Classification of the Images of Gene Expression Patterns Using Neural Networks Based on Multi-valued Neurons
Multi-valued neurons (MVN) are the neural processing elements with complex-valued weights and high functionality. It is possible to implement an arbitrary mapping described by partial-defined multiple-valued function on the single MVN. The MVN-based neural networks are applied to temporal classification of images of gene expression patterns, obtained by confocal scanning microscopy.
KeywordsNeural Network Gene Expression Pattern Image Recognition Cellular Neural Network Discriminant Function Analysis
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- 3.Aizenberg N.N., Aizenberg I.N “CNN based on multi-valued neuron as a model of associative memory for gray-scale images”, Proc. of the 2-d IEEE International Workshop on Cellular Neural Networks and their Applications, Munich, October 12-14 (1992), pp. 36–41, 1992.Google Scholar
- 4.Aizenberg N.N., Aizenberg I.N.. Krivosheev G.A. “Multi-Valued Neurons: Learning, Networks, Application to Image Recognition and Extrapolation of Temporal Series”, Lecture Notes in Computer Science, Vol. 930, (J. Mira, F. Sandoval-Eds.), Springer-Verlag, 389–395, 1985.Google Scholar
- 5.Aizenberg I.N. “Neural networks based on multi-valued and universal binary neurons: theory, application to image processing and recognition”, Lecture Notes in Computer Sciense, vol. 1625 (B. Reusch-Ed.), Springer-Verlag, 1999, pp. 306–316.Google Scholar
- 6.Aizenberg I.N., Aizenberg N.N., Vandewalle J. “Multi-valued and universal binary neurons: theory, learning, applications”, Kluwer Academic Publishers, Boston/Dordrecht/London, 2000.Google Scholar
- 8.Aoki H., Kosugi Y. “An Image Storage System Using Complex-Valued Associative Memory”, Proceedings of the 15th International Conference on Pattern Recognition, Barcelona, Spain September 3-8, 2000, IEEE Computer Society Press, vol. 2, pp. 626–629, 2000.Google Scholar
- 9.Aizenberg I.N., Aizenberg N.N. “Pattern recognition using neural network based on multi-valued neurons”, Lecture Notes in Computer Sciense, vol. 1607-II (J. Mira, J.V. Sanches-Andres-Eds.), Springer-Verlag, pp. 383–392, 1999.Google Scholar
- 10.D. Kosman, J. Reinitz and D.H. Sharp “Automated Assay of Gene Expression at Cellular Resolution”, Proceedings of the 1998 Pacific Symposium on Biocomputing, World Scientific Press, Singapore, pp. 6–17, 1997.Google Scholar
- 13.E. Myasnikova, D. Kosman, J. Reinitz and M. Samsonova “A Method for the Spatiotemporal Registration of the Expression Patterns of Drosophila Segmentation Genes”, Proc. of the 1st Conf. on Modelling and Simulation In Biology, Medicine and Biomedical Engineering, ESIEE, Noisy-le-Grand, France, pp.63–67,1999.Google Scholar