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BioMed Central, BMC Systems Biology, 1(8), 2014

DOI: 10.1186/s12918-014-0102-6

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A Bayesian active learning strategy for sequential experimental design in systems biology

Journal article published in 2014 by Edouard Pauwels, Christian Lajaunie, Jean-Philippe Vert ORCID
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Abstract

Borrowing ideas from Bayesian experimental design and active learning, we propose a new strategy for optimal experimental design in the context of kinetic parameter estimation in systems biology. We describe algorithmic choices that allow to implement this method in a computationally tractable way and make it fully automatic. Based on simulation, we show that it outperforms alternative baseline strategies, and demonstrate the benefit to consider multiple posterior modes of the likelihood landscape, as opposed to traditional schemes based on local and Gaussian approximations. An R package is provided to reproduce all experimental simulations.