The inferelator: An algorithn for learning parsimonious regulatory networks from systems-biology data sets de novo

Richard Bonneau, David J. Reiss, Paul Shannon, Marc Facciotti, Leroy Hood, Nitin S. Baliga, Vesteinn Thorsson

Research output: Contribution to journalArticle

Abstract

We present a method (the Inferelator) for deriving genome-wide transcriptional regulatory interactions, and apply the method to predict a large portion of the regulatory network of the archaeon Halobacterium NRC-I. The Inferelator uses regression and variable selection to identify transcriptional influences on genes based on the integration of genome annotation and expression data. The learned network successfully predicted Halobacterium's global expression under novel perturbations with predictive power similar to that seen over training data. Several specific regulatory predictions were experimentally tested and verified.

Original languageEnglish (US)
Article numberR36
JournalGenome biology
Volume7
Issue number5
DOIs
StatePublished - May 10 2006

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Genetics
  • Cell Biology

Fingerprint Dive into the research topics of 'The inferelator: An algorithn for learning parsimonious regulatory networks from systems-biology data sets de novo'. Together they form a unique fingerprint.

  • Cite this