A hybrid Bayesian network learning method for constructing gene networks
Computational Biology and Chemistry
Bayesian learning of Bayesian networks with informative priors
Annals of Mathematics and Artificial Intelligence
Probabilistic Methods for Bioinformatics: with an Introduction to Bayesian Networks
Probabilistic Methods for Bioinformatics: with an Introduction to Bayesian Networks
Learning gene regulatory networks via globally regularized risk minimization
RECOMB-CG'07 Proceedings of the 2007 international conference on Comparative genomics
Understanding topic influence based on module network
ICADL'07 Proceedings of the 10th international conference on Asian digital libraries: looking back 10 years and forging new frontiers
Construction of gene networks with hybrid approach from expression profile and gene ontology
IEEE Transactions on Information Technology in Biomedicine
Parallelization of module network structure learning and performance tuning on SMP
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
Applying linear models to learn regulation programs in a transcription regulatory module network
EvoBIO'11 Proceedings of the 9th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
Reconstruction of Transcriptional Regulatory Networks by Stability-Based Network Component Analysis
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Methods for learning Bayesian networks can discover dependency structure between observed variables. Although these methods are useful in many applications, they run into computational and statistical problems in domains that involve a large number of variables. In this paper, we consider a solution that is applicable when many variables have similar behavior. We introduce a new class of models, module networks, that explicitly partition the variables into modules, so that the variables in each module share the same parents in the network and the same conditional probability distribution. We define the semantics of module networks, and describe an algorithm that learns the modules' composition and their dependency structure from data. Evaluation on real data in the domains of gene expression and the stock market shows that module networks generalize better than Bayesian networks, and that the learned module network structure reveals regularities that are obscured in learned Bayesian networks.