Mao Ding
Impact in
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- MicroRNA in disease regulation
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- Computational Drug Discovery Methods
Papers in
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- Protein Structure and Dynamics 4
- Machine Learning in Bioinformatics 2
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- Computational Drug Discovery Methods 9
- Co-authors
- Tao Song (6 shared papers)Shudong Wang (6 shared papers)Zhaohong Xie (4 shared papers)Shunliang Xu (4 shared papers)Jianzhong Bi (4 shared papers)Linlin Xu (3 shared papers)Ping Wang (3 shared papers)Zhengyu Zhu (2 shared papers)
In The Last Decade
Mao Ding
22 papers receiving 510 citations
Peers
Comparison fields: 5 of 94
- Cancer Research 120
- Computational Theory and Mathematics 113
- Neurology 46
- Molecular Biology 357
- Biological Psychiatry 12
Countries citing papers authored by Mao Ding
This map shows the geographic impact of Mao Ding'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 Mao Ding with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mao Ding more than expected).
Fields of papers citing papers by Mao Ding
This network shows the impact of papers produced by Mao Ding. 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 Mao Ding. The network helps show where Mao Ding may publish in the future.
Co-authors
The 25 scholars most cited alongside Mao Ding, 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 23 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2018 | 222 | |
| 2 | 2022 | 51 | |
| 3 | 2021 | 43 | |
| 4 | 2021 | 41 | |
| 5 | 2020 | 31 | |
| 6 | 2020 | 19 | |
| 7 | 2022 | 16 | |
| 8 | 2020 | 12 | |
| 9 | 2020 | 11 | |
| 10 | 2021 | 11 | |
| 11 | 2021 | 11 | |
| 12 | 2021 | 9 | |
| 13 | 2023 | 8 | |
| 14 | 2020 | 8 | |
| 15 | 2020 | 6 | |
| 16 | 2021 | 5 | |
| 17 | 2023 | 4 | |
| 18 | 2025 | 2 | |
| 19 | 2025 | 1 | |
| 20 | 2024 | 1 |
About Mao Ding
Mao Ding is a scholar working on Molecular Biology, Computational Theory and Mathematics, Electrical and Electronic Engineering, Civil and Structural Engineering and Cognitive Neuroscience, having authored 23 papers that have together received 514 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (9 papers), Integrated Energy Systems Optimization (6 papers), Protein Structure and Dynamics (4 papers), Infrastructure Resilience and Vulnerability Analysis (3 papers), Machine Learning in Bioinformatics (2 papers), Alzheimer's disease research and treatments (2 papers), Machine Learning in Materials Science (2 papers) and Analytical Chemistry and Chromatography (2 papers). The work is most often cited by research in Cancer Research (120 citations), Computational Theory and Mathematics (113 citations), Neurology (46 citations), Molecular Biology (357 citations) and Biological Psychiatry (12 citations). Mao Ding has collaborated with scholars based in China, Spain and France. Frequent co-authors include Tao Song, Shudong Wang, Zhaohong Xie, Shunliang Xu, Jianzhong Bi, Linlin Xu, Ping Wang, Zhengyu Zhu, Yang Shen and Alfonso Rodríguez‐Patón. Their work appears in journals such as Sustainable Cities and Society, Combinatorial Chemistry & High Throughput Screening, Frontiers in Genetics, Biomedical Materials and Neurochemical Research.
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.