Kai Ge Yang
Impact in
- Geriatrics and Gerontology top 5%
- Sirtuins and Resveratrol in Medicine
- Cancer Research top 5%
- MicroRNA in disease regulation
- Cancer-related molecular mechanisms research
Papers in
-
- MicroRNA in disease regulation 5
- Cancer-related molecular mechanisms research 4
- Oncology 11
- Cancer Immunotherapy and Biomarkers 4
- Co-authors
- Liping Xie (14 shared papers)Qiqi Mao (12 shared papers)Xiangyi Zheng (11 shared papers)Kang Li (16 shared papers)Yan Hou (12 shared papers)Jie Qin (4 shared papers)Ge Lou (7 shared papers)Yu Bai (2 shared papers)
- Journals
- Scientific Reports (4 papers)Metabolomics (4 papers)Frontiers in Immunology (3 papers)Medicine (3 papers)BMC Cancer (2 papers)
- Partner nations
- ChinaUnited StatesCanada
In The Last Decade
Kai Ge Yang
120 papers receiving 2.2k citations
Peers
Comparison fields: 5 of 107
- Geriatrics and Gerontology 75
- Cancer Research 221
- Reproductive Medicine 80
- Oncology 132
- Molecular Biology 320
Countries citing papers authored by Kai Ge Yang
This map shows the geographic impact of Kai Ge Yang'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 Ge Yang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kai Ge Yang more than expected).
Fields of papers citing papers by Kai Ge Yang
This network shows the impact of papers produced by Kai Ge Yang. 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 Ge Yang. The network helps show where Kai Ge Yang may publish in the future.
Co-authors
The 25 scholars most cited alongside Kai Ge Yang, 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 125 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2009 | 171 | |
| 2 | 2014 | 117 | |
| 3 | 2017 | 85 | |
| 4 | 2012 | 83 | |
| 5 | 2018 | 60 | |
| 6 | 2008 | 59 | |
| 7 | 2013 | 56 | |
| 8 | 2021 | 55 | |
| 9 | 2021 | 54 | |
| 10 | 2017 | 53 | |
| 11 | 2011 | 45 | |
| 12 | 2013 | 45 | |
| 13 | 2013 | 44 | |
| 14 | 2016 | 44 | |
| 15 | 2016 | 40 | |
| 16 | 2018 | 39 | |
| 17 | 2015 | 38 | |
| 18 | 2010 | 37 | |
| 19 | 2018 | 37 | |
| 20 | 2012 | 36 |
About Kai Ge Yang
Kai Ge Yang is a scholar working on Cancer Research, Oncology, Modeling and Simulation, Reproductive Medicine and Pulmonary and Respiratory Medicine, having authored 125 papers that have together received 2.2k indexed citations. Recurring topics across this work include Ovarian cancer diagnosis and treatment (6 papers), Metabolomics and Mass Spectrometry Studies (5 papers), MicroRNA in disease regulation (5 papers), COVID-19 epidemiological studies (5 papers), Cancer Immunotherapy and Biomarkers (4 papers), Cancer-related molecular mechanisms research (4 papers), Radiomics and Machine Learning in Medical Imaging (3 papers) and Chronic Obstructive Pulmonary Disease (COPD) Research (3 papers). The work is most often cited by research in Geriatrics and Gerontology (75 citations), Cancer Research (221 citations), Reproductive Medicine (80 citations), Oncology (132 citations) and Molecular Biology (320 citations). Kai Ge Yang has collaborated with scholars based in China, United States and Canada. Frequent co-authors include Liping Xie, Qiqi Mao, Xiangyi Zheng, Kang Li, Yan Hou, Jie Qin, Ge Lou, Yu Bai, Huafeng Shen and Chaofu Ke. Their work appears in journals such as Scientific Reports, Metabolomics, Frontiers in Immunology, Medicine and BMC Cancer.
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.