ArticleBioinformatics (Oxford, England)2021
Comparison Of Observation-Based And Model-Based Identification Of Alert Concentrations From Concentration-Expression Data.
Article in Bioinformatics (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Identification of Changes in Gene Expression.Biometrical journal. Biometrische Zeitschrift · 2026Article
- Adjustment for day-to-day variability in the estimation of effective concentrations for the assessment of mixture toxicity.Archives of toxicology · 2025Article
- AlertGS: determining alerts for gene sets.Bioinformatics (Oxford, England) · 2025Article
- Designs for the simultaneous inference of concentration-response curves.BMC bioinformatics · 2023Article
- Guidance for statistical design and analysis of toxicological dose-response experiments, based on a comprehensive literature review.Archives of toxicology · 2023Article
- Information sharing in high-dimensional gene expression data for improved parameter estimation in concentration-response modelling.PloS one · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationAn important goal of concentration-response studies in toxicology is to determine an 'alert' concentration where a critical level of the response variable is exceeded. In a classical observation-based approach, only measured concentrations are considered as potential alert concentrations. Alternatively, a parametric curve is fitted to the data that describes the relationship between concentration and response. For a prespecified effect level, both an absolute estimate of the alert concentration and an estimate of the lowest concentration where the effect level is exceeded significantly are of interest.
resultsIn a simulation study for gene expression data, we compared the observation-based and the model-based approach for both absolute and significant exceedance of the prespecified effect level. Results show that, compared to the observation-based approach, the model-based approach overestimates the true alert concentration less often and more frequently leads to a valid estimate, especially for genes with large variance. AVAILABILITY AND IMPLEMENTATION: The code used for the simulation studies is available via the GitHub repository: https://github.com/FKappenberg/Paper-IdentificationAlertConcentrations. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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