Boolean implication networks derived from large scale, whole genome microarray datasets.
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.
Sahoo
Debashis
D
Department of Computer Science, Stanford University, Stanford, CA 94305, USA.
Dill
David L
DL
Gentles
Andrew J
AJ
Tibshirani
Robert
R
Plevritis
Sylvia K
SK
D000465:Algorithms; D000818:Animals; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000379:methods*; D016678:Genome; D006801:Humans; D051379:Mice; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated