ArticlePloS one2018
Uncertainty quantification in ToxCast high throughput screening.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
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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.
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Who cites it
22 citing papers in PubMed.
- Improved Machine Learning Predictions of EC50s Using Uncertainty Estimation from Dose-Response Data.Journal of chemical information and modeling · 2025Article
- MLinvitroTox reloaded for high-throughput hazard-based prioritization of high-resolution mass spectrometry data.Journal of cheminformatics · 2025Article
- A Comparison of In Vitro Points of Departure with Human Blood Levels for Per- and Polyfluoroalkyl Substances (PFAS).Toxics · 2024Article
- Chemistry domain of applicability evaluation against existing estrogen receptor high-throughput assay-based activity models.Frontiers in toxicology · 2024Article
- Investigating open access new approach methods (NAM) to assess biological points of departure: A case study with 4 neurotoxic pesticides.Current research in toxicology · 2024Article
- The ToxCast pipeline: updates to curve-fitting approaches and database structure.Frontiers in toxicology · 2023Article
- Evaluating structure-based activity in a high-throughput assay for steroid biosynthesis.Computational toxicology (Amsterdam, Netherlands) · 2022Article
- Impact of High-Throughput Model Parameterization and Data Uncertainty on Thyroid-Based Toxicological Estimates for Pesticide Chemicals.Environmental science & technology · 2022Article
- Food, Beverage, and Feedstock Processing Facility Wastewater: a Unique and Underappreciated Source of Contaminants to U.S. Streams.Environmental science & technology · 2022Article
- The effect of noise on the predictive limit of QSAR models.Journal of cheminformatics · 2021Article
- Cardiotoxicity Hazard and Risk Characterization of ToxCast Chemicals Using Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes from Multiple Donors.Chemical research in toxicology · 2021Article
- Combined Naïve Bayesian, Chemical Fingerprints and Molecular Docking Classifiers to Model and Predict Androgen Receptor Binding Data for Environmentally- and Health-Sensitive Substances.International journal of molecular sciences · 2021Article
- The Alginate Immobilization of Metabolic Enzymes Platform Retrofits an Estrogen Receptor Transactivation Assay With Metabolic Competence.Toxicological sciences : an official journal of the Society of Toxicology · 2020Article
- Selecting a minimal set of androgen receptor assays for screening chemicals.Regulatory toxicology and pharmacology : RTP · 2020Article
- Cytotoxicity Burst? Differentiating Specific from Nonspecific Effects in Tox21Environmental health perspectives · 2020Article
- Identifying and Characterizing Stress Pathways of Concern for Consumer Safety in Next-Generation Risk Assessment.Toxicological sciences : an official journal of the Society of Toxicology · 2020Article
- Potential of ToxCast Data in the Safety Assessment of Food Chemicals.Toxicological sciences : an official journal of the Society of Toxicology · 2020Article
- Utility of In Vitro Bioactivity as a Lower Bound Estimate of In Vivo Adverse Effect Levels and in Risk-Based Prioritization.Toxicological sciences : an official journal of the Society of Toxicology · 2020Article
- Assessing Toxicokinetic Uncertainty and Variability in Risk Prioritization.Toxicological sciences : an official journal of the Society of Toxicology · 2019Article
- The use of evidence from high-throughput screening and transcriptomic data in human health risk assessments.Toxicology and applied pharmacology · 2019Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
High throughput screening (HTS) projects like the U.S. Environmental Protection Agency's ToxCast program are required to address the large and rapidly increasing number of chemicals for which we have little to no toxicity measurements. Concentration-response parameters such as potency and efficacy are extracted from HTS data using nonlinear regression, and models and analyses built from these parameters are used to predict in vivo and in vitro toxicity of thousands of chemicals. How these predictions are impacted by uncertainties that stem from parameter estimation and propagated through the models and analyses has not been well explored. While data size and complexity makes uncertainty quantification computationally expensive for HTS datasets, continued advancements in computational resources have allowed these computational challenges to be met. This study uses nonparametric bootstrap resampling to calculate uncertainties in concentration-response parameters from a variety of HTS assays. Using the ToxCast estrogen receptor model for bioactivity as a case study, we highlight how these uncertainties can be propagated through models to quantify the uncertainty in model outputs. Uncertainty quantification in model outputs is used to identify potential false positives and false negatives and to determine the distribution of model values around semi-arbitrary activity cutoffs, increasing confidence in model predictions. At the individual chemical-assay level, curves with high variability are flagged for manual inspection or retesting, focusing subject-matter-expert time on results that need further input. This work improves the confidence of predictions made using HTS data, increasing the ability to use this data in risk assessment.
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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.