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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- Advanced Text Analysis Techniques 4
- Topic Modeling 4
- Sentiment Analysis and Opinion Mining 3
- Natural Language Processing Techniques 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
- BMC Cancer (2 papers)PLoS ONE (2 papers)Electronics (1 paper)Frontiers in Cell and Developmental Biology (1 paper)Agronomy (1 paper)
- Partner nations
- ChinaSouth KoreaSaudi Arabia
In The Last Decade
Mingyu Ji
29 papers receiving 291 citations
Peers
Comparison fields: 5 of 103
- Infectious Diseases 98
- Microbiology 26
- General Dentistry 6
- Modeling and Simulation 13
- Epidemiology 60
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 | 64 | |
| 2 | 2020 | 55 | |
| 3 | 2019 | 34 | |
| 4 | 2013 | 19 | |
| 5 | 2022 | 16 | |
| 6 | 2023 | 14 | |
| 7 | 2014 | 9 | |
| 8 | 2022 | 9 | |
| 9 | 2020 | 9 | |
| 10 | 2023 | 8 | |
| 11 | 2016 | 8 | |
| 12 | 2016 | 7 | |
| 13 | 2022 | 6 | |
| 14 | 2024 | 5 | |
| 15 | 2023 | 4 | |
| 16 | 2022 | 4 | |
| 17 | 2022 | 3 | |
| 18 | 2022 | 3 | |
| 19 | 2011 | 3 | |
| 20 | 2018 | 3 |
About Mingyu Ji
Mingyu Ji is a scholar working on Artificial Intelligence, Molecular Biology, Epidemiology, Infectious Diseases and Plant Science, having authored 37 papers that have together received 293 indexed citations. Recurring topics across this work include Advanced Text Analysis Techniques (4 papers), SARS-CoV-2 and COVID-19 Research (4 papers), Topic Modeling (4 papers), Sentiment Analysis and Opinion Mining (3 papers), RNA modifications and cancer (3 papers), Natural Language Processing Techniques (3 papers), Cervical Cancer and HPV Research (3 papers) and Research in Cotton Cultivation (2 papers). The work is most often cited by research in Infectious Diseases (98 citations), Microbiology (26 citations), General Dentistry (6 citations), Modeling and Simulation (13 citations) and Epidemiology (60 citations). Mingyu Ji has collaborated with scholars based in China, South Korea and Saudi Arabia. Frequent co-authors include Fengyan Pei, Yunying Zhou, Yunshan Wang, Qianqian Zhao, Huanjie Li, Qingxi Wang, Weihua Yang, Yunshan Wang, Yangyang Wang and Li Wang. Their work appears in journals such as BMC Cancer, PLoS ONE, Electronics, Frontiers in Cell and Developmental Biology and Agronomy.
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.