Figure 1

Outline of our interaction confidence assessment method. In the input interaction network (upper left picture), proteins are labeled with letters (A, B, etc.) and interactions between them are represented by edges. In the first step of the approach, we create the line graph of the given network where nodes represent interactions (labeled A–C, A–D, etc.) and edges represent shared interaction participants. In the second step, we use Markov clustering on this line graph to dissect it into interaction clusters. The clustering granularity is optimized in a previous step of the algorithm. Importantly, proteins can be part of more than one cluster. The relative number of interactions of a protein in a cluster determines how specific a protein is to that cluster. In the third step, we calculate confidence values for every interaction based on how specific both proteins are to the respective clusters. The thickness of interaction links in the lower left picture corresponds to the calculated interaction confidence values for this example network.