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dc.contributor.authorBarrett, Michael
dc.date.accessioned2022-04-06T17:56:21Z
dc.date.available2022-04-06T17:56:21Z
dc.date.issued2022-04-06T17:56:21Z
dc.identifier.urihttp://hdl.handle.net/10222/81511
dc.description.abstractWe present an automated knowledge synthesis and discovery framework to analyze published literature to identify and represent underlying mechanistic associations that aggravate chronic conditions due to COVID-19. Our literature-based discovery approach integrates text mining, knowledge graphs, and medical ontologies to discover hidden and previously unknown pathophysiologic relations between COVID-19 and chronic disease mechanisms as reported in literature that is dispersed across multiple public databases. Our framework applies knowledge graph augmentation methods based on external knowledge (i.e., ontologies) to address the issue of incomplete knowledge captured in relations mined from text (called semantic associations) to improve literature-based discovery of complex mechanistic associations. We applied our approach to discover gene-disease associations for COVID-19 and chronic conditions—i.e. diabetes mellitus and chronic kidney disease—to understand the long-term impact of COVID-19 on patients with chronic diseases. We discovered several novel associations that could help identify mechanisms driving COVID-19 in patients with underlying conditions.en_US
dc.language.isoenen_US
dc.subjectLiterature Based Discoveryen_US
dc.subjectMedical Ontologiesen_US
dc.subjectKnowledge Synthesis and Discoveryen_US
dc.subjectText Miningen_US
dc.subjectKnowledge Graphen_US
dc.subjectHypothesis Generationen_US
dc.subjectInformation Retrievalen_US
dc.subjectKnowledge Representationen_US
dc.subjectSystems Medicineen_US
dc.subjectSystems Biologyen_US
dc.titleApplying Domain Ontologies and Knowledge Graphs To Augment Literature-Based Discovery: Discovering Gene-Disease Associations Between COVID-19, Diabetes Mellitus, And Chronic Kidney Diseaseen_US
dc.date.defence2022-03-11
dc.contributor.departmentFaculty of Computer Scienceen_US
dc.contributor.degreeMaster of Health Informaticsen_US
dc.contributor.external-examinern/aen_US
dc.contributor.graduate-coordinatorDr. Syed Sibte Raza Abidien_US
dc.contributor.thesis-readerDr. Samuel Stewarten_US
dc.contributor.thesis-readerDr. William Van Woenselen_US
dc.contributor.thesis-supervisorDr. Syed Sibte Raza Abidien_US
dc.contributor.thesis-supervisorDr. Samina Abidien_US
dc.contributor.ethics-approvalNot Applicableen_US
dc.contributor.manuscriptsNot Applicableen_US
dc.contributor.copyright-releaseNot Applicableen_US
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