LINEAR AND NONLINEAR METHODS FOR GENE REGULATORY NETWORK INFERENCE

In this work we compare the predictive power of some of the most popular algorithms used for gene network inference, seen as an unsupervised graph learning problem. The data, generated by an artificial model of a gene regulatory network, are taken in different conditions, like at equilibrium or during a time course, and different numbers of samples are considered in the reconstruction. For these data, we see that the performances of the algorithms are neatly superior for steady state data than for time series. Furthermore, we obtain that linear measures are better suited to capture linear behavior (like steady state conditions), while nonlinear measures are more effective for intrinsically nonlinear data (like the time course of our artificial network).