Please use this identifier to cite or link to this item: https://olympias.lib.uoi.gr/jspui/handle/123456789/11251
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dc.contributor.authorExarchos, K.P.,en
dc.contributor.authorRigas, G.,en
dc.contributor.authorGoletsis, Y.,en
dc.contributor.authorFotiadis, D.I.en
dc.date.accessioned2015-11-24T17:04:59Z-
dc.date.available2015-11-24T17:04:59Z-
dc.identifier.urihttps://olympias.lib.uoi.gr/jspui/handle/123456789/11251-
dc.rightsDefault Licence-
dc.subjectOral Cancer, Dynamic Bayesian Networks, Cancer Evolution Monitoringen
dc.titleTowards building a dynamic bayesian network for monitoring oral cancer progression using time course gene expression dataen
heal.typejournalArticle-
heal.type.enJournal articleen
heal.type.elΆρθρο Περιοδικούel
heal.languageen-
heal.accesscampus-
heal.recordProviderΠανεπιστήμιο Ιωαννίνων. Σχολή Οικονομικών και Κοινωνικών Επιστημών. Τμήμα Οικονομικών Επιστημώνel
heal.publicationDate2010-
heal.abstractIn this work we present a methodology for modeling and monitoring the evolvement of oral cancer in remittent patients during the post-treatment follow-up period. Our primary aim is to calculate the probability that a patient will develop a relapse but also to identify the approximate timeframe that this relapse is prone to appear. To this end, we start off by analyzing a broad set of time-course gene expression data in order to identify a set of genes that are mostly differentially expressed between patients with and without relapse and are therefore discriminatory and indicative of a disease reoccurrence evolvement. Next, we employ the maintained genes coupled with a patient-specific risk indicator in order to build upon them a Dynamic Bayesian Network (DBN) able to stratify patients based on their probability for a disease reoccurrence, but also pinpoint an approximate timeframe that the relapse might appear.en
heal.journalNameIEEEen
heal.journalTypepeer reviewed-
heal.fullTextAvailabilityTRUE-
Appears in Collections:Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά) - ΟΕ

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