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.

• Article

  Bender, C., Henjes, F., Fröhlich, H., Wiemann, S., Korf, U. & Beißbarth, T.

  Dynamic deterministic effects propagation networks: learning signalling pathways from longitudinal protein array data.

  Bioinformatics, 2010, Vol. 26(18), pp. i596-i602

• PhD Thesis

  Bender, C.

  Systematic analysis of time resolved high-throughput data using stochastic network inference methods

  University of Heidelberg, Combined Faculties for the Natural Sciences and for Mathematics, 2011

• Article

  Bender, C., Heyde, S. v.d., Henjes, F., Wiemann, S., Korf, U. & Beißbarth, T.

  Inferring signalling networks from longitudinal data using sampling based approaches in the R package 'ddepn'

  BMC Bioinformatics, 2011, 12:291

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