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RECOMB
2004
Springer

Learning Regulatory Network Models that Represent Regulator States and Roles

13 years 2 months ago
Learning Regulatory Network Models that Represent Regulator States and Roles
Abstract. We present an approach to inferring probabilistic models of generegulatory networks that is intended to provide a more mechanistic representation of transcriptional regulation than previous methods. Our approach involves learning Bayesian network models using both gene-expression and genomic-sequence data. One key aspect of our approach is that our models represent states of regulators in addition to their expression levels. For example, the state of a transcription factor may be determined by whether a particular small molecule is bound to it or not. Our models represent these states using hidden nodes in the Bayesian networks. A second key aspect of our approach is that we use known and predicted transcription start sites to determine whether a given transcription factor is more likely to act as an activator or a repressor for a given gene. We refer to this distinction as the role of a regulator with respect to a gene. Determining the roles of a regulator provides a helpful...
Keith Noto, Mark Craven
Added 03 Dec 2009
Updated 03 Dec 2009
Type Conference
Year 2004
Where RECOMB
Authors Keith Noto, Mark Craven
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