ArticleJournal of biomedical informatics2024
FuseLinker: Leveraging LLM's pre-trained text embeddings and domain knowledge to enhance GNN-based link prediction on biomedical knowledge graphs.
Article in Journal of biomedical informatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- CAREPath: semantic context-aware reasoning paths with mechanism-augmented embeddings for drug repurposing.Briefings in bioinformatics · 2026Article
- Intelligent Framework for Adverse Drug Event Identification Using Large Language Models and Retrieval-Augmented Generation: Development and Evaluation Study.Journal of medical Internet research · 2026Article
- KLaR: fusing knowledge graphs and language models for biomedical target discovery.Bioinformatics (Oxford, England) · 2026Article
- iS2C2: a cointelligent platform for mechanistic discovery of disease cellular crosstalk.Signal transduction and targeted therapy · 2026Article
- Development and comparative evaluation of knowledge graph-enhanced large language models for domain-specific question answering in nursing.BMC nursing · 2026Article
- Application and Prospects of Large Language Models in Small-Molecule Drug Discovery.Analytical chemistry · 2025Review
- Harnessing artificial intelligence for brain disease: advances in diagnosis, drug discovery, and closed-loop therapeutics.Frontiers in neurology · 2025Review
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6 authors.
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Abstract
objectiveTo develop the FuseLinker, a novel link prediction framework for biomedical knowledge graphs (BKGs), which fully exploits the graph's structural, textual and domain knowledge information. We evaluated the utility of FuseLinker in the graph-based drug repurposing task through detailed case studies.
methodsFuseLinker leverages fused pre-trained text embedding and domain knowledge embedding to enhance the graph neural network (GNN)-based link prediction model tailored for BKGs. This framework includes three parts: a) obtain text embeddings for BKGs using embedding-visible large language models (LLMs), b) learn the representations of medical ontology as domain knowledge information by employing the Poincaré graph embedding method, and c) fuse these embeddings and further learn the graph structure representations of BKGs by applying a GNN-based link prediction model. We evaluated FuseLinker against traditional knowledge graph embedding models and a conventional GNN-based link prediction model across four public BKG datasets. Additionally, we examined the impact of using different embedding-visible LLMs on FuseLinker's performance. Finally, we investigated FuseLinker's ability to generate medical hypotheses through two drug repurposing case studies for Sorafenib and Parkinson's disease.
resultsBy comparing FuseLinker with baseline models on four BKGs, our method demonstrates superior performance. The Mean Reciprocal Rank (MRR) and Area Under receiver operating characteristic Curve (AUROC) for KEGG50k, Hetionet, SuppKG and ADInt are 0.969 and 0.987, 0.548 and 0.903, 0.739 and 0.928, and 0.831 and 0.890, respectively.
conclusionOur study demonstrates that FuseLinker is an effective novel link prediction framework that integrates multiple graph information and shows significant potential for practical applications in biomedical and clinical tasks. Source code and data are available at https://github.com/YKXia0/FuseLinker.
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