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Medications are typically administered to achieve therapeutic effects. Some drugs can modify an individual's mood and perception, frequently resulting in various enjoyable experiences. However, this can result in drug dependency, a condition marked by continuous drug use despite potential negative consequences. Drug dependency primarily falls into two categories: psychological and physical dependence. Psychological dependence occurs when the pleasurable feelings induced by the drug...
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Drug–drug interactions can precipitate toxicity through multiple mechanisms. Absorption interactions alter how drugs enter the body, exemplified when ranitidine increases the absorption of basic drugs, while cholestyramine decreases the levels of propranolol. Protein binding interactions occur when drugs share the same binding sites on plasma proteins. Drugs like aspirin and warfarin, when bound in excess, can lead to increased free drug concentrations, enhancing the potential for...
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Related Experiment Video

Updated: Feb 16, 2026

C. elegans Positive Butanone Learning, Short-term, and Long-term Associative Memory Assays
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Dependency-based long short term memory network for drug-drug interaction extraction.

Wei Wang1, Xi Yang1, Canqun Yang1

  • 1School of Computer Science, National University of Defense Technology, Changsha, 410073, China.

BMC Bioinformatics
|January 4, 2018
PubMed
Summary

A novel dependency-based deep neural network model improves drug-drug interaction extraction (DDI) by utilizing a Bi-LSTM network. This method achieves state-of-the-art performance, enhancing recall and balancing precision for DDI identification.

Keywords:
Data imbalanceDependency treeLong short term memoryRelation extraction

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Area of Science:

  • Biomedical Informatics
  • Computational Linguistics

Background:

  • Automated drug-drug interaction (DDI) extraction is crucial for managing the growing volume of biomedical literature.
  • Deep neural networks have shown promise in advancing DDI relation identification.

Purpose of the Study:

  • To develop an automated method for DDI extraction using a novel dependency-based deep neural network.
  • To improve the accuracy and efficiency of identifying drug interactions in biomedical texts.

Main Methods:

  • A dependency-based deep neural network model incorporating a Bi-LSTM network was proposed.
  • Three channels (Linear, DFS, BFS) were constructed with embedding, Bi-LSTM, and max pooling layers.
  • Both distance-based and dependency-based features were extracted in the embedding layer.

Main Results:

  • The model achieved a new state-of-the-art F-score of 72.0% on the DDIExtraction 2013 corpus.
  • The proposed approach demonstrated significantly higher Recall compared to existing methods.
  • The model effectively balanced Precision and Recall values.

Conclusions:

  • The dependency-based Bi-LSTM model effectively captures relation information with reduced feature engineering for DDI extraction.
  • The model's performance highlights its capability in accurately identifying drug-drug interactions.
  • This approach offers a robust solution for advancing automated DDI extraction in biomedical research.