Mingquan Ye
- Artificial Intelligence top 10%
- Molecular Biology
- Computer Vision and Pattern Recognition top 10%
- Radiology, Nuclear Medicine and Imaging
- Computational Theory and Mathematics top 10%
- Topics
- Medical Image Segmentation Techniques (6 papers)Rough Sets and Fuzzy Logic (6 papers)Gene expression and cancer classification (5 papers)
- Partner nations
- ChinaUnited States
In The Last Decade
Mingquan Ye
34 papers receiving 513 citations
Hit Papers
Peers
Comparison fields: 5 of 122
- Artificial Intelligence 180
- Molecular Biology 154
- Computer Vision and Pattern Recognition 82
- Radiology, Nuclear Medicine and Imaging 72
- Computational Theory and Mathematics 59
Countries citing papers authored by Mingquan Ye
This map shows the geographic impact of Mingquan Ye'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 Mingquan Ye with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mingquan Ye more than expected).
Fields of papers citing papers by Mingquan Ye
This network shows the impact of papers produced by Mingquan Ye. 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 Mingquan Ye. The network helps show where Mingquan Ye may publish in the future.
Co-authorship network of co-authors of Mingquan Ye
This figure shows the co-authorship network connecting the top 25 collaborators of Mingquan Ye. A scholar is included among the top collaborators of Mingquan Ye 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 Mingquan Ye. Mingquan Ye 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 | 0 | |
| 3 | 3 | |
| 4 | 1 | |
| 5 | 5 | |
| 6 | 5 | |
| 7 | Changing trends in the global burden of mental disorders from 1990 to 2019 and predicted levels in 25 yearsbreakdown → | 66 |
| 8 | 7 | |
| 9 | 56 | |
| 10 | 3 | |
| 11 | 11 | |
| 12 | 3 | |
| 13 | 6 | |
| 14 | 9 | |
| 15 | 12 | |
| 16 | 36 | |
| 17 | 4 | |
| 18 | The Research of Classfication Model Based on Rough Sets and RBF Neural Network | 1 |
| 19 | Application of data mining in medical diagnosis data | 0 |
| 20 | Seasonal Artificial Neural Network Forecasting Model and its Application in the GM(1, 1) Residual Error Correction | 1 |
About Mingquan Ye
Mingquan Ye is a scholar working on Neurology, Computer Vision and Pattern Recognition and Health Information Management, having authored 39 papers that have together received 527 indexed citations. Recurring topics across this work include Medical Image Segmentation Techniques (6 papers), Rough Sets and Fuzzy Logic (6 papers) and Gene expression and cancer classification (5 papers). The work is most often cited by research in Health Informatics (8 citations), Artificial Intelligence (180 citations) and Health Information Management (26 citations). Mingquan Ye has collaborated with scholars based in China and United States. Frequent co-authors include Lingyun Gao, Xuegang Hu, Xindong Wu, Donghui Hu, Xiaocen Liu, Peipei Wang, Hui Yang, Qingqing Li, Kun Lv and Maosheng Wang. Their work appears in journals such as Journal of Neurophysiology, Molecules and Frontiers in Microbiology.
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.