Exploring gene causal interactions using an enhanced constraint-based method

DNA microarray provides a powerful basis for analysis of gene expression. Bayesian networks, which are based on directed acyclic graphs (DAGs) and can provide models of causal influence, have been investigated for gene regulatory networks. The difficulty with this technique is that learning the Bayesian network structure is an NP-hard problem, as the number of DAGs is superexponential in the number of genes, and an exhaustive search is intractable. In this paper, we propose an enhanced constraint-based approach for causal structure learning. We integrate with graphical Gaussian modeling and use its independence graph as an input of our constraint-based causal learning method. We also present graphical decomposition techniques to further improve the performance. Our enhanced method makes it feasible to explore causal interactions among genes interactively. We have tested our methodology using two microarray data sets. The results show that the technique is both effective and efficient in exploring causal structures from microarray data.