General architecture of the magic Bayesian Network. A separate network is instantiated for each pair of genes by initializing bottom-level nodes with evidence. Conditional probability tables for each connection were assessed formally from yeast genetics experts. The network contains discrete nodes and uses the clustering algorithm for belief updating, as initially proposed in ref. . The combination of outputs of expression clustering methods is performed through a single “Coexpression” node, which allows all of the expression analysis method's outputs for one dataset to be combined based on each method's characteristics, such as robustness to noise level in data or optimality for a specific data type (e.g., temporal data). The input nodes for expression-based clustering methods (K-means Clustering, Self Organizing Maps, and Hierarchical Clustering) incorporate pairwise data binned into three categories: high, medium, and low confidence, based on Pearson correlation to the cluster centroid (see supporting information). Nonexpression-based data are incorporated through binary input nodes for colocalization data, experimentally identified transcription factor binding sites, and various experimental evidence for physical or genetic associations of two proteins. The genetic and physical relationship data are divided into experimental evidence types according to the GRID database (http://biodata.mshri.on.ca/grid/servlet/HelpHtmlPages?pageID=3; see supporting information for details).