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Welcome to ddepn project!

DDEPN (Dynamic Deterministic Effects Propagation Networks): Infer signalling networks for high-throughput genomic or proteomic timecourse data, generated after external perturbation like activation or inhibition of network nodes.

This project contains the method 'Dynamic Deterministic Effects Propagation Networks', implemented in the R-package 'ddepn'. The package provides a network inference engine for high-throughput genomic or proteomic expression data, generated after external intervention (inhibitions or stimulations). Two main parts are implemented: an MCMC network structure sampling approach and a Genetic Algorithm network optimisation. Further biological prior knowledge can be included using different prior models.

The method is described in the following publications:

The project summary page you can find here. Also visit Christian Bender's homepage for more information on the author.