We develop a network-based algorithm to predict and verify indirect regulatory interactions in a large-scale genetic regulatory network. Indirect regulations are important as they constitute the majority of experimental data. Our approach is based on the network topology and can be easily incorporated using a matrix formalism. The essence of the method is to extend the transitivity of indirect regulations ( i.e. A regulates B and B regulates C implies A regulates C) to longer cascades and effectively take care of the signs of the regulations. This algorithm is tailored for large and heavily interconnected networks, which are of growing importance due to the accruement of data from high-throughput experiments. We apply the algorithm to the regulatory networks of Homo sapiens, Saccharomyces cerevisiae, Arabidopsis thaliana and Drosophila melanogaster , resulting in novel predictions with calibrated reliability .
We develop a matrix-based approach to predict and verify indirect interactions in gene and protein regulatory networks. It is based on the approximate transitivity of indirect regulations ( e.g. A regulates B and B regulates C often implies that A regulates C) and optimally takes into account the length of a cascade and signs of intermediate interactions. Our method is at its most powerful when applied to large and densely interconnected networks. It successfully predicts both the yet unknown indirect regulations, as well as the sign (activation or repression) of already known ones. The reliability of sign predictions was calibrated using the gold-standard sets of positive and negative interactions. We fine-tuned the parameters of our algorithm by maximizing the area under the Receiver Operating Characteristic (ROC) curve. We then applied the optimized algorithm to large literature-derived networks of all direct and indirect regulatory interactions in several URLanisms ( Homo sapiens, Saccharomyces cerevisiae, Arabidopsis thaliana and Drosophila melanogaster ) .
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network-based algorithm
matrix-based approach
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regulatory interactions in a large-scale genetic regulatory network. Indirect regulations are important as they constitute the majority of experimental data. Our approach
interactions in gene and protein regulatory networks. It
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network topology and can be easily incorporated using a matrix formalism. The essence of the method is to extend the
approximate
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to longer cascades and effectively take care of the signs of the regulations. This algorithm is tailored for large and heavily interconnected networks, which are of growing importance due to the accruement of data from high-throughput experiments. We apply the algorithm to the regulatory networks of
and optimally takes into account the length of a cascade and signs of intermediate interactions. Our method is at its most powerful when applied to large and densely interconnected networks. It successfully predicts both the yet unknown indirect regulations, as well as the sign (activation or repression) of already known ones. The reliability of sign predictions was calibrated using the gold-standard sets of positive and negative interactions. We fine-tuned the parameters of our algorithm by maximizing the area under the Receiver Operating Characteristic (ROC) curve. We then applied the optimized algorithm to large literature-derived networks of all direct and indirect regulatory interactions in several URLanisms (
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, resulting in novel predictions with calibrated reliability
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