ddepn: Dynamic Deterministic Effects Propagation Networks: Infer
signalling networks for timecourse RPPA data
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.
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