Jun Mao
Impact in
-
- Computational Drug Discovery Methods
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- Plant Stress Responses and Tolerance
- Seed Germination and Physiology
- Plant responses to water stress
- Plant Molecular Biology Research
- Plant responses to elevated CO2
Papers in
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- Receptor Mechanisms and Signaling 2
- Cancer therapeutics and mechanisms 2
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- Computational Drug Discovery Methods 5
- Co-authors
- Qinghua Yang (4 shared papers)Huifang Zheng (3 shared papers)Ruixin Shao (3 shared papers)Hui Zhang (7 shared papers)Yifei Qi (2 shared papers)Lan Ding (5 shared papers)John Z. H. Zhang (2 shared papers)Min Wei (1 shared paper)
- Journals
- Photosynthetica (2 papers)Molecular Diversity (1 paper)Journal of Alloys and Compounds (1 paper)Chemico-Biological Interactions (1 paper)Functional Plant Biology (1 paper)
- Partner nations
- ChinaUnited StatesSingapore
In The Last Decade
Jun Mao
20 papers receiving 318 citations
Peers
Comparison fields: 5 of 93
- Computational Theory and Mathematics 81
- Plant Science 122
- Physiology 8
- Endocrine and Autonomic Systems 10
- Health Informatics 2
Countries citing papers authored by Jun Mao
This map shows the geographic impact of Jun Mao'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 Jun Mao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jun Mao more than expected).
Fields of papers citing papers by Jun Mao
This network shows the impact of papers produced by Jun Mao. 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 Jun Mao. The network helps show where Jun Mao may publish in the future.
Co-authors
The 25 scholars most cited alongside Jun Mao, 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 21 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2015 | 72 | |
| 2 | 2022 | 52 | |
| 3 | 2018 | 28 | |
| 4 | 2020 | 25 | |
| 5 | 2018 | 24 | |
| 6 | 2019 | 23 | |
| 7 | 2020 | 16 | |
| 8 | 2015 | 14 | |
| 9 | 2022 | 11 | |
| 10 | 2023 | 11 | |
| 11 | 2020 | 8 | |
| 12 | 2021 | 8 | |
| 13 | 2021 | 7 | |
| 14 | 2004 | 6 | |
| 15 | 2022 | 5 | |
| 16 | 2004 | 3 | |
| 17 | 2021 | 2 | |
| 18 | 2017 | 2 | |
| 19 | 2022 | 1 | |
| 20 | 2024 | 1 |
About Jun Mao
Jun Mao is a scholar working on Molecular Biology, Computational Theory and Mathematics, Plant Science, Cell Biology and Pharmacology, having authored 21 papers that have together received 319 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (5 papers), Microtubule and mitosis dynamics (4 papers), Plant Stress Responses and Tolerance (4 papers), Plant responses to elevated CO2 (4 papers), Cholinesterase and Neurodegenerative Diseases (2 papers), Cancer Treatment and Pharmacology (2 papers), Receptor Mechanisms and Signaling (2 papers) and Cancer therapeutics and mechanisms (2 papers). The work is most often cited by research in Computational Theory and Mathematics (81 citations), Plant Science (122 citations), Physiology (8 citations), Endocrine and Autonomic Systems (10 citations) and Health Informatics (2 citations). Jun Mao has collaborated with scholars based in China, United States and Singapore. Frequent co-authors include Qinghua Yang, Huifang Zheng, Ruixin Shao, Hui Zhang, Yifei Qi, Lan Ding, John Z. H. Zhang, Min Wei, Xudong Zhang and Chun‐Tao Liu. Their work appears in journals such as Photosynthetica, Molecular Diversity, Journal of Alloys and Compounds, Chemico-Biological Interactions and Functional Plant 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.