title: The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo.
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-1. 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.
authors: New York University, Biology Department, Center for Comparative Functional Genomics, New York, NY 10003, USA. bonneau@cs.nyu.edu
authors: Richard
authors: R
authors: Bonneau
authors: David J
authors: DJ
authors: Reiss
authors: Paul
authors: P
authors: Shannon
authors: Marc
authors: M
authors: Facciotti
authors: Leroy
authors: L
authors: Hood
authors: Nitin S
authors: NS
authors: Baliga
authors: Vesteinn
authors: V
authors: Thorsson