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

Journal of Biomedical Semantics

Thomas C Rindflesch¹  Han Zhang²  Marcelo Fiszman¹  Michael J Cairelli¹ 

[1] National Institutes of Health, National Library of Medicine, 38A 9N912A, 8600 Rockville Pike, Bethesda 20892, MD, USA;Department of Medical Informatics, China Medical University, Shenyang 110001, Liaoning, China

关键词: Traumatic brain injury;    Degree centrality;    Natural language processing;    Semantic networks;    Semantic predications;   

Others  :  1209192
DOI  :  10.1186/s13326-015-0022-4

 received in 2014-04-04, accepted in 2015-04-22,  发布年份 2015

【 摘 要 】

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.

【 授权许可】

   
2015 Cairelli et al.; licensee BioMed Central.

【 图 表 】

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

Figure 7.

Figure 8.

【 参考文献 】

• [1]West TD, Marsh JO, Schwarz JJH, Bacchus J, Fisher A, Jumper JP, et al. Rebuilding the trust: report on rehabilitative care and administrative processes at Walter Reed Army Medical Center and National Naval Medical Center. Alexandria, VA; 2007.
• [2]Hart J, Kraut MA, Womack KB, Strain J, Didehbani N, Bartz E, et al.: Neuroimaging of cognitive dysfunction and depression in aging retired national football league players: a cross-sectional study. JAMA Neurol 2013, 70(3):1-10.
• [3]Pellman EJ, Viano DC: National Football League’s Committee on Mild Traumatic Brain Injury: Concussion in professional football: summary of the research conducted by the National Football League’s Committee on Mild Traumatic Brain Injury. Neurosurg Focus 2006, 21(4):E12.
• [4]Jordan BD: Chronic traumatic brain injury associated with boxing. Semin Neurol 2000, 20(2):179-85.
• [5]Guskiewicz KM, McCrea M, Marshall SW, Cantu RC, Randolph C, Barr W, et al.: Cumulative effects associated with recurrent concussion in collegiate football players: The NCAA Concussion Study. JAMA 2003, 290(19):2549-55.
• [6]Hollis SJ, Stevenson MR, McIntosh AS, Shores EA, Collins MW, Taylor CB: Incidence, risk, and protective factors of mild traumatic brain injury in a cohort of Australian nonprofessional male rugby players. Am J Sports Med 2009, 37(12):2328-33.
• [7]Biasca N, Maxwell WL: Minor traumatic brain injury in sports: a review in order to prevent neurological sequelae. Prog Brain Res 2007, 161:263-91.
• [8]Levy ML, Kasasbeh AS, Baird LC, Amene C, Skeen J, Marshall L: Concussions in soccer: a current understanding. World Neurosurg 2012, 78(5):535-44.
• [9]Faul M, Xu L, Wald MM, Coronado VG: Traumatic brain injury in the United States: emergency department visits, hospitalizations, and deaths. Centers for Disease Control and Prevention, National Center for Injury Prevention and Control, Atlanta, Georgia; 2010.
• [10]Cassidy JD, Carroll LJ, Peloso PM, Borg J, von Holst H, Holm L, et al.: Incidence, risk factors and prevention of mild traumatic brain injury: results of the WHO Collaborating Centre Task Force on Mild Traumatic Brain Injury. J Rehabil Med 2004, 43(Suppl):28-60.
• [11]Kiraly M, Kiraly SJ: Traumatic brain injury and delayed sequelae: a review–traumatic brain injury and mild traumatic brain injury (concussion) are precursors to later-onset brain disorders, including early-onset dementia. Sci World J 2007, 7:1768-76.
• [12]Rutherford GW, Corrigan JD: Long-term consequences of traumatic brain injury. J Head Trauma Rehabil 2009, 24(6):421-3.
• [13]Timmons SD: An update on traumatic brain injuries. J Neurosurg Sci 2012, 56(3):191-202.
• [14]Kitano H: Systems biology: a brief overview. Science 2002, 295(5560):1662-4.
• [15]Wang K, Lee I, Carlson G, Hood L, Galas D: Systems biology and the discovery of diagnostic biomarkers. Dis Markers 2010, 28(4):199-207.
• [16]Nadkarni PM, Ohno-Machado L, Chapman WW: Natural language processing: an introduction. J Am Med Inform Assoc 2011, 18(5):544-51.
• [17]Rindflesch TC, Fiszman M: The interaction of domain knowledge and linguistic structure in natural language processing: interpreting hypernymic propositions in biomedical text. J Biomed Inform 2003, 36(6):462-77.
• [18]Aronson AR, Lang FM: An overview of MetaMap: historical perspective and recent advances. J Am Med Inform Assoc 2010, 17(3):229-36.
• [19]Bodenreider O: The Unified Medical Language System (UMLS): integrating biomedical terminology. Nucleic Acids Res 2004, 32(Database issue):D267-70.
• [20]Kilicoglu H, Shin D, Fiszman M, Rosemblat G, Rindflesch TC: SemMedDB: a PubMed-scale repository of biomedical semantic predications. Bioinformatics 2012, 28(23):3158-60.
• [21]Hristovski D, Rindflesch T, Peterlin B: Using Literature-based Discovery to Identify Novel Therapeutic Approaches. Cardiovasc Hematol Agents Med Chem 2012, 11(1):14-24.
• [22]Liu Y, Bill R, Fiszman M, Rindflesch T, Pedersen T, Melton GB, et al.: Using SemRep to Label Semantic Relations Extracted from Clinical Text. AMIA Annu Symp Proc 2012, 2012:587-95.
• [23]Cohen T, Widdows D, Schvaneveldt RW, Davies P, Rindflesch TC: Discovering discovery patterns with predication-based Semantic Indexing. J Biomed Inform 2012, 45(6):1049-65.
• [24]Goodwin JC, Cohen T, Rindflesch TC. Discovery by scent: Closed literature-based discovery system based on the information foraging theory. Presented at the IEEE First International Workshop on the role of Semantic Web in Literature-Based Discovery: 2012, Philadelphia.
• [25]Miller CM, Rindflesch TC, Fiszman M, Hristovski D, Shin D, Rosemblat G, et al.: A closed literature-based discovery technique finds a mechanistic link between hypogonadism and diminished sleep quality in aging men. Sleep 2012, 35(2):279-85.
• [26]Hristovski D, Friedman C, Rindflesch TC, Peterlin B: Exploiting semantic relations for literature-based discovery. AMIA Annu Symp Proc 2006, 2006:349-53.
• [27]Jonnalagadda SR, Del Fiol G, Medlin R, Weir C, Fiszman M, Mostafa J, et al.: Automatically extracting sentences from Medline citations to support clinicians’ information needs. J Am Med Inform Assoc 2012, 20(5):995-1000.
• [28]Zeng QT, Redd D, Rindflesch T, Nebeker J: Synonym, topic model and predicate-based query expansion for retrieving clinical documents. AMIA Annu Symp Proc 2012, 2012:1050-9.
• [29]Shang Y, Li Y, Lin H, Yang Z: Enhancing biomedical text summarization using semantic relation extraction. PLoS One 2011, 6(8):e23862.
• [30]He Y, Kayaalp M: Biological entity recognition with conditional random fields. AMIA Annu Symp Proc 2008, 2008:293-7.
• [31]Hristovski D, Peterlin B, Mitchell JA, Humphrey SM: Using literature-based discovery to identify disease candidate genes. Int J Med Inform 2005, 74(2–4):289-98.
• [32]Bray BE, Fiszman M, Shin D, Rindflesch TC: Using semantic predications to characterize the clinical cardiovascular literature. AMIA Annu Symp Proc 2008, 2008:887.
• [33]Fiszman M, Ortiz E, Bray BE, Rindflesch TC: Semantic processing to support clinical guideline development. AMIA Annu Symp Proc 2008, 2008:187-91.
• [34]Hristovski D, Kastrin A, Peterlin B, Rindflesch TC: Semantic relations for interpreting DNA microarray data. AMIA Annu Symp Proc 2009, 2009:255-9.
• [35]Hristovski D, Revere D, Bugni P, Fuller S, Friedman C, Rindflesch TC: Towards automatic extraction of research findings from the literature. AMIA Annu Symp Proc 2007, 2007:979.
• [36]Cohen T, Schvaneveldt RW, Rindflesch TC: Predication-based semantic indexing: permutations as a means to encode predications in semantic space. AMIA Annu Symp Proc 2009, 2009:114-8.
• [37]Zhang X, Cheng G, Qu Y: Ontology summarization based on RDF sentence graph. Proceedings of the 16th international conference on world wide web 2007, 707-16.
• [38]Zhang H, Fiszman M, Shin D, Miller CM, Rosemblat G, Rindflesch TC: Degree centrality for semantic abstraction summarization of therapeutic studies. J Biomed Inform 2011, 44(5):830-8.
• [39]Forde CT, Karri SK, Young AM, Ogilvy CS: Predictive markers in traumatic brain injury: opportunities for a serum biosignature. Br J Neurosurg 2014, 28(1):8-15.
• [40]Strathmann FG, Schulte S, Goerl K, Petron DJ: Blood-based biomarkers for traumatic brain injury: Evaluation of research approaches, available methods and potential utility from the clinician and clinical laboratory perspectives. Clin Biochem 2014, 47(10-11):876-88.
• [41]Yokobori S, Hosein K, Burks S, Sharma I, Gajavelli S, Bullock R: Biomarkers for the clinical differential diagnosis in traumatic brain injury–a systematic review. CNS Neurosci Ther 2013, 19(8):556-65.
• [42]Di Battista AP, Rhind SG, Baker AJ: Application of blood-based biomarkers in human mild traumatic brain injury. Front Neurol 2013, 4:44.
• [43]Jeter CB, Hergenroeder GW, Hylin MJ, Redell JB, Moore AN, Dash PK: Biomarkers for the diagnosis and prognosis of mild traumatic brain injury/concussion. J Neurotrauma 2013, 30(8):657-70.
• [44]Trugenberger CA, Wälti C, Peregrim D, Sharp ME, Bureeva S: Discovery of novel biomarkers and phenotypes by semantic technologies. BMC Bioinformatics 2013, 14:51. BioMed Central Full Text
• [45]Hur J, Ozgür A, Xiang Z, He Y: Identification of fever and vaccine-associated gene interaction networks using ontology-based literature mining. J Biomed Semantics 2012, 3(1):18. BioMed Central Full Text
• [46]Ozgür A, Xiang Z, Radev DR, He Y: Literature-based discovery of IFN-gamma and vaccine-mediated gene interaction networks. J Biomed Biotechnol 2010, 2010:426479.
• [47]Jordan R, Visweswaran S, Gopalakrishnan V: Semi-automated literature mining to identify putative biomarkers of disease from multiple biofluids. J Clin Bioinforma 2014, 4:13. BioMed Central Full Text
• [48]Chen G, Cairelli MJ, Kilicoglu H, Shin D, Rindflesch TC: Augmenting microarray data with literature-based knowledge to enhance gene regulatory network inference. PLoS Comput Biol 2014, 10(6):e1003666.
• [49]Shang N, Xu H, Rindflesch TC, Cohen T: Identifying plausible adverse drug reactions using knowledge extracted from the literature. J Biomed Inform 2014, 52:293-310.
• [50]Zhang R, Cairelli MJ, Fiszman M, Rosemblat G, Kilicoglu H, Rindflesch TC, et al.: Using semantic predications to uncover drug-drug interactions in clinical data. J Biomed Inform 2014, 49:134-47.
• [51]Ahlers CB, Hristovski D, Kilicoglu H, Rindflesch TC: Using the literature-based discovery paradigm to investigate drug mechanisms. AMIA Annu Symp Proc 2007, 11:6-10.
• [52]Maver A, Hristovski D, Rindflesch TC, Peterlin B: Integration of data from omic studies with the literature-based discovery towards identification of novel treatments for neovascularization in diabetic retinopathy. Biomed Res Int 2013, 2013:848952.
• [53]Cairelli MJ, Miller CM, Fiszman M, Workman TE, Rindflesch TC: Semantic MEDLINE for discovery browsing: using semantic predications and the literature-based discovery paradigm to elucidate a mechanism for the obesity paradox. AMIA Annu Symp Proc 2013, 2013:164-73.
• [54]Cameron D, Bodenreider O, Yalamanchili H, Danh T, Vallabhaneni S, Thirunarayan K, et al.: A graph-based recovery and decomposition of Swanson’s hypothesis using semantic predications. J Biomed Inform 2013, 46(2):238-51.
• [55]Weeber M, Kors JA, Mons B: Online tools to support literature-based discovery in the life sciences. Brief Bioinform 2005, 6(3):277-86.
• [56]Li C, Jimeno-Yepes A, Arregui M, Kirsch H, Rebholz-Schuhmann D: PCorral—interactive mining of protein interactions from MEDLINE. Database 2013, 2013:bat030.
• [57]Kastrin A, Hristovski D: A fast document classification algorithm for gene symbol disambiguation in the BITOLA literature-based discovery support system. AMIA Annu Symp Proc 2008, 6:358-62.
• [58]Gabetta M, Larizza C, Bellazzi R: A Unified Medical Language System (UMLS) based system for Literature-Based Discovery in medicine. Stud Health Technol Inform 2013, 192:412-6.
• [59]van Haagen HH, ‘t Hoen PA, Mons B, Schultes EA: Generic information can retrieve known biological associations: implications for biomedical knowledge discovery. PLoS One 2013, 8(11):e78665.
• [60]Cohen T, Widdows D, Stephan C, Zinner R, Kim J, Rindflesch T, et al.: Predicting high-throughput screening results with scalable literature-based discovery methods. CPT Pharmacometrics Syst Pharmacol 2014, 3:e140.
• [61]Dong W, Liu Y, Zhu W, Mou Q, Wang J, Hu Y: Simulation of Swanson’s literature-based discovery: anandamide treatment inhibits growth of gastric cancer cells in vitro and in silico. PLoS One 2014, 9(6):e100436.
• [62]Andronis C, Sharma A, Virvilis V, Deftereos S, Persidis A: Literature mining, ontologies and information visualization for drug repurposing. Brief Bioinform 2011, 12(4):357-68.
• [63]Liang R, Wang L, Wang G: New insight into genes in association with asthma: literature-based mining and network centrality analysis. Chin Med J (Engl) 2013, 126(13):2472-9.
• [64]Vos R, Aarts S, van Mulligen E, Metsemakers J, van Boxtel MP, Verhey F, et al.: Finding potentially new multimorbidity patterns of psychiatric and somatic diseases: exploring the use of literature-based discovery in primary care research. J Am Med Inform Assoc 2014, 21(1):139-45.
• [65]Hur J, Sullivan KA, Schuyler AD, Hong Y, Pande M, States DJ, et al.: Literature-based discovery of diabetes- and ROS-related targets. BMC Med Genomics 2010, 3:49. BioMed Central Full Text
• [66]Cytoscape: Network data integration, analysis, and visualization in a box. www.cytoscape.org.
• [67]Siman R, Toraskar N, Dang A, McNeil E, McGarvey M, Plaum J, et al.: A panel of neuron-enriched proteins as markers for traumatic brain injury in humans. J Neurotrauma 2009, 26(11):1867-77.
• [68]Bareyre FM, Saatman KE, Helfaer MA, Sinson G, Weisser JD, Brown AL, et al.: Alterations in ionized and total blood magnesium after experimental traumatic brain injury: relationship to neurobehavioral outcome and neuroprotective efficacy of magnesium chloride. J Neurochem 1999, 73(1):271-80.
• [69]Stover JF, Morganti-Kosmann MC, Lenzlinger PM, Stocker R, Kempski OS, Kossmann T: Glutamate and taurine are increased in ventricular cerebrospinal fluid of severely brain-injured patients. J Neurotrauma 1999, 16(2):135-42.
• [70]Lakshmanan R, Loo JA, Drake T, Leblanc J, Ytterberg AJ, McArthur DL, et al.: Metabolic crisis after traumatic brain injury is associated with a novel microdialysis proteome. Neurocrit Care 2010, 12(3):324-36.
• [71]Gasparovic C, Yeo R, Mannell M, Ling J, Elgie R, Phillips J, et al.: Neurometabolite concentrations in gray and white matter in mild traumatic brain injury: an 1H-magnetic resonance spectroscopy study. J Neurotrauma 2009, 26(10):1635-43.
• [72]Yeo RA, Gasparovic C, Merideth F, Ruhl D, Doezema D, Mayer AR: A longitudinal proton magnetic resonance spectroscopy study of mild traumatic brain injury. J Neurotrauma 2011, 28(1):1-11.
• [73]Paparrigopoulos T, Melissaki A, Tsekou H, Efthymiou A, Kribeni G, Baziotis N, et al.: Melatonin secretion after head injury: a pilot study. Brain Inj 2006, 20(8):873-8.
• [74]Seifman MA, Adamides AA, Nguyen PN, Vallance SA, Cooper DJ, Kossmann T, et al.: Endogenous melatonin increases in cerebrospinal fluid of patients after severe traumatic brain injury and correlates with oxidative stress and metabolic disarray. J Cereb Blood Flow Metab 2008, 28(4):684-96.
• [75]Gallagher CN, Carpenter KL, Grice P, Howe DJ, Mason A, Timofeev I, et al.: The human brain utilizes lactate via the tricarboxylic acid cycle: a 13C-labelled microdialysis and high-resolution nuclear magnetic resonance study. Brain 2009, 132(Pt 10):2839-49.
• [76]Defazio MV, Rammo RA, Robles JR, Bramlett HM, Dietrich WD, Bullock MR: The potential utility of blood-derived biochemical markers as indicators of early clinical trends following severe traumatic brain injury. World Neurosurg 2013, 81(1):151-8.
• [77]Lo TY, Jones PA, Minns RA: Pediatric brain trauma outcome prediction using paired serum levels of inflammatory mediators and brain-specific proteins. J Neurotrauma 2009, 26(9):1479-87.