Figure 1.

Workflow of PropaNet. The PropaNet analysis takes three types of input data: time-series gene expression data that are measured at multiple time-points, a template TF network and a set of targeted genes. The goal of the PropaNet analysis is to elucidate time-varying networks at each time point. It uses the influence maximization technique to produce a ranked list of TFs at each time point, in the order of TF that explains DEGs better. Then, a network propagation technique is used to select a group of TFs that explains DEGs best as a whole. The process is done by iterating a network propagation simulation by adding a TF at a time, going down the list of TFs determined by the influence maximization technique.