We used a fully Bayesian approach for modeling our gene-gene relationship network. Parameter estimation is a crucial step in statistical modeling, for which a classical approach is maximum likelihood estimation (MLE). However, unlike MLE, Bayesian techniques involve calculation of posterior probabilities of model parameters by training the model with given data. We assume that the data  follows the generative model , and assign a prior probability Pθ|ℳ to the parameter vector θ under the model . Then Bayes’ rule for calculating posterior probability is as follows: (4) Prθ|ℳ,D=PrD|θ,ℳ×Prθ|ℳZ