DDEPN (Dynamic Deterministic Effects Propagation Networks): Infer signalling networks for timecourse data. Given a matrix of high-throughput genomic or proteomic timecourse data, generated after external perturbation of the biological system, DDEPN models the time-dependent 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.
|Maintainer||Christian Bender <[email protected]>|
|Package repository||View on R-Forge|
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