• DocumentCode
    1905680
  • Title

    Finding Characteristic Biology Patterns in Cancer Microarrays

  • Author

    Vass, Keith ; Grindrod, Peter ; Higham, Des ; Kalna, Gabriela ; Spence, Alastair

  • Author_Institution
    Beatson Inst. for Cancer Res., Strathclyde Univ., Glasgow
  • fYear
    2006
  • fDate
    9-9 Nov. 2006
  • Firstpage
    156
  • Lastpage
    166
  • Abstract
    Genetic and environmental differences are known to affect gene expression. The natural variance of expression of many genes affects the control system in any tissue. A finite set of controls must respond to these perturbations, causing regular patterns of altered gene expression characteristic of the system. In order to examine these ideas, a simple method the summarise and test the observed patterns is presented. In this paper, a classified microarray data is used to find relationships between genes. The quantitative data from microarrays can be classified as up or down, allowing estimation of significance by Monte Carlo methods. Spectral analysis and singular value decomposition were also used to study the gene expression pattern. Successive SVD vectors can identify obvious clusters of related genes.
  • Keywords
    Monte Carlo methods; cancer; genetics; medical diagnostic computing; molecular biophysics; singular value decomposition; spectral analysis; Monte Carlo methods; biology patterns; cancer microarrays; gene expression; singular value decomposition; spectral analysis; tissue;
  • fLanguage
    English
  • Publisher
    iet
  • Conference_Titel
    Signal Processing for Genomics, 2006. The Institution of Engineering and Technology Seminar on
  • Conference_Location
    Cambridge
  • Print_ISBN
    0-86341-716-7
  • Type

    conf

  • Filename
    4126038