ArticleEntropy (Basel, Switzerland)2019
Drug-Drug Interaction Extraction via Recurrent Hybrid Convolutional Neural Networks with an Improved Focal Loss.
Article in Entropy (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed, 99 citations in OpenAlex.
- Unveiling rare drug interactions via a BioGPT-enhanced dual graph framework for robust pharmacovigilance.iScience · 2026Article
- CMCL-DDI: Pharmacophore-aware cross-view contrastive learning for drug-drug interaction prediction.PloS one · 2026Article
- A Domain Adaptive Interpretable Substructure-Aware Graph Attention Network for Drug-Drug Interaction Prediction.Interdisciplinary sciences, computational life sciences · 2025Article
- A meta-contrastive learning approach for clinical drug-drug interaction extraction from biomedical literature.PLoS computational biology · 2025Article
- NCIVISION: A Siamese Neural Network for Molecular Similarity Prediction MEP and RDG Images.Molecules (Basel, Switzerland) · 2025Article
- A comprehensive landscape of AI applications in broad-spectrum drug interaction prediction: a systematic review.Journal of cheminformatics · 2025Review
- Biomedical relation extraction method based on ensemble learning and attention mechanism.BMC bioinformatics · 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
- MTMG: A multi-task model with multi-granularity information for drug-drug interaction extraction.Heliyon · 2023Article
- Biomedical Relation Extraction Using Dependency Graph and Decoder-Enhanced Transformer Model.Bioengineering (Basel, Switzerland) · 2023Article
- MSEDDI: Multi-Scale Embedding for Predicting Drug-Drug Interaction Events.International journal of molecular sciences · 2023Article
- DDI-MuG: Multi-aspect graphs for drug-drug interaction extraction.Frontiers in digital health · 2023Article
- Multitype drug interaction prediction based on the deep fusion of drug features and topological relationships.PloS one · 2022Article
- On the road to explainable AI in drug-drug interactions prediction: A systematic review.Computational and structural biotechnology journal · 2022Review
- Drug-Drug Interactions Prediction Using Fingerprint Only.Computational and mathematical methods in medicine · 2022Article
- Research on Feature Extraction and Chinese Translation Method of Internet-of-Things English Terminology.Computational intelligence and neuroscience · 2022Article
- Convolutional neural networks (CNNs): concepts and applications in pharmacogenomics.Molecular diversity · 2021Article
- Attention-Gated Graph Convolutions for Extracting Drug Interaction Information from Drug Labels.ACM transactions on computing for healthcare · 2021Article
- Text Semantic Classification of Long Discourses Based on Neural Networks with Improved Focal Loss.Computational intelligence and neuroscience · 2021Article
- Identifying side effects of commonly used drugs in the treatment of Covid 19.Scientific reports · 2020Review
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
Abstract
Drug-drug interactions (DDIs) may bring huge health risks and dangerous effects to a patient's body when taking two or more drugs at the same time or within a certain period of time. Therefore, the automatic extraction of unknown DDIs has great potential for the development of pharmaceutical agents and the safety of drug use. In this article, we propose a novel recurrent hybrid convolutional neural network (RHCNN) for DDI extraction from biomedical literature. In the embedding layer, the texts mentioning two entities are represented as a sequence of semantic embeddings and position embeddings. In particular, the complete semantic embedding is obtained by the information fusion between a word embedding and its contextual information which is learnt by recurrent structure. After that, the hybrid convolutional neural network is employed to learn the sentence-level features which consist of the local context features from consecutive words and the dependency features between separated words for DDI extraction. Lastly but most significantly, in order to make up for the defects of the traditional cross-entropy loss function when dealing with class imbalanced data, we apply an improved focal loss function to mitigate against this problem when using the DDIExtraction 2013 dataset. In our experiments, we achieve DDI automatic extraction with a micro F-score of 75.48% on the DDIExtraction 2013 dataset, outperforming the state-of-the-art approach by 2.49%.
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Registered trials
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.