Chenchen Wu
Impact in
- Microbiology top 10%
- Antimicrobial Peptides and Activities
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- Supramolecular Self-Assembly in Materials
Papers in
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- AI in cancer detection 5
- Anomaly Detection Techniques and Applications 2
- Domain Adaptation and Few-Shot Learning 2
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- Digital Imaging for Blood Diseases 2
- Co-authors
- Yucheng Zhang (1 shared paper)Qing Dai (2 shared papers)Peng Tan (1 shared paper)Huiyang Fu (1 shared paper)Tao Wang (1 shared paper)Xi Ma (1 shared paper)Shenrui Xu (1 shared paper)Yujing Wang (1 shared paper)
- Journals
- Advanced Materials (2 papers)Forests (2 papers)Science China Earth Sciences (2 papers)Advanced Engineering Informatics (1 paper)Multimedia Tools and Applications (1 paper)
- Partner nations
- ChinaUnited KingdomGermany
In The Last Decade
Chenchen Wu
23 papers receiving 274 citations
Peers
Comparison fields: 5 of 85
- Microbiology 39
- Biomaterials 33
- Computer Vision and Pattern Recognition 50
- Biomedical Engineering 69
- Artificial Intelligence 48
Countries citing papers authored by Chenchen Wu
This map shows the geographic impact of Chenchen Wu'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 Chenchen Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chenchen Wu more than expected).
Fields of papers citing papers by Chenchen Wu
This network shows the impact of papers produced by Chenchen Wu. 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 Chenchen Wu. The network helps show where Chenchen Wu may publish in the future.
Co-authors
The 25 scholars most cited alongside Chenchen Wu, 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 28 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2023 | 88 | |
| 2 | 2020 | 32 | |
| 3 | 2023 | 22 | |
| 4 | 2024 | 21 | |
| 5 | 2022 | 20 | |
| 6 | 2024 | 15 | |
| 7 | 2018 | 15 | |
| 8 | 2023 | 11 | |
| 9 | 2019 | 9 | |
| 10 | 2025 | 8 | |
| 11 | 2024 | 7 | |
| 12 | 2021 | 7 | |
| 13 | 2019 | 6 | |
| 14 | 2024 | 4 | |
| 15 | 2023 | 3 | |
| 16 | 2019 | 3 | |
| 17 | 2024 | 2 | |
| 18 | 2024 | 1 | |
| 19 | 2023 | 1 | |
| 20 | 2024 | 1 |
About Chenchen Wu
Chenchen Wu is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Molecular Biology, Global and Planetary Change and Biomedical Engineering, having authored 28 papers that have together received 279 indexed citations. Recurring topics across this work include AI in cancer detection (5 papers), Radiomics and Machine Learning in Medical Imaging (3 papers), Digital Imaging for Blood Diseases (2 papers), Machine Fault Diagnosis Techniques (2 papers), Anomaly Detection Techniques and Applications (2 papers), Domain Adaptation and Few-Shot Learning (2 papers), Environmental and Agricultural Sciences (2 papers) and Atmospheric and Environmental Gas Dynamics (2 papers). The work is most often cited by research in Microbiology (39 citations), Biomaterials (33 citations), Computer Vision and Pattern Recognition (50 citations), Biomedical Engineering (69 citations) and Artificial Intelligence (48 citations). Chenchen Wu has collaborated with scholars based in China, United Kingdom and Germany. Frequent co-authors include Yucheng Zhang, Qing Dai, Peng Tan, Huiyang Fu, Tao Wang, Xi Ma, Shenrui Xu, Yujing Wang, Qingyan Wang and Junqiu Yue. Their work appears in journals such as Advanced Materials, Forests, Science China Earth Sciences, Advanced Engineering Informatics and Multimedia Tools and Applications.
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.