title: Boolean implication networks derived from large scale, whole genome microarray datasets.
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.
authors.affiliation: Department of Computer Science, Stanford University, Stanford, CA 94305, USA.
authors.forename: Debashis
authors.initials: D
authors.lastname: Sahoo
authors.forename: David L
authors.initials: DL
authors.lastname: Dill
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Gentles
authors.forename: Robert
authors.initials: R
authors.lastname: Tibshirani
authors.forename: Sylvia K
authors.initials: SK
authors.lastname: Plevritis
mesh_terms: 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