Bioinformatics Advance Access originally published online on July 15, 2006
Bioinformatics 2006 22(17):2129-2135; doi:10.1093/bioinformatics/btl364
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Inferring gene regulatory networks from time series data using the minimum description length principle
1 Department of Electrical and Computer Engineering, Texas A&M University College Station, TX 77843-3128, USA
2 Translational Genomics Research Institute, 400 North Fifth Street Suite 1600, Phoenix, AZ 85004, USA
*To whom correspondence should be addressed.
Motivation: A central question in reverse engineering of genetic networks consists in determining the dependencies and regulating relationships among genes. This paper addresses the problem of inferring genetic regulatory networks from time-series gene-expression profiles. By adopting a probabilistic modeling framework compatible with the family of models represented by dynamic Bayesian networks and probabilistic Boolean networks, this paper proposes a network inference algorithm to recover not only the direct gene connectivity but also the regulating orientations.
Results: Based on the minimum description length principle, a novel network inference algorithm is proposed that greatly shrinks the search space for graphical solutions and achieves a good trade-off between modeling complexity and data fitting. Simulation results show that the algorithm achieves good performance in the case of synthetic networks. Compared with existing state-of-the-art results in the literature, the proposed algorithm exceptionally excels in efficiency, accuracy, robustness and scalability. Given a time-series dataset for Drosophila melanogaster, the paper proposes a genetic regulatory network involved in Drosophila's muscle development.
Availability: Available from the authors upon request.
Contact: wtzhao{at}ece.tamu.edu
Received on January 12, 2006; revised on May 27, 2006; accepted on June 29, 2006
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