Zhaojun Wang
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- Advanced Multi-Objective Optimization Algorithms 6
- Reproductive Medicine top 10%
- Artificial Intelligence top 10%
- Metaheuristic Optimization Algorithms Research 6
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- Air Quality and Health Impacts 6
- Climate Change and Health Impacts 3
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- Occupational and environmental lung diseases 4
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- Genetic and phenotypic traits in livestock 3
- Genetic Mapping and Diversity in Plants and Animals 3
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- Esophageal Cancer Research and Treatment 3
- Journals
- Nature Communications (1 paper)Journal of Lipid Research (1 paper)BMC Public Health (3 papers)
- Partner nations
- ChinaUnited StatesHong Kong
In The Last Decade
Zhaojun Wang
53 papers receiving 593 citations
Hit Papers
Peers
Comparison fields: 5 of 122
- Computational Theory and Mathematics 99
- Reproductive Medicine 47
- Cancer Research 72
- Biophysics 20
- Artificial Intelligence 100
Countries citing papers authored by Zhaojun Wang
This map shows the geographic impact of Zhaojun Wang'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 Zhaojun Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Zhaojun Wang more than expected).
Fields of papers citing papers by Zhaojun Wang
This network shows the impact of papers produced by Zhaojun Wang. 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 Zhaojun Wang. The network helps show where Zhaojun Wang may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Zhaojun Wang, 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 | 0 | |
| 2 | 2025 | 0 | |
| 3 | 2025 | 1 | |
| 4 | 2025 | 0 | |
| 5 | 2024 | 3 | |
| 6 | 2024 | 1 | |
| 7 | 2023 | 17 | |
| 8 | 2023 | 10 | |
| 9 | 2023 | 18 | |
| 10 | 2023 | 7 | |
| 11 | 2023 | 1 | |
| 12 | 2022 | 13 | |
| 13 | 2022 | 7 | |
| 14 | 2022 | 4 | |
| 15 | 2022 | 24 | |
| 16 | 2018 | 5 | |
| 17 | 2018 | 2 | |
| 18 | 2016 | 40 | |
| 19 | 2015 | 18 | |
| 20 | Analysis of the Strategies of Business Marketing according to Green Barrier | 2006 | 1 |
About Zhaojun Wang
Zhaojun Wang is a scholar working on Aging, Health, Toxicology and Mutagenesis and Computational Theory and Mathematics, having authored 59 papers that have together received 602 indexed citations. Recurring topics across this work include Advanced Multi-Objective Optimization Algorithms (6 papers), Metaheuristic Optimization Algorithms Research (6 papers), Air Quality and Health Impacts (6 papers), Occupational and environmental lung diseases (4 papers), Genetic and phenotypic traits in livestock (3 papers), Climate Change and Health Impacts (3 papers), Esophageal Cancer Research and Treatment (3 papers) and Genetic Mapping and Diversity in Plants and Animals (3 papers). The work is most often cited by research in Computational Theory and Mathematics (99 citations), Reproductive Medicine (47 citations) and Cancer Research (72 citations). Zhaojun Wang has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Junhang Zhang, Wenji Li, Zhun Fan, Donglei Shi, Yutong Yuan, Lin Ma, Baoyi Huang, Haifeng Li, Yugen You and Yi Fang. Their work appears in journals such as Nature Communications, Journal of Lipid Research and BMC Public Health.
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.