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Approaches are often used to establish correlation and significance of different components in the experimental data of systems medicine, whereas a network of semantic predications provided by SemRep naturally expresses the network of interactions postulated by systems approaches. Network filtering techniques are used to further suggest significance of the individual concepts and their relationships. By coupling components from these three fields, a novel method of biomarker discovery is proposed.Related workGlycineGlutamate Synthase STIMULATES INHIBITSGlutamate ASSOCIATED_WITHTBIFigure 1 Network of glutamate predications. Subject and object concepts are represented as nodes and predicates are represented as edges. Glutamate is common to all three predications and is, therefore, the most highly connected node in the network.Several manual reviews have been undertaken to survey potential biomarkers for TBI [39,40] and more specifically mTBI [41-43]. These authors search for citations specifically detailing clinical research of mTBI biomarkers and therefore contain only potential biomarkers that have already been investigated. Another limitation of the studies is the small number of citations reviewed (ranging from 26 [42] to 107 [43]) due to the limitations of human review. Although no automated detection of potential TBI biomarkers exists in the literature, there are automatic systems to help diagnose other disorders, for example diabetes and obesity [44]. Although not related to mTBI, there is research related to the literaturebased discovery of other types of interaction networksCairelli et al. Journal of Biomedical Semantics (2015) 6:Page 4 of(though not specifically for biomarkers). One automatically generates an interaction network detailing gene involvement in vaccine-related fever using 170,000 citations from a PubMed search and a vaccine--specific ontology [45]. Another used citations containing the PubMed MeSH term human and containing sentences related to interferon-gamma, from which relationships were extracted and ranked using graph metrics [46]. Jordan et al. [47] present a keyword search method for identifying putative biomarkers for breast and lung cancer by searching for genes and proteins associated with a biological fluid keyword and either cancer. However, none of this work has made use of semantic predications, as we have, in the formation of an interaction network. There is a large body of work on literature-based discovery approaches many of which use SemRep semantic predications [26,48-54]. These CapivasertibCapivasertib Abstract(s)">PubMed ID:https://www.ncbi.nlm.nih.gov/pubmed/16989806 applications to predict various phenomena such as interactions between genes and proteins [46,59], cancer treatments [60,61], adverse drug reactions [49], drug-drug interactions [50], drug repurposing [51,62], asthma gene associations [63], treatments for neovascularization in diabetic retinopathy [52], relations between psychiatric and somatic diseases [64], genes related to reactive oxygen species and diabetes [65], and mechanisms for sleep disturbance [25] and the obesity paradox [53].Figure 2 Overview of methodology. A PubMed search was used to find citations related to nervous system trauma (NST). SemRep extracted predications containing chemical substances from these citations, which were then arranged into a network. The network was filtered by connectedness (degree centrality) and frequency to provide a summary view of the most significant relat.

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