Identifying system dynamics with machine learning
In this meetup, we introduce machine learning techniques dedicated to the identification of system dynamics. From discrete observations of the system, we determine the local influences of its components. In the method we develop, the observations we consider as input are the state transitions of the system. From these observations, we build and refine, transitions after transition, a logic program that captures the dynamics of the system. This method allows, among other things, to learn Boolean networks and to identify cellular automata. This technique can be applied to bioinformatics, especially for the identification of a gene regulatory network from observations obtained through laboratory experiments.