Reassessing the limit of data integration for the prediction of protein-protein interactions in Saccharomyces cerevisiae

This paper investigates the integration of functional genomic data for the prediction of protein-protein interactions (PPI) in Saccharomyces cerevisiae. A previous benchmark study observed a marginal increase in predictive power when integrating diverse features. Classification performance was evaluated using the Receiver Operating Characteristic (ROC) curve. In this study we propose the implementation of a likelihood ratio based Bayesian classifier to reassess the limits of genomic integration. The classifier combines seven genomic features ranging from co-expression to essentiality. Due to the imbalance of the dataset in this study, ROC curves may present an overly optimistic view of the classification performance. We use the true positive/false positive (TP/FP) rate and sensitivity as comparative predictive measures to the ROC curve. Predicted interactions are verified using a Gold Standard constructed from the Munich Database of Interacting Proteins Complex Catalogue. Using the measures TP/FP and sensitivity, a clear increase in classification performance was observed with the integration of features. This framework could be extended to the analysis of PPI in more complex organisms such as Drosophila melanogaster and Homo sapiens.