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

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

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

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

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

author email corresponding author email

Genome Biology 2008, 9:R157doi:10.1186/gb-2008-9-10-r157


Published:	30 October 2008

Subject areas: Bioinformatics, Genetics, Genome studies

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.