Informative structure priors: joint learning of dynamic regulatory networks from multiple types of data.

Conference Paper

We present a method for jointly learning dynamic models of transcriptional regulatory networks from gene expression data and transcription factor binding location data. Models are automatically learned using dynamic Bayesian network inference algorithms; joint learning is accomplished by incorporating evidence from gene expression data through the likelihood, and from transcription factor binding location data through the prior. We propose a new informative structure prior with two advantages. First, the prior incorporates evidence from location data probabilistically, allowing it to be weighed against evidence from expression data. Second, the prior takes on a factorable form that is computationally efficient when learning dynamic regulatory networks. Results obtained from both simulated and experimental data from the yeast cell cycle demonstrate that this joint learning algorithm can recover dynamic regulatory networks from multiple types of data that are more accurate than those recovered from each type of data in isolation.

Duke Authors

Cited Authors

  • Bernard, A; Hartemink, AJ

Published Date

  • January 2005

Published In

Start / End Page

  • 459 - 470

PubMed ID

  • 15759651

Electronic International Standard Serial Number (EISSN)

  • 2335-6936

International Standard Serial Number (ISSN)

  • 2335-6928