Inferring gene regulatory networks from time series data using the minimum description length principle. Academic Article uri icon

abstract

  • 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.

published proceedings

  • Bioinformatics

author list (cited authors)

  • Zhao, W., Serpedin, E., & Dougherty, E. R

citation count

  • 112

complete list of authors

  • Zhao, Wentao||Serpedin, Erchin||Dougherty, Edward R

publication date

  • July 2006