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Prediction of Implicit Protein - Protein Interaction Using Optimal Associative Feature Rule  

Eom, Jae-Hong (서울대학교 전기컴퓨터공학부)
Zhang, Byoung-Tak (서울대학교 전기컴퓨터공학부)
Publication Information
Journal of KIISE:Software and Applications / v.33, no.4, 2006 , pp. 365-377 More about this Journal
Abstract
Proteins are known to perform a biological function by interacting with other proteins or compounds. Since protein interaction is intrinsic to most cellular processes, prediction of protein interaction is an important issue in post-genomic biology where abundant interaction data have been produced by many research groups. In this paper, we present an associative feature mining method to predict implicit protein-protein interactions of Saccharomyces cerevisiae from public protein interaction data. We discretized continuous-valued features by maximal interdependence-based discretization approach. We also employed feature dimension reduction filter (FDRF) method which is based on the information theory to select optimal informative features, to boost prediction accuracy and overall mining speed, and to overcome the dimensionality problem of conventional data mining approaches. We used association rule discovery algorithm for associative feature and rule mining to predict protein interaction. Using the discovered associative feature we predicted implicit protein interactions which have not been observed in training data. According to the experimental results, the proposed method accomplished about 96.5% prediction accuracy with reduced computation time which is about 29.4% faster than conventional method with no feature filter in association rule mining.
Keywords
Protein-protein interaction; Feature association mining; Association rule; Data mining; Bioinformatics;
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