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International Symposium on Neural Networks

ISNN 2006: Advances in Neural Networks - ISNN 2006 pp 690–695Cite as

Neural Feature Association Rule Mining for Protein Interaction Prediction

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3973))

Abstract

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.

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References

  1. Eisen, M.B., et al.: Cluster Analysis and Display of Genome–Wide Expression Patterns. Proc. Natl. Acad. Sci. USA 95, 14863–14868 (1998)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  3. Iossifov, I., et al.: Probabilistic Inference of Molecular Networks from Noisy Data Sources. Bioinformatics 20(8), 1205–1213 (2004)

    Article  Google Scholar 

  4. Ng, S.K., et al.: Integrative Approach for Computationally Inferring Protein Domain Interactions. Bioinformatics 19(8), 923–929 (2003)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. Rangarajan, et al.: Adaptive Neural Network Clustering of Web Users. IEEE Computer 37(4), 34–40 (2004)

    Google Scholar 

  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. 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)

    Chapter  Google Scholar 

  11. Press, W.H., et al.: Numerical Recipes in C. Cambridge University Press, Cambridge (1988)

    MATH  Google Scholar 

  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)

    Chapter  Google Scholar 

  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. Elalfi, A.E., et al.: Extracting Rules from Trained Neural Network Using GA for Managing E-business. Applied Soft Computing 4, 65–77 (2004)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Biointelligence Laboratory, School of Computer Science and Engineering, Seoul National University, Seoul, 151-744, South Korea

    Jae-Hong Eom

Authors
  1. Jae-Hong Eom
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

  3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Zurada

  4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd, 200240, Shanghai, China

    Bao-Liang Lu

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

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© 2006 Springer-Verlag Berlin Heidelberg

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Eom, JH. (2006). Neural Feature Association Rule Mining for Protein Interaction Prediction. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_101

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  • DOI: https://doi.org/10.1007/11760191_101

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34482-7

  • Online ISBN: 978-3-540-34483-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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