Supervised Selective Kernel Fusion for Membrane Protein Prediction

  • Alexander Tatarchuk
  • Valentina Sulimova
  • Ivan Torshin
  • Vadim Mottl
  • David Windridge
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8626)


Membrane protein prediction is a significant classification problem, requiring the integration of data derived from different sources such as protein sequences, gene expression, protein interactions etc. A generalized probabilistic approach for combining different data sources via supervised selective kernel fusion was proposed in our previous papers. It includes, as particular cases, SVM, Lasso SVM, Elastic Net SVM and others. In this paper we apply a further instantiation of this approach, the Supervised Selective Support Kernel SVM and demonstrate that the proposed approach achieves the top-rank position among the selective kernel fusion variants on benchmark data for membrane protein prediction. The method differs from the previous approaches in that it naturally derives a subset of “support kernels” (analogous to support objects within SVMs), thereby allowing the memory-efficient exclusion of significant numbers of irrelevant kernel matrixes from a decision rule in a manner particularly suited to membrane protein prediction.


Multiple Kernel Learning SVM supervised selectivity support kernels membrane protein prediction 


  1. 1.
    Alberts, B., Bray, D., Lewis, J., et al.: Molecular biology of the cell, 3rd edn., p. 1361. Garland Publishing, New York (1994)Google Scholar
  2. 2.
    Overington, J.P., Al-Lazikani, B., Hopkins, A.L.: How many drug targets are there? Nat. Rev. Drug. Discov. 5(12), 993–996 (2006)CrossRefGoogle Scholar
  3. 3.
    Krogh, A., Larsson, B., von Heijne, G., Sonnhammer, E.L.L.: Predicting Transmembrane Protein Topology with a Hidden Markov Model: Application to Complete Genomes. J. Mol. Biol. 305, 567–580 (2001)CrossRefGoogle Scholar
  4. 4.
    Chen, C.P., Rost, B.: State-of-the-art in Membrane Protein Prediction. Applied Bioinformatics 1, 21–35 (2002)Google Scholar
  5. 5.
    Gao, F.P., Cross, T.A.: Recent developments in membrane-protein structural genomics. Genome Biology 6, 244 (2005)CrossRefGoogle Scholar
  6. 6.
    Lanckriet, G., et al.: A statistical framework for genomic data fusion. Bioinformatics 20, 2626–2635 (2004)CrossRefGoogle Scholar
  7. 7.
    Schölkopf, B., Tsuda, K., Vert, J.-P. (eds.): Kernel Methods in Computational Biology. MIT Press (2004)Google Scholar
  8. 8.
    Hofmann, T., Schölkopf, B., Smola, A.J.: Kernel methods in machine learning. Ann. Statist. 36(3), 1171–1220 (2008)CrossRefzbMATHMathSciNetGoogle Scholar
  9. 9.
    Vapnik, V.: Statistical Learning Theory. John-Wiley and Sons, Inc. (1998)Google Scholar
  10. 10.
    Pavlidis, P., Weston, J., Cai, J., Grundy, W.N.: Gene functional classification from heterogeneous data. In: Proceedings of the 5th Annual International Conference on Computational Molecular Biology, pp. 242–248 (2001)Google Scholar
  11. 11.
    Ong, C.S., et al.: Learning the kernel with hyperkernels. J. Mach. Learn. Res. 6, 1043–1071 (2005)zbMATHMathSciNetGoogle Scholar
  12. 12.
    Bie, T., et al.: Kernel-based data fusion for gene prioritization. Bioinformatics 23, 125–132 (2007)CrossRefGoogle Scholar
  13. 13.
    Bach, F.R., et al.: Multiple kernel learning, conic duality, and the SMO algorithm. In: Proceedings of the Twenty-First International Conference on Machine Learning (ICML 2004). Omnipress, Banff (2004)Google Scholar
  14. 14.
    Sonnenburg, S., Rätsch, G., Schäfer, C., Schölkopf, B.: Large Scale Multiple Kernel Learning. Journal of Machine Learning Research 7, 1531–1565 (2006)zbMATHGoogle Scholar
  15. 15.
    Hu, M., Chen, Y., Kwok, J.T.-Y.: Building sparse multiple-kernel SVM classifiers. IEEE Transactions on Neural Networks 20(5), 827–839 (2009)CrossRefGoogle Scholar
  16. 16.
    Gönen, M., Alpayd, E.: Multiple Kernel Machines Using Localized Kernels. In: Proc. of PRIB (2009)Google Scholar
  17. 17.
    Gönen, M., Alpayd, E.: Localized algorithms for multiple kernel learning. Pattern Recognition 46, 795–807 (2013)CrossRefzbMATHGoogle Scholar
  18. 18.
    Liao, L.: Data Fusion with Optimized Block Kernels in LS-SVM for Protein Classification. Engineering 5, 233–236 (2013)CrossRefGoogle Scholar
  19. 19.
    Cortes, C., Mohri, M., Rostamizadeh, A.: Learning non-linear combinations of kernels. In: Bengio, Y., et al. (eds.) Advances in Neural Information Processing Systems, vol. 22, pp. 396–404 (2009)Google Scholar
  20. 20.
    Mottl, V., Tatarchuk, A., Sulimova, V., Krasotkina, O., Seredin, O.: Combining pattern recognition modalities at the sensor level via kernel fusion. In: Haindl, M., Kittler, J., Roli, F. (eds.) MCS 2007. LNCS, vol. 4472, pp. 1–12. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  21. 21.
    Kloft, M., Brefeld, U., Sonnenburg, S., et al.: Efficient and accurate lp-norm multiple kernel learning. In: Bengio, Y., et al. (eds.) Advances in Neural Information Processing Systems, vol. 22, pp. 997–1005. MIT Press (2009)Google Scholar
  22. 22.
    Tatarchuk, A., Mottl, V., Eliseyev, A., Windridge, D.: Selectivity supervision in combining pattern-recognition modalities by feature- and kernel-selective Support Vector Machines. In: Proc. ICPR (2008)Google Scholar
  23. 23.
    Tatarchuk, A., Sulimova, V., Windridge, D., Mottl, V., Lange, M.: Supervised selective combining pattern recognition modalities and its application to signature verification by fusing on-line and off-line kernels. In: Benediktsson, J.A., Kittler, J., Roli, F. (eds.) MCS 2009. LNCS, vol. 5519, pp. 324–334. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  24. 24.
    Tatarchuk, A., Urlov, E., Mottl, V., Windridge, D.: A support kernel machine for supervised selective combining of diverse pattern-recognition modalities. In: El Gayar, N., Kittler, J., Roli, F. (eds.) MCS 2010. LNCS, vol. 5997, pp. 165–174. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  25. 25.
    Bradley, P., Mangasarian, O.: Feature selection via concave minimization and support vector machines. In: International Conference on Machine Learning (1998)Google Scholar
  26. 26.
    Wang, L., Zhu, J., Zou, H.: The doubly regularized support vector machine. Statistica Sinica 16, 589–615 (2006)zbMATHMathSciNetGoogle Scholar
  27. 27.
    De Groot, M.H.: Optimal Statistical Decisions. Wiley Classics Library (2004)Google Scholar
  28. 28.
    Mewes, H.W., Frishman, D., Gruber, C., Geier, B., Haase, D., Kaps, A., Lemcke, K., Mannhaupt, G., Pfeiffer, F., Schüller, C.: MIPS: a database for genomes and protein sequences. Nucleic Acids Research 28, 37–40 (2000)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Alexander Tatarchuk
    • 1
  • Valentina Sulimova
    • 2
  • Ivan Torshin
    • 1
  • Vadim Mottl
    • 1
  • David Windridge
    • 3
  1. 1.Computing Center of the Russian Academy of SciencesMoscowRussia
  2. 2.Tula State UniversityTulaRussia
  3. 3.Centre for Vision, Speech and Signal ProcessingUniversity of SurreyGuildfordUK

Personalised recommendations