期刊
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
卷 146, 期 -, 页码 767-775出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.techfore.2018.08.002
关键词
Tech mining; Bibliometric analysis; Translational science; Gold nanostructures; Nano-medical research
资金
- Innovation Co-lab from Beijing Institute of Technology, China
- Georgia Tech, United States
- University of Manchester, United Kingdom
- US National Science Foundation, United States (NSF) [1064146]
- Natural Science Foundation of Shenzhen University, China (SZU) [2017013]
Clinical translation of scientific discoveries from bench to bedside is typically a challenging process with sporadic progress along its trajectory. Analyzing R&D can provide key intelligence on advancing biomedical innovation in target domains of interest. In this study, we explore the feasibility of using a streamlined tech mining approach for identification of translational indicators and potential opportunities, using observable markers extracted from selected research literature. We apply this strategy to analyze a set of 23,982 PubMed records that involved gold nanostructures (GNSs) research. Nine indicators are generated to assess what different GNSs research activities had achieved and to predict where GNSs research will likely go. We believe such analysis can provide useful translation intelligence for researchers, funding agencies, and pharmaceutical and biotech companies.
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