ArticleBMC bioinformatics2017
An attention-based effective neural model for drug-drug interactions extraction.
Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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26 citing papers in PubMed.
- A parallel dual-stream state-space module for reliable and efficient biomedical relation extraction.PLoS computational biology · 2026Article
- Multimodal Collaborative Modeling of Molecular Structures and Biomedical Text for Accurate Drug-Drug Interaction Extraction.Biomedicines · 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
- Review
- Document-level biomedical relation extraction via hierarchical tree graph and relation segmentation module.Bioinformatics (Oxford, England) · 2024Article
- SubGE-DDI: A new prediction model for drug-drug interaction established through biomedical texts and drug-pairs knowledge subgraph enhancement.PLoS computational biology · 2024Article
- Attention is all you need: utilizing attention in AI-enabled drug discovery.Briefings in bioinformatics · 2023Review
- Drug-drug interaction prediction: databases, web servers and computational models.Briefings in bioinformatics · 2023Review
- Multiple sampling schemes and deep learning improve active learning performance in drug-drug interaction information retrieval analysis from the literature.Journal of biomedical semantics · 2023Article
- Relation Extraction in Biomedical Texts Based on Multi-Head Attention Model With Syntactic Dependency Feature: Modeling Study.JMIR medical informatics · 2022Article
- IMSE: interaction information attention and molecular structure based drug drug interaction extraction.BMC bioinformatics · 2022Article
- On the road to explainable AI in drug-drug interactions prediction: A systematic review.Computational and structural biotechnology journal · 2022Review
- On the robustness of generalization of drug-drug interaction models.BMC bioinformatics · 2021Article
- Medical Information Extraction in the Age of Deep Learning.Yearbook of medical informatics · 2020Review
- Integrated Random Negative Sampling and Uncertainty Sampling in Active Learning Improve Clinical Drug Safety Drug-Drug Interaction Information Retrieval.Frontiers in pharmacology · 2020Article
- Relation extraction between bacteria and biotopes from biomedical texts with attention mechanisms and domain-specific contextual representations.BMC bioinformatics · 2019Article
- Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities.An international journal on information fusion · 2019Article
- Knowledge-guided convolutional networks for chemical-disease relation extraction.BMC bioinformatics · 2019Article
- KMR: knowledge-oriented medicine representation learning for drug-drug interaction and similarity computation.Journal of cheminformatics · 2019Article
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8 authors.
Funding
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Abstract
backgroundDrug-drug interactions (DDIs) often bring unexpected side effects. The clinical recognition of DDIs is a crucial issue for both patient safety and healthcare cost control. However, although text-mining-based systems explore various methods to classify DDIs, the classification performance with regard to DDIs in long and complex sentences is still unsatisfactory.
methodsIn this study, we propose an effective model that classifies DDIs from the literature by combining an attention mechanism and a recurrent neural network with long short-term memory (LSTM) units. In our approach, first, a candidate-drug-oriented input attention acting on word-embedding vectors automatically learns which words are more influential for a given drug pair. Next, the inputs merging the position- and POS-embedding vectors are passed to a bidirectional LSTM layer whose outputs at the last time step represent the high-level semantic information of the whole sentence. Finally, a softmax layer performs DDI classification.
resultsExperimental results from the DDIExtraction 2013 corpus show that our system performs the best with respect to detection and classification (84.0% and 77.3%, respectively) compared with other state-of-the-art methods. In particular, for the Medline-2013 dataset with long and complex sentences, our F-score far exceeds those of top-ranking systems by 12.6%.
conclusionsOur approach effectively improves the performance of DDI classification tasks. Experimental analysis demonstrates that our model performs better with respect to recognizing not only close-range but also long-range patterns among words, especially for long, complex and compound sentences.
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