Networks of neuroinjury semantic predications to identify biomarkers for mild traumatic brain injury

• Published in: Journal of biomedical semantics Vol. 6; no. 1; p. 25
• Main Authors: Cairelli, Michael J, Fiszman, Marcelo, Zhang, Han, Rindflesch, Thomas C
• Format: Journal Article
• Language: English
• Published: England BioMed Central 18.05.2015
• Subjects: BASIC BIOLOGICAL SCIENCES; Degree centrality; Mathematical & Computational Biology; MATHEMATICS AND COMPUTING; Natural language processing; Semantic networks; Semantic predications; Traumatic brain injury; Semantic predications; Natural language processing; Semantic networks; Traumatic brain injury; Degree centrality
• ISSN: 2041-1480; 2041-1480
• DOI: 10.1186/s13326-015-0022-4

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. 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. 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. 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.