ddepn (2.2)
Dynamic Deterministic Effects Propagation Networks: Infer signalling networks for timecourse RPPA data..
http://cran.rproject.org/web/packages/ddepn
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.
Maintainer:
Christian Bender
Author(s): Christian Bender
License: GPL (>= 2)
Uses: coda, gam, gplots, graph, igraph, lattice, BoolNet
Released about 6 years ago.
11 previous versions
 ddepn_2.1.4. Released about 6 years ago.
 ddepn_2.1.2. Released about 7 years ago.
 ddepn_2.1. Released over 7 years ago.
 ddepn_2.0.2. Released almost 8 years ago.
 ddepn_1.9. Released about 8 years ago.
 ddepn_1.8. Released about 8 years ago.
 ddepn_1.7. Released over 8 years ago.
 ddepn_1.6. Released over 8 years ago.
 ddepn_1.5. Released over 8 years ago.
 ddepn_1.4. Released almost 9 years ago.
 ddepn_1.3. Released almost 9 years ago.
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