Elastic Nets for Detection of Up-Regulated Genes in Microarrays

  • Marcos Levano
  • Alejandro Mellado
Part of the Communications in Computer and Information Science book series (CCIS, volume 311)


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.


Elastic net microarrays up-regulated genes clusters 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    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)Google Scholar
  2. 2.
    Kohonen, T.: Self-organizing maps. Springer, Berlin (2001)zbMATHCrossRefGoogle Scholar
  3. 3.
    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)CrossRefGoogle Scholar
  4. 4.
    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)Google Scholar
  5. 5.
    Zinovyev, A.Y., Gorban, A., Popova, T.: Self-organizing approach for automated gene identification. Open Sys. and Information Dyn. 10, 321–333 (2003)MathSciNetzbMATHCrossRefGoogle Scholar
  6. 6.
    Liu, M., Durfee, T., Cabrera, T., Zhao, K., Jin, D., Blattner, F.: Global transcriptional programs reveal a carbon source foraging strategy by Escherichia coli. J. Biol. Chem. 280, 15921–15927 (2005)CrossRefGoogle Scholar
  7. 7.
    Duda, R., Hart, P., Stork, D.: Pattern classification. John Wiley Sons Inc. (2001)Google Scholar
  8. 8.
    Maulik, U., Bandyodpadhyay, S.: Performance evaluation of some clustering algorithms and validity indices. IEEE PAMI 24, 1650–1654 (2002)CrossRefGoogle Scholar
  9. 9.
    Lévano, M., Nowak, H.: New aspects elastic nets of the elastic nets algorithm clusters analysis. J. Neural Computing & Applications 20(6), 835–850 (2011)CrossRefGoogle Scholar
  10. 10.
    Larson, J.W., Briggs, P.R., Tobis, M.: Block-Entropy Analysis of Climate Data. Procedia Computer Science 4, 1592–1601 (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Marcos Levano
    • 1
  • Alejandro Mellado
    • 1
  1. 1.Facultad de Ingeniería, Escuela de Ingeniería InformáticaUniversidad Católica de TemucoTemucoChile

Personalised recommendations