Estimating genomic coexpression networks using first-order conditional independence.

Journal Article (Journal Article)

We describe a computationally efficient statistical framework for estimating networks of coexpressed genes. This framework exploits first-order conditional independence relationships among gene-expression measurements to estimate patterns of association. We use this approach to estimate a coexpression network from microarray gene-expression measurements from Saccharomyces cerevisiae. We demonstrate the biological utility of this approach by showing that a large number of metabolic pathways are coherently represented in the estimated network. We describe a complementary unsupervised graph search algorithm for discovering locally distinct subgraphs of a large weighted graph. We apply this algorithm to our coexpression network model and show that subgraphs found using this approach correspond to particular biological processes or contain representatives of distinct gene families.

Full Text

Duke Authors

Cited Authors

  • Magwene, PM; Kim, J

Published Date

  • 2004

Published In

Volume / Issue

  • 5 / 12

Start / End Page

  • R100 -

PubMed ID

  • 15575966

Pubmed Central ID

  • PMC545795

Electronic International Standard Serial Number (EISSN)

  • 1474-760X

Digital Object Identifier (DOI)

  • 10.1186/gb-2004-5-12-r100


  • eng

Conference Location

  • England