A Novel model for inference of gene regulatory networks

Gene regulatory networks (GRNs) are complex networks consisted of nodes representing genes, transcription factors, microRNAs and edges that represent the interactions between nodes. GRNs can expose and depict underlying cells' gene regulatory mechanisms. In this paper, we propose a new model for inference of GRNs. This model includes prior knowledge into network inference. Our model was applied on generated gene expression time series with different number of genes and time points and one subset of experimental data received from colorectal cancer microarray experiment. To validate the inference capabilities of the proposed model, we compare the ROC curves and AUC values for proposed model and the common used models: dynamic Bayesian networks, Boolean networks and graphical Gaussian models. The proposed model has shown competitive inference capabilities in comparison with other models.