ArticleBioinformatics (Oxford, England)2010
Discovering drug-drug interactions: a text-mining and reasoning approach based on properties of drug metabolism.
Article in Bioinformatics (Oxford, England), 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers, 1 of them a synthesis that pooled it.
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Who cites it
61 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Computational Advances in Drug Safety: Systematic and Mapping Review of Knowledge Engineering Based Approaches.Frontiers in pharmacology · 2019Pooled it
- Contrastive learning-based multi-mechanism disentangled assessment for drug-drug interaction.BMC bioinformatics · 2025Article
- PTB-DDI: An Accurate and Simple Framework for Drug-Drug Interaction Prediction Based on Pre-Trained Tokenizer and BiLSTM Model.International journal of molecular sciences · 2024Article
- DDI-Transform: A neural network for predicting drug-drug interaction events.Quantitative biology (Beijing, China) · 2024Article
- DPSP: a multimodal deep learning framework for polypharmacy side effects prediction.Bioinformatics advances · 2023Article
- Comparing neural models for nested and overlapping biomedical event detection.BMC bioinformatics · 2022Article
- LPInsider: a webserver for lncRNA-protein interaction extraction from the literature.BMC bioinformatics · 2022Article
- Machine learning to predict metabolic drug interactions related to cytochrome P450 isozymes.Journal of cheminformatics · 2022Article
- Search and visualization of gene-drug-disease interactions for pharmacogenomics and precision medicine research using GeneDive.Journal of biomedical informatics · 2021Article
- A Review of Approaches for Predicting Drug-Drug Interactions Based on Machine Learning.Frontiers in pharmacology · 2021Review
- A computational approach to predict multi-pathway drug-drug interactions: A case study of irinotecan, a colon cancer medication.Saudi pharmaceutical journal : SPJ : the official publication of the Saudi Pharmaceutical Society · 2020Article
- Translational Knowledge Discovery Between Drug Interactions and Pharmacogenetics.Clinical pharmacology and therapeutics · 2020Review
- Prediction of Drug-Drug Interactions by Using Profile Fingerprint Vectors and Protein Similarities.Healthcare informatics research · 2020Article
- Feature-Based Learning in Drug Prescription System for Medical Clinics.Neural processing letters · 2020Article
- An Informatics-based Approach to Identify Key Pharmacological Components in Drug-Drug Interactions.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2020Article
- DDIGIP: predicting drug-drug interactions based on Gaussian interaction profile kernels.BMC bioinformatics · 2019Article
- Navigating the disease landscape: knowledge representations for contextualizing molecular signatures.Briefings in bioinformatics · 2019Review
- CuDDI: A CUDA-Based Application for Extracting Drug-Drug Interaction Related Substance Terms from PubMed Literature.Molecules (Basel, Switzerland) · 2019Article
- Detection of drug-drug interactions through data mining studies using clinical sources, scientific literature and social media.Briefings in bioinformatics · 2018Review
- Deep learning improves prediction of drug-drug and drug-food interactions.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
1 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationIdentifying drug-drug interactions (DDIs) is a critical process in drug administration and drug development. Clinical support tools often provide comprehensive lists of DDIs, but they usually lack the supporting scientific evidences and different tools can return inconsistent results. In this article, we propose a novel approach that integrates text mining and automated reasoning to derive DDIs. Through the extraction of various facts of drug metabolism, not only the DDIs that are explicitly mentioned in text can be extracted but also the potential interactions that can be inferred by reasoning.
resultsOur approach was able to find several potential DDIs that are not present in DrugBank. We manually evaluated these interactions based on their supporting evidences, and our analysis revealed that 81.3% of these interactions are determined to be correct. This suggests that our approach can uncover potential DDIs with scientific evidences explaining the mechanism of the interactions.
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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.