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Studying the Conditions for Learning Dynamic Bayesian Networks to Discover Genetic Regulatory Networks

R. J. P. van Berlo

Information and Communication Theory Group, Department of Mediametics, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology P. O. Box 5031 2600 GA Delft, The Netherlands, R.J.P.vanBerlo{at}ewi.tudelft.nl

E. P. van Someren

Information and Communication Theory Group, Department of Mediametics, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology P. O. Box 5031 2600 GA Delft, The Netherlands

M. J. T. Reinders

Information and Communication Theory Group, Department of Mediametics, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology P. O. Box 5031 2600 GA Delft, The Netherlands

Learning regulatory interactions between genes from microarray measurements presents one of the major challenges in functional genomics. This article studies the suitability of learning dynamic Bayesian networks under realistic experimental settings. Through extensive artificial-data experiments, it is investigated how the performance of discovering the true interactions depends on varying data conditions. These experiments show that the performance most strongly deteriorates when the connectivity of the original network increases, and more than a proportional increase in the number of samples is needed to compensate for this. Furthermore, it was found that a lower performance is achieved when the original network size becomes larger, but this decrease can be greatly reduced with increased computational effort. Finally, it is shown that the performance of the search algorithm benefits more from a larger number of restarts rather than from the use of more sophisticated search strategies.

Key Words: Bayesian networks • genetic network modeling • structure search

SIMULATION, Vol. 79, No. 12, 689-702 (2003)
DOI: 10.1177/0037549703040942
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