The max-min high-order dynamic Bayesian network learning for identifying gene regulatory networks from time-series microarray data

We propose a new high-order dynamic Bayesian network (HO-DBN) learning approach, called Max-Min High-Order DBN (MMHO-DBN), for discrete time-series data. MMHO-DBN explicitly models the time lags between parents and target in an efficient manner. It extends the Max-Min Hill-Climbing Bayesian network (MMHC-BN) technique which was originally devised for learning a BN's structure from static data. Both Max-Min approaches are hybrid local learning methods which fuse concepts from both constraint-based Bayesian techniques and search-and-score Bayesian methods. The MMHO-DBN first uses constraint-based ideas to limit the space of potential structure and then applies search-and-score ideas to search for an optimal HO-DBN structure. We evaluated the ability of our MMHO-DBN approach to identify genetic regulatory networks (GRN's) from gene expression time-series data. Preliminary results on artificial and real gene expression time-series are encouraging and show that it is able to learn (long) time-delayed relationships between genes, and faster than current HO-DBN learning methods.

[1]  Wray L. Buntine.  Theory Refinement on Bayesian Networks , 1991, UAI.

[2]  Luonan Chen,et al.  Biomolecular Networks: Methods and Applications in Systems Biology , 2009 .

[3]  Andrew J. Millar,et al.  Using Higher-Order Dynamic Bayesian Networks to Model Periodic Data from the Circadian Clock of Arabidopsis Thaliana , 2009, PRIB.

[4]  Dan Wu,et al.  Modeling Multiple Time Units Delayed Gene Regulatory Network Using Dynamic Bayesian Network , 2006, Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06).

[5]  Kevin P. Murphy,et al.  Learning the Structure of Dynamic Probabilistic Networks , 1998, UAI.

[6]  Dirk Husmeier,et al.  Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks , 2003, Bioinform..

[7]  A. Kudlicki,et al.  Logic of the Yeast Metabolic Cycle: Temporal Compartmentalization of Cellular Processes , 2005, Science.

[8]  W. Hsu,et al.  Handbook of Research on Computational Methodologies in Gene Regulatory Networks , 2009 .

[9]  Jagath C. Rajapakse,et al.  Gene regulatory networks with variable-order dynamic Bayesian networks , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

[10]  Stuart J. Russell,et al.  Dynamic bayesian networks: representation, inference and learning , 2002 .

[11]  David Heckerman,et al.  A Tutorial on Learning with Bayesian Networks , 1998, Learning in Graphical Models.

[12]  David Maxwell Chickering,et al.  Learning Bayesian Networks: The Combination of Knowledge and Statistical Data , 1994, Machine Learning.

[13]  Jagath C. Rajapakse,et al.  Building gene networks with time-delayed regulations , 2010, Pattern Recognit. Lett..

[14]  Gregory F. Cooper,et al.  A Bayesian Method for the Induction of Probabilistic Networks from Data , 1992 .

[15]  Franz von Kutschera,et al.  Causation , 1993, J. Philos. Log..

[16]  Constantin F. Aliferis,et al.  The max-min hill-climbing Bayesian network structure learning algorithm , 2006, Machine Learning.

[17]  Min Zou,et al.  A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data , 2005, Bioinform..

[18]  G. Schwarz.  Estimating the Dimension of a Model , 1978 .

[19]  Michal Linial,et al.  Using Bayesian Networks to Analyze Expression Data , 2000, J. Comput. Biol..