โก Quick Summary
The study introduces FuseLinker, a groundbreaking link prediction framework designed for biomedical knowledge graphs (BKGs). By integrating pre-trained text embeddings and domain knowledge, FuseLinker significantly enhances the performance of graph neural network (GNN) models in drug repurposing tasks.
๐ Key Details
- ๐ Datasets Used: KEGG50k, Hetionet, SuppKG, ADInt
- โ๏ธ Technology: FuseLinker framework utilizing LLMs and GNNs
- ๐ Performance Metrics: MRR and AUROC scores across datasets
- ๐ก Case Studies: Drug repurposing for Sorafenib and Parkinson’s disease
๐ Key Takeaways
- ๐ FuseLinker effectively combines structural, textual, and domain knowledge for improved link prediction.
- ๐ Superior Performance: Achieved MRR of 0.969 and AUROC of 0.987 on KEGG50k dataset.
- ๐ Comprehensive Evaluation: Compared against traditional knowledge graph embedding models.
- ๐ก Embedding Techniques: Utilizes embedding-visible LLMs for text representation.
- ๐ Practical Applications: Shows potential for real-world biomedical and clinical tasks.
- ๐ฌ Hypothesis Generation: Capable of generating medical hypotheses through case studies.
- ๐ Open Source: Source code and data available on GitHub.
๐ Background
The integration of graph neural networks with biomedical knowledge graphs has emerged as a promising approach to enhance link prediction tasks. Traditional methods often fall short in leveraging the rich structural and textual information inherent in these graphs. The development of FuseLinker aims to bridge this gap by utilizing advanced embedding techniques and domain knowledge to improve predictive accuracy in biomedical contexts.
๐๏ธ Study
The researchers developed FuseLinker to address the limitations of existing models in link prediction tasks within BKGs. The framework consists of three main components: obtaining text embeddings using large language models (LLMs), learning representations of medical ontology through the Poincarรฉ graph embedding method, and fusing these embeddings to enhance GNN-based predictions. The study evaluated FuseLinker against traditional models across four public datasets, demonstrating its effectiveness in drug repurposing scenarios.
๐ Results
The results of the study were compelling, with FuseLinker outperforming baseline models on all evaluated datasets. Notably, the Mean Reciprocal Rank (MRR) and Area Under the Receiver Operating Characteristic Curve (AUROC) scores were impressive:
KEGG50k: MRR 0.969, AUROC 0.987;
Hetionet: MRR 0.548, AUROC 0.903;
SuppKG: MRR 0.739, AUROC 0.928;
ADInt: MRR 0.831, AUROC 0.890.
These metrics highlight the framework’s robust performance in predicting links within biomedical contexts.
๐ Impact and Implications
The implications of this study are significant for the field of biomedical informatics. By effectively integrating multiple sources of information, FuseLinker not only enhances link prediction accuracy but also opens avenues for generating new medical hypotheses. This could lead to more efficient drug repurposing strategies and ultimately improve patient outcomes in clinical settings. The potential for practical applications in healthcare is vast, paving the way for future research and development in this area.
๐ฎ Conclusion
The introduction of FuseLinker marks a significant advancement in the realm of link prediction for biomedical knowledge graphs. By leveraging the strengths of LLMs and GNNs, this framework demonstrates remarkable potential for practical applications in drug repurposing and other biomedical tasks. As the integration of AI and machine learning continues to evolve, we anticipate further innovations that will enhance the capabilities of healthcare professionals in their quest for improved patient care.
๐ฌ Your comments
What are your thoughts on the potential of FuseLinker in transforming biomedical research? We invite you to share your insights and engage in a discussion! ๐ฌ Leave your comments below or connect with us on social media:
FuseLinker: Leveraging LLM’s pre-trained text embeddings and domain knowledge to enhance GNN-based link prediction on biomedical knowledge graphs.
Abstract
OBJECTIVE: To 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.
METHODS: FuseLinker 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 Poincarรฉ 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.
RESULTS: By 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.
CONCLUSION: Our 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.
Author: [‘Xiao Y’, ‘Zhang S’, ‘Zhou H’, ‘Li M’, ‘Yang H’, ‘Zhang R’]
Journal: J Biomed Inform
Citation: Xiao Y, et al. FuseLinker: Leveraging LLM’s pre-trained text embeddings and domain knowledge to enhance GNN-based link prediction on biomedical knowledge graphs. FuseLinker: Leveraging LLM’s pre-trained text embeddings and domain knowledge to enhance GNN-based link prediction on biomedical knowledge graphs. 2024; 158:104730. doi: 10.1016/j.jbi.2024.104730