Jun Yin
Impact in
- Cancer Research top 2%
- MicroRNA in disease regulation
- Cancer-related molecular mechanisms research
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- Electrocatalysts for Energy Conversion
Papers in
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- Face and Expression Recognition 31
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- Circular RNAs in diseases 10
- Co-authors
- Xing Chen (5 shared papers)Chuan‐Jian Zhong (33 shared papers)Jin Luo (31 shared papers)Jia Qu (6 shared papers)Xing Chen (7 shared papers)Shiliang Sun (8 shared papers)Yan Zhao (6 shared papers)Rameshwori Loukrakpam (15 shared papers)
- Journals
- Chemistry of Materials (4 papers)Knowledge-Based Systems (4 papers)Information Fusion (3 papers)RNA Biology (3 papers)Expert Systems with Applications (3 papers)
- Partner nations
- ChinaUnited StatesJapan
In The Last Decade
Jun Yin
117 papers receiving 3.5k citations
Peers
Comparison fields: 5 of 144
- Cancer Research 857
- Renewable Energy, Sustainability and the Environment 810
- Computational Mathematics 22
- Electrochemistry 185
- Computer Vision and Pattern Recognition 461
Countries citing papers authored by Jun Yin
This map shows the geographic impact of Jun Yin'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 Yin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jun Yin more than expected).
Fields of papers citing papers by Jun Yin
This network shows the impact of papers produced by Jun Yin. 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 Yin. The network helps show where Jun Yin may publish in the future.
Co-authors
The 25 scholars most cited alongside Jun Yin, 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 125 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2018 | 311 | |
| 2 | 2019 | 178 | |
| 3 | 2011 | 165 | |
| 4 | 2010 | 159 | |
| 5 | 2019 | 138 | |
| 6 | 2011 | 126 | |
| 7 | 2018 | 103 | |
| 8 | 2011 | 94 | |
| 9 | 2012 | 89 | |
| 10 | 2013 | 85 | |
| 11 | 2012 | 84 | |
| 12 | 2011 | 81 | |
| 13 | 2011 | 72 | |
| 14 | 2009 | 71 | |
| 15 | 2021 | 67 | |
| 16 | 2021 | 66 | |
| 17 | 2019 | 65 | |
| 18 | 2018 | 65 | |
| 19 | 2012 | 59 | |
| 20 | 2009 | 57 |
About Jun Yin
Jun Yin is a scholar working on Computer Vision and Pattern Recognition, Molecular Biology, Electrical and Electronic Engineering, Materials Chemistry and Artificial Intelligence, having authored 125 papers that have together received 3.6k indexed citations. Recurring topics across this work include Face and Expression Recognition (31 papers), Electrocatalysts for Energy Conversion (14 papers), Sparse and Compressive Sensing Techniques (13 papers), MicroRNA in disease regulation (13 papers), Cancer-related molecular mechanisms research (12 papers), Catalytic Processes in Materials Science (11 papers), Fuel Cells and Related Materials (10 papers) and Circular RNAs in diseases (10 papers). The work is most often cited by research in Cancer Research (857 citations), Renewable Energy, Sustainability and the Environment (810 citations), Computational Mathematics (22 citations), Electrochemistry (185 citations) and Computer Vision and Pattern Recognition (461 citations). Jun Yin has collaborated with scholars based in China, United States and Japan. Frequent co-authors include Xing Chen, Chuan‐Jian Zhong, Jin Luo, Jia Qu, Xing Chen, Shiliang Sun, Yan Zhao, Rameshwori Loukrakpam, Bridgid N. Wanjala and Bin Fang. Their work appears in journals such as Chemistry of Materials, Knowledge-Based Systems, Information Fusion, RNA Biology and Expert Systems with Applications.
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.