A direct comparison of protein interaction confidence assignment schemes

We measure the extent to which each set of confidence scores correlates with similarity of the interacting proteins in terms of function, expression, pattern of sequence conservation, and homology to interacting proteins in other species. We also employ a new metric, the Signal-to-Noise Ratio of protein complexes embedded in each network, to assess the power of the different methods. Seven confidence assignment schemes, including those of Bader et al., Deane et al., Deng et al., Sharan et al., and Qi et al., are compared in this work.Although the performance of each assignment scheme varies depending on the particular metric used for assessment, we observe that Deng et al. yields the best performance overall (in three out of four viable measures). Importantly, we also find that utilizing any of the probability assignment schemes is always more beneficial than assuming all observed interactions to be true or equally likely.Systematic elucidation of protein-protein interaction networks will be essential for understanding how different behaviors and protein functions are integrated within the cell. Recently, the advent of high-throughput experimental techniques like yeast two-hybrid (Y2H) assays [1] and co-immunoprecipitation (co-IP) screens [2] has led to the elucidation of large-scale protein interaction networks in different species, including S. cerevisiae (yeast) [2-5], D. melanogaster (fly) [6], C. elegans (worm) [7] and H. sapiens (human) [8,9]. These networks, while incorporating thousands or tens of thousands of measured interactions, have so far only partially covered the complete repertoire of protein interactions in an organism, and they have been determined to contain a significant number of false-positive interactions depending on the study [10]. However, recent years have also seen an increase in the accumulation of other sources of biological data such as whole genome sequence, mRNA expression, protein expression and functional annotation. This is parti