Learning Causal Protein-Signaling Networks
Jin Wen Tian, Akshay Deepak · 2008
Graphical Models have been widely used for modelling causal relationships. We use causal Bayesian networks to model protein signaling networks and use the Bayesian approach to learn the network structure from mixed observational and experimental data. We compute the maximum a posteriori (MAP) network for a biological data set originally analyzed by Sachs et al. (2005). Keywords: Causal discovery, Bayesian Network