ArticleJournal of pharmacokinetics and pharmacodynamics2006
A genetic algorithm-based, hybrid machine learning approach to model selection.
Article in Journal of pharmacokinetics and pharmacodynamics, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
24 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Covariate Model Selection Approaches for Population Pharmacokinetics: A Systematic Review of Existing Methods, From SCM to AI.CPT: pharmacometrics & systems pharmacology · 2025Pooled it
- Recent applications of quantitative systems pharmacology and machine learning models across diseases.Journal of pharmacokinetics and pharmacodynamics · 2022Pooled it
- Multi-objective optimization in population pharmacokinetic model selection and optimization: application of NSGA-II in pyDarwin.Journal of pharmacokinetics and pharmacodynamics · 2026Article
- Innovative applications of artificial intelligence technology in pharmacometrics.European journal of clinical pharmacology · 2026Review
- A machine learning approach to population pharmacokinetic modelling automation.Communications medicine · 2025Article
- Leveraging In Silico and Artificial Intelligence Models to Advance Drug Disposition and Response Predictions Across the Lifespan.Clinical and translational science · 2025Review
- pyDarwin machine learning algorithms application and comparison in nonlinear mixed-effect model selection and optimization.Journal of pharmacokinetics and pharmacodynamics · 2024Article
- Development of Predictive Statistical Model for Gaining Valuable Insights in Pharmaceutical Product Recalls.AAPS PharmSciTech · 2024Article
- Go beyond the limits of genetic algorithm in daily covariate selection practice.Journal of pharmacokinetics and pharmacodynamics · 2024Article
- Adoption of Machine Learning in Pharmacometrics: An Overview of Recent Implementations and Their Considerations.Pharmaceutics · 2022Article
- Machine Learning and Pharmacometrics for Prediction of Pharmacokinetic Data: Differences, Similarities and Challenges Illustrated with Rifampicin.Pharmaceutics · 2022Article
- Population pharmacokinetic model selection assisted by machine learning.Journal of pharmacokinetics and pharmacodynamics · 2022Article
- Editor's note on the themed issue: integration of machine learning and quantitative systems pharmacology.Journal of pharmacokinetics and pharmacodynamics · 2022Article
- Genetic Algorithms as a Tool for Dosing Guideline Optimization: Application to Intermittent Infusion Dosing for Vancomycin in Adults.CPT: pharmacometrics & systems pharmacology · 2020Article
- Machine Learning in Drug Discovery and Development Part 1: A Primer.CPT: pharmacometrics & systems pharmacology · 2020Article
- MinimaxHealth systems (Basingstoke, England) · 2019Article
- Artificial Intelligence and Pharmacometrics: Time to Embrace, Capitalize, and Advance?CPT: pharmacometrics & systems pharmacology · 2019Article
- Operating characteristics of stepwise covariate selection in pharmacometric modeling.Journal of pharmacokinetics and pharmacodynamics · 2019Article
- Covariate selection in pharmacometric analyses: a review of methods.British journal of clinical pharmacology · 2015Review
- A genetic algorithm based global search strategy for population pharmacokinetic/pharmacodynamic model selection.British journal of clinical pharmacology · 2015Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We describe a general and robust method for identification of an optimal non-linear mixed effects model. This includes structural, inter-individual random effects, covariate effects and residual error models using machine learning. This method is based on combinatorial optimization using genetic algorithm.
Indexed as
Identifiers
16565924What OpenQuestion holds
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.