Unit:
Κατεύθυνση Βιοπληροφορική-Επιστήμη Βιοϊατρικών ΔεδομένωνΠληροφορική
Author:
Andrinopoulou Christina
Supervisors info:
Θεόδωρος Δαλαμάγκας, Διευθυντής Ερευνών, Ινστιτούτο Πληροφοριακών Συστημάτων, Ερευνητικό Κέντρο “Αθηνά”
Ιωάννης Εμίρης, Πρόεδρος και γενικός διευθυντής, Ερευνητικό Κέντρο “Αθηνά”
Αριστοτέλης Χατζηιωάννου, Διευθυντής Ερευνών, Ιδρυμα Ιατροβιολογικών Ερευνών Ακαδημίας Αθηνών
Original Title:
Biological knowledge extraction and representation in Genomics based on Semantic Network Analysis
Translated title:
Biological knowledge extraction and representation in Genomics based on Semantic Network Analysis
Summary:
The complexity and the volume of biological data are exponentially increasing over the
last decades, due to the maturation of various high-throughput omic technologies. The
challenges of their meaningful interpretation are indeed grand, due to their idiosyncratic
character. In this context, semantic graphs are utilized in order to streamline omic knowl-
edge extraction.
The emerging field of precision medicine benefits the treatment strategies for various dis-
eases, including cancer. Precision medicine focuses on the stratification of patients to
discover groups with shared biological or clinical characteristics, phenotypes, drug re-
sponses, disease subtypes, and more. Stratifying patients facilitates the recommenda-
tion of targeted therapies, in targeted groups, and as a consequence improves the overall
treatment efficacy, in terms of health outcomes for the patient, as well as budgeting for
the health care system.
This study aims to contribute to the demanding field of stratification, utilizing semantic
graphs to interpret the biological data of patients and unsupervised learning techniques
to stratify them. The proposed methodology is structured in a framework called Cancer-
Miner. CancerMiner leverages the functionality of two other tools, the BioInfoMiner, and
the Comparative Analysis tool. It is developed to be part of an existing web platform provid-
ing solutions for interpreting omics data. In this stage, the tool focuses on the stratification
of patients diagnosed with Head and Neck Squamous Cell Carcinoma, with intentions to
include additional cancer types in the future.
Main subject category:
Science
Keywords:
Semantic Networks, Clustering, Precision Medicine, Head and Neck Squamous Cell Carcinoma
File:
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Andrinopoulou_Christina_ds2200013_Thesis.pdf
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File access is restricted only to the intranet of UoA.
