Cairelli Michael J, Fiszman Marcelo, Zhang Han, Rindflesch Thomas C
National Institutes of Health, National Library of Medicine, 38A 9N912A, 8600 Rockville Pike, Bethesda, MD 20892 USA.
Department of Medical Informatics, China Medical University, Shenyang, Liaoning 110001 China.
J Biomed Semantics. 2015 May 18;6:25. doi: 10.1186/s13326-015-0022-4. eCollection 2015.
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.
轻度创伤性脑损伤（mTBI）在军人、运动员以及全球普通人群中（主要因跌倒所致）具有较高的发生率。其后果可能包括一系列神经心理障碍。不幸的是，由于症状难以识别，此类神经损伤往往未被诊断出来，因此发现一种有效的生物标志物将极大地有助于诊断；然而，尚未确定单一的生物标志物。我们确定了几种身体物质作为生物标志物组合的潜在成分，以支持轻度创伤性脑损伤的诊断。
我们发现诊断生物标志物的方法结合了系统医学、自然语言处理和图论的理念与技术。我们创建了一个代表神经损伤的分子相互作用网络，该网络由从文献中自动提取的关系组成。我们检索与神经损伤相关的引文，并提取包含潜在生物标志物的关系（语义谓词）。在将所有关系链接在一起以创建一个代表神经损伤的网络后，我们通过关系频率和概念连通性对网络进行筛选，以将集合缩减为更易于管理的、具有更高研究价值物质的规模。
99437条相关引文产生了26441条独特关系。其中18085条关系包含一个潜在生物标志物作为主体或客体，共有6246个独特概念。通过图指标筛选后，集合缩减为1021条关系和49个独特概念，包括17种潜在生物标志物。
我们创建了一个包含从99437条引文中得出的物质的关系网络，并使用图指标进行筛选，以提供一组17种潜在生物标志物。我们讨论了其中几种物质（谷氨酸、葡萄糖和乳酸）之间的相互作用，作为比目前更有效诊断的基础。这种方法为将湿实验室研究的精力集中在那些作为mTBI生物标志物潜力最高的物质上提供了机会。
