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Institutional Repository [SANDBOX]
Technical University of Crete
Technical University of Crete
EN
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EL
| URI | http://purl.tuc.gr/dl/dias/C3EDCB9E-77F2-4F49-93CF-B9491CA3B6DF | - |
| Identifier | https://doi.org/10.1109/JBHI.2013.2274643 | - |
| Identifier | https://ieeexplore.ieee.org/document/6567891/?tp=&arnumber=6567891&url=http:%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D6567891 | - |
| Language | en | - |
| Extent | 12 pages | en |
| Title | Nonparametric network design and analysis of disease genes in oral cancer progression | en |
| Creator | Zervakis Michail | en |
| Creator | Ζερβακης Μιχαηλ | el |
| Creator | Exarchos Konstantinos P. | en |
| Creator | Fotiadis, Dimitrios Ioannou | en |
| Creator | Bei Aikaterini | en |
| Creator | Μπεη Αικατερινη | el |
| Creator | Garofalakis Minos | en |
| Creator | Γαροφαλακης Μινως | el |
| Creator | Kalantzaki Kalliopi | en |
| Creator | Καλαντζακη Καλλιοπη | el |
| Publisher | Institute of Electrical and Electronics Engineers | en |
| Content Summary | Biological networks in living organisms can be seen as the ultimate means of understanding the underlying mechanisms in complex diseases, such as oral cancer. During the last decade, many algorithms based on high-throughput genomic data have been developed to unravel the complexity of gene network construction and their progression in time. However, the small size of samples compared to the number of observed genes makes the inference of the network structure quite challenging. In this study, we propose a framework for constructing and analyzing gene networks from sparse experimental temporal data and investigate its potential in oral cancer. We use two network models based on partial correlations and kernel density estimation, in order to capture the genetic interactions. Using this network construction framework on real clinical data of the tissue and blood at different time stages, we identified common disease-related structures that may decipher the association between disease state and biological processes in oral cancer. Our study emphasizes an altered MET (hepatocyte growth factor receptor) network during oral cancer progression. In addition, we demonstrate that the functional changes of gene interactions during oral cancer progression might be particularly useful for patient categorization at the time of diagnosis and/or at follow-up periods. | en |
| Type of Item | Peer-Reviewed Journal Publication | en |
| Type of Item | Δημοσίευση σε Περιοδικό με Κριτές | el |
| License | http://creativecommons.org/licenses/by/4.0/ | en |
| Date of Item | 2015-10-24 | - |
| Date of Publication | 2014 | - |
| Subject | Gene network construction | en |
| Subject | KDE | en |
| Subject | Kernel Density Estimation | en |
| Subject | Oral cancer | en |
| Subject | Partial correlation | en |
| Subject | PC | en |
| Bibliographic Citation | K. Kalantzaki, E. S. Bei, K. P. Exarchos, M. Zervakis, M. Garofalakis and D. I. Fotiadis," Nonparametric network design and analysis of disease genes in oral cancer progression," IEEE J. Biomed. Health Inform., vol. 18, no.2, pp. 562-573, Mar. 2014. doi: 10.1109/JBHI.2013.2274643 | en |
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