Bayesian inference using qualitative observations of underlying continuous variables
| Autor: | William S. Hlavacek, Eshan D. Mitra |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Statistics and Probability
Computer science Bayesian probability Parameterized complexity Qualitative property Bayesian inference Machine learning computer.software_genre Biochemistry 03 medical and health sciences 0302 clinical medicine Uncertainty quantification Molecular Biology 030304 developmental biology Likelihood Functions 0303 health sciences Interpretation (logic) Mathematical model business.industry Systems Biology Uncertainty Bayes Theorem Original Papers Computer Science Applications Computational Mathematics Computational Theory and Mathematics Artificial intelligence business computer Software 030217 neurology & neurosurgery |
| Zdroj: | Bioinformatics |
| ISSN: | 1367-4811 1367-4803 |
| Popis: | Motivation Recent work has demonstrated the feasibility of using non-numerical, qualitative data to parameterize mathematical models. However, uncertainty quantification (UQ) of such parameterized models has remained challenging because of a lack of a statistical interpretation of the objective functions used in optimization. Results We formulated likelihood functions suitable for performing Bayesian UQ using qualitative observations of underlying continuous variables or a combination of qualitative and quantitative data. To demonstrate the resulting UQ capabilities, we analyzed a published model for immunoglobulin E (IgE) receptor signaling using synthetic qualitative and quantitative datasets. Remarkably, estimates of parameter values derived from the qualitative data were nearly as consistent with the assumed ground-truth parameter values as estimates derived from the lower throughput quantitative data. These results provide further motivation for leveraging qualitative data in biological modeling. Availability and implementation The likelihood functions presented here are implemented in a new release of PyBioNetFit, an open-source application for analyzing Systems Biology Markup Language- and BioNetGen Language-formatted models, available online at www.github.com/lanl/PyBNF. Supplementary information Supplementary data are available at Bioinformatics online. |
| Databáze: | OpenAIRE |
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