Learning Causal Networks from Microarray Data

We report on a new approach to modelling and identifying dependencies within a gene regulatory cycle. In particular, we aim to learn the structure of a causal network from gene expression microarray data. We model causality in two ways: by using conditional dependence assumptions to model the independence of different causes on a common effect; and by relying on time delays between cause and effect. Networks therefore incorporate both probabilistic and temporal aspects of regulation. We are thus able to deal with cyclic dependencies amongst genes, which is not possible in standard Bayesian networks. However, our model is kept deliberately simple to make it amenable for learning from microarray data, which typically contains a small number of samples for a large number of genes. We have developed a learning algorithm for this model which was implemented and experimentally validated against simulated data and on yeast cell cycle microarray time series data sets.