Application of knowledge graphs in rare disease research

Rare disease data sparsity and heterogeneity: Rare disease research is challenged by sparse and heterogeneous data, contributing to diagnostic delays and limited treatments.

KG privacy and update challenges: Knowledge graph applications in rare disease research face challenges including privacy and dynamic updates.

Rare disease data sparsity and heterogeneity: Rare disease research is challenged by sparse and heterogeneous data, contributing to diagnostic delays and limited treatments.

KG privacy and update challenges: Knowledge graph applications in rare disease research face challenges including privacy and dynamic updates.

Knowledge graph multimodal data integration: Knowledge graphs are presented as a computational approach for integrating multimodal data into structured semantic networks.

Ontology-based data standardization: The review covers standardized ontologies and integration strategies as the data foundation for rare disease knowledge graphs.

KG link prediction for mechanisms: The review examines use of link prediction on knowledge graphs to elucidate pathogenic mechanisms.

Semantic reasoning for clinical diagnosis: The review examines semantic reasoning over knowledge graphs to enhance clinical diagnosis.

GNN-based drug repositioning: The review examines graph neural networks for optimizing drug repositioning in rare disease research.

Rare disease KG application review:

Improved medical decision-making interpretability and precision:

Enhanced clinical diagnosis: