ArticlePloS one2016
Driving the Model to Its Limit: Profile Likelihood Based Model Reduction.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers.
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
52 citing papers in PubMed.
- PEtab Select: Specification standard and supporting software for automated model selection.PLoS computational biology · 2026Article
- A Nonparametric Approach to Practical Identifiability of Nonlinear Mixed Effects Models.Bulletin of mathematical biology · 2026Article
- Identification and characterization of virtual sub-populations through phenotype-guided filtering. The challenging case of nonidentifiable models in the context of therapeutic evaluation.Journal of pharmacokinetics and pharmacodynamics · 2025Article
- A linear pathway for inositol pyrophosphate metabolism revealed by 18O labeling and model reduction.PLoS computational biology · 2025Article
- Quantitative Assessment of Biological Dynamics with Aggregate Data.Bulletin of mathematical biology · 2025Article
- Optimizing Early Ophthalmology Clinical Trials: Home OCT and Modeling Can Reduce Sample Size by 20% to 40.Translational vision science & technology · 2025Article
- Guidelines for mechanistic modeling and analysis in cardiovascular research.American journal of physiology. Heart and circulatory physiology · 2024Review
- Longitudinal single-cell data informs deterministic modelling of inflammatory bowel disease.NPJ systems biology and applications · 2024Article
- How robust are estimates of key parameters in standard viral dynamic models?PLoS computational biology · 2024Article
- Modeling resistance to the broadly neutralizing antibody PGT121 in people living with HIV-1.PLoS computational biology · 2024Article
- Basal MET phosphorylation is an indicator of hepatocyte dysregulation in liver disease.Molecular systems biology · 2024Article
- Index analysis: An approach to understand signal transduction with application to the EGFR signalling pathway.PLoS computational biology · 2024Article
- Quantifying single-cell diacylglycerol signaling kinetics after uncaging.Biophysical journal · 2023Article
- Estimating global identifiability using conditional mutual information in a Bayesian framework.Scientific reports · 2023Article
- CD8Nature communications · 2023Article
- Activator-blocker model of transcriptional regulation by pioneer-like factors.Nature communications · 2023Article
- Likelihood-ratio test statistic for the finite-sample case in nonlinear ordinary differential equation models.PLoS computational biology · 2023Article
- A Continuation Technique for Maximum Likelihood Estimators in Biological Models.Bulletin of mathematical biology · 2023Article
- Geometric analysis enables biological insight from complex non-identifiable models using simple surrogates.PLoS computational biology · 2023Article
- A quantitative model for human neurovascular coupling with translated mechanisms from animals.PLoS computational biology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In systems biology, one of the major tasks is to tailor model complexity to information content of the data. A useful model should describe the data and produce well-determined parameter estimates and predictions. Too small of a model will not be able to describe the data whereas a model which is too large tends to overfit measurement errors and does not provide precise predictions. Typically, the model is modified and tuned to fit the data, which often results in an oversized model. To restore the balance between model complexity and available measurements, either new data has to be gathered or the model has to be reduced. In this manuscript, we present a data-based method for reducing non-linear models. The profile likelihood is utilised to assess parameter identifiability and designate likely candidates for reduction. Parameter dependencies are analysed along profiles, providing context-dependent suggestions for the type of reduction. We discriminate four distinct scenarios, each associated with a specific model reduction strategy. Iterating the presented procedure eventually results in an identifiable model, which is capable of generating precise and testable predictions. Source code for all toy examples is provided within the freely available, open-source modelling environment Data2Dynamics based on MATLAB available at http://www.data2dynamics.org/, as well as the R packages dMod/cOde available at https://github.com/dkaschek/. Moreover, the concept is generally applicable and can readily be used with any software capable of calculating the profile likelihood.
Indexed as
Identifiers
What 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.