ArticleScience translational medicine2012
Data-driven prediction of drug effects and interactions.
Article in Science translational medicine, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 377 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
377 citing papers in PubMed.
- Human cardiovascular disease model predicts xanthine oxidase inhibitor cardiovascular risk.PloS one · 2023Trial
- Prospective adverse event risk evaluation in clinical trials.Health care management science · 2022Trial
- Robust prediction of drug combination side effects in realistic settings.PLoS computational biology · 2026Article
- IMF-DDI: Information Mapping and Fusion Framework for Drug-drug Interaction Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- Decoupling topological and molecular features for interpretable biomolecular interaction prediction.Briefings in bioinformatics · 2026Article
- Using Artificial Intelligence to Identify Patterns and Predictors of Adverse Drug Reactions in Cardiovascular Patients: A Comprehensive Narrative Review.American journal of cardiovascular drugs : drugs, devices, and other interventions · 2026Review
- Computational Prediction of the Severity of Adverse Drug Reactions Caused by Drug-Drug Interactions.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Exploring Risks of Adverse Drug Events From Single Drug to Three-Drug Combination.Clinical and translational science · 2026Article
- Article
- Multi-GraphDDI: Multi-Feature Fusion and Interaction for Graph-Based Drug-Drug Interaction Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- VITAGRAPH: building a knowledge graph for biologically relevant learning tasks.Scientific data · 2026Article
- Multimodal Collaborative Modeling of Molecular Structures and Biomedical Text for Accurate Drug-Drug Interaction Extraction.Biomedicines · 2026Article
- A quantum graph-based differential model for drug-drug interaction prediction.Journal of computer-aided molecular design · 2026Article
- A Scalable Sign-Aware Multi-Omics Knowledge Graph Foundation Model for Mechanistic Drug Action and Clinical Response Predictions.bioRxiv : the preprint server for biology · 2026Article
- AdaptMol: domain adaptation for molecular image recognition with limited supervision.Journal of cheminformatics · 2026Article
- Explainable drug side effect prediction in central neural system via biologically informed graph neural network.Translational psychiatry · 2026Article
- RAG-based architectures for drug side effect retrieval using compact LLMs.Scientific reports · 2026Article
- Machine Learning in Preclinical Development of Antiviral Peptide Candidates: A Review of the Current Landscape.Viruses · 2026Review
- Leveraging Large Language Models in Extracting Drug Safety Information from Prescription Drug Labels.Drug safety · 2026Article
- Real-time clinical analytics at scale: a platform built on large language models-powered knowledge graphs.JAMIA open · 2026Article
317 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Adverse drug events remain a leading cause of morbidity and mortality around the world. Many adverse events are not detected during clinical trials before a drug receives approval for use in the clinic. Fortunately, as part of postmarketing surveillance, regulatory agencies and other institutions maintain large collections of adverse event reports, and these databases present an opportunity to study drug effects from patient population data. However, confounding factors such as concomitant medications, patient demographics, patient medical histories, and reasons for prescribing a drug often are uncharacterized in spontaneous reporting systems, and these omissions can limit the use of quantitative signal detection methods used in the analysis of such data. Here, we present an adaptive data-driven approach for correcting these factors in cases for which the covariates are unknown or unmeasured and combine this approach with existing methods to improve analyses of drug effects using three test data sets. We also present a comprehensive database of drug effects (Offsides) and a database of drug-drug interaction side effects (Twosides). To demonstrate the biological use of these new resources, we used them to identify drug targets, predict drug indications, and discover drug class interactions. We then corroborated 47 (P < 0.0001) of the drug class interactions using an independent analysis of electronic medical records. Our analysis suggests that combined treatment with selective serotonin reuptake inhibitors and thiazides is associated with significantly increased incidence of prolonged QT intervals. We conclude that confounding effects from covariates in observational clinical data can be controlled in data analyses and thus improve the detection and prediction of adverse drug effects and 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.