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Examining drug and side effect relation using author–entity pair bipartite networks

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  • Jeong, Yoo Kyung
  • Xie, Qing
  • Yan, Erjia
  • Song, Min

Abstract

The current study has two objectives. First, we explore the characteristics of biological entities, such as drugs, and their side effects using an author–entity pair bipartite network. Second, we use the constructed network to examine whether there are outstanding features of relations between drugs and side effects. We extracted drug and side effect names from 169,766 PubMed abstracts published between 2010 to 2014 and constructed author–entity pair bipartite networks after ambiguous author names were processed. We propose a new ranking algorithm that takes into consideration the characteristics of bipartite networks to identify top-ranked biological drug and side effect pairs. To investigate the relationship between a particular drug and a side effect, we compared the drug and side effect pairs obtained from the network containing both drug and side effect with those observed in SIDER, a human expert-curated database. The results of this study indicate that our approach was able to identify a wide range of patterns of drug–side effect relations from the perspective of authors’ research interests. Further, our approach also identified the unique characteristics of the relation of biomedical entities obtained using an author–entity pair bipartite network.

Suggested Citation

  • Jeong, Yoo Kyung & Xie, Qing & Yan, Erjia & Song, Min, 2020. "Examining drug and side effect relation using author–entity pair bipartite networks," Journal of Informetrics, Elsevier, vol. 14(1).
    • Handle: RePEc:eee:infome:v:14:y:2020:i:1:s1751157719302354
    DOI: 10.1016/j.joi.2019.100999
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    Cited by:

    1. Zhao, Zhili & Wu, Simin & Luo, Ge & Zhang, Nana & Hu, Ahui & Liu, Jun, 2024. "Mining odd-length paths for link prediction in bipartite networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 646(C).
    2. Dongin Nam & Jiwon Kim & Jeeyoung Yoon & Chaemin Song & Seongdeok Kim & Min Song, 2024. "Examining knowledge entities and its relationships based on citation sentences using a multi-anchor bipartite network," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(11), pages 7197-7228, November.
    3. Wang, Zhenhua & Ren, Ming & Gao, Dong & Li, Zhuang, 2023. "A Zipf's law-based text generation approach for addressing imbalance in entity extraction," Journal of Informetrics, Elsevier, vol. 17(4).

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