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

Tsakaneli Stavroula

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URIhttp://purl.tuc.gr/dl/dias/0BBECE1F-7316-4842-9704-885D51A3B9D4-
Identifierhttps://doi.org/10.26233/heallink.tuc.40671-
Languageen-
Extent91 pagesen
TitleVisualization and comparison of topological networks from multiple approaches for cancer prognosisen
CreatorTsakaneli Stavroulaen
CreatorΤσακανελη Σταυρουλαel
Contributor [Thesis Supervisor]Zervakis Michalisen
Contributor [Thesis Supervisor]Ζερβακης Μιχαληςel
Contributor [Committee Member]Mania Aikaterinien
Contributor [Committee Member]Μανια Αικατερινηel
Contributor [Committee Member]Petrakis Evripidisen
Contributor [Committee Member]Πετρακης Ευριπιδηςel
PublisherΠολυτεχνείο Κρήτηςel
PublisherTechnical University of Creteen
Academic UnitTechnical University of Crete::School of Electronic and Computer Engineeringen
Academic UnitΠολυτεχνείο Κρήτης::Σχολή Ηλεκτρονικών Μηχανικών και Μηχανικών Υπολογιστώνel
Content SummaryThe 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.en
Type of ItemΔιπλωματική Εργασίαel
Type of ItemDiploma Worken
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2015-10-12-
Date of Publication2015-
SubjectΔίκτυα γονιδίωνel
SubjectCircuits, Geneen
SubjectGene circuitsen
SubjectGene modulesen
SubjectGene networksen
SubjectGenetic regulatory networksen
SubjectGRNs (Gene regulatory networks)en
SubjectModules, Geneen
SubjectNetworks, Gene regulatoryen
SubjectNetworks, Transcriptionalen
SubjectRegulatory networks, Geneen
SubjectTranscriptional networksen
Subjectgene regulatory networksen
Subjectcircuits geneen
Subjectgene circuitsen
Subjectgene modulesen
Subjectgene networksen
Subjectgenetic regulatory networksen
Subjectgrns gene regulatory networksen
Subjectmodules geneen
Subjectnetworks gene regulatoryen
Subjectnetworks transcriptionalen
Subjectregulatory networks geneen
Subjecttranscriptional networksen
Bibliographic CitationStavroula Tsakaneli, "Visualization and comparison of topological networks from multiple approaches for cancer prognosis", Diploma Work, School of Electronic and Computer Engineering, Technical University of Crete, Chania, Greece, 2015en

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