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Indexing important drugs from medical literature

Author

Listed:
  • Riad Alharbey

    (University of Jeddah)

  • Jong In Kim

    (Yonsei University)

  • Ali Daud

    (University of Jeddah
    Zhejiang Ocean University)

  • Min Song

    (Yonsei University)

  • Abdulrahman A. Alshdadi

    (University of Jeddah)

  • Malik Khizar Hayat

    (The University of Haripur
    Macquarie University)

Abstract

Health maintenance is one of the foremost pillars of human society which needs up-to-date solutions to medical problems. The advancement in the biomedical field has intensified the—information load that exists in the form of clinic reports, research papers, or lab tests, etc. Extracting meaningful insights from this corpus is equally important as its progress—to make it valuable for recent medicine. In terms of biomedical text mining, the areas explored include protein–protein interactions, entity-relationship detection, and so on. The biomedical effects of drugs have significance when administered to a living organism. Biomedical literature is not widely explored in terms of gene-drug relations, hence needs investigation. Indexing methods can be used for ranking gene-drug relations. In scientific literature, Hirsch’s the h-index is usually used to quantify the impact of an individual author. Likewise, in this research, we propose the Drug-Index, a quantifiable measure that can be used to detect gene-drug relations. It is useful in drug discovery, diagnosing, personalized treatment using suitable drugs for relevant genes. For a strong and reliable gene-drug relationship discovery, drugs are extracted from a subset of MEDLINE—a bibliographic medical database. The detected drugs are verified from the PharmacoGenomics KnowledgeBase (PharmGKB)—a publicly available medical knowledgebase by Stanford University.

Suggested Citation

  • Riad Alharbey & Jong In Kim & Ali Daud & Min Song & Abdulrahman A. Alshdadi & Malik Khizar Hayat, 2022. "Indexing important drugs from medical literature," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(5), pages 2661-2681, May.
    • Handle: RePEc:spr:scient:v:127:y:2022:i:5:d:10.1007_s11192-022-04340-7
    DOI: 10.1007/s11192-022-04340-7
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    References listed on IDEAS

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    1. Xiaoying Li & Suyuan Peng & Jian Du, 2021. "Towards medical knowmetrics: representing and computing medical knowledge using semantic predications as the knowledge unit and the uncertainty as the knowledge context," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 6225-6251, July.
    2. Kathleen M. Giacomini & Ronald M. Krauss & Dan M. Roden & Michel Eichelbaum & Michael R. Hayden & Yusuke Nakamura, 2007. "When good drugs go bad," Nature, Nature, vol. 446(7139), pages 975-977, April.
    3. Xuefeng Wang & Shuo Zhang & Yao Wu & Xuemei Yang, 2021. "Revealing potential drug-disease-gene association patterns for precision medicine," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 3723-3748, May.
    4. Gianluca Fabiano & Andrea Marcellusi & Giampiero Favato, 2020. "Public–private contribution to biopharmaceutical discoveries: a bibliometric analysis of biomedical research in UK," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(1), pages 153-168, July.
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    Cited by:

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    3. Emmons, Karen M. & Mendez, Samuel & Lee, Rebekka M. & Erani, Diana & Mascioli, Lynette & Abreu, Marlene & Adams, Susan & Daly, James & Bierer, Barbara E., 2023. "Data sharing in the context of community-engaged research partnerships," Social Science & Medicine, Elsevier, vol. 325(C).

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