Predicting protein-protein interactions using numerical associational features

We investigate the problem of predicting protein-protein interaction (PPI) using numerical features constructed from parent-child relation of a partial network constructed from known protein interactions. For each pair of proteins, we use a validation-based approach to normalize these features, which are based on association rule interestingness measures. The primary contribution of this work is the parametric normalization formula we derive and calibrate using data for the PPI task. This formula improves basic interestingness measures through taking sizes of itemset into account. Our derived itemset size-sensitive measures consider those rare but significant relationships among the children and the parents of set of proteins. We evaluate our work using k-nearest neighbor and rule-based classification approach.