Comparison of Protein-Protein Interaction Confidence Assignment Schemes

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Recent technological advances have enabled high-throughput measurements of protein-protein interactions in the cell, producing protein interaction networks for various species at an ever increasing pace. However, common technologies like yeast two-hybrid can experience high rates of false positive detection. To combat these errors, many methods have been developed which associate confidence scores with each interaction. Here we perform the first comparative analysis and performance assessment among these different methods using the fact that interacting proteins have similar biological attributes such as function, expression, and evolutionary conservation. We also introduce a new measure, the signal to noise ratio of protein complexes embedded in each network, to assess the quality of the different methods. We observe that utilizing any probability scheme is always more beneficial than assuming all observed interactions to be real. Also, schemes that assign probabilities to individual interactions generally perform better than those assessing the reliability of a set of interactions obtained from an experiment or a database.