In this paper, we propose a probabilistic framework to predict the interaction probability of proteins.
The notion of domain combination and domain combination pair is newly introduced and the prediction
model in the framework takes domain combination pair as a basic unit of protein interactions to overcome
the limitations of the conventional domain pair based prediction systems. The framework largely consists
of prediction preparation and service stages. In the prediction preparation stage, two appearance pro-
bability matrices, which hold information on appearance frequencies of domain combination pairs in the
interacting and non-interacting sets of protein pairs, are constructed. Based on the appearance probability
matrix, a probability equation is devised. The equation maps a protein pair to a real number in the range
of 0 to 1. Two distributions of interacting and non-interacting set of protein pairs are obtained using the
equation. In the prediction service stage, the interaction probability of a protein pair is predicted using the
distributions and the equation. The validity of the prediction model is evaluated for the interacting set of
protein pairs in Yeast organism and artificially generated non-interacting set of protein pairs. When 80%
of the set of interacting protein pairs in DIP database are used as learning set of interacting protein pairs,
very high sensitivity(86%) and specificity(56%) are achieved within our framework.