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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.
Studies in Health Technology and Informatics
|May 23, 2026
Summary
This study introduces an AI-driven cancer knowledge graph (KG) built from millions of research articles. The KG reveals new mechanistic relationships and cancer pathways, aiding personalized cancer treatment discovery.
Area of Science:
- Computational biology
- Bioinformatics
- Cancer research
Background:
- Cancer incidence is influenced by complex mechanistic relationships.
- Understanding these mechanisms is crucial for developing personalized cancer interventions.
- Artificial intelligence (AI)-driven biomedical knowledge graphs (KGs) can integrate multifaceted medical knowledge.
Purpose of the Study:
- To develop and present a cancer-specific knowledge graph (KG).
- To discover, represent, and visualize mechanistic relationships between cancer, therapeutic agents, and biomedical concepts.
- To identify novel cancer-related interactions and pathways using AI.
Main Methods:
- Constructed a cancer KG by analyzing over 2.5 million PubMed articles.
- Abstracted 7 distinct cancer-related entities and 30 relationship types.
- Employed link prediction techniques to identify missing interactions within the KG.
Main Results:
- Successfully developed a comprehensive cancer knowledge graph.
- Identified novel mechanistic relationships between cancer entities.
- Discovered extended cancer pathways by predicting missing interactions.
Conclusions:
- AI-driven KGs are effective tools for uncovering complex biological mechanisms in cancer.
- The developed cancer KG facilitates the discovery of new therapeutic targets and pathways.
- This approach supports advancements in personalized cancer medicine.
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