Nucl. Acids Res. (2016) doi: 10.1093/nar/gkw963
The arrows in Figure 1 were missed during the art conversion process. A new Figure 1 is provided below. The Editors and Publisher apologise to the Authors and Readers for this error and inconvenience caused.
Figure 1.
Overview of our approach to integrate diverse regulatory genomic datasets as structure priors. 
and 
denote a regulator (such as a transcription factor) and a target gene respectively. For each candidate edge 
, different sources of prior networks can be used. The figure shows three different types of prior networks: ChIP, Motif and Knockout. Each prior network can be weighted, for example 
denotes the weight of the regulatory edge in the motif network. Different prior networks are combined to specify the prior probability of an edge using a single logistic function with prior parameters β0, βM, βK, βC. β° controls for network sparsity, while the other parameters specify importance of the prior networks. The regulatory network is itself represented as a dependency network learned by estimating a set of conditional probability distributions. In the example, the conditional distribution of the target gene X4 is specified by three regulators, X1, X2, X3. Ψ denotes a particular form of conditional probability distributions that is parameterized by Θ.