Learning Bayesian networks with integration of indirect prior knowledge.
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.
Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269, USA. baikang.pei@engr.uconn.edu
Baikang
B
Pei
David W
DW
Rowe
Dong-Guk
DG
Shin
D000465:Algorithms; D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods*; D016208:Databases, Factual; D053263:Gene Regulatory Networks / Q000235:genetics*