Prediction of Implicit Protein-Protein Interaction by Optimal Associative Feature Mining

Abstract

Proteins are known to perform a biological function by interacting with other proteins or compounds. Since protein-protein interaction is intrinsic to most cellular processes, protein interaction prediction is an important issue in post-genomic biology where abundant interaction data has been produced by many research groups. In this paper, we present an associative feature mining method to predict implicit protein-protein interactions of S.cerevisiae from public protein-protein interaction data. To overcome the dimensionality problem of conventional data mining approach, we employ feature dimension reduction filter (FDRF) method based on the information theory to select optimal informative features and to speed up the overall mining procedure. As a mining method to predict interaction, we use association rule discovery algorithm for associative feature and rule mining. Using the discovered associative feature we predict implicit protein interactions which have not been observed in training data. According to the experimental results, the proposed method accomplishes about 94.8% prediction accuracy with reduced computation time which is 32.5% faster than conventional method that has no feature filter.