Application of knowledge graphs in rare disease research

Area of Science:

• Biomedical Informatics
• Computational Biology
• Genomics

Background:

• Rare disease research is hampered by sparse, heterogeneous data, causing diagnostic delays and treatment limitations.
• Knowledge Graphs (KGs) provide a computational framework to integrate multimodal data into structured semantic networks.

Purpose of the Study:

• To review the technical paradigms and applications of KGs in the rare disease workflow.
• To explore KG integration with Large Language Models (LLMs) for improved medical decision-making.

Main Methods:

• Data foundation: standardized ontologies (e.g., Human Phenotype Ontology - HPO) and integration strategies.
• Core applications: link prediction for pathogenic mechanisms, semantic reasoning for clinical diagnosis, and Graph Neural Networks for drug repositioning.
• Emerging integration: KGs with LLMs, specifically Retrieval-Augmented Generation (RAG), for enhanced interpretability and precision.

Main Results:

• KGs effectively integrate multimodal data, addressing sparsity and heterogeneity in rare diseases.
• KG applications demonstrate potential in elucidating disease mechanisms, improving diagnosis, and optimizing drug repositioning.
• LLM integration with KGs shows promise for more precise and interpretable medical decision-making.

Conclusions:

• Knowledge Graphs offer a robust computational solution for rare disease research challenges.
• The integration of KGs with LLMs represents a significant advancement in precision medicine.
• Future directions include addressing privacy concerns and dynamic data updates through methods like federated learning.