Kai Yu
Impact in
- Cancer Research top 5%
- MicroRNA in disease regulation
- Cancer-related molecular mechanisms research
- Cancer, Hypoxia, and Metabolism
- Geriatrics and Gerontology top 10%
Papers in
-
- Circular RNAs in diseases 9
- RNA Research and Splicing 5
- Peroxisome Proliferator-Activated Receptors 3
-
- MicroRNA in disease regulation 12
- Cancer-related molecular mechanisms research 7
- Co-authors
- Qiang Huang (11 shared papers)Nan Yang (9 shared papers)Yue Zhong (8 shared papers)Peiyu Pu (3 shared papers)Anling Zhang (3 shared papers)Zhifan Jia (3 shared papers)Guangxiu Wang (3 shared papers)Chunsheng Kang (2 shared papers)
- Journals
- Current Medicinal Chemistry (3 papers)Cell Cycle (2 papers)Aquaculture (2 papers)Experimental Biology and Medicine (2 papers)Tumor Biology (2 papers)
- Partner nations
- ChinaUnited StatesHong Kong
In The Last Decade
Kai Yu
64 papers receiving 864 citations
Peers
Comparison fields: 5 of 100
- Cancer Research 268
- Geriatrics and Gerontology 29
- Molecular Biology 403
- Aquatic Science 38
- Immunology 80
Countries citing papers authored by Kai Yu
This map shows the geographic impact of Kai Yu'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 Kai Yu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kai Yu more than expected).
Fields of papers citing papers by Kai Yu
This network shows the impact of papers produced by Kai Yu. 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 Kai Yu. The network helps show where Kai Yu may publish in the future.
Co-authors
The 25 scholars most cited alongside Kai Yu, 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 67 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2015 | 81 | |
| 2 | 2017 | 56 | |
| 3 | 2016 | 51 | |
| 4 | 2018 | 46 | |
| 5 | 2018 | 42 | |
| 6 | 2018 | 35 | |
| 7 | 2015 | 34 | |
| 8 | 2020 | 33 | |
| 9 | 2017 | 29 | |
| 10 | 2018 | 25 | |
| 11 | 2020 | 24 | |
| 12 | 2021 | 24 | |
| 13 | 2020 | 23 | |
| 14 | 2017 | 22 | |
| 15 | 2007 | 20 | |
| 16 | 2013 | 20 | |
| 17 | 2023 | 20 | |
| 18 | 2021 | 19 | |
| 19 | 2020 | 19 | |
| 20 | 2020 | 19 |
About Kai Yu
Kai Yu is a scholar working on Molecular Biology, Cancer Research, Immunology, Pulmonary and Respiratory Medicine and Oncology, having authored 67 papers that have together received 872 indexed citations. Recurring topics across this work include MicroRNA in disease regulation (12 papers), Circular RNAs in diseases (9 papers), Cancer-related molecular mechanisms research (7 papers), RNA Research and Splicing (5 papers), Ferroptosis and cancer prognosis (5 papers), Aquaculture disease management and microbiota (5 papers), Aquaculture Nutrition and Growth (4 papers) and Peroxisome Proliferator-Activated Receptors (3 papers). The work is most often cited by research in Cancer Research (268 citations), Geriatrics and Gerontology (29 citations), Molecular Biology (403 citations), Aquatic Science (38 citations) and Immunology (80 citations). Kai Yu has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Qiang Huang, Nan Yang, Yue Zhong, Peiyu Pu, Anling Zhang, Zhifan Jia, Guangxiu Wang, Chunsheng Kang, Bingcheng Ren and Fang Dai. Their work appears in journals such as Current Medicinal Chemistry, Cell Cycle, Aquaculture, Experimental Biology and Medicine and Tumor Biology.
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.