MinReg: A Scalable Algorithm for Learning Parsimonious Regulatory Networks in Yeast and Mammals
Dana Pe'er, Amos Tanay, Aviv Regev; 7(7):167−189, 2006.
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.
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