ArticleBMC bioinformatics2024
Biomedical relation extraction method based on ensemble learning and attention mechanism.
Article in BMC bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- A parallel dual-stream state-space module for reliable and efficient biomedical relation extraction.PLoS computational biology · 2026Article
- Dual channel drug-drug interactions extraction based on cross attention.BMC bioinformatics · 2026Article
- Unveiling rare drug interactions via a BioGPT-enhanced dual graph framework for robust pharmacovigilance.iScience · 2026Article
- A meta-contrastive learning approach for clinical drug-drug interaction extraction from biomedical literature.PLoS computational biology · 2025Article
- Sex-Specific Ensemble Models for Type 2 Diabetes Classification in the Mexican Population.Diabetes, metabolic syndrome and obesity : targets and therapy · 2025Article
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Authors and funding
5 authors.
Funding
Abstract
backgroundRelation extraction (RE) plays a crucial role in biomedical research as it is essential for uncovering complex semantic relationships between entities in textual data. Given the significance of RE in biomedical informatics and the increasing volume of literature, there is an urgent need for advanced computational models capable of accurately and efficiently extracting these relationships on a large scale.
resultsThis paper proposes a novel approach, SARE, combining ensemble learning Stacking and attention mechanisms to enhance the performance of biomedical relation extraction. By leveraging multiple pre-trained models, SARE demonstrates improved adaptability and robustness across diverse domains. The attention mechanisms enable the model to capture and utilize key information in the text more accurately. SARE achieved performance improvements of 4.8, 8.7, and 0.8 percentage points on the PPI, DDI, and ChemProt datasets, respectively, compared to the original BERT variant and the domain-specific PubMedBERT model.
conclusionsSARE offers a promising solution for improving the accuracy and efficiency of relation extraction tasks in biomedical research, facilitating advancements in biomedical informatics. The results suggest that combining ensemble learning with attention mechanisms is effective for extracting complex relationships from biomedical texts. Our code and data are publicly available at: https://github.com/GS233/Biomedical .
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