ArticleBioinformatics (Oxford, England)2020
GpABC: a Julia package for approximate Bayesian computation with Gaussian process emulation.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Article
- HAPNEST: efficient, large-scale generation and evaluation of synthetic datasets for genotypes and phenotypes.Bioinformatics (Oxford, England) · 2023Article
- The minimal intrinsic stochasticity of constitutively expressed eukaryotic genes is sub-Poissonian.Science advances · 2023Article
- Julia for biologists.Nature methods · 2023Review
- Efficient Bayesian inference for stochastic agent-based models.PLoS computational biology · 2022Article
- Article
- Cluster mean-field theory accurately predicts statistical properties of large-scale DNA methylation patterns.Journal of the Royal Society, Interface · 2022Article
- Efficient exact inference for dynamical systems with noisy measurements using sequential approximate Bayesian computation.Bioinformatics (Oxford, England) · 2020Article
- Global search metaheuristics for neural mass model calibration.Imaging neuroscience (Cambridge, Mass.)Article
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Authors and funding
7 authors.
Funding
Abstract
motivationApproximate Bayesian computation (ABC) is an important framework within which to infer the structure and parameters of a systems biology model. It is especially suitable for biological systems with stochastic and nonlinear dynamics, for which the likelihood functions are intractable. However, the associated computational cost often limits ABC to models that are relatively quick to simulate in practice.
resultsWe here present a Julia package, GpABC, that implements parameter inference and model selection for deterministic or stochastic models using (i) standard rejection ABC or sequential Monte Carlo ABC or (ii) ABC with Gaussian process emulation. The latter significantly reduces the computational cost. AVAILABILITY AND IMPLEMENTATION: https://github.com/tanhevg/GpABC.jl.
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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.