Journal
BIOINFORMATICS
Volume 27, Issue 11, Pages 1529-1536Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btr166
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Funding
- Graduate School of Mathematical Analysis of Evolution, Information and Complexity
- Excellence Initiative of German governments [GSC 270]
- Federal ministry of education and research (BMBF) [PKB-01GS08]
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Results: To detect regulatory dependencies in a network, we examined how the expression of different genes correlates to successive network states. For this purpose, we used Pearson correlation as an elementary correlation measure. Given a Boolean network containing only monotone Boolean functions, we prove that the correlation of successive states can identify the dependencies in the network. This method not only finds dependencies in randomly created artificial networks to very high percentage, but also reconstructed large fractions of both a published Escherichia coli regulatory network from simulated data and a yeast cell cycle network from real microarray data.
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