Abstract
DNA analysis by microarrays is a powerful tool that allows replication of the RNA of hundreds of thousands of genes at the same time, generating a large amount of data in multidimensional space that must be analyzed using informatics tools. Various clustering techniques have been applied to analyze the microarrays, but they do not offer a systematic form of analysis. This paper proposes the use of Gorban’s Elastic Neural Net in an iterative way to find patterns of expressed genes. The new method proposed (Iterative Elastic Neural Net, IENN) has been evaluated with up-regulated genes of the Escherichia Coli bacterium and is compared with the Self-Organizing Maps (SOM) technique frequently used in this kind of analysis. The results show that the proposed method finds 86.7% of the up-regulated genes, compared to 65.2% of genes found by the SOM. A comparative analysis of Receiver Operating Characteristic (ROC) with SOM shows that the proposed method is 11.5% more effective.
This work was supported by projects FONDECYT 1050082, FONDECYT 1040354, FONDECYT 1040365 and MILENIO P02-054-F, Chile.
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References
Molla, M., Waddell, M., Page, D., Shavlik, J.: Using machine learning to design and interpret gene-expression microarrays. Artificial Intelligence Magazine 25, 23–44 (2004)
Kohonen, T.: Self-organizing maps. Springer, Berlin (2001)
Hautaniemi, S., Yli-Harja, O., Astola, J.: Analysis and visualization of gene expression microarray data in human cancer using self-organizing maps. Machine Learning 52, 45–66 (2003)
Tamayo, P., Slonim, D., Mesirov, J., Zhu, Q., Kitareewan, S., Dmitrovsky, E., Lander, E., Golub, T.: Interpreting patterns of expression with self-organizing maps: Methods and application to hematopoietic differentiation. Genetics 96, 2907–2912 (1999)
Monti, S., Tamayo, P., Mesirov, J., Golub, T.: Consensus clustering: a resampling-based method for class discovery and visualization of gene expression microarray data. Machine Learning 52, 91–118 (2003), http://www.springerlink.com/content/w12815643056/
Gorban, A., Zinovyev, A.: Method of elastic maps and its applications in data visualization and data modeling. International Journal of Computing Anticipatory Systems, CHAOS 12, 353–369 (2001)
Gorban, A., Zinovyev, A., Wunsch, D.: Application of the method of elastic maps in analysis of genetic texts. In: Proc. International Joint Conference on Neural Networks (IJCNN), Portland, Oregon, July 20-24 (2003)
Zinovyev, A.Y., Gorban, A., Popova, T.: Self-organizing approach for automated gene identification. Open Sys. and Information Dyn. 10, 321–333 (2003)
Liu, M., Durfee, T., Cabrera, T., Zhao, K., Jin, D., Blattner, F.: Global transcriptional programs reveal a carbon source foraging strategy by E. Coli. J. Biol. Chem. 280, 15921–15927 (2005)
Gorban, A., Zinovyev, A.: Elastic principal graphs and manifolds and their practical applications. Computing 75, 359–379 (2005)
Quackenbush, J.: Microarrays data normalization and transformation. Nature Reviews Genetics 2, 418–427 (2001)
Maulik, U., Bandyodpadhyay, S.: Performance evaluation of some clustering algorithms and validity indices. IEEE PAMI 24, 1650–1654 (2002)
Salas, R., Allende, H., Moreno, S., Saavedra, C.: Flexible architecture of self organizing maps for changing environments. In: Sanfeliu, A., Cortés, M.L. (eds.) CIARP 2005. LNCS, vol. 3773, pp. 642–653. Springer, Heidelberg (2005)
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Chacón, M., Lévano, M., Allende, H., Nowak, H. (2007). Detection of Gene Expressions in Microarrays by Applying Iteratively Elastic Neural Net. In: Beliczynski, B., Dzielinski, A., Iwanowski, M., Ribeiro, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2007. Lecture Notes in Computer Science, vol 4432. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71629-7_40
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DOI: https://doi.org/10.1007/978-3-540-71629-7_40
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