Yongquan Yang
- Nephrology top 10%
- Acute Kidney Injury Research 6
- Chronic Kidney Disease and Diabetes 3
- Artificial Intelligence top 10%
- Machine Learning and Data Classification 5
- AI in cancer detection 4
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- Radiomics and Machine Learning in Medical Imaging 4
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- Advanced Neural Network Applications 3
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- Lipoproteins and Cardiovascular Health 5
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- Cell Image Analysis Techniques 4
- Co-authors
- Ning ChenHong BuJie ChenPing HeFengling LiShiqun ChenMing YingBo Wang
- Journals
- Multimedia Tools and Applications (5 papers)Frontiers in Cardiovascular Medicine (3 papers)Artificial Intelligence Review (2 papers)
- Partner nations
- ChinaUnited StatesJapan
In The Last Decade
Yongquan Yang
33 papers receiving 481 citations
Hit Papers
Peers
Comparison fields: 5 of 117
- Nephrology 57
- Artificial Intelligence 185
- Radiology, Nuclear Medicine and Imaging 103
- Computer Vision and Pattern Recognition 68
- Cancer Research 46
Countries citing papers authored by Yongquan Yang
This map shows the geographic impact of Yongquan Yang'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 Yongquan Yang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yongquan Yang more than expected).
Fields of papers citing papers by Yongquan Yang
This network shows the impact of papers produced by Yongquan Yang. 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 Yongquan Yang. The network helps show where Yongquan Yang may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Yongquan Yang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2024 | 2 | |
| 2 | 2024 | 1 | |
| 3 | 2024 | 5 | |
| 4 | 2024 | 5 | |
| 5 | 2024 | 3 | |
| 6 | 2024 | 5 | |
| 7 | 2023 | 5 | |
| 8 | 2022 | 8 | |
| 9 | 2022 | 1 | |
| 10 | 2022 | 3 | |
| 11 | 2022 | 3 | |
| 12 | 2021 | 3 | |
| 13 | 2021 | 12 | |
| 14 | 2021 | 2 | |
| 15 | 2021 | 25 | |
| 16 | 2021 | 30 | |
| 17 | 2021 | 63 | |
| 18 | 2021 | 13 | |
| 19 | 2020 | 8 | |
| 20 | 2020 | 6 |
About Yongquan Yang
Yongquan Yang is a scholar working on Nephrology, Biophysics and Computer Vision and Pattern Recognition, having authored 35 papers that have together received 500 indexed citations. Recurring topics across this work include Acute Kidney Injury Research (6 papers), Lipoproteins and Cardiovascular Health (5 papers), Machine Learning and Data Classification (5 papers), Radiomics and Machine Learning in Medical Imaging (4 papers), AI in cancer detection (4 papers), Cell Image Analysis Techniques (4 papers), Chronic Kidney Disease and Diabetes (3 papers) and Advanced Neural Network Applications (3 papers). The work is most often cited by research in Nephrology (57 citations), Artificial Intelligence (185 citations) and Radiology, Nuclear Medicine and Imaging (103 citations). Yongquan Yang has collaborated with scholars based in China, United States and Japan. Frequent co-authors include Ning Chen, Hong Bu, Jie Chen, Ping He, Fengling Li, Shiqun Chen, Ning Chen, Ming Ying, Bo Wang and Yong Liu. Their work appears in journals such as Multimedia Tools and Applications, Frontiers in Cardiovascular Medicine, Artificial Intelligence Review, BMC Cardiovascular Disorders and Journal of Nephrology.
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.