ArticleScientific reports2015
Label Propagation Prediction of Drug-Drug Interactions Based on Clinical Side Effects.
Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 80 papers.
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Who cites it
80 citing papers in PubMed.
- Mol2Image: an enhanced DDI prediction framework leveraging drug molecular descriptors.BMC bioinformatics · 2026Article
- Genetic underpinnings of type-2 diabetes (T2D) with colorectal cancer (CRC): In-silico discovery of common molecular signatures, pathogenetic processes and therapeutic candidates.Journal, genetic engineering & biotechnology · 2026Article
- Machine learning models for drug-drug interaction prediction from computational discovery to clinical application.NPJ digital medicine · 2026Review
- Multi-view knowledge-guided flow subgraphs with substructure initialization for explainable DDI prediction.Briefings in functional genomics · 2026Article
- Research on drug-drug interaction prediction using capsule neural network based on self-attention mechanism.BMC bioinformatics · 2025Article
- Contrastive learning-based multi-mechanism disentangled assessment for drug-drug interaction.BMC bioinformatics · 2025Article
- PS3N: leveraging protein sequence-structure similarity for novel drug-drug interaction discovery.Scientific reports · 2025Article
- Adaptive Multi-Kernel Graph Neural Network for Drug-Drug Interaction Prediction.Interdisciplinary sciences, computational life sciences · 2025Article
- In-silico discovery of type-2 diabetes-causing host key genes that are associated with the complexity of monkeypox and repurposing common drugs.Briefings in bioinformatics · 2025Article
- Leveraging large language models to mimic domain expert labeling in unstructured text-based electronic healthcare records in non-english languages.BMC medical informatics and decision making · 2025Article
- Screening of common genomic biomarkers to explore common drugs for the treatment of pancreatic and kidney cancers with type-2 diabetes through bioinformatics analysis.Scientific reports · 2025Article
- Article
- Common molecular links and therapeutic insights between type 2 diabetes and kidney cancer.PloS one · 2025Article
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- Improving drug-drug interaction prediction via in-context learning and judging with large language models.Frontiers in pharmacology · 2025Article
- Machine learning-based drug-drug interaction prediction: a critical review of models, limitations, and data challenges.Frontiers in pharmacology · 2025Review
- Comprehensive applications of the artificial intelligence technology in new drug research and development.Health information science and systems · 2024Review
- Article
- DrugDAGT: a dual-attention graph transformer with contrastive learning improves drug-drug interaction prediction.BMC biology · 2024Article
- DDI-Transform: A neural network for predicting drug-drug interaction events.Quantitative biology (Beijing, China) · 2024Article
20 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drug-drug interaction (DDI) is an important topic for public health, and thus attracts attention from both academia and industry. Here we hypothesize that clinical side effects (SEs) provide a human phenotypic profile and can be translated into the development of computational models for predicting adverse DDIs. We propose an integrative label propagation framework to predict DDIs by integrating SEs extracted from package inserts of prescription drugs, SEs extracted from FDA Adverse Event Reporting System, and chemical structures from PubChem. Experimental results based on hold-out validation demonstrated the effectiveness of the proposed algorithm. In addition, the new algorithm also ranked drug information sources based on their contributions to the prediction, thus not only confirming that SEs are important features for DDI prediction but also paving the way for building more reliable DDI prediction models by prioritizing multiple data sources. By applying the proposed algorithm to 1,626 small-molecule drugs which have one or more SE profiles, we obtained 145,068 predicted DDIs. The predicted DDIs will help clinicians to avoid hazardous drug interactions in their prescriptions and will aid pharmaceutical companies to design large-scale clinical trial by assessing potentially hazardous drug combinations. All data sets and predicted DDIs are available at http://astro.temple.edu/~tua87106/ddi.html.
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