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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)
Konstantinos P. Exarchos ; Unit of Medical Technology and Intelligent Information Systems, Dept of Materials Science and Engineering, University of Ioannina, GR 45110, Greece ; George Rigas ; Yorgos Goletsis ; Dimitrios I. Fotiadis

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:

Proceedings of the 10th IEEE International Conference on Information Technology and Applications in Biomedicine

Date of Conference:

3-5 Nov. 2010