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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
Published on: October 13, 2023
A Knowledge Graph to Represent and Predict Cancer Mechanistic Associations
Mehrana Calagari1, Samina Abidi2, Syed Sibte Raza Abidi1
1NICHE Research Group, Faculty of Computer Science, Dalhousie University, Canada.
None:
A range of underlying mechanistic relationships influences the incidence of cancer. Understanding these mechanisms can help develop personalized interventions to manage cancer progression. AI-driven biomedical knowledge graphs (KG) can represent complex medical knowledge from multiple sources. We present a cancer KG to discover, represent, and visualize mechanistic relationships between cancer, therapeutic agents, and associated biomedical concepts. We developed a cancer knowledge graph by analyzing over 2.5 million PubMed articles related to cancer, abstracting 7 different cancer-related entities, and 30 different relationships. We applied link prediction to discover missing interactions among cancer entities and were able to find new relationships and longer cancer pathways between entities.
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