Abstract
Recently, Self Organizing Maps have been a popular approach to analyze gene expression data. Our paper presents an improved SOM-based algorithm called Supervised Network Self Organizing Map (sNet-SOM), which overcomes the main drawbacks of existing techniques by adaptively determining the number of clusters with a dynamic extension process and integrating unsupervised and supervised learning in an effort to make use of prior knowledge on data. The process is driven by an inhomogeneous measure that balances unsupervised/supervised learning and model complexity criteria. Multiple models are dynamically constructed by the algorithm, each corresponding to an unsupervised/supervised balance, model selection criteria being used to select the optimum one. The design allows us to effectively utilize multiple functional class labeling.
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References
Eisen, M. B., Spellman P. T., Brown, P., Botstein D., (1998) Cluster Analysis and Display of Genome-wide Expression Patterns, Proc. Natl. Acad. Science, Vol. 95, pp. 14863–14868.
Hastie T., Tibshirani R., Botstein D., Brown P., (2001) Supervised Harvesting of expression trees, Genome Biology, 2(1).
Friedman, N., M. Linial, I. Nachman, and D’ Pe’er, (2000) Using Bayesian Networks to Analyze Expression Data, J. Comp. Bio. 7, pp. 601–620.
Kohonen T., (1997) Self-Organized Maps, Springer-Verlag, Second Edition.
Fritzke B., (1995) Growing Grid-a Self Organizing Network with Constant Neighborhood Range and Adaptation Strength, Neural Processing Letters, Vol. 2, No. 5, pp. 9–13.
Alakahoon D., Halgamuge S., Srinivasan B.(2000) Dyamic SOM with Controlled Growth for Knowledge Discovery, IEEE Trans. on Neural Networks, Vol.11, No.3, pp 601–614.
Brown M., Grundy W. N., Lin D., Cristianini N., Sugnet C. W., Furey T., Ares M., Haussler D., (1997) Knowledge-based Analysis of Microarray Gene Expression Data By Using Support Vector Machines, Proc. Natl. Acad. Science, Vol 97, No 1, pp. 262–267.
Brazma A., Jaak V., (2000) Gene Expression Data Analysis, FEBS Letters, 480, pp. 17–24.
Haykin S, (1999) Neural Networks, Prentice Hall International, Second Edition.
Sable, C. L., Hatzivassiloglou, V., (1999) Text-Based Approaches for the Categorization of Images, 3rd Annual Conf. on Research and Advanced Techn. for Digital Libraries, Paris.
Herrero J., Valencia A., and Dopazo J. (2001) A Hierarchical Unsupervised Growing Neural Network for Clustering Gene Expression Patterns. Bioinformatics, 17, 126–136.
Papadimitriou S., Mavroudi S., Vladutu L., Bezerianos A.(2001) Ischemia detection with a Self Organizing Map Supplemented by Supervised Learning, IEEE Trans. On Neural Networks, Vol.12, No.3, pp. 503–515.
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Mavroudi, S., Dragomir, A., Papadimitriou, S., Bezerianos, A. (2003). Integrating Supervised and Unsupervised Learning in Self Organizing Maps for Gene Expression Data Analysis. In: Kaynak, O., Alpaydin, E., Oja, E., Xu, L. (eds) Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003. ICANN ICONIP 2003 2003. Lecture Notes in Computer Science, vol 2714. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44989-2_32
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DOI: https://doi.org/10.1007/3-540-44989-2_32
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