Dai Shi
Impact in
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- Pain Mechanisms and Treatments
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- Neuroinflammation and Neurodegeneration Mechanisms
- Intracranial Aneurysms: Treatment and Complications
Papers in
- Oncology 13
- Cancer Immunotherapy and Biomarkers 6
- Peptidase Inhibition and Analysis 5
- Chemokine receptors and signaling 3
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- Radiopharmaceutical Chemistry and Applications 5
- Medical Imaging Techniques and Applications 4
- Co-authors
- Yuke Tian (5 shared papers)Fei Cao (3 shared papers)Anne Manyande (3 shared papers)Huilian Bu (3 shared papers)Feng Gao (3 shared papers)Bing Ying Xu (2 shared papers)Dengfeng Cheng (17 shared papers)Xuehai Guan (2 shared papers)
- Journals
- Molecular Pharmaceutics (6 papers)Experimental Neurology (3 papers)ACS Omega (2 papers)Journal of Cellular and Molecular Medicine (2 papers)Cell Death and Disease (2 papers)
- Partner nations
- ChinaUnited StatesUnited Kingdom
In The Last Decade
Dai Shi
58 papers receiving 740 citations
Peers
Comparison fields: 5 of 111
- Physiology 149
- Neurology 42
- Radiology, Nuclear Medicine and Imaging 101
- Cancer Research 54
- Media Technology 33
Countries citing papers authored by Dai Shi
This map shows the geographic impact of Dai Shi'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 Dai Shi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dai Shi more than expected).
Fields of papers citing papers by Dai Shi
This network shows the impact of papers produced by Dai Shi. 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 Dai Shi. The network helps show where Dai Shi may publish in the future.
Co-authors
The 25 scholars most cited alongside Dai Shi, 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 60 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2014 | 87 | |
| 2 | 2018 | 80 | |
| 3 | 2014 | 46 | |
| 4 | 2016 | 39 | |
| 5 | 2016 | 37 | |
| 6 | 2021 | 37 | |
| 7 | 2013 | 31 | |
| 8 | 2019 | 31 | |
| 9 | 2021 | 29 | |
| 10 | 2023 | 27 | |
| 11 | 2019 | 26 | |
| 12 | 2015 | 25 | |
| 13 | 2022 | 17 | |
| 14 | 2019 | 15 | |
| 15 | 2022 | 15 | |
| 16 | 2016 | 14 | |
| 17 | 2019 | 13 | |
| 18 | 2020 | 13 | |
| 19 | 2018 | 12 | |
| 20 | 2023 | 12 |
About Dai Shi
Dai Shi is a scholar working on Oncology, Radiology, Nuclear Medicine and Imaging, Immunology, Physiology and Cellular and Molecular Neuroscience, having authored 60 papers that have together received 747 indexed citations. Recurring topics across this work include Cancer Immunotherapy and Biomarkers (6 papers), Radiopharmaceutical Chemistry and Applications (5 papers), Peptidase Inhibition and Analysis (5 papers), Immune cells in cancer (4 papers), Medical Imaging Techniques and Applications (4 papers), Pain Mechanisms and Treatments (4 papers), User Authentication and Security Systems (3 papers) and Chemokine receptors and signaling (3 papers). The work is most often cited by research in Physiology (149 citations), Neurology (42 citations), Radiology, Nuclear Medicine and Imaging (101 citations), Cancer Research (54 citations) and Media Technology (33 citations). Dai Shi has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Yuke Tian, Fei Cao, Anne Manyande, Huilian Bu, Feng Gao, Bing Ying Xu, Dengfeng Cheng, Xuehai Guan, Sixian Chan and Yan Chang. Their work appears in journals such as Molecular Pharmaceutics, Experimental Neurology, ACS Omega, Journal of Cellular and Molecular Medicine and Cell Death and Disease.
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.