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The triangle of biomedicine framework to analyze the impact of citations on the dissemination of categories in the PubMed database

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  • Pech, Gerson
  • Mreła, Aleksandra
  • Osińska, Veslava
  • Sokolov, Oleksandr

Abstract

Processing scientific literature metadata allows us to verify the assignment of articles to predefined categories. The Triangle of Biomedicine (TB) is a convenient space for considering the positions of biomedical papers according to human, animal, and molecular-cellular subdisciplines. The placement of PubMed papers in the TB using citations and, what is more interesting, the dynamics of the changing positions of papers (because of citations) have not been examined to date. This research presents a method for finding the article citation vectors of directly cited papers whose components are the MeSH terms shares offered by the PubMed database. The citation vectors allow finding the paper's position in the TB and comparing it with the original position of the publication. The analysis of sets of citation vectors enables locating their position on the translational line to show the distance between human research and animal-molecular studies. Moreover, applying information entropy, the dynamics of entropies in four different sets of articles are studied.

Suggested Citation

  • Pech, Gerson & Mreła, Aleksandra & Osińska, Veslava & Sokolov, Oleksandr, 2025. "The triangle of biomedicine framework to analyze the impact of citations on the dissemination of categories in the PubMed database," Journal of Informetrics, Elsevier, vol. 19(2).
  • Handle: RePEc:eee:infome:v:19:y:2025:i:2:s1751157725000124
    DOI: 10.1016/j.joi.2025.101648
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    References listed on IDEAS

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    1. 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.
    2. Zhao, Qihang & Feng, Xiaodong, 2022. "Utilizing citation network structure to predict paper citation counts: A Deep learning approach," Journal of Informetrics, Elsevier, vol. 16(1).
    3. 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.
    4. Travis A. Hoppe & Salsabil Arabi & B. Ian Hutchins, 2023. "Predicting substantive biomedical citations without full text," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 120(30), pages 2213697120-, July.
    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. Lutz Bornmann & Robin Haunschild & Rüdiger Mutz, 2021. "Growth rates of modern science: a latent piecewise growth curve approach to model publication numbers from established and new literature databases," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 8(1), pages 1-15, December.
    7. Dongyu Zang & Chunli Liu, 2023. "Correction: 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 2417-2418, April.
    8. Li, Xin & Tang, Xuli, 2021. "Characterizing interdisciplinarity in drug research: A translational science perspective," Journal of Informetrics, Elsevier, vol. 15(4).
    9. Kong, Ling & Zhang, Wei & Hu, Haotian & Liang, Zhu & Han, Yonggang & Wang, Dongbo & Song, Min, 2024. "Transdisciplinary fine-grained citation content analysis: A multi-task learning perspective for citation aspect and sentiment classification," Journal of Informetrics, Elsevier, vol. 18(3).
    10. Scott R. Rosas & Jeffrey T. Schouten & Marie T. Cope & Jonathan M. Kagan, 2013. "Modeling the dissemination and uptake of clinical trials results," Research Evaluation, Oxford University Press, vol. 22(3), pages 179-186, May.
    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. Mao, Jin & Liang, Zhentao & Cao, Yujie & Li, Gang, 2020. "Quantifying cross-disciplinary knowledge flow from the perspective of content: Introducing an approach based on knowledge memes," Journal of Informetrics, Elsevier, vol. 14(4).
    13. Vancraeynest, Bram & Pham, Hoang-Son & Ali-Eldin, Amr, 2024. "A new approach to computing the distances between research disciplines based on researcher collaborations and similarity measurement techniques," Journal of Informetrics, Elsevier, vol. 18(3).
    14. Ke, Qing, 2020. "An analysis of the evolution of science-technology linkage in biomedicine," Journal of Informetrics, Elsevier, vol. 14(4).
    15. 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.
    16. 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.
    17. W. Glänzel & A. Schubert & H. -J. Czerwon, 1999. "An item-by-item subject classification of papers published in multidisciplinary and general journals using reference analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 44(3), pages 427-439, March.
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