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Bioinformatics for Traumatic Brain Injury: Proteomic Data Mining

In: Data Mining in Biomedicine

Author

Listed:
  • Su-Shing Chen

    (University of Florida
    University of Florida)

  • William E. Haskins

    (University of Florida
    University of Florida
    University of Florida)

  • Andrew K. Ottens

    (University of Florida
    University of Florida
    University of Florida)

  • Ronald L. Hayes

    (University of Florida
    University of Florida
    University of Florida)

  • Nancy Denslow

    (University of Florida
    University of Florida)

  • Kevin K. W. Wang

    (University of Florida
    University of Florida
    University of Florida
    University of Florida)

Abstract

The importance of neuroproteomic studies is that they will help elucidate the currently poorly understood biochemical mechanisms or pathways underlying various psychiatric, neurological and neurodegenerative diseases. In this chapter, we focus on traumatic brain injury (TBI), a neurological disorder currently with no FDA approved therapeutic treatment. This chapter describes data mining strategies for proteomic analysis in traumatic brain injury research so that the diagnosis and treatment of TBI can be developed. We should note that brain imaging provides only coarse resolutions and proteomic analysis yields much finer resolutions to these two problems. Our data mining approach is not only at the collected data level, but rather an integrated scheme of animal modeling, instrumentation and data analysis.

Suggested Citation

  • Su-Shing Chen & William E. Haskins & Andrew K. Ottens & Ronald L. Hayes & Nancy Denslow & Kevin K. W. Wang, 2007. "Bioinformatics for Traumatic Brain Injury: Proteomic Data Mining," Springer Optimization and Its Applications, in: Panos M. Pardalos & Vladimir L. Boginski & Alkis Vazacopoulos (ed.), Data Mining in Biomedicine, pages 363-387, Springer.
    • Handle: RePEc:spr:spochp:978-0-387-69319-4_20
    DOI: 10.1007/978-0-387-69319-4_20
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