Dynamic Bayesian networks (DBN) are widely applied in modeling various biological networks, including the gene regulatory network. Due to several NP-hardness results on learning static Bayesian network, most methods for learning DBN are heuristic, that employ either local search such as greedy hill-climbing, or a meta optimization framework such as genetic algorithm or simulated annealing.
We present GlobalMIT, a toolbox for learning the globally optimal DBN structure using a recently introduced information theoretic based scoring metric named mutual information test (MIT). Under MIT, learning the globally optimal DBN can be efficiently achieved in polynomial time. The toolbox is implemented in Matlab, with also a C++ stand-alone implementation of the search engine for improved performance.Reference:
1. 'GlobalMIT: Learning Globally Optimal Dynamic Bayesian Network with the Mutual Information Test (MIT) Criterion', Vinh, N. X., Chetty, M., Coppel, R., and Wangikar, P. P. (2011), Bioinformatics [ERA rank A*], in press Pre-publication PDF Technical Report.
A variant of GlobalMIT which learns globally optimal DBNs under the MIT and MDL metrics, without the equi-cardinality constraints on the variables, is available here.
2. 'Local and Global Algorithms for Learning Dynamic Bayesian Networks', Vinh Nguyen, Madhu Chetty, Pramod Wangikar and Ross Coppel, The IEEE International Conference on Data Mining (ICDM), 2012 [Full paper, acceptance rate 10.7%].
Code for computing the Adjusted Mutual Information (AMI) in Matlab
1. 'Information Theoretic Measures for Clusterings Comparison: Variants, Properties, Normalization and Correction for Chance', N.X. Vinh, Epps, J. and Bailey, J. (2010), the Journal of Machine Learning Research (JMLR), 11(Oct), 2837-54, [ERA rank A] PDF.
2. 'Information Theoretic Measures for Clusterings Comparison: Is a Correction for Chance Necessary?', N.X. Vinh, Epps, J. and Bailey, J., in Proc. the 26th International Conference on Machine Learning (ICML'09), June 2009, Montreal, Canada, PDF [ERA rank A, acceptance rate: 23.5%].
Code for the minCEntropy algorithm for alternative clustering (Matlab)
1. 'minCEntropy: a Novel Information Theoretic Approach for the Generation of Alternative Clusterings,' N. X. Vinh, Epps, J., the 10th IEEE Int. Conf. on Data Mining (ICDM'10), 2010, [ERA rank A, acceptance rate: 9% full paper]. PDF
Code for the Spherical K-means clustering algorithm (Matlab)
1. 'Gene Clustering on the Unit Hypersphere with the Spherical K-means algorithm: coping with extremely large number of local optima', N.X. Vinh, In Proc. the 2008 International Conference on Bioinformatics and Computational Biology (BIOCOMP), Las Vegas, 14-17 Jul 2008. PDF.