Elucidating the structure of genetic regulatory networks: a study of a second order dynamical model on artificial data

International audienceLearning regulatory networks from time-series of gene expression is a challenging task. We propose to use synthetic data to analyze the ability of a state-space model to retrieve the network structure while varying a number of relevant problem parameters. ROC curves together with new tools such as spectral clustering of local solutions found by EM are used to analyze these results and provide relevant insights