Abstract
A circular Self-Organising Map (SOM) based on a temporal metric has been proposed for clustering and characterising gene expressions. Expression profiles are first modelled with Radial Basis Functions. The co-expression coefficient, defined as the uncentred correlation of the differentiation of the models, is combined in a circular SOM for grouping and ordering the modelled expressions based on their temporal properties. In the proposed method the topology has been extended to temporal and cyclic ordering of the expressions. An example and a test on a microarray dataset are presented to demonstrate the advantages of the proposed method.
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References
Duggan, D., Bittner, M., Chen, Y., Meltzer, P., Trent, J.: Expresssion profiling using cDNA microarrays. Nature 21, 10–14 (1999)
Möller-Levet, C.S., Yin, H., Cho, K.-H., Wolkenhauer, O.: Modelling gene expression time-series with radial basis function neural networks. In: Proc. of the International Joint Conference on Neural Networks, Budapest, Hungary, vol. II, pp. 1191–1196 (2004)
Möller-Levet, C.S., Yin, H.: Modelling and clustering of gene expressions using RBFs and a shape similarity metric. In: Yang, Z.R., Yin, H., Everson, R.M. (eds.) IDEAL 2004. LNCS, vol. 3177, pp. 1–10. Springer, Heidelberg (2004)
Möller-Levet, C.S., Yin, H.: Modeling and analysis of gene expressions based on coexpressions. International Journal of Neural Systems (2005) (submitted)
Kohonen, T.: The self-organizing map. Proc. IEEE 78(9), 1464–1480 (1990)
Tavazoie, S., Hughes, J.D., Campbell, M.J., Cho, R.J., Church, G.M.: Systematic determination of genetic network architecture. Nature Genetics 22, 281–285 (1999)
Guimaraes, G., Moura-Pires, F.: An essay in classifying Self-Organizing Maps for temporal sequence processing. In: Allison, N., Yin, H., Allison, L., Slack, J. (eds.) Advances in Self-Organising Maps, pp. 259–260 (2001)
Kohonen, T.: The hypermap architecture. In: Kohonen, T., Mkisara, K., Simula, O., Kangas, J. (eds.) Artificial Neural Networks, vol. 2. Elsevier Science Publishers, The Netherlands (1991)
Chappel, G.J., Taylor, J.G.: The temporal Kohonen map. Neural Networks 6, 441–445 (1993)
Varsta, M., Millan, J.D.R., Heinkkonen, J.: A recurrent Self-Organizing Map for temporal sequence processing. In: Gerstner, W., Hasler, M., Germond, A., Nicoud, J.-D. (eds.) ICANN 1997. LNCS, vol. 1327, pp. 421–426. Springer, Heidelberg (1997)
Koskela, T., Varsta, M., Heikkonen, J., Kaski, K.: Temporal sequence processing using recurrent SOM. In: Second Int. Conf. on Knowldege-Based Intelligent Engineering Systems, April 1998, vol. 1, pp. 290–297 (1998)
Kemke, C., Wichert, A.: Hierarchical Self-Organizing Feature Map for speech recognition. In: Proc. of the World Congress on Neural Networks, Hillsdale, NY, USA, vol. 3, pp. 45–47 (1993)
Zhang, B.-T., Yang, J., Chi, S.W.: Self-Organizing latent lattice models for temporal gene expression profiling. Machine Learning 52, 67–89 (2003)
Chen, S., Cowan, C.F.N., Grant, P.M.: Orthogonal least squares learning algorithm for radial basis function networks. IEEE Transactions on Neural Networks 2(2), 302–309 (1991)
Schwarz, G.: Estimating the dimension of a model. The Annuals of Statistics 6(2), 461–464 (1978)
Yin, H., Allison, N.M.: A Bayesian self-oganising map for Gaussian mixture. IEE Proc.- Vision, Image and Signal Processing 148(4), 234–240 (2001)
Spellman, P.T., Sherlock, G., Zhang, M.Q., Iyer, V.R., Anders, K., Eisen, M.B., Brown, P.O., Botstein, D., Futcher, B.: Comprehensive identification of cell cycleregulated genes of yeast Saccharamyces cerevisiae by microarray hybridization. Molecular Biology of the Cell 9, 3273–3297 (1998)
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Möller-Levet, C.S., Yin, H. (2005). Circular SOM for Temporal Characterisation of Modelled Gene Expressions. In: Gallagher, M., Hogan, J.P., Maire, F. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2005. IDEAL 2005. Lecture Notes in Computer Science, vol 3578. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11508069_42
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DOI: https://doi.org/10.1007/11508069_42
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