Abstract
In this paper, we present an association rule based protein interaction prediction method. We use neural network to cluster protein interaction data and feature selection method to reduce protein feature dimension. After this model training, association rules for protein interaction prediction are generated by decoding a set of learned weights of trained neural network and association rule mining. For model training, the initial network model was constructed with existing protein interaction data in terms of their functional categories and interactions. The protein interaction data of Yeast (S.cerevisiae) from MIPS and SGD are used. The prediction performance was compared with traditional simple association rule mining method. According to the experimental results, proposed method shows about 96.1% accuracy compared to simple association mining approach which achieved about 91.4%.
This research was supported by the NRL Program of the Korea Ministry of Science and by the BK21-IT Program from the Ministry of Education and Human Resources Development of Korea. The ICT at Seoul National University provided research facilities for this study.
An erratum to this chapter can be found at http://dx.doi.org/10.1007/11550907_163 .
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Eisen, M.B., et al.: Cluster analysis and display of genome–wide expression patterns. In: Proc. Natl. Acad. Sci. USA, vol. 95, pp. 14863–14868 (1998)
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., et al.: Probabilistic inference of molecular networks from noisy data sources. Bioinformatics 20(8), 1205–1213 (2004)
Ng, S.K., et al.: Integrative approach for computationally inferring protein domain interactions. Bioinformatics 19(8), 923–929 (2003)
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. In: Internal Report 95-35, Dept. of Computer Science, Leiden University, Netherlands, pp. 174–185 (1995)
Rangarajan, 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 2003, pp. 856–863 (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)
Press, W.H., et al.: Numerical recipes in C. Cambridge University Press, Cambridge (1988)
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)
Eom, J.-H.: Prediction of Yeast Protein–Protein Interactions by Neural Association Rule. In: Internal Report 04-03, Dept. of Computer Sci.&Eng., Seoul National University, Republic of Korea, pp. 1–12 (2004)
Elalfi, A.E., et al.: Extracting rules from trained neural network using GA for managing E-business. Applied Soft Computing 4, 65–77 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Eom, JH., Zhang, BT. (2005). Prediction of Yeast Protein–Protein Interactions by Neural Feature Association Rule. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3697. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550907_77
Download citation
DOI: https://doi.org/10.1007/11550907_77
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28755-1
Online ISBN: 978-3-540-28756-8
eBook Packages: Computer ScienceComputer Science (R0)