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Ιδρυματικό Αποθετήριο [SANDBOX]
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| URI | http://purl.tuc.gr/dl/dias/1B73E0C5-1C73-41F7-A42D-2C44251C4F9E | - |
| Αναγνωριστικό | https://ieeexplore.ieee.org/document/7590977/ | - |
| Αναγνωριστικό | https://doi.org/10.1109/EMBC.2016.7590977 | - |
| Γλώσσα | en | - |
| Μέγεθος | 4 pages | en |
| Τίτλος | Biclustering strategies for genetic marker selection in gynecologic tumor cell lines | en |
| Δημιουργός | Alevyzaki Androniki | en |
| Δημιουργός | Αλεβυζακη Ανδρονικη | el |
| Δημιουργός | Sfakianakis Stylianos | en |
| Δημιουργός | Σφακιανακης Στυλιανος | el |
| Δημιουργός | Bei Aikaterini | en |
| Δημιουργός | Μπεη Αικατερινη | el |
| Δημιουργός | Obermayr Eva | en |
| Δημιουργός | Zeillinger Robert | en |
| Δημιουργός | Fotiadis D. I. | en |
| Δημιουργός | Zervakis Michail | en |
| Δημιουργός | Ζερβακης Μιχαηλ | el |
| Εκδότης | Institute of Electrical and Electronics Engineers | en |
| Περίληψη | Over the past few decades great interest has been focused on cell lines derived from tumors, because of their usability as models to understand the biology of cancer. At the same time, advanced technologies such as DNA-microarrays have been broadly used to study the expression level of thousands of genes in primary tumors or cancer cell lines in a single experiment. Results from microarray analysis approaches have provided valuable insights into the underlying biology and proven useful for tumor classification, prognostication and prediction. Our approach utilizes biclustering methods for the discovery of genes with coherent expression across a subset of conditions (cell lines of a tumor type). More specifically, we present a novel modification on Cheng & Church's algorithm that searches for differences across the studied conditions, but also enforces consistent intensity characteristics of each cluster within each condition. The application of this approach on a gynecologic panel of cell lines succeeds to derive discriminant groups of compact bi-clusters across four types of tumor cell lines. In this form, the proposed approach is proven efficient for the derivation of tumor-specific markers. | en |
| Τύπος | Πλήρης Δημοσίευση σε Συνέδριο | el |
| Τύπος | Conference Full Paper | en |
| Άδεια Χρήσης | http://creativecommons.org/licenses/by/4.0/ | en |
| Ημερομηνία | 2018-10-09 | - |
| Ημερομηνία Δημοσίευσης | 2016 | - |
| Θεματική Κατηγορία | Gene expression | en |
| Θεματική Κατηγορία | Cancer | en |
| Θεματική Κατηγορία | Tumors | en |
| Θεματική Κατηγορία | Clustering algorithms | en |
| Θεματική Κατηγορία | Artificial intelligence | en |
| Θεματική Κατηγορία | Electronic mail | en |
| Βιβλιογραφική Αναφορά | A. Alevyzaki, S. Sfakianakis, E. S. Bei, E. Obermayr, R. Zeillinger, D. Fotiadis, M. Zervakis, "Biclustering strategies for genetic marker selection in gynecologic tumor cell lines," in 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2016, pp. 1430-1433. doi: 10.1109/EMBC.2016.7590977 | en |
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