Discovering condition-dependent Bayesian networks for gene regulation
Among the main interests in many biological studies are the structure of gene regulatory network, and in particular differences in the regulatory interactions between differ- ent conditions. However, since the number of available samples is always very small and estimating the network structure is extremely hard, most current algorithms have to assume that the gene regulation does not change be- tween conditions. We propose a new Bayesian network algorithm which (i) utilizes all the samples for estimat- ing regulatory relations that remain the same across con- ditions, and (ii) explicitly searches for regulatory relation- ships that are active only in one of the conditions. The result is an easily interpretable map of changes in regula- tion in several conditions.