ArticleALTEX2024
Why adverse outcome pathways need to be FAIR.
Article in ALTEX, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Towards Bayesian-based quantitative adverse outcome pathways using in vitro data from open literature and continuous variables: a case example for liver fibrosis.Toxicological sciences : an official journal of the Society of Toxicology · 2026Article
- Cardiotoxicity adverse outcome pathway network: towards mechanistic and quantitative modelling.Frontiers in toxicology · 2026Article
- Filling the mechanistic and methodological gaps for predicting cytokine release syndrome in humans.Frontiers in pharmacology · 2026Article
- De-risking seizure liability: integrating adverse outcome pathways (AOPs), new approach methodologies (NAMs), and in silico approaches while highlighting knowledge gaps.Toxicological sciences : an official journal of the Society of Toxicology · 2025Review
- Going digital to boost safe and sustainable materials innovation markets. The digital safe-and-sustainability-by-design innovation approach of the PINK project.Computational and structural biotechnology journal · 2025Article
- Building an Adverse Outcome Pathway network for COVID-19.Frontiers in systems biology · 2024Article
- Comprehensive mapping of the AOP-Wiki database: identifying biological and disease gaps.Frontiers in toxicology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
Adverse outcome pathways (AOPs) provide evidence for demonstrating and assessing causality between measurable toxicological mechanisms and human or environmental adverse effects. AOPs have gained increasing attention over the past decade and are believed to provide the necessary steppingstone for more effective risk assessment of chemicals and materials and moving beyond the need for animal testing. However, as with all types of data and knowledge today, AOPs need to be reusable by machines, i.e., machine-actionable, in order to reach their full impact potential. Machine-actionability is supported by the FAIR principles, which guide findability, accessibility, interoperability, and reusability of data and knowledge. Here, we describe why AOPs need to be FAIR and touch on aspects such as the improved visibility and the increased trust that FAIRification of AOPs provides.
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.