John J. Won

2.5k citations
2 papers · 1.7k · 2 hit papers · h-index 2

Impact in

    • SARS-CoV-2 and COVID-19 Research
    • COVID-19 Clinical Research Studies
    • Viral gastroenteritis research and epidemiology
    • SARS-CoV-2 detection and testing
  • Neurology top 5%
    • Long-Term Effects of COVID-19

Papers in

John J. Won

2 papers receiving 1.6k citations

John J. Won's Hit Papers

Comparative therapeutic efficacy of remdesivir and combination lopinavir, ritonavir, and interferon beta against MERS-CoV 2020 · 1.3k citations
1.3k0+2+4Years since publication4008001.2k

Peers

John J. Won
Comparison fields: 5 of 103
  • Infectious Diseases 1.3k
  • Neurology 306
  • Modeling and Simulation 55
  • Computational Theory and Mathematics 180
  • Pharmacology 56
Replace Darius Babusis with:
Darius Babusis United States
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Zhongsi Hong China
Ariane J. Brown United States
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Scott Sellers United States
Calvin J. Gordon Canada
Prathanporn Kaewpreedee Hong Kong
Shamsah H. Al-Ahmed Saudi Arabia
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John J. Won relative to Darius Babusis United States Darius Babusis's profile →
Citations per field
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Darius Babusis · 1×
Citations per year

Countries citing papers authored by John J. Won

Since Specialization
Citations

This map shows the geographic impact of John J. Won's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by John J. Won with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John J. Won more than expected).

Fields of papers citing papers by John J. Won

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by John J. Won. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by John J. Won. The network helps show where John J. Won may publish in the future.

Co-authors

The 19 scholars most cited alongside John J. Won, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with John J. Won Line = papers co-authored together John J. Won links everyone, so they are left out of the graph.

All Works

2 of 2 papers shown
#Work
1
Comparative therapeutic efficacy of remdesivir and combination lopinavir, ritonavir, and interferon beta against MERS-CoV
Hit paper breakdown →
20201274
2
Broad spectrum antiviral remdesivir inhibits human endemic and zoonotic deltacoronaviruses with a highly divergent RNA dependent RNA polymerase
Hit paper breakdown →
2019385

About John J. Won

John J. Won is a scholar working on Animal Science and Zoology, Infectious Diseases, Genetics, Immunology and Organic Chemistry, having authored 2 papers that have together received 1.7k indexed citations. Recurring topics across this work include Animal Virus Infections Studies (2 papers), COVID-19 Clinical Research Studies (1 paper), SARS-CoV-2 and COVID-19 Research (1 paper), Virus-based gene therapy research (1 paper) and interferon and immune responses (1 paper). The work is most often cited by research in Infectious Diseases (1.3k citations), Neurology (306 citations), Modeling and Simulation (55 citations), Computational Theory and Mathematics (180 citations) and Pharmacology (56 citations). John J. Won has collaborated with scholars based in United States. Frequent co-authors include Tomáš Cihlář, Timothy P. Sheahan, Amy Sims, Mark R. Denison, Ralph S. Baric, Ariane J. Brown, Joy Y. Feng, Alison Hogg, Robert Jordan and Michael O. Clarke. Their work appears in journals such as Antiviral Research and Nature Communications.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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