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Inference and uncertainty quantification of stochastic gene expression via synthetic models
Estimating uncertainty in model predictions is a central task in quantitative biology. Biological models at the single-cell level are intrinsically stochastic and nonlinear, creating formidable challenges for their statistical estimation which inevitably has to rely on approximations that trade accu...
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
The Royal Society
2022
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9277240/ https://www.ncbi.nlm.nih.gov/pubmed/35858045 http://dx.doi.org/10.1098/rsif.2022.0153 |