Xiaoying Ye
Impact in
- Aging top 1%
- Genetics, Aging, and Longevity in Model Organisms
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- Circadian rhythm and melatonin
Papers in
-
- Pluripotent Stem Cells Research 20
- CRISPR and Genetic Engineering 11
- Physiology 17
- Telomeres, Telomerase, and Senescence 7
- Nitric Oxide and Endothelin Effects 6
- Co-authors
- Lin Liu (20 shared papers)Jonathan V. Sweedler (7 shared papers)Josip Blonder (16 shared papers)David L. Keefe (10 shared papers)Stanislav S. Rubakhin (6 shared papers)Timothy D. Veenstra (11 shared papers)Yu Yin (6 shared papers)Mengyuan Liu (3 shared papers)
- Journals
- Cell Research (3 papers)Cell Reports (3 papers)Electrophoresis (3 papers)PLoS ONE (3 papers)Stem Cells and Development (2 papers)
- Partner nations
- ChinaUnited StatesCanada
In The Last Decade
Xiaoying Ye
77 papers receiving 2.7k citations
Peers
Comparison fields: 5 of 131
- Aging 167
- Endocrine and Autonomic Systems 179
- Reproductive Medicine 212
- Geriatrics and Gerontology 95
- Molecular Biology 1.5k
Countries citing papers authored by Xiaoying Ye
This map shows the geographic impact of Xiaoying Ye'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 Xiaoying Ye with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xiaoying Ye more than expected).
Fields of papers citing papers by Xiaoying Ye
This network shows the impact of papers produced by Xiaoying Ye. 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 Xiaoying Ye. The network helps show where Xiaoying Ye may publish in the future.
Co-authors
The 25 scholars most cited alongside Xiaoying Ye, 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 84 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2013 | 236 | |
| 2 | 2012 | 193 | |
| 3 | 2012 | 173 | |
| 4 | 2012 | 145 | |
| 5 | 2014 | 128 | |
| 6 | 2011 | 122 | |
| 7 | 2012 | 115 | |
| 8 | 2009 | 93 | |
| 9 | 2011 | 77 | |
| 10 | 2008 | 72 | |
| 11 | 2013 | 65 | |
| 12 | 2016 | 60 | |
| 13 | 2022 | 54 | |
| 14 | 2018 | 52 | |
| 15 | 2008 | 51 | |
| 16 | 2008 | 51 | |
| 17 | 2009 | 48 | |
| 18 | 2007 | 47 | |
| 19 | 2014 | 41 | |
| 20 | 2008 | 41 |
About Xiaoying Ye
Xiaoying Ye is a scholar working on Molecular Biology, Physiology, Public Health, Environmental and Occupational Health, Spectroscopy and Bioengineering, having authored 84 papers that have together received 2.7k indexed citations. Recurring topics across this work include Pluripotent Stem Cells Research (20 papers), CRISPR and Genetic Engineering (11 papers), Advanced Proteomics Techniques and Applications (10 papers), Analytical Chemistry and Sensors (9 papers), Reproductive Biology and Fertility (8 papers), Mass Spectrometry Techniques and Applications (7 papers), Telomeres, Telomerase, and Senescence (7 papers) and Nitric Oxide and Endothelin Effects (6 papers). The work is most often cited by research in Aging (167 citations), Endocrine and Autonomic Systems (179 citations), Reproductive Medicine (212 citations), Geriatrics and Gerontology (95 citations) and Molecular Biology (1.5k citations). Xiaoying Ye has collaborated with scholars based in China, United States and Canada. Frequent co-authors include Lin Liu, Jonathan V. Sweedler, Josip Blonder, David L. Keefe, Stanislav S. Rubakhin, Timothy D. Veenstra, Yu Yin, Mengyuan Liu, Ming Zeng and Qiang Zhao. Their work appears in journals such as Cell Research, Cell Reports, Electrophoresis, PLoS ONE and Stem Cells and Development.
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.