ArticleDose-response : a publication of International Hormesis Society
Nonlinear Dose-Response Modeling of High-Throughput Screening Data Using an Evolutionary Algorithm.
Article in Dose-response : a publication of International Hormesis Society. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Creatine supplementation on fatigue related to post-COVID-19 condition-fatigue study: a randomized controlled trial.Frontiers in nutrition · 2026Article
- Modeling omics dose-response at the pathway level with DoseRider.Computational and structural biotechnology journal · 2025Article
- Extending the lymphoblastoid cell line model for drug combination pharmacogenomics.Pharmacogenomics · 2021Review
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Abstract
Nonlinear dose-response relationships exist extensively in the cellular, biochemical, and physiologic processes that are affected by varying levels of biological, chemical, or radiation stress. Modeling such responses is a crucial component of toxicity testing and chemical screening. Traditional model fitting methods such as nonlinear least squares (NLS) are very sensitive to initial parameter values and often had convergence failure. The use of evolutionary algorithms (EAs) has been proposed to address many of the limitations of traditional approaches, but previous methods have been limited in the types of models they can fit. Therefore, we propose the use of an EA for dose-response modeling for a range of potential response model functional forms. This new method can not only fit the most commonly used nonlinear dose-response models (eg, exponential models and 3-, 4-, and 5-parameter logistic models) but also select the best model if no model assumption is made, which is especially useful in the case of high-throughput curve fitting. Compared with NLS, the new method provides stable and robust solutions without sensitivity to initial values.
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