ArticleBioinformatics (Oxford, England)2015
SYSBIONS: nested sampling for systems biology.
Article in Bioinformatics (Oxford, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- A Bayesian semi-parametric model for thermal proteome profiling.Communications biology · 2021Article
- Likelihood-free nested sampling for parameter inference of biochemical reaction networks.PLoS computational biology · 2020Article
- Systems Bioinformatics: increasing precision of computational diagnostics and therapeutics through network-based approaches.Briefings in bioinformatics · 2019Review
- Bayesian inference of agent-based models: a tool for studying kidney branching morphogenesis.Journal of mathematical biology · 2018Article
- Graphics processing units in bioinformatics, computational biology and systems biology.Briefings in bioinformatics · 2017Review
- BCM: toolkit for Bayesian analysis of Computational Models using samplers.BMC systems biology · 2016Article
- Dizzy-Beats: a Bayesian evidence analysis tool for systems biology.Bioinformatics (Oxford, England) · 2015Article
- Topological sensitivity analysis for systems biology.Proceedings of the National Academy of Sciences of the United States of America · 2014Article
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3 authors.
Funding
Abstract
motivationModel selection is a fundamental part of the scientific process in systems biology. Given a set of competing hypotheses, we routinely wish to choose the one that best explains the observed data. In the Bayesian framework, models are compared via Bayes factors (the ratio of evidences), where a model's evidence is the support given to the model by the data. A parallel interest is inferring the distribution of the parameters that define a model. Nested sampling is a method for the computation of a model's evidence and the generation of samples from the posterior parameter distribution.
resultsWe present a C-based, GPU-accelerated implementation of nested sampling that is designed for biological applications. The algorithm follows a standard routine with optional extensions and additional features. We provide a number of methods for sampling from the prior subject to a likelihood constraint. AVAILABILITY AND IMPLEMENTATION: The software SYSBIONS is available from http://www.theosysbio.bio.ic.ac.uk/resources/sysbions/ CONTACT: m.stumpf@imperial.ac.uk, robert.johnson11@imperial.ac.uk.
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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.