Domain combination based protein-protein interaction possibility ranking method

With the accumulation of protein and its related data on the Internet, many domain based computational techniques to predict protein interactions have been developed. However most of the techniques still have many limitations to be used in real fields. They usually suffer from low accuracy problem in prediction and do not provide any interaction possibility ranking method for multiple protein pairs. In this paper, we reevaluate a domain combination based protein interaction prediction method and develop an interaction possibility ranking method for multiple protein pairs. Using the ranking method, one can discern which protein pair is more probable to interact with each other than other protein pairs in multiple protein pairs. In the reevaluation, we have found that the accuracy of the prediction is improved as the size of non-interacting set of protein pairs is increased. When the size of non-interacting set of protein pairs is increased to 20 times bigger than that of interacting set of protein pairs in learning sets, 84% sensitivity and 75% specificity were achieved in yeast organism. In the validation of the ranking method, we revealed that there exist some correlations between the interacting probability and the accuracy of the prediction in case of the protein pair group having the matching PIP values in the interacting or non-interacting PIP distributions.