Investigation for Genetic Signature of Radiosensitivity – Data Analysis

  • Joanna Zyla
  • Paul Finnon
  • Robert Bulman
  • Simon Bouffler
  • Christophe Badie
  • Joanna Polanska
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 242)

Abstract

The aim of the study was to develop a data analysis strategy capable of discovering the genetic background of radiosensitivity. Radiosensitivity is the relative susceptibility of cells, tissues, organs or organisms to the harmful effect of radiation. Effects of radiation include the mutation of DNA specialy in genes responsible for DNA repair. Identification of polymorphisms and genes responsible for an organisms’ radiosensitivity increases the knowledge about the cell cycle and the mechanism of radiosensitivity, possibly providing the researchers with a better understanding of the process of carcinogenesis. To obtain this results, mathematical modeling and data mining methods were used.

Keywords

radiosensitivity data mining mathematical modeling 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Adzhubei, I.A., Schmidt, S., Peshkin, L., Ramensky, V.E., Gerasimova, A., Bork, P., Kondrashov, A.S., Sunyaev, S.R.: A method and server for predicting damaging missense mutations. Nature Methods 7(4), 248–249 (2010)CrossRefGoogle Scholar
  2. 2.
    Bromberg, Y., Rost, B.: SNAP: predict effect of non-synonymous polymorphisms on function. Nucleic Acids Research 35(11), 3823–3835 (2007)CrossRefGoogle Scholar
  3. 3.
    Capriotti, E., Calabrese, R., Casadio, R.: Predicting the insurgence of human genetic diseases associated to single point protein mutations with support vector machines and evolutionary information. Bioinformatics 22(22), 2729–2734 (2006)CrossRefGoogle Scholar
  4. 4.
    Ewans, W., Grant, G.: Statistical methods in bioinformatics. An introduction. Statistics for Biology and Health. Springer (2001)Google Scholar
  5. 5.
    Hastie, T., Tibshiranit, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer (2009)Google Scholar
  6. 6.
    Hubert, M., Vandervieren, E.: An adjusted boxplot for skewed distributions. Computational Statistics and Data Analysis 52(12), 5186–5201 (2008)MathSciNetCrossRefMATHGoogle Scholar
  7. 7.
    Karlin, S., Macken, C.A.: Assessment of inhomogeneities in E.Coli physical map. Nucleic Acids Research 19(15), 4241–4246 (1991)CrossRefGoogle Scholar
  8. 8.
    Kumur, P., Henikoff, S., Ng, P.C.: Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nature Protocols 4(9), 1073–1081 (2009)CrossRefGoogle Scholar
  9. 9.
    Szatkiewicz, J.P., Beane, G.L., Ding, Y., Hutchins, L., Padro-Manuel de Villena, F., Churchill, G.A.: An imputed genotype resource for the laboratory mouse. Mammalian Genome 19(3), 199–208 (2008)CrossRefGoogle Scholar
  10. 10.
    Thomas, P.D., Kejariwal, A.: Coding single-nucleotide polymorphisms associated with complex vs. mendelian disease: Evolutionary evidence for differences molecular effects. Proceedings of the National Academy of Sciences of the United States of America 101(43), 15,398–15,403 (2004)Google Scholar
  11. 11.
    Xue, Y., Ren, J., Gao, X., Jin, C., Wen, L., Yao, X.: GPS 2.0, a tool to predict kinase-specific phosphorylation sites in hierarchy. Molecular and Cellular Proteomics 7(9), 1598–1608 (2008)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Joanna Zyla
    • 1
  • Paul Finnon
    • 2
  • Robert Bulman
    • 2
  • Simon Bouffler
    • 2
  • Christophe Badie
    • 2
  • Joanna Polanska
    • 1
  1. 1.Institute of Automatic ControlSilesian University of TechnologyGliwicePoland
  2. 2.Center for Radiation Chemical and Enviromental HazardsPublic Health EnglandChiltonUnited Kingdom

Personalised recommendations