Mining protein kinases regulation using graphical models

Abnormal kinase activity is a frequent cause of diseases, which makes kinases a promising pharmacological target. Thus, it is critical to identify the characteristics of protein kinases regulation by studying the activation and inhibition of kinase subunits in response to varied stimuli. Bayesian network (BN) is a formalism for probabilistic reasoning that has been widely used for learning dependency models. However, for high-dimensional discrete random vectors the set of plausible models becomes large and a full comparison of all the posterior probabilities related to the competing models becomes infeasible. A solution to this problem is based on the Markov Chain Monte Carlo (MCMC) method. This paper proposes a BN-based framework to discover the dependency correlations of kinase regulation. Our approach is to apply the MCMC method to generate a sequence of samples from a probability distribution, by which to approximate the distribution. The frequent connections (edges) are identified from the obtained sampling graphical models. Our results point to a number of novel candidate regulation patterns that are interesting in biology and include inferred associations that were unknown.