Haijun Wen
Impact in
- Modeling and Simulation top 10%
-
- Genetic Mapping and Diversity in Plants and Animals
- Evolution and Genetic Dynamics
- Genetic diversity and population structure
Papers in
-
- Genomics and Phylogenetic Studies 3
- CRISPR and Genetic Engineering 3
- Genetics 9
- Evolution and Genetic Dynamics 7
- Co-authors
- Chung‐I Wu (16 shared papers)Xuemei Lu (7 shared papers)Ziwen He (3 shared papers)Xionglei He (5 shared papers)Richard R. Hudson (1 shared paper)Weiwei Zhai (2 shared papers)Tian Tang (1 shared paper)Yu Wang (1 shared paper)
- Journals
- Molecular Biology and Evolution (6 papers)National Science Review (6 papers)eLife (3 papers)Oncotarget (1 paper)Science Bulletin (1 paper)
- Partner nations
- ChinaUnited StatesKazakhstan
In The Last Decade
Haijun Wen
19 papers receiving 320 citations
Peers
Comparison fields: 5 of 80
- Modeling and Simulation 26
- Genetics 141
- Infectious Diseases 60
- Plant Science 114
- Cancer Research 40
Countries citing papers authored by Haijun Wen
This map shows the geographic impact of Haijun Wen'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 Wen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Haijun Wen more than expected).
Fields of papers citing papers by Haijun Wen
This network shows the impact of papers produced by Haijun Wen. 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 Wen. The network helps show where Haijun Wen may publish in the future.
Co-authors
The 25 scholars most cited alongside Haijun Wen, 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 | 2011 | 138 | |
| 2 | 2018 | 33 | |
| 3 | 2008 | 21 | |
| 4 | 2020 | 17 | |
| 5 | 2021 | 16 | |
| 6 | 2019 | 15 | |
| 7 | 2022 | 13 | |
| 8 | 2019 | 11 | |
| 9 | 2020 | 11 | |
| 10 | 2023 | 9 | |
| 11 | 2019 | 8 | |
| 12 | 2022 | 7 | |
| 13 | 2020 | 7 | |
| 14 | 2023 | 6 | |
| 15 | 2022 | 4 | |
| 16 | 2017 | 3 | |
| 17 | 2024 | 2 | |
| 18 | 2025 | 2 | |
| 19 | 2024 | 1 | |
| 20 | 2024 | 0 |
About Haijun Wen
Haijun Wen is a scholar working on Molecular Biology, Genetics, Cancer Research, Infectious Diseases and Modeling and Simulation, having authored 20 papers that have together received 324 indexed citations. Recurring topics across this work include Cancer Genomics and Diagnostics (7 papers), Evolution and Genetic Dynamics (7 papers), SARS-CoV-2 and COVID-19 Research (6 papers), COVID-19 epidemiological studies (3 papers), Viral Infections and Outbreaks Research (3 papers), Genomics and Phylogenetic Studies (3 papers), CRISPR and Genetic Engineering (3 papers) and CAR-T cell therapy research (2 papers). The work is most often cited by research in Modeling and Simulation (26 citations), Genetics (141 citations), Infectious Diseases (60 citations), Plant Science (114 citations) and Cancer Research (40 citations). Haijun Wen has collaborated with scholars based in China, United States and Kazakhstan. Frequent co-authors include Chung‐I Wu, Xuemei Lu, Ziwen He, Xionglei He, Richard R. Hudson, Weiwei Zhai, Tian Tang, Yu Wang, Anthony J. Greenberg and Suhua Shi. Their work appears in journals such as Molecular Biology and Evolution, National Science Review, eLife, Oncotarget and Science Bulletin.
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.