IDEAS home Printed from https://ideas.repec.org/h/spr/spochp/978-1-4614-2107-8_8.html
   My bibliography  Save this book chapter

Data Mining for Cancer Biomarkers with Raman Spectroscopy

In: Data Mining for Biomarker Discovery

Author

Listed:
  • Michael B. Fenn

    (University of Florida
    University of Florida)

  • Vijay Pappu

    (University of Florida
    University of Florida)

Abstract

Raman spectroscopy has the potential to play an important role in the diagnosis and treatment of cancer as a unique type of biomarker technology. Raman spectra can provide a collective picture of the overall composition of biological samples as well as highly sensitive, targeting of specific biomolecular moieties depending upon the application. In the field of Oncology, Raman Spectroscopy can help in the identification of biomarkers for use in drug discovery, cancer-risk assessment, histopathology, and in vivo clinical applications. Continued advancements to data analysis techniques could prove vital in realization of such biomedical applications. This chapter provides a brief overview of some of the more common data analysis methods as well as outlines several of the technical challenges encountered in the implementation of these methods. The development of standardized data techniques with incorporation into fully functional integrated software platforms will also be necessary for clinical applications in future.

Suggested Citation

  • Michael B. Fenn & Vijay Pappu, 2012. "Data Mining for Cancer Biomarkers with Raman Spectroscopy," Springer Optimization and Its Applications, in: Panos M. Pardalos & Petros Xanthopoulos & Michalis Zervakis (ed.), Data Mining for Biomarker Discovery, edition 127, chapter 0, pages 143-168, Springer.
    • Handle: RePEc:spr:spochp:978-1-4614-2107-8_8
    DOI: 10.1007/978-1-4614-2107-8_8
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Panagopoulos, Orestis P. & Pappu, Vijay & Xanthopoulos, Petros & Pardalos, Panos M., 2016. "Constrained subspace classifier for high dimensional datasets," Omega, Elsevier, vol. 59(PA), pages 40-46.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:spochp:978-1-4614-2107-8_8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.