A method to integrate, assess and characterize the protein-protein interactions

Recently, large-scale protein-protein interactions were recovered using the similar two-hybrid system for the model systems. This information allows us to investigate the protein interaction network from a systematic point of view. However, experimentally determined interactions are susceptible to errors. A previous assessment estimated that only similar to 10% of the interactions can be supported by more than one independent experiment, and about half of the interactions may be false positives. These false positives might unnecessarily link unrelated proteins, resulting in huge apparent interaction clusters, which complicate elucidation for the biological importance of these interactions. Address this problem, we present an approach to integrate, assess and characterize all available protein-protein interactions in model organisms yeast and fly. We first integrate all available protein-protein interaction databases of yeast and fly, and merge all the datasets. We then use machine learning techniques to score the reliability for each interaction, and to rigorously validate the scoring scheme of yeast protein-protein interactions from different aspects. Our results show that this scoring scheme provides a good basis for selecting reliable protein-protein interaction dataset.