Syndromics: A Bioinformatics Approach for Neurotrauma Research

Adam R. Ferguson, Ellen D. Stück, Jessica L. Nielson

Research output: Contribution to journalReview articlepeer-review

20 Scopus citations

Abstract

Substantial scientific progress has been made in the past 50 years in delineating many of the biological mechanisms involved in the primary and secondary injuries following trauma to the spinal cord and brain. These advances have highlighted numerous potential therapeutic approaches that may help restore function after injury. Despite these advances, bench-to-bedside translation has remained elusive. Translational testing of novel therapies requires standardized measures of function for comparison across different laboratories, paradigms, and species. Although numerous functional assessments have been developed in animal models, it remains unclear how to best integrate this information to describe the complete translational "syndrome" produced by neurotrauma. The present paper describes a multivariate statistical framework for integrating diverse neurotrauma data and reviews the few papers to date that have taken an information-intensive approach for basic neurotrauma research. We argue that these papers can be described as the seminal works of a new field that we call "syndromics", which aim to apply informatics tools to disease models to characterize the full set of mechanistic inter-relationships from multi-scale data. In the future, centralized databases of raw neurotrauma data will enable better syndromic approaches and aid future translational research, leading to more efficient testing regimens and more clinically relevant findings.

Original languageEnglish (US)
Pages (from-to)438-454
Number of pages17
JournalTranslational Stroke Research
Volume2
Issue number4
DOIs
StatePublished - Dec 2011
Externally publishedYes

Bibliographical note

Funding Information:
Acknowledgments The authors wish to thank Dr. Michael S. Beattie, Dr. Jacqueline C. Bresnahan, and Dr. J. Russell Huie for comments on an earlier version of this manuscript. This work was supported by National Institutes of Health (NIH) National Institute of Neurological Disorders and Stroke (NINDS) grants R01-NS069537 and R01-NS067092 to ARF.

Keywords

  • Assessment
  • Multivariate statistics
  • Outcome measures
  • Spinal cord injury
  • Traumatic brain injury

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