Visual Analysis Tool in Comparative Genomics

  • Juan F. De PazEmail author
  • Carolina Zato
  • María Abáigar
  • Ana Rodríguez-Vicente
  • Rocío Benito
  • Jesús M. Hernández
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 154)


Detecting regions with mutations associated with different pathologies is an important step in selecting relevant genes, proteins or diseases. The corresponding information of the mutations and genes is distributed in different public sources and databases, so it is necessary to use systems that can contrast different sources and select conspicuous information. This work presents a visual analysis tool that automatically selects relevant segments and the associated genes or proteins that could determine different pathologies.


Comparative Genomic Hybridization Gain Function Detect Copy Number Variation Comparative Genomic Hybridization Microarrays Relevant Segment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Aha, D., Kibler, D., Albert, M.K.: Instance-based learning algorithms. Machine Learning 6, 37–66 (1991)Google Scholar
  2. 2.
    Breiman, L., Fried, J.H., Olshen, R.A., Stone, C.J.: Classification and regression trees. Wadsworth International Group. (1984)Google Scholar
  3. 3.
    Chen, W., Erdogan, F., Ropers, H., Lenzner, S., Ullmann, R.: CGHPRO- a comprehensive data analysis tool for array CGH. BMC Bioinformatics 6(85), 299–303 (2005)Google Scholar
  4. 4.
    Cohen, W.W.: Fast effective rule induction. In: Proceedings of the 12th International Conference on Machine Learning, pp. 115–123. Morgan Kaufmann, San Francisco (1995)Google Scholar
  5. 5.
    De Haan, J.R., Bauerschmidt, S., van Schaik, R.C., Piek, E., Buydens, L.M.C., Wehrens, R.: Robust ANOVA for microarray data. Chemometrics and Intelligent Laboratory Systems 98(1), 38–44 (2009)CrossRefGoogle Scholar
  6. 6.
    Duda, R.O., Hart, P.: Pattern classification and Scene Analysis. John Wisley & Sons, New York (1973)zbMATHGoogle Scholar
  7. 7.
    Holmes, G., Hall, M., Prank, E.: Generating Rule Sets from Model Trees. Advanced Topics in Artificial Intelligence 1747(1999), 1–12 (2007)Google Scholar
  8. 8.
    Holte, R.C.: Very simple classification rules perform well on most commonly used datasets. Machine Learning 11, 63–91 (1993)zbMATHCrossRefGoogle Scholar
  9. 9.
    Kim, S.Y., Nam, S.W., Lee, S.H., Park, W.S., Yoo, N.J., Lee, J.Y., Chung, Y.J.: ArrayCyGHt, a web application for analysis and visualization of array-CGH data. Bioinformatics 21(10), 2554–2555 (2005)CrossRefGoogle Scholar
  10. 10.
    Lingjaerde, O.C., Baumbush, L.O., Liestol, K., Glad, I.K., Borresen-Dale, A.L.: CGH-explorer, a program for analysis of array-CGH data. Bioinformatics 21(6), 821–822 (2005)CrossRefGoogle Scholar
  11. 11.
    Mantripragada, K.K., Buckley, P.G., Diaz de Stahl, T., Dumanski, J.P.: Genomic microarrays in the spotlight. Trends Genetics 20(2), 87–94 (2004)CrossRefGoogle Scholar
  12. 12.
    Menten, B., Pattyn, F., De Preter, K., Robbrecht, P., Michels, E., Buysse, K., Mortier, G., De Paepe, A., van Vooren, S., Vermeesh, J., et al.: ArrayCGHbase: an analysis platform for comparative genomic hybridization microarrays. BMC Bioinformatics 6(124), 179–187 (2006)Google Scholar
  13. 13.
    Pinkel, D., Albertson, D.G.: Array comparative genomic hybridization and its applications in cancer. Nature Genetics 37, 11–17 (2005)CrossRefGoogle Scholar
  14. 14.
    Quinlan, J.R.: C4.5: Programs For Machine Learning. Morgan Kaufmann Publishers Inc. (1993)Google Scholar
  15. 15.
    Rosa, P., Viara, E., Hupé, P., Pierron, G., Liva, S., Neuvial, P., Brito, I., Lair, S., Servant, N., Robine, N., Manié, E., Brennetot, C., Janoueix-Lerosey, I., Raynal, V., Gruel, N., Rouveirol, C., Stransky, N., Stern, M., Delattre, O., Aurias, A., Radvanyi, F., Barillot, E.: Visualization and analysis of array-CGH, transcriptome and other molecular profiles. Bioinformatics 22(17), 2066–2073 (2006)CrossRefGoogle Scholar
  16. 16.
    Wang, P., Young, K., Pollack, J., Narasimham, B., Tibshirani, R.: A method for callong gains and losses in array CGH data. Biostat. 6(1), 45–58 (2005)zbMATHCrossRefGoogle Scholar
  17. 17.
    Xia, X., McClelland, M., Wang, Y.: WebArray, an online platform for microarray data analysis. BMC Bionformatics 6(306), 1737–1745 (2005)Google Scholar
  18. 18.
    Ylstra, B., Van den Ijssel, P., Carvalho, B., Meijer, G.: BAC to the future! or oligonucleotides: a perspective for microarray comparative genomic hybridization (array CGH). Nucleic Acids Research 34, 445–450 (2006)CrossRefGoogle Scholar
  19. 19.
    Yue, S., Wang, C.: The influence of serial correlation on the Mann-Whitney test for detecting a shift in median. Advances in Water Resources 25(3), 325–333 (2002)CrossRefGoogle Scholar
  20. 20.
  21. 21.
    Kruskal, W., Wallis, W.: Use of ranks in one-criterion variance analysis. Journal of American Statistics Association (1952)Google Scholar
  22. 22.
    Kenney, J.F., Keeping, E.S.: Mathematics of Statistics, Pt. 2, 2nd edn. Van Nostrand, Princeton (1951)Google Scholar
  23. 23.
    Van de Wiel, M.A., Kim, K.I., Vosse, S.J., Van Wieringen, W.N., Wilting, S.M., Ylstra, B.: CGHcall: calling aberrations for array CGH tumor profiles. Bioinformatics 23(7), 892–894 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Juan F. De Paz
    • 1
    Email author
  • Carolina Zato
    • 1
  • María Abáigar
    • 2
  • Ana Rodríguez-Vicente
    • 2
  • Rocío Benito
    • 2
  • Jesús M. Hernández
    • 2
    • 3
  1. 1.Department of Computer Science and AutomationUniversity of SalamancaSalamancaSpain
  2. 2.IBMCC, Cancer Research CenterUniversity of Salamanca-CSICSalamancaSpain
  3. 3.Servicio de Hematología, Hospital Universitario de SalamancaSalamancaSpain

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