Learning Regulatory Network Models that Represent Regulator States and Roles

@inproceedings{Noto2004LearningRN,
  title={Learning Regulatory Network Models that Represent Regulator States and Roles},
  author={Keith Noto and Mark Craven},
  booktitle={Regulatory Genomics},
  year={2004}
}
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… CONTINUE READING
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