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Towards building a Dynamic Bayesian Network for monitoring oral cancer progression using time-course gene expression data

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4 Author(s)
Exarchos, K.P. ; Dept of Mater. Sci. & Eng., Univ. of Ioannina, Ioannina, Greece ; 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 time-frame 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 time-frame that the relapse might appear.

Published in:

Information Technology and Applications in Biomedicine (ITAB), 2010 10th IEEE International Conference on

Date of Conference:

3-5 Nov. 2010