Dai Dai
Impact in
- Immunology top 10%
- Immune Cell Function and Interaction
- T-cell and B-cell Immunology
- Psoriasis: Treatment and Pathogenesis
- Cognitive Neuroscience top 10%
- Functional Brain Connectivity Studies
- EEG and Brain-Computer Interfaces
Papers in
-
- Immune Cell Function and Interaction 5
- T-cell and B-cell Immunology 2
-
- Systemic Lupus Erythematosus Research 4
- Co-authors
- Huiguang He (3 shared papers)Nan Shen (12 shared papers)Fangfang Qu (1 shared paper)Youcun Qian (1 shared paper)Xiao He (1 shared paper)Jingjing Wang (1 shared paper)Hanchao Gao (1 shared paper)Honglin Wang (1 shared paper)
- Journals
- Arthritis & Rheumatology (3 papers)The Journal of Rheumatology (1 paper)Lupus Science & Medicine (1 paper)Arthritis Research & Therapy (1 paper)Machine Vision and Applications (1 paper)
- Partner nations
- ChinaUnited StatesJapan
In The Last Decade
Dai Dai
19 papers receiving 635 citations
Peers
Comparison fields: 5 of 92
- Immunology 220
- Cognitive Neuroscience 137
- Psychiatry and Mental health 83
- Rheumatology 64
- Neurology 33
Countries citing papers authored by Dai Dai
This map shows the geographic impact of Dai 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 Dai Dai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dai Dai more than expected).
Fields of papers citing papers by Dai Dai
This network shows the impact of papers produced by Dai 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 Dai Dai. The network helps show where Dai Dai may publish in the future.
Co-authors
The 25 scholars most cited alongside Dai 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 21 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2015 | 185 | |
| 2 | 2012 | 129 | |
| 3 | 2017 | 62 | |
| 4 | 2014 | 47 | |
| 5 | 2015 | 35 | |
| 6 | 2012 | 35 | |
| 7 | 2014 | 27 | |
| 8 | 2020 | 23 | |
| 9 | 2022 | 18 | |
| 10 | 2023 | 17 | |
| 11 | 2022 | 16 | |
| 12 | 2023 | 15 | |
| 13 | 2022 | 15 | |
| 14 | 2020 | 6 | |
| 15 | 2022 | 6 | |
| 16 | 2024 | 2 | |
| 17 | 2008 | 2 | |
| 18 | 2025 | 1 | |
| 19 | 2013 | 1 | |
| 20 | 2025 | 0 |
About Dai Dai
Dai Dai is a scholar working on Immunology, Rheumatology, Cognitive Neuroscience, Cancer Research and Computer Networks and Communications, having authored 21 papers that have together received 642 indexed citations. Recurring topics across this work include Immune Cell Function and Interaction (5 papers), Systemic Lupus Erythematosus Research (4 papers), Functional Brain Connectivity Studies (3 papers), Advanced Neuroimaging Techniques and Applications (2 papers), T-cell and B-cell Immunology (2 papers), Domain Adaptation and Few-Shot Learning (1 paper), Network Security and Intrusion Detection (1 paper) and Peer-to-Peer Network Technologies (1 paper). The work is most often cited by research in Immunology (220 citations), Cognitive Neuroscience (137 citations), Psychiatry and Mental health (83 citations), Rheumatology (64 citations) and Neurology (33 citations). Dai Dai has collaborated with scholars based in China, United States and Japan. Frequent co-authors include Huiguang He, Nan Shen, Fangfang Qu, Youcun Qian, Xiao He, Jingjing Wang, Hanchao Gao, Honglin Wang, Ju Qiu and Xinyang Song. Their work appears in journals such as Arthritis & Rheumatology, The Journal of Rheumatology, Lupus Science & Medicine, Arthritis Research & Therapy and Machine Vision 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.