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Tracking biomedical articles along the translational continuum: a measure based on biomedical knowledge representation

Author

Listed:
  • Xin Li

    (Tongji Medical College, Huazhong University of Science and Technology)

  • Xuli Tang

    (Central China Normal University)

  • Wei Lu

    (Wuhan University)

Abstract

Keeping track of translational research is essential to evaluating the performance of programs on translational medicine. Despite several indicators in previous studies, a consensus measure is still needed to represent the translational features of biomedical research at the article level. In this study, we first trained semantic representations of biomedical entities and documents (i.e., bio-entity2vec and bio-doc2vec) based on over 30 million PubMed articles. With these vectors, we then developed a new measure called Translational Progression (TP) for tracking biomedical articles along the translational continuum. We validated the effectiveness of TP from two perspectives (Clinical trial phase identification and ACH classification), which showed excellent consistency between TP and other indicators. Meanwhile, TP has several advantages. First, it can track the degree of translation of biomedical research dynamically and in real-time. Second, it is straightforward to interpret and operationalize. Third, it doesn’t require labor-intensive MeSH labeling and it is suitable for big scholarly data as well as papers that are not indexed in PubMed. In addition, we examined the translational progressions of biomedical research from three dimensions (including overall distribution, time, and research topic), which revealed three significant findings. The proposed measure in this study could be used by policymakers to monitor biomedical research with high translational potential in real-time and make better decisions. It can also be adopted and improved for other domains, such as physics or computer science, to assess the application value of scientific discoveries.

Suggested Citation

  • Xin Li & Xuli Tang & Wei Lu, 2023. "Tracking biomedical articles along the translational continuum: a measure based on biomedical knowledge representation," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(2), pages 1295-1319, February.
    • Handle: RePEc:spr:scient:v:128:y:2023:i:2:d:10.1007_s11192-022-04607-z
    DOI: 10.1007/s11192-022-04607-z
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    References listed on IDEAS

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    1. Ke, Qing, 2020. "The citation disadvantage of clinical research," Journal of Informetrics, Elsevier, vol. 14(1).
    2. E. Decullier & P. V. Tang & L. Huot & H. Maisonneuve, 2021. "Why an automated tracker finds poor sharing of clinical trial results for an academic sponsor: a bibliometric analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1239-1248, February.
    3. B Ian Hutchins & Matthew T Davis & Rebecca A Meseroll & George M Santangelo, 2019. "Predicting translational progress in biomedical research," PLOS Biology, Public Library of Science, vol. 17(10), pages 1-25, October.
    4. Paul Donner & Ulrich Schmoch, 2020. "The implicit preference of bibliometrics for basic research," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(2), pages 1411-1419, August.
    5. Li, Xin & Tang, Xuli & Cheng, Qikai, 2022. "Predicting the clinical citation count of biomedical papers using multilayer perceptron neural network," Journal of Informetrics, Elsevier, vol. 16(4).
    6. Li, Xin & Tang, Xuli, 2021. "Characterizing interdisciplinarity in drug research: A translational science perspective," Journal of Informetrics, Elsevier, vol. 15(4).
    7. Narin, Francis & Rozek, Richard P., 1988. "Bibliometric analysis of U.S. pharmaceutical industry research performance," Research Policy, Elsevier, vol. 17(3), pages 139-154, June.
    8. Grant Lewison & Guillermo Paraje, 2004. "The classification of biomedical journals by research level," Scientometrics, Springer;Akadémiai Kiadó, vol. 60(2), pages 145-157, June.
    9. Yeon Hak Kim & Aaron D. Levine & Eric J. Nehl & John P. Walsh, 2020. "A bibliometric measure of translational science," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2349-2382, December.
    10. Boyack, Kevin W. & Patek, Michael & Ungar, Lyle H. & Yoon, Patrick & Klavans, Richard, 2014. "Classification of individual articles from all of science by research level," Journal of Informetrics, Elsevier, vol. 8(1), pages 1-12.
    11. Du, Jian & Li, Peixin & Guo, Qianying & Tang, Xiaoli, 2019. "Measuring the knowledge translation and convergence in pharmaceutical innovation by funding-science-technology-innovation linkages analysis," Journal of Informetrics, Elsevier, vol. 13(1), pages 132-148.
    12. Francis Narin & Gabriel Pinski & Helen Hofer Gee, 1976. "Structure of the Biomedical Literature," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 27(1), pages 25-45, January.
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    Cited by:

    1. Meijun Liu & Sijie Yang & Yi Bu & Ning Zhang, 2023. "Female early-career scientists have conducted less interdisciplinary research in the past six decades: evidence from doctoral theses," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-16, December.

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