Mingxuan Wang
Impact in
- Artificial Intelligence top 5%
- Natural Language Processing Techniques
- Topic Modeling
- Speech Recognition and Synthesis
- Biochemistry top 10%
Papers in
-
- Topic Modeling 19
- Natural Language Processing Techniques 19
- Co-authors
- Lei Li (13 shared papers)Liwei Wu (4 shared papers)Hao Zhou (4 shared papers)Jianhua Zhu (4 shared papers)Rongmin Yu (4 shared papers)Wei Wen (1 shared paper)Hong Xu (4 shared papers)Zehui Lin (2 shared papers)
- Journals
- Journal of Agricultural and Food Chemistry (4 papers)Applied Microbiology and Biotechnology (3 papers)Plant Disease (3 papers)PLoS ONE (2 papers)Frontiers in Microbiology (2 papers)
- Partner nations
- ChinaUnited StatesUnited Kingdom
In The Last Decade
Mingxuan Wang
76 papers receiving 1.3k citations
Peers
Comparison fields: 5 of 127
- Artificial Intelligence 417
- Biochemistry 64
- Computer Vision and Pattern Recognition 164
- Plant Science 289
- Biophysics 34
Countries citing papers authored by Mingxuan Wang
This map shows the geographic impact of Mingxuan 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 Mingxuan Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mingxuan Wang more than expected).
Fields of papers citing papers by Mingxuan Wang
This network shows the impact of papers produced by Mingxuan 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 Mingxuan Wang. The network helps show where Mingxuan Wang may publish in the future.
Co-authors
The 25 scholars most cited alongside Mingxuan 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
Showing the 20 most-cited of 90 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2019 | 172 | |
| 2 | 2021 | 94 | |
| 3 | 2015 | 76 | |
| 4 | 2020 | 68 | |
| 5 | 2020 | 60 | |
| 6 | 2020 | 56 | |
| 7 | 2023 | 52 | |
| 8 | 2013 | 48 | |
| 9 | 2021 | 39 | |
| 10 | 2021 | 32 | |
| 11 | 2019 | 32 | |
| 12 | 2022 | 32 | |
| 13 | 2018 | 31 | |
| 14 | 2021 | 30 | |
| 15 | 2021 | 30 | |
| 16 | 2010 | 30 | |
| 17 | 2024 | 29 | |
| 18 | 2013 | 27 | |
| 19 | 2022 | 26 | |
| 20 | 2021 | 22 |
About Mingxuan Wang
Mingxuan Wang is a scholar working on Artificial Intelligence, Molecular Biology, Computer Vision and Pattern Recognition, Plant Science and Biomedical Engineering, having authored 90 papers that have together received 1.3k indexed citations. Recurring topics across this work include Topic Modeling (19 papers), Natural Language Processing Techniques (19 papers), Multimodal Machine Learning Applications (9 papers), Plant-Microbe Interactions and Immunity (8 papers), Lipid metabolism and biosynthesis (5 papers), Plant Pathogens and Fungal Diseases (5 papers), Spectroscopy and Chemometric Analyses (4 papers) and Nanomaterials for catalytic reactions (4 papers). The work is most often cited by research in Artificial Intelligence (417 citations), Biochemistry (64 citations), Computer Vision and Pattern Recognition (164 citations), Plant Science (289 citations) and Biophysics (34 citations). Mingxuan Wang has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Lei Li, Liwei Wu, Hao Zhou, Jianhua Zhu, Rongmin Yu, Wei Wen, Hong Xu, Zehui Lin, Xiaohai Feng and Yong Q. Chen. Their work appears in journals such as Journal of Agricultural and Food Chemistry, Applied Microbiology and Biotechnology, Plant Disease, PLoS ONE and Frontiers in Microbiology.
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.