A hybrid Bayesian network learning method for constructing gene networks
Computational Biology and Chemistry
Detecting reliable gene interactions by a hierarchy of Bayesian network classifiers
Computer Methods and Programs in Biomedicine
Journal of Biomedical Informatics
Improved Bayesian Network inference using relaxed gene ordering
International Journal of Data Mining and Bioinformatics
Ensemble transcript interaction networks: A case study on Alzheimer's disease
Computer Methods and Programs in Biomedicine
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In recent years, there has been a growing interest in applying Bayesian networks and their extensions to reconstruct regulatory networks from gene expression data. Since the gene expression domain involves a large number of variables and a limited number of samples, it poses both computational and statistical challenges to Bayesian network learning algorithms. Here we define a constrained family of Bayesian network structures suitable for this domain and devise an efficient search algorithm that utilizes these structural constraints to find high scoring networks from data. Interestingly, under reasonable assumptions on the underlying probability distribution, we can provide performance guarantees on our algorithm. Evaluation on real data from yeast and mouse, demonstrates that our method cannot only reconstruct a high quality model of the yeast regulatory network, but is also the first method to scale to the complexity of mammalian networks and successfully reconstructs a reasonable model over thousands of variables.