Pingye Zhang
Impact in
- Dermatology top 10%
- Cancer and Skin Lesions
- Cutaneous lymphoproliferative disorders research
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- Nonmelanoma Skin Cancer Studies
Papers in
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- Statistical Methods in Clinical Trials 6
- Advanced Causal Inference Techniques 2
- Statistical Methods and Inference 2
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- Optimal Experimental Design Methods 4
- Co-authors
- Ramona F. Swaby (4 shared papers)Abhishek Joshi (3 shared papers)Jessica R. Bauman (2 shared papers)Nicole Basset‐Séguin (2 shared papers)Josep M. Piulats (2 shared papers)Brett Hughes (3 shared papers)Olga Vornicova (2 shared papers)Nicolás Meyer (3 shared papers)
- Journals
- Cancer Research (2 papers)Statistics in Biopharmaceutical Research (2 papers)Journal of Clinical Oncology (1 paper)Journal of Biopharmaceutical Statistics (1 paper)Dermatology and Therapy (1 paper)
- Partner nations
- United StatesSpainFrance
In The Last Decade
Pingye Zhang
10 papers receiving 247 citations
Peers
Comparison fields: 5 of 29
- Dermatology 62
- Epidemiology 156
- Oncology 120
- Statistics and Probability 24
- Otorhinolaryngology 9
Countries citing papers authored by Pingye Zhang
This map shows the geographic impact of Pingye Zhang'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 Pingye Zhang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pingye Zhang more than expected).
Fields of papers citing papers by Pingye Zhang
This network shows the impact of papers produced by Pingye Zhang. 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 Pingye Zhang. The network helps show where Pingye Zhang may publish in the future.
Co-authors
The 25 scholars most cited alongside Pingye Zhang, 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 | 2020 | 192 | |
| 2 | 2018 | 23 | |
| 3 | 2021 | 9 | |
| 4 | 2021 | 8 | |
| 5 | 2021 | 6 | |
| 6 | 2020 | 6 | |
| 7 | 2021 | 3 | |
| 8 | 2021 | 3 | |
| 9 | 2023 | 1 | |
| 10 | 2023 | 1 |
About Pingye Zhang
Pingye Zhang is a scholar working on Statistics and Probability, Management Science and Operations Research, Oncology, Immunology and Biomedical Engineering, having authored 10 papers that have together received 252 indexed citations. Recurring topics across this work include Statistical Methods in Clinical Trials (6 papers), Optimal Experimental Design Methods (4 papers), Cancer Immunotherapy and Biomarkers (2 papers), Advanced Causal Inference Techniques (2 papers), Statistical Methods and Inference (2 papers), Innovative Microfluidic and Catalytic Techniques Innovation (2 papers), Nonmelanoma Skin Cancer Studies (1 paper) and Head and Neck Cancer Studies (1 paper). The work is most often cited by research in Dermatology (62 citations), Epidemiology (156 citations), Oncology (120 citations), Statistics and Probability (24 citations) and Otorhinolaryngology (9 citations). Pingye Zhang has collaborated with scholars based in United States, Spain and France. Frequent co-authors include Ramona F. Swaby, Abhishek Joshi, Jessica R. Bauman, Nicole Basset‐Séguin, Josep M. Piulats, Brett Hughes, Olga Vornicova, Nicolás Meyer, Jean‐Jacques Grob and Jacob Schachter. Their work appears in journals such as Cancer Research, Statistics in Biopharmaceutical Research, Journal of Clinical Oncology, Journal of Biopharmaceutical Statistics and Dermatology and Therapy.
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.