Fig. 1.

The computational approach. The first step involves a network N (e.g. physical interaction) of size m and for each gene, i in N the algorithm calculates its degree centrality, DC (di_N). The second step involves a list of functional pathways (e.g. pathway P) and for each gene i, the DC (di_p) is calculated using the information on interacting genes from N. For the assumption of equal probability for existing edges between nodes, the algorithm calculates the expected DC of gene i in the pathway P. If the DC observed for the gene i (di_p) is higher than expected (di_{p_expected}), i could be a potential driver in the pathway P