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GNN-based Biomedical Knowledge Graph Mining in Drug Development
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Integrating biomedical knowledge graphs with GNNs can enhance the performance and interpretation of KG inference problems, which will benefit in generating new hypotheses and new drug development.
“Furthermore, integrating biomedical knowledge graphs (KGs) with GNNs can enhance the performance and interpretation of KG inference problems, which will benefit in generating new hypotheses and new drug development [69].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [69]
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Cited by 10
Metadata
- Authors
- Su, C., Hou, Y., & Wang, F.
- Affiliations
- Weill Cornell Medicine (US)
- Corresponding author
- Su, Chang
- Venue
- Graph Neural Networks: Foundations, Frontiers, and Applications
- Publisher
- Springer Nature Singapore
- Published
- 2022
- Pages
- 517-540
- OpenAlex
- W4205371418
- Cited by
- 10
- References
- 0
- Citations by year
- 2026: 4 · 2025: 2 · 2024: 1 · 2023: 3
- FWCI
- 2.51
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Springer eBooks (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Biomedical Text Mining and Ontologies
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Keywords
- graph neural networks, biomedical knowledge graphs, drug discovery, drug development, heterogeneous data integration, Parkinson's disease, COVID-19 pandemic, biomedical literature, knowledge retrieval
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