Learning Bayesian networks with integration of indirect prior knowledge

Abstract

A Bayesian network model can be used to study the structures of gene regulatory networks. It has the ability to integrate information from both prior knowledge and experimental data. In this study, we propose an approach to efficiently integrate global ordering information into model learning, where the ordering information specifies the indirect relationships among genes. We demonstrate that, compared with a traditional Bayesian network model that uses only local prior knowledge, utilising additional global ordering knowledge can significantly improve the model's performance. The magnitude of this improvement depends on abundance of global ordering information and data quality.