Ming Yang
Impact in
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- T-cell and B-cell Immunology
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- Advanced biosensing and bioanalysis techniques
- CRISPR and Genetic Engineering
- Inflammasome and immune disorders
Papers in
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- Advanced biosensing and bioanalysis techniques 5
- CRISPR and Genetic Engineering 5
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- Internet Traffic Analysis and Secure E-voting 7
- Co-authors
- Junzhou Luo (7 shared papers)Zhen Ling (5 shared papers)Qianjin Lu (7 shared papers)Haijing Wu (9 shared papers)Xinwen Fu (4 shared papers)Ming Zhao (4 shared papers)Zhongfei Zhang (4 shared papers)Wei Yu (3 shared papers)
- Journals
- Sensors and Actuators B Chemical (3 papers)ACS Nano (2 papers)Heliyon (2 papers)iScience (2 papers)Frontiers in Endocrinology (2 papers)
- Partner nations
- ChinaUnited StatesHong Kong
In The Last Decade
Ming Yang
96 papers receiving 909 citations
Peers
Comparison fields: 5 of 124
- Immunology 120
- Molecular Biology 277
- Artificial Intelligence 127
- Cancer Research 53
- Computer Networks and Communications 69
Countries citing papers authored by Ming Yang
This map shows the geographic impact of Ming Yang'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 Ming Yang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ming Yang more than expected).
Fields of papers citing papers by Ming Yang
This network shows the impact of papers produced by Ming Yang. 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 Ming Yang. The network helps show where Ming Yang may publish in the future.
Co-authors
The 25 scholars most cited alongside Ming Yang, 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 112 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2019 | 81 | |
| 2 | 2021 | 64 | |
| 3 | 2024 | 48 | |
| 4 | 2024 | 42 | |
| 5 | 2022 | 40 | |
| 6 | 2022 | 35 | |
| 7 | 2011 | 24 | |
| 8 | 2024 | 22 | |
| 9 | 2023 | 22 | |
| 10 | 2024 | 21 | |
| 11 | 1999 | 20 | |
| 12 | 2023 | 18 | |
| 13 | 2025 | 18 | |
| 14 | 2019 | 17 | |
| 15 | 2023 | 17 | |
| 16 | 2015 | 17 | |
| 17 | Multi-Task Learning with Gaussian Matrix Generalized Inverse Gaussian Model | 2013 | 16 |
| 18 | 2021 | 15 | |
| 19 | 2022 | 15 | |
| 20 | 2021 | 14 |
About Ming Yang
Ming Yang is a scholar working on Molecular Biology, Artificial Intelligence, Immunology, Electrical and Electronic Engineering and Biomedical Engineering, having authored 112 papers that have together received 929 indexed citations. Recurring topics across this work include Internet Traffic Analysis and Secure E-voting (7 papers), Autophagy in Disease and Therapy (5 papers), Advanced biosensing and bioanalysis techniques (5 papers), Immune Cell Function and Interaction (5 papers), Network Security and Intrusion Detection (5 papers), CRISPR and Genetic Engineering (5 papers), Biosensors and Analytical Detection (4 papers) and Cancer-related molecular mechanisms research (4 papers). The work is most often cited by research in Immunology (120 citations), Molecular Biology (277 citations), Artificial Intelligence (127 citations), Cancer Research (53 citations) and Computer Networks and Communications (69 citations). Ming Yang has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Junzhou Luo, Zhen Ling, Qianjin Lu, Haijing Wu, Xinwen Fu, Ming Zhao, Zhongfei Zhang, Wei Yu, Changyu Zhou and Di Long. Their work appears in journals such as Sensors and Actuators B Chemical, ACS Nano, Heliyon, iScience and Frontiers in Endocrinology.
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.