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

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 dif...

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Autores principales: Cairelli, Michael J, Fiszman, Marcelo, Zhang, Han, Rindflesch, Thomas C
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4436163/
https://www.ncbi.nlm.nih.gov/pubmed/25992264
http://dx.doi.org/10.1186/s13326-015-0022-4
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author Cairelli, Michael J
Fiszman, Marcelo
Zhang, Han
Rindflesch, Thomas C
author_facet Cairelli, Michael J
Fiszman, Marcelo
Zhang, Han
Rindflesch, Thomas C
author_sort Cairelli, Michael J
collection PubMed
description 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. CONCLUSION: 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.
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spelling pubmed-44361632015-05-20 Networks of neuroinjury semantic predications to identify biomarkers for mild traumatic brain injury Cairelli, Michael J Fiszman, Marcelo Zhang, Han Rindflesch, Thomas C J Biomed Semantics Research Article 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. CONCLUSION: 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. BioMed Central 2015-05-18 /pmc/articles/PMC4436163/ /pubmed/25992264 http://dx.doi.org/10.1186/s13326-015-0022-4 Text en © Cairelli et al.; licensee BioMed Central. 2015 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Cairelli, Michael J
Fiszman, Marcelo
Zhang, Han
Rindflesch, Thomas C
Networks of neuroinjury semantic predications to identify biomarkers for mild traumatic brain injury
title Networks of neuroinjury semantic predications to identify biomarkers for mild traumatic brain injury
title_full Networks of neuroinjury semantic predications to identify biomarkers for mild traumatic brain injury
title_fullStr Networks of neuroinjury semantic predications to identify biomarkers for mild traumatic brain injury
title_full_unstemmed Networks of neuroinjury semantic predications to identify biomarkers for mild traumatic brain injury
title_short Networks of neuroinjury semantic predications to identify biomarkers for mild traumatic brain injury
title_sort networks of neuroinjury semantic predications to identify biomarkers for mild traumatic brain injury
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4436163/
https://www.ncbi.nlm.nih.gov/pubmed/25992264
http://dx.doi.org/10.1186/s13326-015-0022-4
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