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Comparing genomic network methodologies: a combined approach for cancer prognosis

Tsakaneli Stavroula, Bei Aikaterini, Zervakis Michail

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URIhttp://purl.tuc.gr/dl/dias/6A8FE480-564D-4B46-8442-6EACE371A609-
Identifierhttps://doi.org/10.1007/978-3-319-32703-7_99-
Identifierhttps://link.springer.com/chapter/10.1007/978-3-319-32703-7_99-
Languageen-
Extent6 pagesen
TitleComparing genomic network methodologies: a combined approach for cancer prognosisen
CreatorTsakaneli Stavroulaen
CreatorΤσακανελη Σταυρουλαel
CreatorBei Aikaterinien
CreatorΜπεη Αικατερινηel
CreatorZervakis Michailen
CreatorΖερβακης Μιχαηλel
PublisherSpringer Verlagen
Content SummaryOne of the goals of cancer research is to understand the genetic causes of disease pathology and specify the exact ways that genetic components interact to enable a complex living system exhibit the disease phenotype. Consequently, research efforts must be addressed to elucidate various phenome components, such as trancriptome, metabolome and proteome, with the aim to derive a prognostic phenotype. In this work, we attempt to model causal effects among genes and proteins using their interactions in the form of biological networks. Two spatial network approaches are examined in breast cancer in association with established genomic signatures, in order to derive tight subnetworks linked to explicit biological processes. These approaches include the HotNet2 and Activity Vector algorithms, which create gene interaction subnetworks after processing and evaluating gene expression data. Finally, we evaluate the results for their biological significance and their statistical prediction in an independent dataset. The proposed network analysis provides a blueprint to explore diagnostic and/or therapeutic opportunities.en
Type of ItemΠλήρης Δημοσίευση σε Συνέδριοel
Type of ItemConference Full Paperen
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2018-10-30-
Date of Publication2016-
SubjectActivity vectoren
SubjectBiological networksen
SubjectGene expressionen
SubjectGenomic signatureen
SubjectHotNet2en
Bibliographic CitationS. Tsakaneli, E. S. Bei and M. Zervakis, "Comparing genomic network methodologies: A combined approach for cancer prognosis," in 14th Mediterranean Conference on Medical and Biological Engineering and Computing, 2016, pp. 506-511. doi: 10.1007/978-3-319-32703-7_99en

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