Jun Dong
Impact in
- Physiology top 10%
- Diet and metabolism studies
- Adipose Tissue and Metabolism
Papers in
-
- Metabolomics and Mass Spectrometry Studies 21
- Gut microbiota and health 5
-
- Diabetes, Cardiovascular Risks, and Lipoproteins 6
- Co-authors
- Ruiyue Yang (41 shared papers)Wenxiang Chen (33 shared papers)Hongxia Li (22 shared papers)Siming Wang (23 shared papers)Hongna Mu (15 shared papers)Qi Zhou (5 shared papers)Xianghui Li (5 shared papers)Le Cai (4 shared papers)
- Journals
- Clinical Chemistry and Laboratory Medicine (CCLM) (4 papers)Clinica Chimica Acta (3 papers)Journal of Chromatography B (3 papers)Nutrition Metabolism and Cardiovascular Diseases (3 papers)Journal of Lipid Research (2 papers)
- Partner nations
- ChinaUnited StatesItaly
In The Last Decade
Jun Dong
69 papers receiving 1.3k citations
Peers
Comparison fields: 5 of 116
- Biological Psychiatry 24
- Physiology 223
- Endocrinology, Diabetes and Metabolism 140
- Molecular Biology 512
- Pharmacology 44
Countries citing papers authored by Jun Dong
This map shows the geographic impact of Jun Dong'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 Jun Dong with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jun Dong more than expected).
Fields of papers citing papers by Jun Dong
This network shows the impact of papers produced by Jun Dong. 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 Jun Dong. The network helps show where Jun Dong may publish in the future.
Co-authors
The 25 scholars most cited alongside Jun Dong, 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 72 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2019 | 164 | |
| 2 | 2020 | 150 | |
| 3 | 2021 | 84 | |
| 4 | 2014 | 83 | |
| 5 | 2011 | 49 | |
| 6 | 2015 | 46 | |
| 7 | 2017 | 37 | |
| 8 | 2012 | 34 | |
| 9 | 2013 | 34 | |
| 10 | 2011 | 31 | |
| 11 | 2019 | 30 | |
| 12 | 1997 | 29 | |
| 13 | 2022 | 27 | |
| 14 | 2018 | 25 | |
| 15 | 2007 | 25 | |
| 16 | 2010 | 25 | |
| 17 | 2015 | 21 | |
| 18 | 2017 | 20 | |
| 19 | 2017 | 20 | |
| 20 | 2018 | 19 |
About Jun Dong
Jun Dong is a scholar working on Molecular Biology, Endocrinology, Diabetes and Metabolism, Physiology, Surgery and Pathology and Forensic Medicine, having authored 72 papers that have together received 1.3k indexed citations. Recurring topics across this work include Metabolomics and Mass Spectrometry Studies (21 papers), Diabetes, Cardiovascular Risks, and Lipoproteins (6 papers), Gut microbiota and health (5 papers), Cholesterol and Lipid Metabolism (5 papers), Analytical Chemistry and Chromatography (5 papers), Liver Disease Diagnosis and Treatment (4 papers), Diet and metabolism studies (4 papers) and Nutrition and Health in Aging (3 papers). The work is most often cited by research in Biological Psychiatry (24 citations), Physiology (223 citations), Endocrinology, Diabetes and Metabolism (140 citations), Molecular Biology (512 citations) and Pharmacology (44 citations). Jun Dong has collaborated with scholars based in China, United States and Italy. Frequent co-authors include Ruiyue Yang, Wenxiang Chen, Hongxia Li, Siming Wang, Hongna Mu, Qi Zhou, Xianghui Li, Le Cai, Liang Sun and Weiqing Tang. Their work appears in journals such as Clinical Chemistry and Laboratory Medicine (CCLM), Clinica Chimica Acta, Journal of Chromatography B, Nutrition Metabolism and Cardiovascular Diseases and Journal of Lipid Research.
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.