[HTML][HTML] Boolean implication networks derived from large scale, whole genome microarray datasets
Genome biology, 2008•Springer
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.
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.
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.
Springer
