Center for Complex Network Research, Department of Physics, Northeastern University, Boston, Massachusetts, USA.
J Am Med Inform Assoc. 2019 Jun 1;26(6):516-523. doi: 10.1093/jamia/ocy177.
Translational science aims at "translating" basic scientific discoveries into clinical applications. The identification of translational science has practicality such as evaluating the effectiveness of investments made into large programs like the Clinical and Translational Science Awards. Despite several proposed methods that group publications-the primary unit of research output-into some categories, we still lack a quantitative way to place articles onto the full, continuous spectrum from basic research to clinical medicine.
I learn vector representations of controlled vocabularies assigned to Medline articles to obtain a translational axis that points from basic science to clinical medicine. The projected position of a term on the translational axis, expressed by a continuous quantity, indicates the term's "appliedness." The position of an article, determined by the average location over its terms, quantifies the degree of its appliedness, which I term the level score.
I validate the present method by comparing with previous techniques, showing excellent agreement yet uncovering significant variations of scores of articles in previously defined categories. The measure allows us to characterize the standing of journals, disciplines, and the entire biomedical literature along the basic-applied spectrum. Analysis on large-scale citation network reveals 2 main findings. First, direct citations mainly occurred between articles with similar scores. Second, shortest paths are more likely ended up with an article closer to the basic end of the spectrum, regardless of where the starting article is on the spectrum.
The proposed method provides a quantitative way to identify translational science.
转化科学旨在将基础科学发现“转化”为临床应用。转化科学的识别具有实用性，例如评估投入到临床和转化科学奖等大型计划中的投资的效果。尽管有几种将出版物（研究产出的主要单位）分组到某些类别的方法，但我们仍然缺乏一种将文章置于从基础研究到临床医学的完整连续光谱的定量方法。
我学习分配给 Medline 文章的受控词汇的向量表示，以获得从基础科学到临床医学的翻译轴。术语在翻译轴上的投影位置，用连续数量表示，指示术语的“应用程度”。文章的位置由其术语的平均位置确定，量化了其应用程度，我将其称为水平得分。
我通过与以前的技术进行比较验证了该方法，显示出极好的一致性，但揭示了以前定义的类别中文章得分的显著差异。该测量允许我们沿着基础应用光谱来描述期刊、学科以及整个生物医学文献的地位。对大规模引文网络的分析揭示了两个主要发现。首先，直接引文主要发生在具有相似得分的文章之间。其次，最短路径更有可能以更接近光谱基础端的文章结束，而不管起始文章在光谱上的位置如何。
所提出的方法提供了一种识别转化科学的定量方法。
