This study focuses on dynamical system identification, with the reverse modeling of a gene regulatory network as motivating application. An active learning approach is used to iteratively select the most informative experiments needed to improve the parameters and hidden variables estimates in a dynamical model given a budget for experiments. The design of experiments under these budgeted resources is formalized in terms of sequential optimization. A local optimization criterion (reward) is designed to assess each experiment in the sequence, and the global optimization of the sequence is tackled in a game-inspired setting, within the Upper Confidence Tree framework combining Monte-Carlo tree-search and multi-armed bandits. The approach, called EDEN for Experimental Design for parameter Estimation in a Network, shows very good performances on several realistic simulated problems of gene regulatory network reverse-modeling, inspired from the international challenge DREAM7. © 2014 Springer-Verlag.
CITATION STYLE
Llamosi, A., Mezine, A., D’Alché-Buc, F., Letort, V., & Sebag, M. (2014). Experimental design in dynamical system identification: A bandit-based active learning approach. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8725 LNAI, pp. 306–321). Springer Verlag. https://doi.org/10.1007/978-3-662-44851-9_20
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