Biclustering strategies for genetic marker selection in gynecologic tumor cell linesBiclustering strategies for genetic marker selection in gynecologic tumor cell lines Πλήρης Δημοσίευση σε Συνέδριο Conference Full Paper 2018-10-092016enOver 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. http://creativecommons.org/licenses/by/4.0/1430-143338th Annual International Conference of the IEEE Engineering in Medicine and Biology SocietyProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS Alevyzaki Androniki Αλεβυζακη Ανδρονικη Sfakianakis Stylianos Σφακιανακης Στυλιανος Bei Aikaterini Μπεη Αικατερινη Obermayr Eva Zeillinger Robert Fotiadis D. I. Zervakis Michail Ζερβακης Μιχαηλ Institute of Electrical and Electronics Engineers Gene expression Cancer Tumors Clustering algorithms Artificial intelligence Electronic mail