ArticleBMC bioinformatics2017
Dependency-based long short term memory network for drug-drug interaction 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 19 papers.
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19 citing papers in PubMed, 71 citations in OpenAlex.
- 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
- Biomedical relation extraction method based on ensemble learning and attention mechanism.BMC bioinformatics · 2024Article
- MTMG: A multi-task model with multi-granularity information for drug-drug interaction extraction.Heliyon · 2023Article
- 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
- DDI-MuG: Multi-aspect graphs for drug-drug interaction extraction.Frontiers in digital health · 2023Article
- Application of multivariate time-series model for high performance computing (HPC) fault prediction.PloS one · 2023Article
- IMSE: interaction information attention and molecular structure based drug drug interaction extraction.BMC bioinformatics · 2022Article
- DDInter: an online drug-drug interaction database towards improving clinical decision-making and patient safety.Nucleic acids research · 2022Article
- On the road to explainable AI in drug-drug interactions prediction: A systematic review.Computational and structural biotechnology journal · 2022Review
- Mining a stroke knowledge graph from literature.BMC bioinformatics · 2021Article
- Using drug descriptions and molecular structures for drug-drug interaction extraction from literature.Bioinformatics (Oxford, England) · 2021Article
- Social Network Analysis of an Online Smoking Cessation Community to Identify Users' Smoking Status.Healthcare informatics research · 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
- BO-LSTM: classifying relations via long short-term memory networks along biomedical ontologies.BMC bioinformatics · 2019Article
- Extraction of chemical-protein interactions from the literature using neural networks and narrow instance representation.Database : the journal of biological databases and curation · 2019Article
- Constructing a database for the relations between CNV and human genetic diseases via systematic text mining.BMC bioinformatics · 2018Article
- A bioinformatics potpourri.BMC genomics · 2018Article
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDrug-drug interaction extraction (DDI) needs assistance from automated methods to address the explosively increasing biomedical texts. In recent years, deep neural network based models have been developed to address such needs and they have made significant progress in relation identification.
methodsWe propose a dependency-based deep neural network model for DDI extraction. By introducing the dependency-based technique to a bi-directional long short term memory network (Bi-LSTM), we build three channels, namely, Linear channel, DFS channel and BFS channel. All of these channels are constructed with three network layers, including embedding layer, LSTM layer and max pooling layer from bottom up. In the embedding layer, we extract two types of features, one is distance-based feature and another is dependency-based feature. In the LSTM layer, a Bi-LSTM is instituted in each channel to better capture relation information. Then max pooling is used to get optimal features from the entire encoding sequential data. At last, we concatenate the outputs of all channels and then link it to the softmax layer for relation identification.
resultsTo the best of our knowledge, our model achieves new state-of-the-art performance with the F-score of 72.0% on the DDIExtraction 2013 corpus. Moreover, our approach obtains much higher Recall value compared to the existing methods.
conclusionsThe dependency-based Bi-LSTM model can learn effective relation information with less feature engineering in the task of DDI extraction. Besides, the experimental results show that our model excels at balancing the Precision and Recall values.
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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.