Weiming Mou
Impact in
- Health Informatics top 5%
- Artificial Intelligence in Healthcare and Education
Papers in
- Oncology 13
- Cancer Immunotherapy and Biomarkers 8
- CAR-T cell therapy research 3
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- Artificial Intelligence in Healthcare and Education 10
- Co-authors
- Lingxuan Zhu (21 shared papers)Anqi Lin (22 shared papers)Peng Luo (22 shared papers)Jian Zhang (14 shared papers)Aimin Jiang (9 shared papers)Zaoqu Liu (6 shared papers)Quan Cheng (8 shared papers)Cheng Quan (2 shared papers)
- Journals
- International Journal of Surgery (3 papers)BMC Medicine (2 papers)Gut Microbes (1 paper)JMIR mhealth and uhealth (1 paper)Technology in Cancer Research & Treatment (1 paper)
- Partner nations
- ChinaHong KongUnited States
In The Last Decade
Weiming Mou
29 papers receiving 229 citations
Weiming Mou's Hit Papers
Peers
Comparison fields: 5 of 67
- Health Informatics 39
- Biological Psychiatry 7
- Oncology 70
- Cancer Research 24
- Immunology 32
Countries citing papers authored by Weiming Mou
This map shows the geographic impact of Weiming Mou'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 Weiming Mou with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Weiming Mou more than expected).
Fields of papers citing papers by Weiming Mou
This network shows the impact of papers produced by Weiming Mou. 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 Weiming Mou. The network helps show where Weiming Mou may publish in the future.
Co-authors
The 25 scholars most cited alongside Weiming Mou, 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 30 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | From chaos to order: optimizing fecal microbiota transplantation for enhanced immune checkpoint inhibitors efficacy Hit paper breakdown → | 2025 | 34 |
| 2 | 2021 | 27 | |
| 3 | 2024 | 22 | |
| 4 | 2025 | 14 | |
| 5 | 2024 | 14 | |
| 6 | 2024 | 13 | |
| 7 | 2024 | 12 | |
| 8 | 2024 | 12 | |
| 9 | 2022 | 11 | |
| 10 | 2024 | 10 | |
| 11 | 2025 | 10 | |
| 12 | 2025 | 10 | |
| 13 | 2025 | 7 | |
| 14 | 2024 | 6 | |
| 15 | 2025 | 5 | |
| 16 | 2024 | 5 | |
| 17 | 2025 | 4 | |
| 18 | 2022 | 4 | |
| 19 | 2022 | 3 | |
| 20 | 2025 | 3 |
About Weiming Mou
Weiming Mou is a scholar working on Oncology, Health Informatics, Molecular Biology, Artificial Intelligence and Pulmonary and Respiratory Medicine, having authored 30 papers that have together received 235 indexed citations. Recurring topics across this work include Artificial Intelligence in Healthcare and Education (10 papers), Cancer Immunotherapy and Biomarkers (8 papers), Gut microbiota and health (5 papers), CAR-T cell therapy research (3 papers), Radiomics and Machine Learning in Medical Imaging (3 papers), Topic Modeling (3 papers), COVID-19 diagnosis using AI (2 papers) and Immune Cell Function and Interaction (2 papers). The work is most often cited by research in Health Informatics (39 citations), Biological Psychiatry (7 citations), Oncology (70 citations), Cancer Research (24 citations) and Immunology (32 citations). Weiming Mou has collaborated with scholars based in China, Hong Kong and United States. Frequent co-authors include Lingxuan Zhu, Anqi Lin, Peng Luo, Jian Zhang, Aimin Jiang, Zaoqu Liu, Quan Cheng, Cheng Quan, Yancheng Lai and Ting Wei. Their work appears in journals such as International Journal of Surgery, BMC Medicine, Gut Microbes, JMIR mhealth and uhealth and Technology in Cancer Research & Treatment.
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.