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Visualization and comparison of topological networks from multiple approaches for cancer prognosis

Tsakaneli Stavroula

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Περίληψη

The ultimate goal of the genomic revolution, is understanding the genetic causes, the blueprint that specifies the exact ways that genetic components, like genes and proteins, interact to make a complex living system, behind phenotypic characteristics of organisms. Nowadays, genome-wide gene expression technologies have been available and are of great importance in many scientific areas such as clinical prognosis, diagnosis and treatment. This availability has made at least a part of this goal closer and led, both biologists and computational scientists, to introduce a variety of methodological approaches, well suited for both qualitative and quantitative level modeling and simulation, for the analysis of genetic interactions in terms of predicting the genetic and proteomic associations as well as modeling the relationships among the studied genetic components. These approaches have the potential to elucidate the effect of the nature and topology of interactions on the systemic properties of organisms.In this thesis, we model and process, by implementing two different methodological approaches, the relationships between genes and proteins, in order to examine relationships as well as novel genomic signatures, fundamental and of great significance in the creation of breast cancer and cancer metastasis. These approaches are two different algorithms, HotNet2 and Activity Vector, which create gene interaction subnetworks after processing gene expression data, which have been selected from a larger dataset, and protein-protein interaction networks. Finally, we evaluate the results, for their biological significance and their statistical prediction in an independent dataset.

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