Prediction of Protein Interaction with Neural Network-Based Feature Association Rule Mining
Prediction of protein interactions is one of the central problems in post–genomic biology. In this paper, we present an association rule-based protein interaction prediction method. We adopted neural network to cluster protein interaction data, and used information theory based feature selection method to reduce protein feature dimension. After model training, feature association rules are generated to interaction prediction by decoding a set of learned weights of trained neural network and by mining association rules. For model training, an initial network model was constructed with public Yeast protein interaction data considering their functional categories, set of features, and interaction partners. The prediction performance was compared with traditional simple association rule mining method. The experimental results show that proposed method has about 96.1% interaction prediction accuracy compared to simple association mining approach which achieved about 91.4% accuracy.
KeywordsFeature Selection Association Rule Association Rule Mining Trained Neural Network Adaptive Resonance Theory
Unable to display preview. Download preview PDF.
- 3.Pavlidis, P., Weston, J.: Gene functional classification from heterogeneous data. In: Pro. of the 5th Int’l Conf. on Comp. Molecular Biology, pp. 249–255 (2001)Google Scholar
- 9.Barbara, M.: ART1 and pattern clustering. In: Proceedings of the 1988 connectionist models summer 1988, pp. 174–185. Morgan Kaufmann, San Francisco (1988)Google Scholar
- 10.Heins, L.G., Tauritz, D.R.: Adaptive resonance theory (ART): an introduction. Internal Report 95-35, Dept. of Comp. Sci., Leiden University, Netherlands, 174–85 (1995)Google Scholar
- 13.Rangarajan, S.K., Phoha, V.V., et al.: Adaptive neural network clustering of web users. IEEE Computer 37(4), 34–40 (2004)Google Scholar
- 14.Yu, L., Liu, H.: Feature selection for high dimensional data: a fast correlation-based filter solution. In: Proceeding of ICML-03, pp. 856–863 (2003)Google Scholar
- 18.Press, W.H., Flannery, B.P., et al.: Numerical recipes in C: The Art of Scientific Computing, 2nd edn., pp. 633–634. Cambridge University Press, Cambridge (1992)Google Scholar
- 21.Eom, J.-H., Zhang, B.-T.: Prediction of implicit protein–protein interaction using optimal associative feature rule. Journal of Korea Information Science Society: Software and Application 33(4), 365–377 (2006)Google Scholar