ddepn (2.1.2)
Dynamic Deterministic Effects Propagation Networks: Infer signalling networks for timecourse RPPA data..
http://cran.r-project.org/web/packages/ddepn
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
Author(s): Christian Bender
License: GPL (>= 2)
Uses: cluster, coda, gam, gplots, graph, igraph0, lattice, RBGL, multicore, BoolNet
Released 11 months ago.
9 previous versions
- ddepn_2.1. Released about 1 year ago.
- ddepn_2.0.2. Released over 1 year ago.
- ddepn_1.9. Released almost 2 years ago.
- ddepn_1.8. Released almost 2 years ago.
- ddepn_1.7. Released about 2 years ago.
- ddepn_1.6. Released about 2 years ago.
- ddepn_1.5. Released over 2 years ago.
- ddepn_1.4. Released over 2 years ago.
- ddepn_1.3. Released over 2 years ago.
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