ArticlePloS one2018
Drug drug interaction extraction from the literature using a recursive neural network.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed, 118 citations in OpenAlex.
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- A parallel dual-stream state-space module for reliable and efficient biomedical relation extraction.PLoS computational biology · 2026Article
- Enhanced drug-drug interaction extraction from biomedical text using deep learning-based sentence representations.Scientific reports · 2025Article
- A comprehensive landscape of AI applications in broad-spectrum drug interaction prediction: a systematic review.Journal of cheminformatics · 2025Review
- BioBBC: a multi-feature model that enhances the detection of biomedical entities.Scientific reports · 2024Article
- The role of artificial intelligence in drug screening, drug design, and clinical trials.Frontiers in pharmacology · 2024Review
- Clinical Decision Support Systems to Predict Drug-Drug Interaction Using Multilabel Long Short-Term Memory with an Autoencoder.International journal of environmental research and public health · 2023Article
- IMSE: interaction information attention and molecular structure based drug drug interaction extraction.BMC bioinformatics · 2022Article
- How can natural language processing help model informed drug development?: a review.JAMIA open · 2022Review
- Exploiting document graphs for inter sentence relation extraction.Journal of biomedical semantics · 2022Article
- Bioactivity assessment of natural compounds using machine learning models trained on target similarity between drugs.PLoS computational biology · 2022Article
- On the road to explainable AI in drug-drug interactions prediction: A systematic review.Computational and structural biotechnology journal · 2022Review
- MarkerGenie: an NLP-enabled text-mining system for biomedical entity relation extraction.Bioinformatics advances · 2022Article
- Multitype drug interaction prediction based on the deep fusion of drug features and topological relationships.PloS one · 2022Article
- BioVerbNet: a large semantic-syntactic classification of verbs in biomedicine.Journal of biomedical semantics · 2021Article
- Attention-Gated Graph Convolutions for Extracting Drug Interaction Information from Drug Labels.ACM transactions on computing for healthcare · 2021Article
- Discovering microbe-disease associations from the literature using a hierarchical long short-term memory network and an ensemble parser model.Scientific reports · 2021Article
- Identifying side effects of commonly used drugs in the treatment of Covid 19.Scientific reports · 2020Review
- Improving biomedical named entity recognition with syntactic information.BMC bioinformatics · 2020Article
- Medical Information Extraction in the Age of Deep Learning.Yearbook of medical informatics · 2020Review
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Detecting drug-drug interactions (DDI) is important because information on DDIs can help prevent adverse effects from drug combinations. Since there are many new DDI-related papers published in the biomedical domain, manually extracting DDI information from the literature is a laborious task. However, text mining can be used to find DDIs in the biomedical literature. Among the recently developed neural networks, we use a Recursive Neural Network to improve the performance of DDI extraction. Our recursive neural network model uses a position feature, a subtree containment feature, and an ensemble method to improve the performance of DDI extraction. Compared with the state-of-the-art models, the DDI detection and type classifiers of our model performed 4.4% and 2.8% better, respectively, on the DDIExtraction Challenge'13 test data. We also validated our model on the PK DDI corpus that consists of two types of DDIs data: in vivo DDI and in vitro DDI. Compared with the existing model, our detection classifier performed 2.3% and 6.7% better on in vivo and in vitro data respectively. The results of our validation demonstrate that our model can automatically extract DDIs better than existing models.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.