Can Chen
Impact in
- Infectious Diseases top 5%
- COVID-19 Clinical Research Studies
- Viral Infections and Vectors
- SARS-CoV-2 and COVID-19 Research
- Viral gastroenteritis research and epidemiology
- Modeling and Simulation top 10%
Papers in
-
- COVID-19 Clinical Research Studies 10
- Viral gastroenteritis research and epidemiology 5
- Co-authors
- Kefeng Li (3 shared papers)Peng Li (2 shared papers)Jianbo Yan (2 shared papers)Yaxin Dai (2 shared papers)Hongling Wang (1 shared paper)Xi Cheng (1 shared paper)Shigui Yang (16 shared papers)Yuqing Zhou (8 shared papers)
- Journals
- International Journal of Infectious Diseases (3 papers)European Journal of Clinical Pharmacology (3 papers)Medicine (3 papers)Virus Research (2 papers)Scientific Reports (2 papers)
- Partner nations
- ChinaUnited StatesHong Kong
In The Last Decade
Can Chen
81 papers receiving 933 citations
Peers
Comparison fields: 5 of 127
- Infectious Diseases 299
- Modeling and Simulation 32
- Hepatology 54
- Cardiology and Cardiovascular Medicine 132
- Neurology 74
Countries citing papers authored by Can Chen
This map shows the geographic impact of Can Chen'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 Can Chen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Can Chen more than expected).
Fields of papers citing papers by Can Chen
This network shows the impact of papers produced by Can Chen. 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 Can Chen. The network helps show where Can Chen may publish in the future.
Co-authors
The 25 scholars most cited alongside Can Chen, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 84 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2018 | 76 | |
| 2 | 2013 | 73 | |
| 3 | 2020 | 50 | |
| 4 | 2018 | 47 | |
| 5 | 2021 | 39 | |
| 6 | 2020 | 38 | |
| 7 | 2013 | 34 | |
| 8 | 2022 | 33 | |
| 9 | 2021 | 32 | |
| 10 | 2019 | 28 | |
| 11 | 2021 | 25 | |
| 12 | 2019 | 23 | |
| 13 | 2015 | 22 | |
| 14 | 2020 | 22 | |
| 15 | 2018 | 19 | |
| 16 | 2020 | 18 | |
| 17 | 2020 | 17 | |
| 18 | 2022 | 16 | |
| 19 | 2021 | 16 | |
| 20 | 2020 | 15 |
About Can Chen
Can Chen is a scholar working on Infectious Diseases, Cardiology and Cardiovascular Medicine, Epidemiology, Surgery and Oncology, having authored 84 papers that have together received 946 indexed citations. Recurring topics across this work include COVID-19 Clinical Research Studies (10 papers), Long-Term Effects of COVID-19 (7 papers), Viral gastroenteritis research and epidemiology (5 papers), COVID-19 epidemiological studies (5 papers), Influenza Virus Research Studies (4 papers), Hepatitis Viruses Studies and Epidemiology (4 papers), COVID-19 diagnosis using AI (3 papers) and Hepatitis B Virus Studies (3 papers). The work is most often cited by research in Infectious Diseases (299 citations), Modeling and Simulation (32 citations), Hepatology (54 citations), Cardiology and Cardiovascular Medicine (132 citations) and Neurology (74 citations). Can Chen has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Kefeng Li, Peng Li, Jianbo Yan, Yaxin Dai, Hongling Wang, Xi Cheng, Shigui Yang, Yuqing Zhou, Zipeng Liu and Junwen Wang. Their work appears in journals such as International Journal of Infectious Diseases, European Journal of Clinical Pharmacology, Medicine, Virus Research and Scientific Reports.
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.