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Protein Classification with Kernelized Softassign

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Graph-Based Representations in Pattern Recognition (GbRPR 2005)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3434))

Abstract

In this paper we address the problem of comparing and classifying protein surfaces through a kernelized version of the Softassign graph-matching algorithm. Preliminary experiments with random-generated graphs have suggested that weighting the quadratic cost function of Softassign with information coming from the computation of diffusion kernels on graphs attenuate the performance decay with increasing noise levels. Our experimental results show that this approach yields a useful similarity measure to cluster proteins with similar structure, to automatically find prototypical graphs representing families of proteins and also to classify proteins in terms of their distance to these prototypes. We also show that the role of kernel-based information is to smooth the obtained matching fields, which in turn results in noise-free prototype estimation.

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References

  1. Halperin, I., Ma, B., Wolfson, H., Nussinov, R.: Principles of docking: An overview of search algorithms and a guide to scoring functions. Proteins 47, 409–443 (2002)

    Article  Google Scholar 

  2. Connolly, M.: Analytical molecular surface calculation. J. Appl. Crys. 16, 548–558 (1983)

    Article  Google Scholar 

  3. Wolfson, H.J., Lamdan, Y.: Geometric hashing; A general and efficient model-based recognition scheme. In: Proc. of the IEEE Int. Conf. on Computer Vision, pp. 238–249 (1988)

    Google Scholar 

  4. Nussinov, R., Wolfson, H.J.: Efficient detection of three-dimensional motifs in biological macromolecules by computer vision techniques. Proc. of the Natl. Acad. Sci. USA 88, 10495–10499 (1991)

    Article  Google Scholar 

  5. Gardiner, E.J., Willet, P., Artymiuk, P.J.: Graph-theoretic techniques for macromolecular docking. J. Chem. Inform. Comput. Sci. 40, 273–279 (2000)

    Google Scholar 

  6. Pickering, S.J., Bulpitt, A.J., Efford, N., Gold, N.D., Westhead, D.R.: AI-based algorithms for protein surface comparisons. Computers and Chemistry 26, 79–84 (2001)

    Article  Google Scholar 

  7. Gold, S., Rangarajan, A.: A graduated assignment algorithm for graph matching. IEEE Trans. on Patt. Anal. and Mach. Int. 18(4), 377–388 (1996)

    Article  Google Scholar 

  8. Finch, A.M., Wilson, R.C., Hancock, E.: An energy function and continuous edit process for graph matching. Neural Computation 10(7), 1873–1894 (1998)

    Article  Google Scholar 

  9. Pelillo, M.: Replicator equations, maximal cliques, and graph isomorphism. Neural Computation 11, 1933–1955 (1999)

    Article  Google Scholar 

  10. Luo, B., Hancock, E.R.: Structural graph matching using the EM algorithm and singular value decomposition. IEEE Trans. on Patt. Anal. and Mach. Int. 23(10), 1120–1136 (2001)

    Article  Google Scholar 

  11. Lozano, M.A., Escolano, F.: A significant improvement of softassign with diffusion kernels. In: Proc. of the IAPR Workshop on Synt. and Struct. Patt. Rec. LNCS. Springer, Heidelberg (2004) (accepted for publication)

    Google Scholar 

  12. Kondor, R., Lafferty, J.: Diffusion kernels on graphs and other discrete input spaces. In: Proc. of the Intl. Conf. on Mach. Learn., pp. 315–322. Morgan-Kauffman, San Francisco (2002)

    Google Scholar 

  13. Smola, A., Kondor, R.I.: Kernels and regularization on graphs. In: Schölkopf, B., Warmuth, M.K. (eds.) COLT/Kernel 2003. LNCS, vol. 2777, pp. 144–158. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  14. Chung, F.R.K.: Spectral graph theory. In: Conference Board of the Mathematical Sciences(CBMS), vol. 92. American Mathematical Society, Providence (1997)

    Google Scholar 

  15. Berman, H.M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T.N., Weissig, H., Shindyalov, I.N., Bourne, P.E.: The Protein Data Bank. Nucleic Acids Research 28, 235–242 (2000)

    Article  Google Scholar 

  16. Lozano, M.A., Escolano, F.: EM algorithm for clustering an ensemble of graphs with comb matching. In: Rangarajan, A., Figueiredo, M.A.T., Zerubia, J. (eds.) EMMCVPR 2003. LNCS, vol. 2683, pp. 52–67. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  17. Holm, L., Sander, C.: Protein structure comparison by alignment of distance matrices. J. Mol. Biol. 233(1), 123–138 (1993)

    Article  Google Scholar 

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Authors and Affiliations

  1. Robot Vision Group, Departamento de Ciencia de la Computación e Inteligencia Artificial, Universidad de Alicante, Spain

    Miguel Angel Lozano & Francisco Escolano

Authors
  1. Miguel Angel Lozano
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  2. Francisco Escolano
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Editors and Affiliations

  1. GREYC CNRS UMR 6072, Image Team, Université de Caen Basse-Normandie, 6, Boulevard Maréchal Juin, 14050, Caen Cedex, France

    Luc Brun

  2. Dipartimento di Ingegneria dell’Informazione ed Ingegneria Elettrica, Università di Salerno, via P.te Don Melillo, I-84084, Fisciano, SA), Italy

    Mario Vento

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Lozano, M.A., Escolano, F. (2005). Protein Classification with Kernelized Softassign. In: Brun, L., Vento, M. (eds) Graph-Based Representations in Pattern Recognition. GbRPR 2005. Lecture Notes in Computer Science, vol 3434. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31988-7_32

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  • DOI: https://doi.org/10.1007/978-3-540-31988-7_32

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25270-2

  • Online ISBN: 978-3-540-31988-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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