionThe early diagnosis of diseases in patients is a key objective of biomedical science and one of the most important factors in the treatment of diseases such as cancer. The early detection of cancer can make the di erence between a successful treatment and the dead of the patient. Ovarian cancer is diagnosed at late clinical stage in more than 80% of patients and the 5-year survival rate is around 35% of population, while in early diagnosed patients it exceeds 90%. The aim of this work is to present techniques for the early detection of ovarian cancers based in probabilistic analysis of proteomic spectra.
dc.language.isoeng
dc.publisherUniversitat Politècnica de Catalunya
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Ciències de la salut::Medicina::Diagnòstic per la imatge
dc.subjectÀrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Bioinformàtica
dc.subject.lcshOvaries--Cancer--Diagnosis
dc.subject.lcshStatistics--Graphic methods
dc.subject.lcshBayesian statistical decision theory
dc.titleBayesian Gaussian network classifiers for mass spectra classification
dc.typeMaster thesis
dc.subject.lemacOvaris--Càncer--Diagnòstic
dc.subject.lemacEstadística--Mètodes gràfics
dc.subject.lemacEstadística bayesiana
dc.rights.accessOpen Access
dc.audience.educationlevelMàster
dc.audience.mediatorFacultat d'Informàtica de Barcelona
dc.audience.degreeMÀSTER UNIVERSITARI EN INTEL·LIGÈNCIA ARTIFICIAL (Pla 2009)


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Attribution-NonCommercial-NoDerivs 3.0 Spain
Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 3.0 Spain