Neural Feature Association Rule Mining for Protein Interaction Prediction

  • Jae-Hong Eom
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)


The prediction of protein interactions is an important problem in post–genomic biology. In this paper, we present an association rule mining method for protein interaction prediction. A neural network is used to cluster protein interaction data and a feature selection is used to reduce the dimension of protein features. For model training, the preliminary network model was constructed with existing protein interaction data in terms of their functional categories and interactions. A set of association rules for protein interaction prediction are derived by decoding a set of learned weights of trained neural network after this model training. The protein interaction data of Yeast from public databases are used. The prediction performance was compared with simple association rule-based approach. According to the experimental results, proposed method achieved about 95.5% accuracy.


Association Rule Association Rule Mining Trained Neural Network Adaptive Resonance Theory Adaptive Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Eisen, M.B., et al.: Cluster Analysis and Display of Genome–Wide Expression Patterns. Proc. Natl. Acad. Sci. USA 95, 14863–14868 (1998)CrossRefGoogle Scholar
  2. 2.
    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)CrossRefGoogle Scholar
  3. 3.
    Iossifov, I., et al.: Probabilistic Inference of Molecular Networks from Noisy Data Sources. Bioinformatics 20(8), 1205–1213 (2004)CrossRefGoogle Scholar
  4. 4.
    Ng, S.K., et al.: Integrative Approach for Computationally Inferring Protein Domain Interactions. Bioinformatics 19(8), 923–929 (2003)CrossRefGoogle Scholar
  5. 5.
    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)CrossRefGoogle Scholar
  6. 6.
    Barbara, M.: ART1 and pattern clustering. In: Proceedings of the Summer School of Connectionist Models, pp. 174–185. Morgan Kaufmann, San Francisco (1988)Google Scholar
  7. 7.
    Heins, L.G., Tauritz, D.R.: Adaptive Resonance Theory (ART): An Introduction. Internal Report 95-35, Dept. of Computer Science, Leiden University, Netherlands, 174–85 (1995)Google Scholar
  8. 8.
    Rangarajan, et al.: Adaptive Neural Network Clustering of Web Users. IEEE Computer 37(4), 34–40 (2004)Google Scholar
  9. 9.
    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)Google Scholar
  10. 10.
    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)CrossRefGoogle Scholar
  11. 11.
    Press, W.H., et al.: Numerical Recipes in C. Cambridge University Press, Cambridge (1988)MATHGoogle Scholar
  12. 12.
    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)CrossRefGoogle Scholar
  13. 13.
    Eom, J.-H.: Prediction of Yeast Protein–Protein Interactions by Neural Association Rule. Internal Report 04-03, Dept. of Computer Sci.&Eng., Seoul National University, Republic of Korea, 1–12 (2004)Google Scholar
  14. 14.
    Elalfi, A.E., et al.: Extracting Rules from Trained Neural Network Using GA for Managing E-business. Applied Soft Computing 4, 65–77 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jae-Hong Eom
    • 1
  1. 1.Biointelligence Laboratory, School of Computer Science and EngineeringSeoul National UniversitySeoulSouth Korea

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