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Improved automatic classification of biological particles from electron-microscopy images using genetic neural nets

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Engineering Applications of Bio-Inspired Artificial Neural Networks (IWANN 1999)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1607))

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Abstract

In this paper several neural network classification algorithms have been applied to a real-world data case of electron microscopy data classification. Using several labeled sets as a reference, the parameters and architecture of the classifiers, LVQ (Learning Vector Quantization) trained codebooks and BP (backpropagation) trained feedforward neural-nets were optimized using a genetic algorithm. The automatic process of training and optimization is implemented using a new version of the g-lvq (genetic learning vector quantization) and G-Prop (genetic back-propagation) algorithms, and compared to a non-optimized version of the algorithms, Kohonen's LVQ and MLP trained with QuickProp. Dividing the all available samples in three sets, for training, testing and validation, the results presented here show a low average error for unknown samples. In this problem, G-Prop outperforms G-LVQ, but G-LVQ obtains codebooks with less parameters than the perceptrons obtained by G-Prop. The implication of this kind of automatic classification algorithms in the determination of three dimensional structure of biological particles is finally discused.

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References

  1. M. C. San Martín, N.P.J. Stamford, N. Dammeranova, N.E. Dixon, and J.M. Carazo. A structural model for the Echerichia coli DnaB helicase based on electron microscopy data. J. Struct. Biol., (114):167–176, 1995.

    Article  Google Scholar 

  2. X. Yu, M.J. Jezewska, W. Bujalowski, and E.H. Egelman. The hexameric e.coli dnab helicase can exist in different quaternary states. J. Mol. Biol., (259):7–14, 1996.

    Article  Google Scholar 

  3. M. Bárcena, M.C. San Martín, F. Weise, S. Ayora, J.C. Alonso, and J.M. Carazo. Polymorphic quaternary organization of the bacilus subtilis bacteriophage SPP1 replicative helicase (G40P). J. Mol. Biol., (283):809–819, 1998.

    Article  Google Scholar 

  4. C. San Martin, C. Gruss, and J.M. Carazo. Six molecules of SV40 large T antigen assemble in a propeller-shaped particle around a channel. Journal of Molecular biology, (269), 1997.

    Google Scholar 

  5. Teuvo Kohonen. The self-organizing map. Proc. IEEE 78:1464–1480, 1990.

    Article  Google Scholar 

  6. R. Marabini, and J.M. Carazo. Pattern recognition and classification of images of biological macromolecules using artificial neural networks. Biophysics Journal, 66:1804–1814, 1994.

    Article  Google Scholar 

  7. R.A. Crowther, and L.A. Amos. Harmonic analysis of electron microscope images with rotational symmetry. J. Mol. Biol., (60):123–130, 1971.

    Article  Google Scholar 

  8. Jose-Jesus Fernández and Jose-Maria Carazo. Analysis of structural variability within two-dimensional biological crystals by a combination of patch averaging techniques and self-organizing maps. Ultramicroscopy, 65:81–93, 1996.

    Article  Google Scholar 

  9. J. J. Merelo and A. Prieto. G-LVQ, a combination of genetic algorithms and LVQ. In N.C.Steele D.W.Pearson and R.F. Albrecht, editors, Artificial Neural Nets and Genetic Algorithms, pages 92–95. Springer-Verlag, 1995.

    Google Scholar 

  10. T. Kohonen. The self-organizing map. Procs. IEEE, 78:1464 ff., 1990.

    Article  Google Scholar 

  11. Ethem Alpaydim. GAL: Networks that grow when they learn and shrink when they forget. Technical Report TR-91-032, International Computer Science Institute, May 1991.

    Google Scholar 

  12. Enrique Monte, D. Hidalgo, J. Mariño, and I. Hernáez. A vector quantization algorithm based on genetic algorithms and LVQ. In NATO-ASI Bubión, page 231 ff., 1993.

    Google Scholar 

  13. S.R. Johnson, J.M. Sutter, H.L. Engelhardt, P.C. Jurs, J. Whiteand J.S. Kauer, T.A. Dickinson, and D.R. Walt. Identification of multiple analytes using an optical sensor array and pattern recognition neural networks. Anal. Chem., (69):4641, 1997.

    Article  Google Scholar 

  14. P. Fränti, J. Kivijärvi, T. Kaukoranta, and O. Nevalainen. Genetic algorithms for codebook generation in vq. In Proc. 3rd Nordic Workshop on Genetic Algorithms, Helsinki, Finlan, pages 207–222, 1997.

    Google Scholar 

  15. Juan-Carlos Perez and Enrique Vidal. Constructive design of LVQ and DSM classifiers. In J. Mira, J. Cabestany, and A. Prieto, editors, New Trends in Neural Computation, Lecture Notes in Computer Science No. 686, pages 335–339. Springer, 1993.

    Google Scholar 

  16. Xin Yao and Yong Liu. Towards Designing Artificial Neural Networks by Evolution. Applied Mathematics and Computation, 91(1):83–90, 1998.

    Article  MATH  MathSciNet  Google Scholar 

  17. P.A. Castillo; J. González; J.J. Merelo; V. Rivas; G. Romero; A. Prieto. G-rop: Global Optimization of Multilayer Perceptrons using GAs. submitted to Neurocomputing, 1998.

    Google Scholar 

  18. P.A. Castillo; J. González; J.J. Merelo; V. Rivas; G. Romero; A. Prieto. SA-Prop: Optimization of Multilayer Perceptron Parameters using Simulated Annealing. submitted to IWANN99, 1998.

    Google Scholar 

  19. J. J. Merelo; A. Prieto; F. Morán; R. Marabini; J. M. Carazo. Automatic classification of biological particles from electron-microscopy images using conventional and genetic-algorithm optimized learning vector quantization. Neural Processing Letters, 8:55–65, 1998.

    Article  Google Scholar 

  20. A. Pascual, M. Brcena, and J.M. Carazo. Application of the fuzzy kohonen clustering network to biological macromolecules images classification. In submitted to IWANN99, 1999.

    Google Scholar 

  21. David H. Ackley. A connectionist algorithm for genetic search. In John. J Grefenstette, editor, Proceedings of the First International Conference on Genetic Algorithms and their Applications, pages 121–135, Hillsdale, New Jersey, 1985. Lawrence Erlbaum Associates.

    Google Scholar 

  22. I. Harvey. Species adaptation genetic algorithms: a basis for a continuing SAGA. In F. J. Varela and P. Bourgine, editors, Proceedings of the First European Conference on Artificial Life. Toward a Practice of Autonomous Systems, pages 346–354, Paris, France, 11–13 December 1991. MIT Press, Cambridge, MA.

    Google Scholar 

  23. J. D. Schaffer and J. J. Grefenstette. Multi-objective learning via genetic algorithms. In Procs. of the 9th international Conference on Artificial Intelligence, pages 593–595, 1985.

    Google Scholar 

  24. S.E. Fahlman. Faster-Learning Variations on Back-Propagation: An Empirical Study. Proceedings of the 1988 Connectionist Models Summer School, Morgan Kaufmann, 1988.

    Google Scholar 

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Authors and Affiliations

  1. Depto. Arquitectura y Tecnología de las Computadoras, Facultad de Ciencias, GeNeura Team, Campus Fuentenueva, s/n, 18071, Granada, (Spain)

    J. J. Merelo, G. Romero & P. Castillo

  2. Departmento de Informática, Universidad de Jaén, 28040, Madrid, (Spain)

    V. Rivas

  3. Grupo de Biocomputación, CNB, 28040, Madrid, (Spain)

    A. Pascual & J. M. Carazo

Authors
  1. J. J. Merelo
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  2. V. Rivas
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  3. G. Romero
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  4. P. Castillo
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  5. A. Pascual
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  6. J. M. Carazo
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José Mira Juan V. Sánchez-Andrés

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© 1999 Springer-Verlag Berlin Heidelberg

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Merelo, J.J., Rivas, V., Romero, G., Castillo, P., Pascual, A., Carazo, J.M. (1999). Improved automatic classification of biological particles from electron-microscopy images using genetic neural nets. In: Mira, J., Sánchez-Andrés, J.V. (eds) Engineering Applications of Bio-Inspired Artificial Neural Networks. IWANN 1999. Lecture Notes in Computer Science, vol 1607. Springer, Berlin, Heidelberg . https://doi.org/10.1007/BFb0100504

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  • DOI: https://doi.org/10.1007/BFb0100504

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  • Print ISBN: 978-3-540-66068-2

  • Online ISBN: 978-3-540-48772-2

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