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Wiley InterScience | |||||||||||||||||||||
   Annals of the New York Academy of SciencesVolume 1158 Issue The Challenges of Systems Biology Community Efforts to Harness Biological Complexity, Pages 36 - 43 Published Online: 30 Mar 2009 © 2010 The New York Academy of Sciences
Abstract | References | Full Text: HTML, PDF (Size: 291K) | Related Articles | Citation Tracking  Reverse-Engineering Transcriptional Modules from Gene Expression Data Copyright © 2009 The New York Academy of Sciences KEYWORDS reverse engineering • transcriptional modules • probabilistic graphical models • ensemble methods ABSTRACT"Module networks" are a framework to learn gene regulatory networks from expression data using a probabilistic model in which coregulated genes share the same parameters and conditional distributions. We present a method to infer ensembles of such networks and an averaging procedure to extract the statistically most significant modules and their regulators. We show that the inferred probabilistic models extend beyond the dataset used to learn the models. | 
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