Analyzing the Effect of Prior Knowledge in Genetic Regulatory Network Inference

Inferring the metabolic pathways that control the cell cycles is a challenging and difficult task. Its importance in the process of understanding living organisms has motivated the development of several models to infer gene regulatory networks from DNA microarray data. In the last years, many works have been adding biological information to those models to improve the obtained results. In this work, we add prior biological knowledge into a Bayesian Network model with non parametric regression and analyze the effects of such information in the results.