Ze Wang
Impact in
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- Pharmacological Effects of Natural Compounds
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- Computational Drug Discovery Methods
Papers in
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- Computational Drug Discovery Methods 5
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- Plant-based Medicinal Research 2
- Co-authors
- Guixia Liu (3 shared papers)Yun Tang (4 shared papers)Weihua Li (3 shared papers)Yimeng Wang (1 shared paper)Jie Yu (1 shared paper)Zhi Yong Huang (1 shared paper)Zhuohang Yu (1 shared paper)Yu Yan Jiang (1 shared paper)
- Journals
- International Journal of Molecular Medicine (2 papers)RSC Advances (2 papers)Laser & Photonics Review (1 paper)Materials Research Letters (1 paper)Animals (1 paper)
- Partner nations
- ChinaUnited StatesSouth Korea
In The Last Decade
Ze Wang
42 papers receiving 231 citations
Peers
Comparison fields: 5 of 87
- Pharmacology 16
- Computational Theory and Mathematics 18
- Complementary and alternative medicine 7
- Biomedical Engineering 40
- Computational Mechanics 15
Countries citing papers authored by Ze Wang
This map shows the geographic impact of Ze Wang'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 Ze Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ze Wang more than expected).
Fields of papers citing papers by Ze Wang
This network shows the impact of papers produced by Ze Wang. 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 Ze Wang. The network helps show where Ze Wang may publish in the future.
Co-authors
The 25 scholars most cited alongside Ze Wang, 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 48 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2023 | 24 | |
| 2 | 2023 | 19 | |
| 3 | 2024 | 15 | |
| 4 | 2024 | 13 | |
| 5 | 2024 | 13 | |
| 6 | 2023 | 12 | |
| 7 | 2024 | 11 | |
| 8 | 2020 | 10 | |
| 9 | 2024 | 9 | |
| 10 | Learning to Learn Dense Gaussian Processes for Few-Shot Learning | 2021 | 8 |
| 11 | 2024 | 8 | |
| 12 | 2021 | 7 | |
| 13 | 2025 | 6 | |
| 14 | 2019 | 6 | |
| 15 | 2019 | 6 | |
| 16 | 2023 | 6 | |
| 17 | 2024 | 5 | |
| 18 | 2025 | 5 | |
| 19 | [Prediction of drug bioavailability by genetic algorithm and artificial neural network]. | 2006 | 4 |
| 20 | 2024 | 4 |
About Ze Wang
Ze Wang is a scholar working on Computational Theory and Mathematics, Pharmacology, Bioengineering, Artificial Intelligence and Biomedical Engineering, having authored 48 papers that have together received 238 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (5 papers), Bioinformatics and Genomic Networks (3 papers), Nanoplatforms for cancer theranostics (2 papers), Domain Adaptation and Few-Shot Learning (2 papers), Advanced Sensor and Energy Harvesting Materials (2 papers), Plant-based Medicinal Research (2 papers), Text and Document Classification Technologies (2 papers) and Electrical Contact Performance and Analysis (2 papers). The work is most often cited by research in Pharmacology (16 citations), Computational Theory and Mathematics (18 citations), Complementary and alternative medicine (7 citations), Biomedical Engineering (40 citations) and Computational Mechanics (15 citations). Ze Wang has collaborated with scholars based in China, United States and South Korea. Frequent co-authors include Guixia Liu, Yun Tang, Weihua Li, Yimeng Wang, Jie Yu, Zhi Yong Huang, Zhuohang Yu, Yu Yan Jiang, Zengrui Wu and Weidong Peng. Their work appears in journals such as International Journal of Molecular Medicine, RSC Advances, Laser & Photonics Review, Materials Research Letters and Animals.
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.