Prophetic Granger Causality to infer gene regulatory networks.
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 perturb...
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| Main Authors: | Daniel E Carlin, Evan O Paull, Kiley Graim, Christopher K Wong, Adrian Bivol, Peter Ryabinin, Kyle Ellrott, Artem Sokolov, Joshua M Stuart |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2017-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0170340 |
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