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Data Mining Strategies Applied in Brain Injury Models

  • Stefania MondelloEmail author
  • Firas Kobeissy
  • Isaac Fingers
  • Zhiqun Zhang
  • Ronald L. Hayes
  • Kevin K. W. Wang
Chapter
  • 1.4k Downloads
Part of the Springer Optimization and Its Applications book series (SOIA, volume 65)

Abstract

Traumatic brain injury or traumatic head injury is characterized as a direct physical impact or trauma to the head, causing brain injury. It represents a major national health problem without a US Food and Drug Administration-approved therapy. The application of neuroproteomics/neurogenomics has revolutionized the characterization of protein/gene dynamics, leading to a greater understanding of post-injury biochemistry. Neuroproteomics and Neurogenomics fields have undertaken major advances in the area of neurotrauma research focusing on biomarker identification. Several candidate markers have been identified and are being evaluated for their efficacy as biological biomarkers utilizing these “omics approaches”. The identification of these differentially expressed candidate markers using these techniques is proving to be only the first step in the biomarker development process. However, to translate these findings into the clinic, data-driven development cycle incorporating data-mining steps for discovery, qualification, verification, and clinical validation is needed. Data mining steps extend beyond the collected data level into an integrated scheme of animal modeling, instrumentation, and functional data analysis. In this chapter, we provide an introductory review of data-mining/systems biology coupled approaches that have been applied to biomarker discovery and clinical validation; in addition, the need for strengthening the integral roles of these disciplines in establishing a comprehensive understanding of specific brain disorder and biomarker identification in general.

Keywords

Traumatic Brain Injury Brain Injury Severe Traumatic Brain Injury Clinical Validation Pretest Probability 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Stefania Mondello
    • 1
    Email author
  • Firas Kobeissy
    • 1
  • Isaac Fingers
    • 1
  • Zhiqun Zhang
    • 1
  • Ronald L. Hayes
    • 1
  • Kevin K. W. Wang
    • 1
  1. 1.Banyan BiomarkersAlachuaUSA

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