ArticleHeliyon2023
MultiGML: Multimodal graph machine learning for prediction of adverse drug events.
Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- AI-based multimodal integration of genomics and electronic health records.Nature reviews. Genetics · 2026Review
- Advancing Drug-Drug Interaction Prediction with Biomimetic Improvements: Leveraging the Latest Artificial Intelligence Techniques to Guide Researchers in the Field.Biomimetics (Basel, Switzerland) · 2026Review
- A scoping review of artificial intelligence applications in clinical trial risk assessment.NPJ digital medicine · 2025Article
- Developmental toxicity: artificial intelligence-powered assessments.Trends in pharmacological sciences · 2025Review
- An Evaluation Benchmark for Adverse Drug Event Prediction from Clinical Trial Results.Scientific data · 2025Article
- Explainable Artificial Intelligence in the Field of Drug Research.Drug design, development and therapy · 2025Review
- Graph Artificial Intelligence in Medicine.Annual review of biomedical data science · 2024Review
- Attention is all you need: utilizing attention in AI-enabled drug discovery.Briefings in bioinformatics · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Adverse drug events constitute a major challenge for the success of clinical trials. Several computational strategies have been suggested to estimate the risk of adverse drug events in preclinical drug development. While these approaches have demonstrated high utility in practice, they are at the same time limited to specific information sources. Thus, many current computational approaches neglect a wealth of information which results from the integration of different data sources, such as biological protein function, gene expression, chemical compound structure, cell-based imaging and others. In this work we propose an integrative and explainable
Indexed as
Identifiers
What OpenQuestion holds
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.