We are applying causal discovery algorithms using the framework of Bayesian networks which are structurally directed acyclic graphs with the vertices representing domain variables and the directed edges denoting the probabilistic dependency relationships. The parameters of the model are specified as marginal probabilities for the root nodes and conditional probabilities for non-root nodes. By applying this method to the proteomic data from different disease states we hope to discover the cause and effect interaction networks of the disease biomarkers which will facilitate targeting them by drugs.