Towards building a dynamic bayesian network for monitoring oral cancer progression using time course gene expression data (Journal article)

Exarchos, K.P.,/ Rigas, G.,/ Goletsis, Y.,/ Fotiadis, D.I.

In 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.
Institution and School/Department of submitter: Πανεπιστήμιο Ιωαννίνων. Σχολή Οικονομικών και Κοινωνικών Επιστημών. Τμήμα Οικονομικών Επιστημών
Keywords: Oral Cancer, Dynamic Bayesian Networks, Cancer Evolution Monitoring
Appears in Collections:Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά)

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