Prophetic Granger Causality to infer gene regulatory networks.
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ABSTRACT:
SUBMITTER: Carlin DE
PROVIDER: S-EPMC5718405 | biostudies-literature | 2017
REPOSITORIES: biostudies-literature
Publications
Prophetic Granger Causality to infer gene regulatory networks.
Carlin Daniel E DE Paull Evan O EO Graim Kiley K Wong Christopher K CK Bivol Adrian A Ryabinin Peter P Ellrott Kyle K Sokolov Artem A Stuart Joshua M JM
PloS one 20171206 12
We introduce a novel method called Prophetic Granger Causality (PGC) for inferring gene regulatory networks (GRNs) from protein-level time series data. The method uses an L1-penalized regression adaptation of Granger Causality to model protein levels as a function of time, stimuli, and other perturbations. When combined with a data-independent network prior, the framework outperformed all other methods submitted to the HPN-DREAM 8 breast cancer network inference challenge. Our investigations rev ...[more]
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