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dc.contributor.authorNtokou, Eleni-
dc.description.abstractDrug repurposing involves exploring new pharmaceutical purpose for already approved drugs. The drug development process comes with a high development risk, as it is demanding in terms of both time and cost. Drug repurposing tries to address these issues, as it focuses on drugs already on the market, thus alleviating the need for clinical trials to assess the already established safety profiles of drugs, requires less resources and is historically associated with higher success rates. In the present work, a knowledge graph has been created with the intention to identify potential drug candidates for schizophrenia, major depressive disorder, and anxiety disorders, with the use of ontology-based reasoning. The resulted knowledge graph involves seventy-three classes with three of them being the defined classes, designed to generate new knowledge. Twelve thousand nine hundred twenty five individuals are imported to the ontology from Kyoto Encyclopedia of Genes and Genomes and DrugBank databases containing genes and proteins, drugs, biological pathways, and the aforementioned human diseases. To this end, two potential candidate drugs for schizophrenia were identified, as well as four for major depressive disorder – two of which are already in clinical trials, emerged. For anxiety disorders, the candidates retrieved were disproportionately high in prevalence, suggesting that further investigation is required to make the inference process more selective.en_US
dc.rightsAll rights reserveden_US
dc.subjectKnowledge graphen_US
dc.subjectDrug repurposingen_US
dc.subjectMajor depressive disorderen_US
dc.subjectAnxiety disordersen_US
dc.titleGraph-based inference: A case study in identifying potential drug candidates for the treatment of schizophrenia, major depressive disorder & anxiety disordersen_US
dcterms.thesisSupervisorDrakakis, Georgios-
dcterms.licenseCC BY-NCen_US
dcterms.thesisCommittee.MemberVogiatzis, Dimitrios-
dcterms.thesisApprovedByKrepapa, Areti-
Appears in Collections:Program in Data Science

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