ArticleDose-response : a publication of International Hormesis Society2011
Optimization of nonlinear dose- and concentration-response models utilizing evolutionary computation.
Article in Dose-response : a publication of International Hormesis Society, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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.
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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
10 citing papers in PubMed.
- Inclusion of Control Data in Fits to Concentration-Response Curves Improves Estimates of Half-Maximal Concentrations.Journal of medicinal chemistry · 2023Article
- Evaluation of statistical approaches for association testing in noisy drug screening data.BMC bioinformatics · 2022Article
- Current Methods for Quantifying Drug Synergism.Proteomics & bioinformatics : current research · 2019Article
- An Introduction to Terminology and Methodology of Chemical Synergy-Perspectives from Across Disciplines.Frontiers in pharmacology · 2017Review
- Contextualizing Hepatocyte Functionality of Cryopreserved HepaRG Cell Cultures.Drug metabolism and disposition: the biological fate of chemicals · 2016Article
- Beyond ICJournal of pharmacogenomics & pharmacoproteomics · 2014Article
- A comparison of association methods for cytotoxicity mapping in pharmacogenomics.Frontiers in genetics · 2011Article
- Nonlinear Dose-Response Modeling of High-Throughput Screening Data Using an Evolutionary Algorithm.Dose-response : a publication of International Hormesis SocietyArticle
- Ex-Vivo Modeling for Heritability Assessment and Genetic Mapping in Pharmacogenomics.Proceedings. American Statistical Association. Annual MeetingArticle
- A Universal Delayed Difference Model Fitting Dose-response Curves.Dose-response : a publication of International Hormesis SocietyArticle
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
An essential part of toxicity and chemical screening is assessing the concentrated related effects of a test article. Most often this concentration-response is a nonlinear, necessitating sophisticated regression methodologies. The parameters derived from curve fitting are essential in determining a test article's potency (EC(50)) and efficacy (E(max)) and variations in model fit may lead to different conclusions about an article's performance and safety. Previous approaches have leveraged advanced statistical and mathematical techniques to implement nonlinear least squares (NLS) for obtaining the parameters defining such a curve. These approaches, while mathematically rigorous, suffer from initial value sensitivity, computational intensity, and rely on complex and intricate computational and numerical techniques. However if there is a known mathematical model that can reliably predict the data, then nonlinear regression may be equally viewed as parameter optimization. In this context, one may utilize proven techniques from machine learning, such as evolutionary algorithms, which are robust, powerful, and require far less computational framework to optimize the defining parameters. In the current study we present a new method that uses such techniques, Evolutionary Algorithm Dose Response Modeling (EADRM), and demonstrate its effectiveness compared to more conventional methods on both real and simulated data.
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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.