title: Networks of neuroinjury semantic predications to identify biomarkers for mild traumatic brain injury.
abstract: OBJECTIVE
  Mild traumatic brain injury (mTBI) has high prevalence in the military, among athletes, and in the general population worldwide (largely due to falls). Consequences can include a range of neuropsychological disorders. Unfortunately, such neural injury often goes undiagnosed due to the difficulty in identifying symptoms, so the discovery of an effective biomarker would greatly assist diagnosis; however, no single biomarker has been identified. We identify several body substances as potential components of a panel of biomarkers to support the diagnosis of mild traumatic brain injury.
  
  
  METHODS
  Our approach to diagnostic biomarker discovery combines ideas and techniques from systems medicine, natural language processing, and graph theory. We create a molecular interaction network that represents neural injury and is composed of relationships automatically extracted from the literature. We retrieve citations related to neurological injury and extract relationships (semantic predications) that contain potential biomarkers. After linking all relationships together to create a network representing neural injury, we filter the network by relationship frequency and concept connectivity to reduce the set to a manageable size of higher interest substances.
  
  
  RESULTS
  99,437 relevant citations yielded 26,441 unique relations. 18,085 of these contained a potential biomarker as subject or object with a total of 6246 unique concepts. After filtering by graph metrics, the set was reduced to 1021 relationships with 49 unique concepts, including 17 potential biomarkers.
  
  
  CONCLUSIONS
  We created a network of relationships containing substances derived from 99,437 citations and filtered using graph metrics to provide a set of 17 potential biomarkers. We discuss the interaction of several of these (glutamate, glucose, and lactate) as the basis for more effective diagnosis than is currently possible. This method provides an opportunity to focus the effort of wet bench research on those substances with the highest potential as biomarkers for mTBI.
authors.affiliation: National Institutes of Health, National Library of Medicine, 38A 9N912A, 8600 Rockville Pike, Bethesda, MD 20892 USA.
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Cairelli
authors.affiliation: National Institutes of Health, National Library of Medicine, 38A 9N912A, 8600 Rockville Pike, Bethesda, MD 20892 USA.
authors.forename: Marcelo
authors.initials: M
authors.lastname: Fiszman
authors.affiliation: Department of Medical Informatics, China Medical University, Shenyang, Liaoning 110001 China.
authors.forename: Han
authors.initials: H
authors.lastname: Zhang
authors.affiliation: National Institutes of Health, National Library of Medicine, 38A 9N912A, 8600 Rockville Pike, Bethesda, MD 20892 USA.
authors.forename: Thomas C
authors.initials: TC
authors.lastname: Rindflesch
keywords: Degree centrality; Natural language processing; Semantic networks; Semantic predications; Traumatic brain injury