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Summary
This summary is machine-generated.

This study introduces entity metrics for analyzing COVID-19 research, identifying key genes like ACE-2 and C-reactive protein, and chemicals such as lopinavir and ritonavir for better knowledge diffusion insights.

Keywords:
BibliometricsCOVID-19EntityEntitymetricsKnowledge graphScientific publications

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Area of Science:

  • Biomedical Informatics
  • Network Science
  • Virology

Background:

  • Over 109 million COVID-19 cases and 2.4 million deaths highlight the pandemic's global impact.
  • Thousands of papers and bibliometric analyses exist, but none focus on domain entities within scientific publications.
  • Entity metrics, analyzing bio-entities and their relationships, can reveal knowledge usage and diffusion patterns.

Purpose of the Study:

  • To perform an entitymetric analysis on the scientific literature concerning COVID-19.
  • To identify key biological and chemical entities and their significance within the COVID-19 research landscape.
  • To explore the application of network analysis for understanding knowledge diffusion in a specific scientific domain.

Main Methods:

  • Construction of an entity-entity co-occurrence network from COVID-19 literature.
  • Application of network indicators to analyze extracted biological and chemical entities.
  • Comparative analysis of entity importance irrespective of ranking methodologies.

Main Results:

  • Identification of Angiotensin-Converting Enzyme 2 (ACE-2) and C-reactive protein as crucial genes in COVID-19 research.
  • Determination of lopinavir and ritonavir as significant chemical entities within the analyzed literature.
  • The study highlights the utility of entity metrics in uncovering domain-specific knowledge structures.

Conclusions:

  • Entity metrics provide valuable insights into knowledge usage and diffusion within the COVID-19 scientific domain.
  • ACE-2 and C-reactive protein are identified as central genes, while lopinavir and ritonavir are key chemicals in COVID-19 research.
  • Network analysis of domain entities offers a novel perspective beyond traditional bibliometrics.