Using graphical models and genomic expression data to statistically validate models of genetic regulatory networks.

Published

Journal Article

We propose a model-driven approach for analyzing genomic expression data that permits genetic regulatory networks to be represented in a biologically interpretable computational form. Our models permit latent variables capturing unobserved factors, describe arbitrarily complex (more than pair-wise) relationships at varying levels of refinement, and can be scored rigorously against observational data. The models that we use are based on Bayesian networks and their extensions. As a demonstration of this approach, we utilize 52 genomes worth of Affymetrix GeneChip expression data to correctly differentiate between alternative hypotheses of the galactose regulatory network in S. cerevisiae. When we extend the graph semantics to permit annotated edges, we are able to score models describing relationships at a finer degree of specification.

Full Text

Duke Authors

Cited Authors

  • Hartemink, AJ; Gifford, DK; Jaakkola, TS; Young, RA

Published Date

  • January 2001

Published In

Start / End Page

  • 422 - 433

PubMed ID

  • 11262961

Pubmed Central ID

  • 11262961

Electronic International Standard Serial Number (EISSN)

  • 2335-6936

International Standard Serial Number (ISSN)

  • 2335-6928

Language

  • eng