Work

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

Year: 2008

Type: article

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.

Source: Genome biology

DOI: 10.1186/gb-2008-9-10-r157

Authors: Debashis Sahoo, David L Dill, Andrew J Gentles, Robert Tibshirani, Sylvia K Plevritis

Institutions: Stanford University, Stanford Medicine

Language: English

Topic: Gene expression and cancer classification

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $5,490

Funders: National Institutes of Health, National Cancer Institute