Abstract
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.
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References
Goffeau, A., Barrell, B.G., et al.: Life with 6000 genes. Science 274, 546–567 (1996)
Eisen, M.B., Spellman, P.T., et al.: Cluster analysis and display of genomewide expression patterns. Proc. Natl. Acad. Sci. USA 95, 14863–14868 (1998)
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)
Park, J., et al.: Mapping protein family interactions: intramolecular and intermolecular protein family interaction repertoires in the PDB and yeast. J. Mol. Biol. 307, 929–939 (2001)
Iossifov, I., Krauthammer, M., et al.: Probabilistic inference of molecular networks from noisy data sources. Bioinformatics 20(8), 1205–1213 (2004)
Ng, S.K., Zhang, Z., et al.: Integrative approach for computationally inferring protein domain interactions. Bioinformatics 19(8), 923–929 (2003)
Eom, J.-H., et al.: Prediction of implicit protein–protein interaction by optimal associative feature mining. In: Yang, Z.R., Yin, H., Everson, R.M. (eds.) IDEAL 2004. LNCS, vol. 3177, pp. 85–91. Springer, Heidelberg (2004)
Carpenter, G.A., Grossberg, S.: A massively parallel architecture for a self-organizing neural pattern recognition machine. Computer Vision, Graphics and Image Processing 37, 54–115 (1987)
Barbara, M.: ART1 and pattern clustering. In: Proceedings of the 1988 connectionist models summer 1988, pp. 174–185. Morgan Kaufmann, San Francisco (1988)
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)
Mewes, H.W., Amid, C., et al.: MIPS: analysis and annotation of proteins from whole genomes. Nucleic Acids Res. 32(1), D41–44 (2004)
Christie, K.R., Weng, S., et al.: Saccharomyces Genome Database (SGD) provides tools to identify and analyze sequences from Saccharomyces cerevisiae and related sequences from other organisms. Nucleic Acids Res. 32(1), D311–314 (2004)
Rangarajan, S.K., Phoha, V.V., et al.: Adaptive neural network clustering of web users. IEEE Computer 37(4), 34–40 (2004)
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)
Csank, C., Costanzo, M.C., et al.: Three yeast proteome databases: YPD, PombePD, and CalPD (MycoPathPD). Methods Enzymol. 350, 347–373 (2002)
Ito, T., Chiba, T., et al.: A comprehensive two-hybrid analysis to explore the yeast protein interactome. Proc. Natl Acad. Sci. USA 98, 4569–4574 (2001)
Uetz, P., Giot, L., et al.: A comprehensive analysis of protein–protein interactions in Saccharomyces cerevisiae. Nature 403, 623–627 (2000)
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)
Eom, J.-H., et al.: Adaptive Neural Network based Clustering of Yeast Protein-Protein Interactions. In: Das, G., Gulati, V.P. (eds.) CIT 2004. LNCS, vol. 3356, pp. 49–57. Springer, Heidelberg (2004)
Oyama, T., Kitano, K., et al.: Extraction of knowledge on protein–protein interaction by association rule discovery. Bioinformatics 18, 705–714 (2002)
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)
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Eom, JH., Zhang, BT. (2006). Prediction of Protein Interaction with Neural Network-Based Feature Association Rule Mining. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893295_4
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DOI: https://doi.org/10.1007/11893295_4
Publisher Name: Springer, Berlin, Heidelberg
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