Conduite d'expériences par apprentissage actif pour l'identification de systèmes dynamiques biologiques : application à l'estimation de paramètres d'équations différentielles ordinaires

Abstract : Continuous progress in screening and high-throughput sequencing techniques in recent years paves the way for the identification of dynamic biological systems such as gene regulatory networks. However, the scarcity of the experimental data often leads to anuncertain estimation of parameters of interest. These uncertainties can be solved by generating new data in different experimental conditions, which induces additional costs. This thesis proposes a general active learning approach to develop tools of sequential experimental design for the identification of dynamical biological systems. The problem is formulated as a one-player game : the player has a budget dedicated for his experiments, each experiment has a different cost ; at every turn, he chooses one or more experiments to be performed on the system with the ultimate aim of maximizing the quality of the estimate, until the available budget is exhausted. The proposed approach called Experimental DEsign for Network inference (EDEN), is based on UCT (Upper Confident bounds for Trees) algorithm which combines Monte-Carlo tree search algorithms with multi-arm bandits to perform an effective exploration of the possible sequences of experiments. A strong point of the approach is anticipation : an experiment is selected at each round, knowing that this round will be followed by a number of other experiments, according to the available budget. This generic approach is rolled out in parameter estimation in nonlinear ordinary differential equations using partial observations. EDEN is applied on two problems : signaling network and gene regulatory network identification. Compared to the competitors, it exhibits very good results on a DREAM7 challenge where a limited budget and a wide range of experiments (perturbations, measurements) are available.
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Adel Mezine. Conduite d'expériences par apprentissage actif pour l'identification de systèmes dynamiques biologiques : application à l'estimation de paramètres d'équations différentielles ordinaires. Apprentissage [cs.LG]. Université Paris-Saclay; Université d'Evry-Val-d'Essonne, 2016. Français. ⟨NNT : 2016SACLE030⟩. ⟨tel-01762779⟩

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