Inferring time derivatives including cell growth rates using Gaussian processes
High-throughput time-series data is increasingly available, yet estimating time-derivatives from such data can remain a challenge. Here, the authors provide a non-parametric method for inferring the first and second time-derivatives from multiple replicates of time-series data and for estimating err...
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| Autores principales: | , , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2016
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/6aefe21cd35443ab9d0e44b3d219f660 |
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| Sumario: | High-throughput time-series data is increasingly available, yet estimating time-derivatives from such data can remain a challenge. Here, the authors provide a non-parametric method for inferring the first and second time-derivatives from multiple replicates of time-series data and for estimating errors in this inference and in any summary statistics. |
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