Xiaomeng Dai
Impact in
- Cancer Research top 5%
- MicroRNA in disease regulation
- Oncology top 5%
- Cancer Immunotherapy and Biomarkers
- Cancer Cells and Metastasis
Papers in
- Oncology 25
- Cancer Immunotherapy and Biomarkers 11
- CAR-T cell therapy research 5
- Cancer Cells and Metastasis 4
- Co-authors
- Honglin Jin (8 shared papers)Chao Wan (8 shared papers)Kunyu Yang (7 shared papers)Jonathan F. Lovell (6 shared papers)Zi-Hao Liu (1 shared paper)Bin Du (1 shared paper)Yajie Sun (6 shared papers)Jingshu Meng (5 shared papers)
- Journals
- Theranostics (3 papers)Advanced Science (3 papers)Translational Oncology (3 papers)Chemical Engineering Journal (2 papers)Nano Today (2 papers)
- Partner nations
- ChinaUnited StatesHong Kong
In The Last Decade
Xiaomeng Dai
53 papers receiving 1.9k citations
Xiaomeng Dai's Hit Papers
Peers
Comparison fields: 5 of 108
- Cancer Research 504
- Oncology 644
- Immunology 466
- Hepatology 98
- Molecular Biology 826
Countries citing papers authored by Xiaomeng Dai
This map shows the geographic impact of Xiaomeng Dai'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 Xiaomeng Dai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xiaomeng Dai more than expected).
Fields of papers citing papers by Xiaomeng Dai
This network shows the impact of papers produced by Xiaomeng Dai. 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 Xiaomeng Dai. The network helps show where Xiaomeng Dai may publish in the future.
Co-authors
The 25 scholars most cited alongside Xiaomeng Dai, 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 54 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 195 | |
| 2 | 2020 | 189 | |
| 3 | 2019 | 154 | |
| 4 | 2015 | 145 | |
| 5 | 2017 | 89 | |
| 6 | 2021 | 82 | |
| 7 | 2021 | 81 | |
| 8 | 2020 | 76 | |
| 9 | 2020 | 70 | |
| 10 | Nano-enhanced immunotherapy: Targeting the immunosuppressive tumor microenvironment Hit paper breakdown → | 2024 | 68 |
| 11 | 2021 | 57 | |
| 12 | 2018 | 54 | |
| 13 | 2018 | 51 | |
| 14 | 2017 | 49 | |
| 15 | 2023 | 46 | |
| 16 | 2017 | 44 | |
| 17 | 2017 | 40 | |
| 18 | 2017 | 39 | |
| 19 | 2024 | 35 | |
| 20 | 2022 | 35 |
About Xiaomeng Dai
Xiaomeng Dai is a scholar working on Oncology, Molecular Biology, Immunology, Cancer Research and Pulmonary and Respiratory Medicine, having authored 54 papers that have together received 1.9k indexed citations. Recurring topics across this work include Cancer Immunotherapy and Biomarkers (11 papers), Immune cells in cancer (7 papers), Nanoplatforms for cancer theranostics (6 papers), Immunotherapy and Immune Responses (6 papers), Intensive Care Unit Cognitive Disorders (5 papers), CAR-T cell therapy research (5 papers), Hepatocellular Carcinoma Treatment and Prognosis (4 papers) and Cancer Cells and Metastasis (4 papers). The work is most often cited by research in Cancer Research (504 citations), Oncology (644 citations), Immunology (466 citations), Hepatology (98 citations) and Molecular Biology (826 citations). Xiaomeng Dai has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Honglin Jin, Chao Wan, Kunyu Yang, Jonathan F. Lovell, Zi-Hao Liu, Bin Du, Yajie Sun, Jingshu Meng, Gang Wu and Jing Huang. Their work appears in journals such as Theranostics, Advanced Science, Translational Oncology, Chemical Engineering Journal and Nano Today.
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.