Zhangming Niu
- Molecular Biology
- Computational Theory and Mathematics top 1%
- Radiology, Nuclear Medicine and Imaging top 5%
- Artificial Intelligence top 5%
- Materials Chemistry
- Co-authors
- Evandro Fei FangGuang YangXiangxiang ZengXianglu XiaoYinghui JiangWade Menpes-SmithShuangjia ZhengYuedong Yang
- Topics
- Computational Drug Discovery Methods (16 papers)Machine Learning in Materials Science (10 papers)Protein Structure and Dynamics (9 papers)
- Cited by
- Health InformaticsComputational Theory and MathematicsRadiology, Nuclear Medicine and Imaging
- Partner nations
- ChinaUnited KingdomUnited States
In The Last Decade
Zhangming Niu
27 papers receiving 1.1k citations
Hit Papers
Peers
Comparison fields: 5 of 130
- Molecular Biology 424
- Computational Theory and Mathematics 360
- Radiology, Nuclear Medicine and Imaging 339
- Artificial Intelligence 286
- Materials Chemistry 174
Countries citing papers authored by Zhangming Niu
This map shows the geographic impact of Zhangming Niu'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 Zhangming Niu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Zhangming Niu more than expected).
Fields of papers citing papers by Zhangming Niu
This network shows the impact of papers produced by Zhangming Niu. 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 Zhangming Niu. The network helps show where Zhangming Niu may publish in the future.
Co-authorship network of co-authors of Zhangming Niu
This figure shows the co-authorship network connecting the top 25 collaborators of Zhangming Niu. A scholar is included among the top collaborators of Zhangming Niu 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 Zhangming Niu. Zhangming Niu 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 | 7 | |
| 3 | 2 | |
| 4 | 0 | |
| 5 | 16 | |
| 6 | 2 | |
| 7 | 39 | |
| 8 | 35 | |
| 9 | 41 | |
| 10 | 44 | |
| 11 | 27 | |
| 12 | 16 | |
| 13 | 83 | |
| 14 | 32 | |
| 15 | 3 | |
| 16 | 9 | |
| 17 | 29 | |
| 18 | 24 | |
| 19 | 40 | |
| 20 | 5 |
About Zhangming Niu
Zhangming Niu is a scholar working on Computational Theory and Mathematics, Health Informatics and Geriatrics and Gerontology, having authored 29 papers that have together received 1.1k indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (16 papers), Machine Learning in Materials Science (10 papers) and Protein Structure and Dynamics (9 papers). The work is most often cited by research in Health Informatics (79 citations), Computational Theory and Mathematics (360 citations) and Radiology, Nuclear Medicine and Imaging (339 citations). Zhangming Niu has collaborated with scholars based in China, United Kingdom and United States. Frequent co-authors include Evandro Fei Fang, Guang Yang, Xiangxiang Zeng, Xianglu Xiao, Yinghui Jiang, Wade Menpes-Smith, Shuangjia Zheng, Yuedong Yang, Jun Xia and Minhao Wang. Their work appears in journals such as PLoS Medicine, IEEE Access and Advanced Science.
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.