The Inferelator: an Algorithm for Learning Parsimonious Regulatory Networks from Systems-biology Data Sets De Novo

Overview

Journal Genome Biol

Specialties Biology
Genetics

Date 2006 May 12

PMID 16686963

Citations 253

Authors

Richard Bonneau

David J Reiss

Paul Shannon

Marc Facciotti

Leroy Hood

Nitin S Baliga

Vesteinn Thorsson

Affiliations

Soon will be listed here.

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.

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