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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Exarchos, K.P. Rigas, G. Goletsis, Y. Fotiadis, D.I. |
| Copyright Year | 2010 |
| Description | Author affiliation: Unit of Medical Technology and Intelligent Information Systems, Dept of Materials Science and Engineering, University of Ioannina, GR 45110, Greece (Exarchos, K.P.; Rigas, G.; Fotiadis, D.I.) || Dept of Economics, University of Ioannina, GR, 45110, Greece (Goletsis, Y.) |
| Abstract | 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. |
| Starting Page | 1 |
| Ending Page | 4 |
| File Size | 676915 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424465590 |
| e-ISBN | 9781424465613 |
| DOI | 10.1109/ITAB.2010.5687744 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-11-03 |
| Publisher Place | Greece |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Predictive models Neck Data models Proteins Cancer Joints Cancer Evolution Monitoring Oral Cancer Dynamic Bayesian Networks |
| Content Type | Text |
| Resource Type | Article |
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