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The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo

Richard Bonneau12*, David J Reiss3, Paul Shannon3, Marc Facciotti3, Leroy Hood3, Nitin S Baliga3 and Vesteinn Thorsson3

Author Affiliations

1 New York University, Biology Department, Center for Comparative Functional Genomics, New York, NY 10003, USA

2 Courant Institute, NYU Department of Computer Science, New York, NY 10003, USA

3 Institute for Systems Biology, Seattle, WA 98103-8904, USA

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Genome Biology 2006, 7:R36  doi:10.1186/gb-2006-7-5-r36

Published: 10 May 2006

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.