University of North Carolina at Chapel Hill Graduate School
Doi
Abstract
In the span of a decade, we have brought about a fundamental shift in the way we structure, organize, store, and conceptualize biomedical datasets. Data which had previously been siloed has been gathered, organized, and aggregated into central repositories, interlinked with each other by categorizing these vast sums of knowledge into well defined ontologies. These interlinked databases, better known as knowledge graphs, have come to redefine our ability to explore the current state of our knowledge, answer complex questions about how objects relate to each other, and invent novel connections in vastly different research disciplines. With these knowledge graphs, new ideas can be quickly formulated, instead of relying upon the insight of a single scientist or small team of experts, these ideas can be made leveraging the vast historical catalog of research progress that has been captured in biomedical databases. Knowledge graphs can be used to propose hypotheses which narrow the nearly infinite array of possible explorations which can link any pair of ideas to only those which have some historical and practical considerations. In this way, we hope to utilize these knowledge graphs to produce hypotheses, promote those which are viable, and provide them to biomedical experts. In this work, we aim to develop methodologies to produce meaningful hypotheses using these graphs as inputs. We approach this problem by (i) utilizing intrinsic mathematical properties of the intermediate nodes along a pathways, (ii) translating existing biomedical ideas into graphical structures, and (iii) incorporating niche domain-specific biomedical datasets to explore domain problems. We have shown the ability of these methods to produce practical and useful hypotheses and pathways which can be utilized by experts for immediate exploration.Doctor of Philosoph
