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

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

Journal of Informetrics, 2020, vol. 14, issue 1

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.

Keywords: Bipartite network; Ranking algorithm; Knowledge structure; Knowledge discovery; Biological entity relation (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:infome:v:14:y:2020:i:1:s1751157719302354

DOI: 10.1016/j.joi.2019.100999

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