Kui K. Chan
Impact in
- Infectious Diseases top 5%
- SARS-CoV-2 and COVID-19 Research
- COVID-19 Clinical Research Studies
- SARS-CoV-2 detection and testing
- Biochemistry top 5%
Papers in
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- Porphyrin Metabolism and Disorders 6
- vaccines and immunoinformatics approaches 3
- Protein Structure and Dynamics 2
-
- SARS-CoV-2 and COVID-19 Research 8
- SARS-CoV-2 detection and testing 3
- COVID-19 Clinical Research Studies 3
- Co-authors
- Erik Procko (8 shared papers)Shawn A. Abbasi (1 shared paper)Danielle E. Dorosky (1 shared paper)John M. Dye (1 shared paper)David M. Kranz (1 shared paper)Preeti Sharma (1 shared paper)Andrew S. Herbert (1 shared paper)J.A. Gerlt (7 shared papers)
- Journals
- Biochemistry (4 papers)Journal of the American Chemical Society (3 papers)EMBO Molecular Medicine (1 paper)Molecular Therapy — Methods & Clinical Development (1 paper)The Journal of Physical Chemistry B (1 paper)
- Partner nations
- United StatesHong KongHungary
In The Last Decade
Kui K. Chan
18 papers receiving 935 citations
Peers
Comparison fields: 5 of 72
- Infectious Diseases 478
- Biochemistry 83
- Molecular Biology 498
- Neurology 54
- Animal Science and Zoology 51
Countries citing papers authored by Kui K. Chan
This map shows the geographic impact of Kui K. Chan'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 Kui K. Chan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kui K. Chan more than expected).
Fields of papers citing papers by Kui K. Chan
This network shows the impact of papers produced by Kui K. Chan. 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 Kui K. Chan. The network helps show where Kui K. Chan may publish in the future.
Co-authors
The 25 scholars most cited alongside Kui K. Chan, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 397 | |
| 2 | 2021 | 88 | |
| 3 | 1985 | 73 | |
| 4 | 2008 | 69 | |
| 5 | 2009 | 56 | |
| 6 | 2022 | 53 | |
| 7 | 2007 | 41 | |
| 8 | 2009 | 31 | |
| 9 | 2009 | 31 | |
| 10 | 2022 | 31 | |
| 11 | 2021 | 23 | |
| 12 | 2010 | 21 | |
| 13 | 2008 | 16 | |
| 14 | 2023 | 7 | |
| 15 | 2016 | 5 | |
| 16 | 2022 | 1 | |
| 17 | 2015 | 1 | |
| 18 | 2024 | 1 | |
| 19 | 2021 | 0 |
About Kui K. Chan
Kui K. Chan is a scholar working on Molecular Biology, Infectious Diseases, Materials Chemistry, Neurology and Computational Theory and Mathematics, having authored 19 papers that have together received 945 indexed citations. Recurring topics across this work include Enzyme Structure and Function (8 papers), SARS-CoV-2 and COVID-19 Research (8 papers), Porphyrin Metabolism and Disorders (6 papers), SARS-CoV-2 detection and testing (3 papers), COVID-19 Clinical Research Studies (3 papers), vaccines and immunoinformatics approaches (3 papers), Biochemical Acid Research Studies (2 papers) and Protein Structure and Dynamics (2 papers). The work is most often cited by research in Infectious Diseases (478 citations), Biochemistry (83 citations), Molecular Biology (498 citations), Neurology (54 citations) and Animal Science and Zoology (51 citations). Kui K. Chan has collaborated with scholars based in United States, Hong Kong and Hungary. Frequent co-authors include Erik Procko, Shawn A. Abbasi, Danielle E. Dorosky, John M. Dye, David M. Kranz, Preeti Sharma, Andrew S. Herbert, J.A. Gerlt, B.M. Wood and Tina L. Amyes. Their work appears in journals such as Biochemistry, Journal of the American Chemical Society, EMBO Molecular Medicine, Molecular Therapy — Methods & Clinical Development and The Journal of Physical Chemistry B.
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.