An open question in gene regulation is how spatio-temporal patterns of gene expression is encoded in the genome. We discussed this paper in our lab meeting. This paper talks about  a two-step approach to predicting spatial  and temporal gene expression patterns in Drosophila. This prediction task is tackled as a two-step approach: (a) first find cis-regulatory modules that exhibit spatio or temporal activity, (b) second link the crms to predict spatio-temporal expression. Assume space and time is captured by the term “context”.  To do (a) the authors use a Bayesian network which is trained on known CRM-context relationships. To do (b) the authors use additional data based on insulators and H3K4me1 to predict which CRMs are associated with which genes. In (a) the CRM-context information is known only for a few hundred of the total 8000 crms that are present. So the authors use an EM idea where they use the trained Bayesian network to predict the context-specific activity of each CRM. Then using the soft labels of each CRM they predict the expression of the gene. This second model is also a Bayesian network but has additional variables for the distance between CRMs and genes and whether there is an insulator binding site.