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 Zinovyev’s Elastic Net in an iterative way to find patterns of up-regulated genes. The new method proposed has been evaluated with up-regulated genes of the Escherichia Coli k12 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 87% of the up-regulated genes, compared to 65% of genes found by the SOM. A comparative analysis of Receiver Operating Characteristic with SOM shows that the proposed method is 12% more effective.
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Levano, M., Mellado, A. (2012). Elastic Nets for Detection of Up-Regulated Genes in Microarrays. In: Jayne, C., Yue, S., Iliadis, L. (eds) Engineering Applications of Neural Networks. EANN 2012. Communications in Computer and Information Science, vol 311. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32909-8_19
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DOI: https://doi.org/10.1007/978-3-642-32909-8_19
Publisher Name: Springer, Berlin, Heidelberg
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