Learning Causal Protein-Signaling Networks
Jin Tian and Akshay Deepak – Iowa State University
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).