Haijun Deng
Impact in
- Infectious Diseases top 0.5%
- SARS-CoV-2 and COVID-19 Research
- COVID-19 Clinical Research Studies
- SARS-CoV-2 detection and testing
- Modeling and Simulation top 1%
- COVID-19 epidemiological studies
Papers in
-
- RNA modifications and cancer 4
- Epidemiology 16
- Hepatitis B Virus Studies 12
- Liver Disease Diagnosis and Treatment 6
- Co-authors
- Ailong Huang (26 shared papers)Quanxin Long (17 shared papers)Jieli Hu (7 shared papers)Juan Chen (13 shared papers)Jun Yuan (2 shared papers)Kun Su (1 shared paper)Fan Zhang (1 shared paper)Yong Zhang (1 shared paper)
In The Last Decade
Haijun Deng
55 papers receiving 2.8k citations
Haijun Deng's Hit Papers
Peers
Comparison fields: 5 of 126
- Infectious Diseases 1.6k
- Modeling and Simulation 275
- Neurology 319
- Biological Psychiatry 34
- Obstetrics and Gynecology 73
Countries citing papers authored by Haijun Deng
This map shows the geographic impact of Haijun Deng'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 Haijun Deng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Haijun Deng more than expected).
Fields of papers citing papers by Haijun Deng
This network shows the impact of papers produced by Haijun Deng. 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 Haijun Deng. The network helps show where Haijun Deng may publish in the future.
Co-authors
The 25 scholars most cited alongside Haijun Deng, 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 64 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Clinical and immunological assessment of asymptomatic SARS-CoV-2 infections Hit paper breakdown → | 2020 | 1712 |
| 2 | 2021 | 186 | |
| 3 | 2020 | 131 | |
| 4 | Deficiency of gluconeogenic enzyme PCK1 promotes metabolic-associated fatty liver disease through PI3K/AKT/PDGF axis activation in male mice Hit paper breakdown → | 2023 | 105 |
| 5 | 2021 | 90 | |
| 6 | 2021 | 72 | |
| 7 | 2019 | 48 | |
| 8 | 2023 | 45 | |
| 9 | 2020 | 42 | |
| 10 | 2015 | 38 | |
| 11 | 2011 | 31 | |
| 12 | 2019 | 28 | |
| 13 | 2010 | 28 | |
| 14 | 2023 | 22 | |
| 15 | 2022 | 20 | |
| 16 | 2023 | 17 | |
| 17 | 2017 | 16 | |
| 18 | 2024 | 14 | |
| 19 | 2023 | 14 | |
| 20 | 2023 | 14 |
About Haijun Deng
Haijun Deng is a scholar working on Molecular Biology, Epidemiology, Infectious Diseases, Hepatology and Cancer Research, having authored 64 papers that have together received 2.9k indexed citations. Recurring topics across this work include Hepatitis B Virus Studies (12 papers), Hepatitis C virus research (8 papers), SARS-CoV-2 and COVID-19 Research (7 papers), COVID-19 Clinical Research Studies (6 papers), Liver Disease Diagnosis and Treatment (6 papers), RNA modifications and cancer (4 papers), Muscle metabolism and nutrition (3 papers) and Cancer Cells and Metastasis (3 papers). The work is most often cited by research in Infectious Diseases (1.6k citations), Modeling and Simulation (275 citations), Neurology (319 citations), Biological Psychiatry (34 citations) and Obstetrics and Gynecology (73 citations). Haijun Deng has collaborated with scholars based in China, Malaysia and Thailand. Frequent co-authors include Ailong Huang, Quanxin Long, Jieli Hu, Juan Chen, Jun Yuan, Kun Su, Fan Zhang, Yong Zhang, Jingfu Qiu and Wei Xü. Their work appears in journals such as Journal of Medical Virology, Genes & Diseases, Journal of Clinical Investigation, Nature Communications and Advanced Science.
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.