Journal
CHALLENGES OF SYSTEMS BIOLOGY: COMMUNITY EFFORTS TO HARNESS BIOLOGICAL COMPLEXITY
Volume 1158, Issue -, Pages 302-313Publisher
WILEY-BLACKWELL
DOI: 10.1111/j.1749-6632.2008.03757.x
Keywords
regulatory networks; computational methods
Ask authors/readers for more resources
This paper describes the technique designated best performer in the 2nd conference on Dialogue for Reverse Engineering Assessments and Methods (DREAM2) Challenge 5 (unsigned genome-scale network prediction from blinded microarray data). Existing algorithms use the pairwise correlations of the expression levels of genes, which provide valuable but insufficient information for the inference of regulatory interactions. Here we present a computational approach based on the recently developed context likelihood of related (CLR) algorithm, extracting additional complementary information using the information theoretic measure of synergy and assigning a score to each ordered pair of genes measuring the degree of confidence that the first gene regulates the second. When tested on a set of publicly available Escherichia coli gene-expression data with known assumed ground truth, the synergy augmented CLR (SA-CLR) algorithm had significantly improved prediction performance when compared to CLR. There is also enhanced potential for biological discovery as a result of the identification of the most likely synergistic partner genes involved in the interactions.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available