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Predicting translational progress in biomedical research

Author

Listed:
  • B Ian Hutchins
  • Matthew T Davis
  • Rebecca A Meseroll
  • George M Santangelo

Abstract

Fundamental scientific advances can take decades to translate into improvements in human health. Shortening this interval would increase the rate at which scientific discoveries lead to successful treatment of human disease. One way to accomplish this would be to identify which advances in knowledge are most likely to translate into clinical research. Toward that end, we built a machine learning system that detects whether a paper is likely to be cited by a future clinical trial or guideline. Despite the noisiness of citation dynamics, as little as 2 years of postpublication data yield accurate predictions about a paper’s eventual citation by a clinical article (accuracy = 84%, F1 score = 0.56; compared to 19% accuracy by chance). We found that distinct knowledge flow trajectories are linked to papers that either succeed or fail to influence clinical research. Translational progress in biomedicine can therefore be assessed and predicted in real time based on information conveyed by the scientific community’s early reaction to a paper.Fundamental scientific advances can take decades to translate into improvements in human health. This study shows that a machine learning model can accurately predict whether an article is likely to be cited by a future clinical trial or guideline, using as little as two years of post-publication citation data.

Suggested Citation

  • 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.
  • Handle: RePEc:plo:pbio00:3000416
    DOI: 10.1371/journal.pbio.3000416
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    References listed on IDEAS

    as
    1. B Ian Hutchins & Xin Yuan & James M Anderson & George M Santangelo, 2016. "Relative Citation Ratio (RCR): A New Metric That Uses Citation Rates to Measure Influence at the Article Level," PLOS Biology, Public Library of Science, vol. 14(9), pages 1-25, September.
    2. repec:nas:journl:v:115:y:2018:p:2329-2334 is not listed on IDEAS
    3. John P A Ioannidis, 2008. "Measuring Co-Authorship and Networking-Adjusted Scientific Impact," PLOS ONE, Public Library of Science, vol. 3(7), pages 1-8, July.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. 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).
    2. Dongyu Zang & Chunli Liu, 2023. "Exploring the clinical translation intensity of papers published by the world’s top scientists in basic medicine," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(4), pages 2371-2416, April.
    3. Zhifeng Liu & Chenlin Wang & Ruojia Wang, 2024. "From bench to bedside: determining what drives academic citations in clinical trials," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(11), pages 6813-6837, November.
    4. Li, Xin & Tang, Xuli & Lu, Wei, 2024. "Investigating clinical links in edge-labeled citation networks of biomedical research: A translational science perspective," Journal of Informetrics, Elsevier, vol. 18(3).
    5. 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.
    6. Li, Xin & Tang, Xuli, 2021. "Characterizing interdisciplinarity in drug research: A translational science perspective," Journal of Informetrics, Elsevier, vol. 15(4).
    7. 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.
    8. Higashide, Noriyuki & Zhang, Yi & Asatani, Kimitaka & Miura, Takahiro & Sakata, Ichiro, 2024. "Quantifying advances from basic research to applied research in material science," Technovation, Elsevier, vol. 135(C).
    9. Xin Li & Xuli Tang & Wei Lu, 2024. "How biomedical papers accumulated their clinical citations: a large-scale retrospective analysis based on PubMed," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(6), pages 3315-3339, June.

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