A Neural Procedure for Gene Function Prediction

Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 19)


The graph classification problem consists, given a weighted graph and a partial node labeling, in extending the labels to all nodes. In many real-world context, such as Gene Function Prediction, the partial labeling is unbalanced: positive labels are much less than negatives. In this paper we present a new neural algorithm for predicting labels in presence of label imbalance. This algorithm is based on a family of Hopfield networks, described by 2 continuous parameters and 1 discrete parameter, and it consists of two main steps: 1) the network parameters are learnt through a cost-sensitive optimization procedure based on local search; 2) a suitable Hopfield network restricted to unlabeled nodes is considered and simulated. The reached equilibrium point induces the classification of unlabeled nodes. An experimental analysis on real-world unbalanced data in the context of genome-wide prediction of gene functions show the effectiveness of the proposed approach.


Neural Network Hopfield Network Gene Function Prediction 


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  1. 1.
    Belkin, M., Matveeva, I., Niyogi, P.: Regularization and Semi-supervised Learning on Large Graphs. In: Shawe-Taylor, J., Singer, Y. (eds.) COLT 2004. LNCS (LNAI), vol. 3120, pp. 624–638. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  2. 2.
    Bengio, Y., Delalleau, O., Le Roux, N.: Label Propagation and Quadratic Criterion. In: Chapelle, O., Scholkopf, B., Zien, A. (eds.) Semi-Supervised Learning, pp. 193–216. MIT Press (2006)Google Scholar
  3. 3.
    Bertoni, A., Frasca, M., Valentini, G.: COSNet: A Cost Sensitive Neural Network for Semi-supervised Learning in Graphs. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011, Part I. LNCS, vol. 6911, pp. 219–234. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  4. 4.
    Borgatti, S., Mehra, A., Brass, D., Labianca, G.: Network Analysis in the Social Sciences. Science 232, 892–895 (2009)CrossRefGoogle Scholar
  5. 5.
    Deng, M., Chen, T., Sun, F.: An integrated probabilistic model for functional prediction of proteins. J. Comput. Biol. 11, 463–475 (2004)CrossRefGoogle Scholar
  6. 6.
    Dorogovtsev, S., Mendes, J.: Evolution of networks: From biological nets to the Internet and WWW. Oxford University Press, Oxford (2003)MATHGoogle Scholar
  7. 7.
    Elkan, C.: The foundations of cost-sensitive learning. In: Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence, pp. 973–978 (2001)Google Scholar
  8. 8.
    Hopfield, J.: Neural networks and physical systems with emergent collective compatational abilities. Proc. Natl. Acad. Sci. USA 79, 2554–2558 (1982)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Karaoz, U., et al.: Whole-genome annotation by using evidence integration in functional-linkage networks. Proc. Natl. Acad. Sci. USA 101, 2888–2893 (2004)CrossRefGoogle Scholar
  10. 10.
    Lin, H.T., Lin, C.J., Weng, R.: A note on platt’s probabilistic outputs for support vector machines. Machine Learning 68(3), 267–276 (2007)CrossRefGoogle Scholar
  11. 11.
    Marcotte, E., Pellegrini, M., Thompson, M., Yeates, T., Eisenberg, D.: A combined algorithm for genome-wide prediction of protein function. Nature 402, 83–86 (1999)CrossRefGoogle Scholar
  12. 12.
    Ruepp, A., et al.: The FunCat, a functional annotation scheme for systematic classification of proteins from whole genomes. Nucleic Acids Research 32(18), 5539–5545 (2004)CrossRefGoogle Scholar
  13. 13.
    Szummer, M., Jaakkola, T.: Partially labeled classification with Markov random walks. In: Advances in Neural Information Processing Systems (NIPS), vol. 14, pp. 945–952. MIT Press (2001)Google Scholar
  14. 14.
    Tsuda, K., Shin, H., Scholkopf, B.: Fast protein classification with multiple networks. Bioinformatics 21(suppl. 2), ii59–ii65 (2005)Google Scholar
  15. 15.
    Wilcoxon, F.: Individual comparisons by ranking methods. Biometrics 1, 80–83 (1945)CrossRefGoogle Scholar
  16. 16.
    Wuchty, S., Ravasz, E., Barabsi, A.L.: The architecture of biological networks. Complex Systems in Biomedicine 5259, 165–181 (2003)Google Scholar
  17. 17.
    Zhu, X., Ghahramani, Z., Lafferty, J.: Semi-supervised learning using gaussian fields and harmonic functions. In: ICML, pp. 912–919 (2003)Google Scholar

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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Dipartimento di Scienze dell’InformazioneUniversità degli Studi di MilanoMilanoItaly

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