Jun Feng
Impact in
- Cancer Research top 10%
- Cancer, Hypoxia, and Metabolism
- MicroRNA in disease regulation
- Cancer, Lipids, and Metabolism
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- Peptidase Inhibition and Analysis
Papers in
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- Metabolism, Diabetes, and Cancer 3
- Angiogenesis and VEGF in Cancer 3
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- Cancer, Hypoxia, and Metabolism 5
- Cancer, Lipids, and Metabolism 2
- Co-authors
- Jichang Wang (9 shared papers)Peijun Liu (6 shared papers)Xin Sun (5 shared papers)Shaoying Lu (6 shared papers)Jianlin Liu (2 shared papers)Jingyuan Deng (1 shared paper)Yuxin Li (1 shared paper)Yanhua Yang (1 shared paper)
- Journals
- Journal of Medicinal Chemistry (2 papers)Neuroscience (2 papers)Lipids in Health and Disease (2 papers)Journal of Cellular and Molecular Medicine (2 papers)Korean Journal of Radiology (1 paper)
- Partner nations
- ChinaUnited States
In The Last Decade
Jun Feng
30 papers receiving 755 citations
Peers
Comparison fields: 5 of 83
- Cancer Research 178
- Oncology 139
- Endocrinology, Diabetes and Metabolism 74
- Immunology 95
- Molecular Biology 310
Countries citing papers authored by Jun Feng
This map shows the geographic impact of Jun Feng'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 Feng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jun Feng more than expected).
Fields of papers citing papers by Jun Feng
This network shows the impact of papers produced by Jun Feng. 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 Feng. The network helps show where Jun Feng may publish in the future.
Co-authors
The 25 scholars most cited alongside Jun Feng, 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 32 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2019 | 112 | |
| 2 | 2010 | 110 | |
| 3 | 2014 | 93 | |
| 4 | 2018 | 78 | |
| 5 | 2018 | 51 | |
| 6 | 2010 | 47 | |
| 7 | 2011 | 30 | |
| 8 | 2017 | 30 | |
| 9 | 2021 | 22 | |
| 10 | 2023 | 19 | |
| 11 | 2018 | 19 | |
| 12 | 2014 | 18 | |
| 13 | 2019 | 18 | |
| 14 | 2024 | 17 | |
| 15 | 2012 | 16 | |
| 16 | 2015 | 11 | |
| 17 | 2009 | 10 | |
| 18 | 2019 | 10 | |
| 19 | 2017 | 8 | |
| 20 | 2023 | 8 |
About Jun Feng
Jun Feng is a scholar working on Molecular Biology, Cancer Research, Cardiology and Cardiovascular Medicine, Pulmonary and Respiratory Medicine and Oncology, having authored 32 papers that have together received 759 indexed citations. Recurring topics across this work include Cerebrovascular and Carotid Artery Diseases (5 papers), Cancer, Hypoxia, and Metabolism (5 papers), Metabolism, Diabetes, and Cancer (3 papers), Angiogenesis and VEGF in Cancer (3 papers), Neuroscience and Neuropharmacology Research (2 papers), Coronary Interventions and Diagnostics (2 papers), Cancer, Lipids, and Metabolism (2 papers) and HER2/EGFR in Cancer Research (2 papers). The work is most often cited by research in Cancer Research (178 citations), Oncology (139 citations), Endocrinology, Diabetes and Metabolism (74 citations), Immunology (95 citations) and Molecular Biology (310 citations). Jun Feng has collaborated with scholars based in China and United States. Frequent co-authors include Jichang Wang, Peijun Liu, Xin Sun, Shaoying Lu, Jianlin Liu, Jingyuan Deng, Yuxin Li, Yanhua Yang, Weiping Zhang and Yanwei Shen. Their work appears in journals such as Journal of Medicinal Chemistry, Neuroscience, Lipids in Health and Disease, Journal of Cellular and Molecular Medicine and Korean Journal of Radiology.
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.