DDEPN (Dynamic Deterministic Effects Propagation Networks): Infer signalling networks for timecourse data. Given a matrix of highthroughput genomic or proteomic timecourse data, generated after external perturbation of the biological system, DDEPN models the timedependent propagation of active and passive states depending on a network structure. Optimal network structures given the experimental data are reconstructed. Two network inference algorithms can be used: inhibMCMC, a Markov Chain Monte Carlo sampling approach and GA, a Genetic Algorithm network optimisation. Inclusion of prior biological knowledge can be done using different network prior models.
Package details 


Author  Christian Bender 
Maintainer  Christian Bender <[email protected]> 
License  GPL (>=2) 
Version  2.2.3 
Package repository  View on RForge 
Installation 
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