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Identifying translational indicators and technology opportunities for nanomedical research using tech mining: The case of gold nanostructures

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  • Ma, Jing
  • Abrams, Natalie F.
  • Porter, Alan L.
  • Zhu, Donghua
  • Farrell, Dorothy

Abstract

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.

Suggested Citation

  • Ma, Jing & Abrams, Natalie F. & Porter, Alan L. & Zhu, Donghua & Farrell, Dorothy, 2019. "Identifying translational indicators and technology opportunities for nanomedical research using tech mining: The case of gold nanostructures," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 767-775.
    • Handle: RePEc:eee:tefoso:v:146:y:2019:i:c:p:767-775
    DOI: 10.1016/j.techfore.2018.08.002
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    References listed on IDEAS

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    Cited by:

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    3. Ren, Haiying & Zhao, Yuhui, 2021. "Technology opportunity discovery based on constructing, evaluating, and searching knowledge networks," Technovation, Elsevier, vol. 101(C).
    4. Jing Ma & Yaohui Pan & Chih-Yi Su, 2022. "Organization-oriented technology opportunities analysis based on predicting patent networks: a case of Alzheimer’s disease," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5497-5517, September.
    5. Wang, Chang & Geng, Hongjun & Sun, Rui & Song, Huiling, 2022. "Technological potential analysis and vacant technology forecasting in the graphene field based on the patent data mining," Resources Policy, Elsevier, vol. 77(C).
    6. Shin, Hyunjin & Woo, Hyun Goo & Sohn, Kyung-Ah & Lee, Sungjoo, 2023. "Comparing research trends with patenting activities in the biomedical sector: The case of dementia," Technological Forecasting and Social Change, Elsevier, vol. 195(C).

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