Constraint Minimization for Efficient Modeling of Gene Regulatory Network

  • Ramesh Ram
  • Madhu Chetty
  • Dieter Bulach
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5265)


Due to various complexities, as well as noise and high dimensionality, reconstructing a gene regulatory network (GRN) from a high-throughput microarray data becomes computationally intensive.In our earlier work on causal model approach for GRN reconstruction, we had shown the superiority of Markov blanket (MB) algorithm compared to the algorithm using the existing Y and V causal models. In this paper, we show the MB algorithm can be enhanced further by application of the proposed constraint logic minimization (CLM) technique. We describe a framework for minimizing the constraint logic involved (condition independent tests) by exploiting the Markov blanket learning methods developed for a Bayesian network (BN). The constraint relationships are represented in the form of logic using K-map and with the aid of CLM increase the algorithm efficiency and the accuracy. We show improved results by investigations on both the synthetic as well as the real life yeast cell cycle data sets.


Causal model Markov blanket Constraint minimization Gene regulatory network 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Ramesh Ram
    • 1
  • Madhu Chetty
    • 1
  • Dieter Bulach
    • 2
  1. 1.Gippsland School of ITMonash UniversityChurchillAustralia
  2. 2.CSIRO Livestock IndustriesAustralian Animal Health LaboratoryGeelongAustralia

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