Jin‐Zhen Wu
- Surgery top 10%
- Molecular Biology
- Endocrinology, Diabetes and Metabolism top 5%
- Cardiology and Cardiovascular Medicine top 5%
- Genetics top 10%
- Co-authors
- Rui‐Xing YinYang DezhaiShang‐Ling PanLin WeixiongXiaoli CaoDong‐Feng WuWeixiong LinLynn Htet Htet Aung
- Topics
- Diabetes, Cardiovascular Risks, and Lipoproteins (20 papers)Lipoproteins and Cardiovascular Health (15 papers)Genetic Associations and Epidemiology (15 papers)
- Cited by
- Endocrinology, Diabetes and MetabolismCardiology and Cardiovascular MedicineCancer Research
- Partner nations
- ChinaUnited States
In The Last Decade
Jin‐Zhen Wu
66 papers receiving 1.4k citations
Peers
Comparison fields: 5 of 109
- Surgery 468
- Molecular Biology 395
- Endocrinology, Diabetes and Metabolism 392
- Cardiology and Cardiovascular Medicine 355
- Genetics 274
Countries citing papers authored by Jin‐Zhen Wu
This map shows the geographic impact of Jin‐Zhen Wu'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 Jin‐Zhen Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jin‐Zhen Wu more than expected).
Fields of papers citing papers by Jin‐Zhen Wu
This network shows the impact of papers produced by Jin‐Zhen Wu. 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 Jin‐Zhen Wu. The network helps show where Jin‐Zhen Wu may publish in the future.
Co-authorship network of co-authors of Jin‐Zhen Wu
This figure shows the co-authorship network connecting the top 25 collaborators of Jin‐Zhen Wu. A scholar is included among the top collaborators of Jin‐Zhen Wu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Jin‐Zhen Wu. Jin‐Zhen Wu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 6 | |
| 3 | 2 | |
| 4 | 14 | |
| 5 | 5 | |
| 6 | 22 | |
| 7 | 13 | |
| 8 | 12 | |
| 9 | Association between the MARS rs6782181 polymorphism and serum lipid levels. | 3 |
| 10 | 4 | |
| 11 | 8 | |
| 12 | 28 | |
| 13 | 19 | |
| 14 | 25 | |
| 15 | 25 | |
| 16 | 39 | |
| 17 | 47 | |
| 18 | 27 | |
| 19 | 42 | |
| 20 | 47 |
About Jin‐Zhen Wu
Jin‐Zhen Wu is a scholar working on Endocrinology, Diabetes and Metabolism, Cardiology and Cardiovascular Medicine and Biochemistry, having authored 68 papers that have together received 1.4k indexed citations. Recurring topics across this work include Diabetes, Cardiovascular Risks, and Lipoproteins (20 papers), Lipoproteins and Cardiovascular Health (15 papers) and Genetic Associations and Epidemiology (15 papers). The work is most often cited by research in Endocrinology, Diabetes and Metabolism (392 citations), Cardiology and Cardiovascular Medicine (355 citations) and Cancer Research (168 citations). Jin‐Zhen Wu has collaborated with scholars based in China and United States. Frequent co-authors include Rui‐Xing Yin, Yang Dezhai, Shang‐Ling Pan, Lin Weixiong, Xiaoli Cao, Dong‐Feng Wu, Weixiong Lin, Lynn Htet Htet Aung, Wu‐Xian Chen and Feng Huang. Their work appears in journals such as Journal of the American College of Cardiology, PLoS ONE 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.