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RECOMB
2004
Springer

Learning Regulatory Network Models that Represent Regulator States and Roles

9 years 10 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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