Haitong Wan
Impact in
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- Traditional Chinese Medicine Analysis
- Pharmacology top 1%
- Pharmacological Effects of Natural Compounds
- Plant-based Medicinal Research
Papers in
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- Metabolomics and Mass Spectrometry Studies 7
-
- Traditional Chinese Medicine Analysis 29
- Co-authors
- Yu He (60 shared papers)Jiehong Yang (65 shared papers)Huifen Zhou (47 shared papers)Li Yu (26 shared papers)Chang Li (20 shared papers)Weifeng Jin (13 shared papers)Guangwei Chen (4 shared papers)Ping Huang (4 shared papers)
- Journals
- Frontiers in Pharmacology (12 papers)Molecules (6 papers)Journal of Ethnopharmacology (6 papers)Phytomedicine (5 papers)Biomedicine & Pharmacotherapy (5 papers)
- Partner nations
- ChinaUnited StatesRussia
In The Last Decade
Haitong Wan
128 papers receiving 2.0k citations
Peers
Comparison fields: 5 of 141
- Complementary and alternative medicine 412
- Pharmacology 266
- Neurology 165
- Filtration and Separation 36
- Biochemistry 85
Countries citing papers authored by Haitong Wan
This map shows the geographic impact of Haitong Wan'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 Haitong Wan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Haitong Wan more than expected).
Fields of papers citing papers by Haitong Wan
This network shows the impact of papers produced by Haitong Wan. 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 Haitong Wan. The network helps show where Haitong Wan may publish in the future.
Co-authors
The 25 scholars most cited alongside Haitong Wan, 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 132 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2022 | 125 | |
| 2 | 2022 | 115 | |
| 3 | 2022 | 85 | |
| 4 | 2022 | 58 | |
| 5 | 2019 | 57 | |
| 6 | 2021 | 56 | |
| 7 | 2019 | 53 | |
| 8 | 2010 | 47 | |
| 9 | 2024 | 41 | |
| 10 | 2021 | 36 | |
| 11 | 2019 | 36 | |
| 12 | 2015 | 32 | |
| 13 | 2022 | 32 | |
| 14 | 2020 | 31 | |
| 15 | 2020 | 31 | |
| 16 | 2021 | 28 | |
| 17 | 2012 | 28 | |
| 18 | 2011 | 28 | |
| 19 | 2021 | 27 | |
| 20 | 2018 | 27 |
About Haitong Wan
Haitong Wan is a scholar working on Molecular Biology, Complementary and alternative medicine, Pharmacology, Epidemiology and Plant Science, having authored 132 papers that have together received 2.0k indexed citations. Recurring topics across this work include Traditional Chinese Medicine Analysis (29 papers), Pharmacological Effects of Natural Compounds (12 papers), Immune Response and Inflammation (8 papers), Neuroinflammation and Neurodegeneration Mechanisms (8 papers), interferon and immune responses (7 papers), Metabolomics and Mass Spectrometry Studies (7 papers), Sunflower and Safflower Cultivation (6 papers) and Influenza Virus Research Studies (6 papers). The work is most often cited by research in Complementary and alternative medicine (412 citations), Pharmacology (266 citations), Neurology (165 citations), Filtration and Separation (36 citations) and Biochemistry (85 citations). Haitong Wan has collaborated with scholars based in China, United States and Russia. Frequent co-authors include Yu He, Jiehong Yang, Huifen Zhou, Li Yu, Chang Li, Weifeng Jin, Guangwei Chen, Ping Huang, Chongyu Shao and Haixia Du. Their work appears in journals such as Frontiers in Pharmacology, Molecules, Journal of Ethnopharmacology, Phytomedicine and Biomedicine & Pharmacotherapy.
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.