Learning Bayesian networks with integration of indirect prior knowledge

Authors:
Baikang Pei;David W. Rowe;Dong-Guk Shin
Affiliations:
Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269, USA.;Department of Genetics and Developmental Biology, University of Connecticut Health Center, Farmington, CT 06269, USA.;Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269, USA
Venue:
International Journal of Data Mining and Bioinformatics
Year:
2010

Citing 5
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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.