Mingyu Ji
Impact in
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- SARS-CoV-2 and COVID-19 Research
- SARS-CoV-2 detection and testing
- COVID-19 Clinical Research Studies
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- Reproductive tract infections research
Papers in
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- Topic Modeling 4
- Advanced Text Analysis Techniques 4
- Natural Language Processing Techniques 3
- Sentiment Analysis and Opinion Mining 3
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- RNA modifications and cancer 3
- Co-authors
- Fengyan Pei (9 shared papers)Yunying Zhou (9 shared papers)Yunshan Wang (8 shared papers)Qianqian Zhao (4 shared papers)Huanjie Li (5 shared papers)Qingxi Wang (4 shared papers)Weihua Yang (4 shared papers)Yunshan Wang (3 shared papers)
- Journals
- PLoS ONE (2 papers)BMC Cancer (2 papers)Frontiers in Plant Science (1 paper)Frontiers in Cellular and Infection Microbiology (1 paper)Journal of Computational Chemistry (1 paper)
- Partner nations
- ChinaSaudi ArabiaSouth Korea
In The Last Decade
Mingyu Ji
31 papers receiving 307 citations
Peers
Comparison fields: 5 of 98
- Infectious Diseases 87
- Microbiology 21
- Modeling and Simulation 11
- Applied Microbiology and Biotechnology 4
- General Dentistry 3
Countries citing papers authored by Mingyu Ji
This map shows the geographic impact of Mingyu Ji'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 Mingyu Ji with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mingyu Ji more than expected).
Fields of papers citing papers by Mingyu Ji
This network shows the impact of papers produced by Mingyu Ji. 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 Mingyu Ji. The network helps show where Mingyu Ji may publish in the future.
Co-authors
The 25 scholars most cited alongside Mingyu Ji, 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 37 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 65 | |
| 2 | 2020 | 55 | |
| 3 | 2019 | 34 | |
| 4 | 2013 | 20 | |
| 5 | 2022 | 17 | |
| 6 | 2023 | 16 | |
| 7 | 2022 | 10 | |
| 8 | 2014 | 9 | |
| 9 | 2020 | 9 | |
| 10 | 2022 | 8 | |
| 11 | 2023 | 8 | |
| 12 | 2016 | 8 | |
| 13 | 2016 | 7 | |
| 14 | 2024 | 6 | |
| 15 | 2023 | 5 | |
| 16 | 2022 | 4 | |
| 17 | 2022 | 3 | |
| 18 | 2024 | 3 | |
| 19 | 2022 | 3 | |
| 20 | 2025 | 3 |
About Mingyu Ji
Mingyu Ji is a scholar working on Artificial Intelligence, Molecular Biology, Infectious Diseases, Plant Science and Epidemiology, having authored 37 papers that have together received 310 indexed citations. Recurring topics across this work include Topic Modeling (4 papers), SARS-CoV-2 and COVID-19 Research (4 papers), Advanced Text Analysis Techniques (4 papers), Natural Language Processing Techniques (3 papers), Sentiment Analysis and Opinion Mining (3 papers), RNA modifications and cancer (3 papers), Cervical Cancer and HPV Research (2 papers) and COVID-19 Clinical Research Studies (2 papers). The work is most often cited by research in Infectious Diseases (87 citations), Microbiology (21 citations), Modeling and Simulation (11 citations), Applied Microbiology and Biotechnology (4 citations) and General Dentistry (3 citations). Mingyu Ji has collaborated with scholars based in China, Saudi Arabia and South Korea. Frequent co-authors include Fengyan Pei, Yunying Zhou, Yunshan Wang, Qianqian Zhao, Huanjie Li, Qingxi Wang, Weihua Yang, Yunshan Wang, Huailong Zhao and Li Wang. Their work appears in journals such as PLoS ONE, BMC Cancer, Frontiers in Plant Science, Frontiers in Cellular and Infection Microbiology and Journal of Computational Chemistry.
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.