ArticleBioinformatics (Oxford, England)2012
A Bayesian approach to targeted experiment design.
Article in Bioinformatics (Oxford, England), 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed.
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- Investigating Active Learning and Meta-Learning for Iterative Peptide Design.Journal of chemical information and modeling · 2021Article
- Optimal experimental design for mathematical models of haematopoiesis.Journal of the Royal Society, Interface · 2021Article
- Accelerated knowledge discovery from omics data by optimal experimental design.Nature communications · 2020Article
- Bayesian inference for dynamical systems.Infectious Disease Modelling · 2020Article
- Computational optimization of associative learning experiments.PLoS computational biology · 2020Article
- Uncertainty quantification, propagation and characterization by Bayesian analysis combined with global sensitivity analysis applied to dynamical intracellular pathway models.Bioinformatics (Oxford, England) · 2019Article
- The finite state projection based Fisher information matrix approach to estimate information and optimize single-cell experiments.PLoS computational biology · 2019Article
- Sequential infection experiments for quantifying innate and adaptive immunity during influenza infection.PLoS computational biology · 2019Article
- Quantifying the relative importance of experimental data points in parameter estimation.BMC systems biology · 2018Article
- PEITH(Θ): perfecting experiments with information theory in Python with GPU support.Bioinformatics (Oxford, England) · 2018Article
- Optimal Quantification of Contact Inhibition in Cell Populations.Biophysical journal · 2017Article
- A confidence building exercise in data and identifiability: Modeling cancer chemotherapy as a case study.Journal of theoretical biology · 2017Article
- How to deal with parameters for whole-cell modelling.Journal of the Royal Society, Interface · 2017Review
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4 authors.
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Abstract
motivationSystems biology employs mathematical modelling to further our understanding of biochemical pathways. Since the amount of experimental data on which the models are parameterized is often limited, these models exhibit large uncertainty in both parameters and predictions. Statistical methods can be used to select experiments that will reduce such uncertainty in an optimal manner. However, existing methods for optimal experiment design (OED) rely on assumptions that are inappropriate when data are scarce considering model complexity.
resultsWe have developed a novel method to perform OED for models that cope with large parameter uncertainty. We employ a Bayesian approach involving importance sampling of the posterior predictive distribution to predict the efficacy of a new measurement at reducing the uncertainty of a selected prediction. We demonstrate the method by applying it to a case where we show that specific combinations of experiments result in more precise predictions. AVAILABILITY AND IMPLEMENTATION: Source code is available at: http://bmi.bmt.tue.nl/sysbio/software/pua.html.
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