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Boolean implication networks derived from large scale, whole genome microarray datasets

Debashis Sahoo1, David L Dill2*, Andrew J Gentles3, Robert Tibshirani4 and Sylvia K Plevritis3

Author Affiliations

1 Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA

2 Department of Computer Science, Stanford University, Stanford, CA 94305, USA

3 Department of Radiology, Stanford University, Stanford, CA 94305, USA

4 Department of Health Research and Policy and Department of Statistics, Stanford University, Stanford, CA 94305, USA

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Genome Biology 2008, 9:R157  doi:10.1186/gb-2008-9-10-r157

Published: 30 October 2008

Abstract

We describe a method for extracting Boolean implications (if-then relationships) in very large amounts of gene expression microarray data. A meta-analysis of data from thousands of microarrays for humans, mice, and fruit flies finds millions of implication relationships between genes that would be missed by other methods. These relationships capture gender differences, tissue differences, development, and differentiation. New relationships are discovered that are preserved across all three species.