Ld is principally set to provide a visually comprehensible network in the result, though such a visualization is not required. Reducing the threshold for inclusion would expand the list with significant compounds, including microRNA. When we filter by frequency of occurrence with a cutoff of 2 instances we eliminate 78.8 of the predications. This step risks eliminating predications that occur only once because they are completely new and have only been stated once. Figure 8a shows that 7.8 of predications were from citations in 2010. As seen in Figure 8b, when all predications that occur only one time are removed, the 2010 fraction increases to 7.9 . This shows that there is not a disproportionate elimination of predications from the most recent citations and the loss of unique predications due to their novelty likely plays a much less significant role than the elimination of inaccurate extraction by SemRep. On the other hand, as SemRep precision continues to improve, additional attention to date of publication may be required.Future directionsb)Figure 8 The relative distribution of predication frequency by year. a) All frequencies. b) Predications that have at least 2 occurrences. 78.8 of predications occurred only once.Creating a map of neural injury interactions offers significant potential for basic science research. Additionally, our refinement of the network to identify the most significant interactions according to their degree centrality and frequency facilitates the quick translation of published research data into clinical practice. The resulting compound list is clearly interesting in the context of clinical applicability and merits further study. This technique allows the investigation of potential biomarkers tobe focused, potentially reducing the wet-lab effort and reducing the time of assay development. Now that we have outlined a basic methodology, we would like to compare this method with various other methods combining information extraction and network analysis to understand the advantages and disadvantages to different approaches. Our current methodology can be expanded as noted above to include different subsets of substances in theCairelli et al. Journal of Biomedical Semantics (2015) 6:Page 12 offinal result. Additionally, this methodology is not limited to biomarker discovery but can also be applied to other areas of medical discovery, including novel therapeutic targets, drug repurposing, and others.Ridge Institute for Science and Education through an inter-agency agreement between the US Department of Energy and the National Library of Medicine. This study was supported in part by the Intramural Research Program of the National Capivasertib Institutes of Health, National Library of Medicine. Author details 1 National Institutes of Health, National Library of Medicine, 38A 9N912A, 8600 Rockville Pike, Bethesda, MD 20892, USA. 2Department of Medical Informatics, China Medical University, Shenyang, Liaoning 110001, China. Received: 4 April 2014 Accepted: 22 AprilConclusion We have explored the creation of a molecular interaction network that represents neural injury and is composed of semantic predications automatically extracted from the literature. We achieved our goal of providing substances with potential as biomarkers to support the diagnosis of mTBI. The methodology is based on a network of semantic predications representing the PubMed ID:https://www.ncbi.nlm.nih.gov/pubmed/7500280 interaction of substances observed subsequent to neural insult. Combining semantic predicat.