Examining drug and side effect relation using author–entity pair bipartite networks
作者：
Highlights：
• The paper explores the characteristics of biological entities, such as drugs, and their side effects using an author–entity pair bipartite network.
• We propose a new ranking algorithm that takes into consideration the characteristics of bipartite networks to identify top-ranked entity pairs.
• We compared the drug and side effect pairs obtained from the network containing both drug and side effect with those observed in SIDER.
• Our approach was able to identify a wide range of patterns of drug–side effect relations from the perspective of authors’ research interests.
摘要
•The paper explores the characteristics of biological entities, such as drugs, and their side effects using an author–entity pair bipartite network.•We propose a new ranking algorithm that takes into consideration the characteristics of bipartite networks to identify top-ranked entity pairs.•We compared the drug and side effect pairs obtained from the network containing both drug and side effect with those observed in SIDER.•Our approach was able to identify a wide range of patterns of drug–side effect relations from the perspective of authors’ research interests.
论文关键词：Bipartite network,Ranking algorithm,Knowledge structure,Knowledge discovery,Biological entity relation
论文评审过程：Received 16 July 2019, Revised 9 November 2019, Accepted 9 December 2019, Available online 21 December 2019, Version of Record 21 December 2019.
论文官网地址：https://doi.org/10.1016/j.joi.2019.100999
