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

Learning regulatory networks from time-series of gene expres-
sion 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.