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Citation algorithms for identifying research milestones driving biomedical innovation

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  • Jordan A. Comins

    (Virginia Tech Applied Research Corporation)

  • Loet Leydesdorff

    (University of Amsterdam)

Abstract

Scientific activity plays a major role in innovation for biomedicine and healthcare. For instance, fundamental research on disease pathologies and mechanisms can generate potential targets for drug therapy. This co-evolution is punctuated by papers which provide new perspectives and open new domains. Despite the relationship between scientific discovery and biomedical advancement, identifying these research milestones that truly impact biomedical innovation can be difficult and is largely based solely on the opinions of subject matter experts. Here, we consider whether a new class of citation algorithms that identify seminal scientific works in a field, Reference Publication Year Spectroscopy (RPYS) and multi-RPYS, can identify the connections between innovation (e.g., therapeutic treatments) and the foundational research underlying them. Specifically, we assess whether the results of these analytic techniques converge with expert opinions on research milestones driving biomedical innovation in the treatment of Basal Cell Carcinoma. Our results show that these algorithms successfully identify the majority of milestone papers detailed by experts (Wong and Dlugosz in J Investig Dermatol 134(e1):E18–E22, 2014)—thereby validating the power of these algorithms to converge on independent opinions of seminal scientific works derived by subject matter experts. These advances offer an opportunity to identify scientific activities enabling innovation in biomedicine.

Suggested Citation

  • Jordan A. Comins & Loet Leydesdorff, 2017. "Citation algorithms for identifying research milestones driving biomedical innovation," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(3), pages 1495-1504, March.
    • Handle: RePEc:spr:scient:v:110:y:2017:i:3:d:10.1007_s11192-016-2238-1
    DOI: 10.1007/s11192-016-2238-1
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    References listed on IDEAS

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    Cited by:

    1. Comins, Jordan A. & Carmack, Stephanie A. & Leydesdorff, Loet, 2018. "Patent citation spectroscopy (PCS): Online retrieval of landmark patents based on an algorithmic approach," Journal of Informetrics, Elsevier, vol. 12(4), pages 1223-1231.
    2. Mariani, Manuel Sebastian & Medo, Matúš & Lafond, François, 2019. "Early identification of important patents: Design and validation of citation network metrics," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 644-654.
    3. Xue Wang & Xuemei Yang & Jian Du & Xuwen Wang & Jiao Li & Xiaoli Tang, 2021. "A deep learning approach for identifying biomedical breakthrough discoveries using context analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 5531-5549, July.

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