While high throughput technologies provide experimental tools to identify protein-protein interactions (PPIs), their false positive rate can be as high as about 50%. Thus, it is critical to provide computational methods that can assess the experimentally identified PPIs, detect the potential spurious interactions, and reliably predict new PPIs. In this paper, we propose a novel in-silico method for PPI prediction and assessment. Our model is based on new features extracted from different organisms and a Bayesian network that integrates heterogeneous data sources. We successfully apply the novel model to predict human PPIs from three model organisms Saccharomyces cerevisiae, C. elegans, and Drosophila melanogaster. For those PPIs with mapping information from the three model organisms, our model can reach 80% in sensitivity with a specificity of 70%.
['Xiaotong Lin', 'M. X. Liu', 'Xue Wen Chen']
Protein-Protein Interaction Prediction and Assessment from Model Organisms