Ding Ai
- Biochemistry top 0.5%
- Eicosanoids and Hypertension Pharmacology 22
- Cell Biology top 1%
- Hippo pathway signaling and YAP/TAZ 15
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- Hormonal Regulation and Hypertension 9
- Diet, Metabolism, and Disease 5
- Cancer Research top 5%
- Immunology top 5%
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- Fatty Acid Research and Health 6
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- Angiogenesis and VEGF in Cancer 5
- Metabolomics and Mass Spectrometry Studies 4
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- Liver Disease Diagnosis and Treatment 5
Ding Ai
83 papers receiving 3.6k citations
Hit Papers
Peers
Comparison fields: 5 of 123
- Biochemistry 803
- Cell Biology 785
- Endocrinology, Diabetes and Metabolism 523
- Cancer Research 457
- Immunology 484
Countries citing papers authored by Ding Ai
This map shows the geographic impact of Ding Ai'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 Ding Ai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ding Ai more than expected).
Fields of papers citing papers by Ding Ai
This network shows the impact of papers produced by Ding Ai. 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 Ding Ai. The network helps show where Ding Ai may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Ding Ai, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 1 | |
| 2 | 2025 | 3 | |
| 3 | 2024 | 3 | |
| 4 | 2024 | 14 | |
| 5 | 2024 | 7 | |
| 6 | 2024 | 4 | |
| 7 | 2023 | 3 | |
| 8 | 2023 | 1 | |
| 9 | 2023 | 35 | |
| 10 | 2021 | 32 | |
| 11 | 2020 | 80 | |
| 12 | 2020 | 93 | |
| 13 | 2017 | 90 | |
| 14 | 2014 | 7 | |
| 15 | 2013 | 52 | |
| 16 | 2009 | 8 | |
| 17 | 2007 | 134 | |
| 18 | Research and development of the automatic modeling system for Monte Carlo particle transport simulation | 2006 | 16 |
| 19 | Computer Simulation of City Traffic Design | 2000 | 1 |
| 20 | Shoot Regeneration of Protoplasts Isolated from the Embryogenic Suspension Cell Lines of Apple | 1992 | 1 |
About Ding Ai
Ding Ai is a scholar working on Biochemistry, Cell Biology, Endocrinology, Diabetes and Metabolism, Molecular Biology and Aging, having authored 84 papers that have together received 3.6k indexed citations. Recurring topics across this work include Eicosanoids and Hypertension Pharmacology (22 papers), Hippo pathway signaling and YAP/TAZ (15 papers), Hormonal Regulation and Hypertension (9 papers), Fatty Acid Research and Health (6 papers), Angiogenesis and VEGF in Cancer (5 papers), Liver Disease Diagnosis and Treatment (5 papers), Diet, Metabolism, and Disease (5 papers) and Metabolomics and Mass Spectrometry Studies (4 papers). The work is most often cited by research in Biochemistry (803 citations), Cell Biology (785 citations), Endocrinology, Diabetes and Metabolism (523 citations), Cancer Research (457 citations) and Immunology (484 citations). Ding Ai has collaborated with scholars based in China, United States and Romania. Frequent co-authors include Yi Zhu, Xu Zhang, Jinlong He, Chunjiong Wang, Bochuan Li, Bruce D. Hammock, Alan R. Tall, Hongfeng Jiang, Huizhen Lv and Nanping Wang. Their work appears in journals such as Circulation Research, Journal of Clinical Investigation, British Journal of Pharmacology, Circulation and Proceedings of the National Academy of Sciences.
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.