Inferring gene regulatory network based on Bayesian mean average method.

There has been a lot of interest in reverse engineering gene regulatory networks from gene expression data. In order to deal with a wide range of problems, a variety of models and methodologies have been developed. Although these strategies are focused on particular biological and experimental systems, they often require experimental data that is difficult or impossible to collect. More genetic disruption and higher-throughput sequencing data make it possible to create more complete methods for inferring gene regulation networks. In order to infer gene regulatory networks from perturbed gene expression data, we provide in this paper an approach based on Euclidean distance and the Bayesian mean average method. Existing methods were compared to the new one, and the results indicate that the new method performs better. [ABSTRACT FROM AUTHOR]