Bioinformatics for Traumatic Brain Injury: Proteomic Data Mining

Su-Shing Chen, William E. Haskins, Andrew K. Ottens, Ronald L. Hayes, Nancy Denslow and Kevin K. W. Wang
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Su-Shing Chen: University of Florida
William E. Haskins: University of Florida
Andrew K. Ottens: University of Florida
Ronald L. Hayes: University of Florida
Nancy Denslow: University of Florida
Kevin K. W. Wang: University of Florida

A chapter in Data Mining in Biomedicine, 2007, pp 363-387 from Springer

Abstract: 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.

Keywords: Traumatic Brain Injury; Protein Identification; Traumatic Brain Injury Patient; Protein Separation; Control Cortical Impact (search for similar items in EconPapers)
Date: 2007
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DOI: 10.1007/978-0-387-69319-4_20

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