COVID-19 cases have surpassed the 109 + million markers, with deaths tallying up to 2.4 million. Tens of thousands of papers regarding COVID-19 have been published along with countless bibliometric analyses done on COVID-19 literature. Despite this, none of the analyses have focused on domain entities occurring in scientific publications. However, analysis of these bio-entities and the relations among them, a strategy called entity metrics, could offer more insights into knowledge usage and diffusion in specific cases. Thus, this paper presents an entitymetric analysis on COVID-19 literature. We construct an entity–entity co-occurrence network and employ network indicators to analyze the extracted entities. We find that ACE-2 and C-reactive protein are two very important genes and that lopinavir and ritonavir are two very important chemicals, regardless of the results from either ranking.
Bibliographical noteFunding Information:
The authors are grateful to Allen Institute of AI for publicly sharing the COVID-19 open research dataset and Drs. Zhiyong Lu and Chih-Hsuan Wei for their kind help on PubTator usage. The authors thank the anonymous reviewer to offer many suggestions to improve the quality of this paper. Qi Yu acknowledges financial supports from National Natural Science Foundation of China (Grant Number: 71573162) and the Shanxi Scholarship Council of China (Grant Number: HGKY2019057). This work was supported by Min Song acknowledges financial supports from the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (Grant Number: 2020S1A5B1104865). Ying Ding acknowledges financial support from the NSF RAPID (Grant Number: 2028717). Baitong Chen acknowledges financial support from the Ministry of Education of China Project of Humanities and Social Sciences (Grant Number: 18YJC870002).
© 2021, Akadémiai Kiadó, Budapest, Hungary.
All Science Journal Classification (ASJC) codes
- Social Sciences(all)
- Computer Science Applications
- Library and Information Sciences