Probabilistic prediction and ranking of human protein-protein interactions

Background: Although the prediction of protein-protein interactions has been extensively investigated for yeast, few such datasets exist for the far larger proteome in human. Furthermore, it has recently been estimated that the overall average false positive rate of available computational and high-throughput experimental interaction datasets is as high as 90%. Results: The prediction of human protein-protein interactions was investigated by combining orthogonal protein features within a probabilistic framework. The features include co-expression, orthology to known interacting proteins and the full-Bayesian combination of subcellular localization, co-occurrence of domains and post-translational modifications. A novel scoring function for local network topology was also investigated. This topology feature greatly enhanced the predictions and together with the full-Bayes combined features, made the largest contribution to the predictions. Using a conservative threshold, our most accura...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Michelle S. Scott, Geoffrey J. Barton
• Keywords: BMCBI 2007, Human Interactions, Interaction Datasets, Protein-protein Interactions