ArticleRisk analysis : an official publication of the Society for Risk Analysis2020
Predicting the Probability that a Chemical Causes Steatosis Using Adverse Outcome Pathway Bayesian Networks (AOPBNs).
Article in Risk analysis : an official publication of the Society for Risk Analysis, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 32 citations in OpenAlex.
- 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
- A Method to Calibrate Chemical-Agnostic Quantitative Adverse Outcome Pathways on Multiple Chemical Data.Chemical research in toxicology · 2026Article
- Quantitative adverse outcome pathway modeling for cigarette-smoke-induced airway mucus hypersecretion. Part 1: adverse-outcome-pathway-basedFrontiers in toxicology · 2025Article
- Adverse Outcome Pathways Mechanistically Describing Hepatotoxicity.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Computational Strategies for Assessing Adverse Outcome Pathways: Hepatic Steatosis as a Case Study.International journal of molecular sciences · 2024Review
- Proof of concept for quantitative adverse outcome pathway modeling of chronic toxicity in repeated exposure.Scientific reports · 2024Article
- Deriving time-concordant event cascades from gene expression data: A case study for Drug-Induced Liver Injury (DILI).PLoS computational biology · 2022Article
- Probabilistic modelling of developmental neurotoxicity based on a simplified adverse outcome pathway network.Computational toxicology (Amsterdam, Netherlands) · 2022Article
- From Qualitative to Quantitative AOP: A Case Study of Neurodegeneration.Frontiers in toxicology · 2022Article
- From Causal Networks to Adverse Outcome Pathways: A Developmental Neurotoxicity Case Study.Frontiers in toxicology · 2022Article
- Beyond adverse outcome pathways: making toxicity predictions from event networks, SAR models, data and knowledge.Toxicology research · 2021Article
- Quantification of an Adverse Outcome Pathway Network by Bayesian Regression and Bayesian Network Modeling.Integrated environmental assessment and management · 2021Article
- Quantitative adverse outcome pathway (qAOP) models for toxicity prediction.Archives of toxicology · 2020Review
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 3 countries.
Funding
Abstract
Adverse outcome pathway Bayesian networks (AOPBNs) are a promising avenue for developing predictive toxicology and risk assessment tools based on adverse outcome pathways (AOPs). Here, we describe a process for developing AOPBNs. AOPBNs use causal networks and Bayesian statistics to integrate evidence across key events. In this article, we use our AOPBN to predict the occurrence of steatosis under different chemical exposures. Since it is an expert-driven model, we use external data (i.e., data not used for modeling) from the literature to validate predictions of the AOPBN model. The AOPBN accurately predicts steatosis for the chemicals from our external data. In addition, we demonstrate how end users can utilize the model to simulate the confidence (based on posterior probability) associated with predicting steatosis. We demonstrate how the network topology impacts predictions across the AOPBN, and how the AOPBN helps us identify the most informative key events that should be monitored for predicting steatosis. We close with a discussion of how the model can be used to predict potential effects of mixtures and how to model susceptible populations (e.g., where a mutation or stressor may change the conditional probability tables in the AOPBN). Using this approach for developing expert AOPBNs will facilitate the prediction of chemical toxicity, facilitate the identification of assay batteries, and greatly improve chemical hazard screening strategies.
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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.