Mingyang Wang
Impact in
- Inorganic Chemistry top 2%
- Asymmetric Hydrogenation and Catalysis
Papers in
-
- Topic Modeling 7
- Advanced Text Analysis Techniques 7
-
- Computational Drug Discovery Methods 15
- Rough Sets and Fuzzy Logic 7
- Co-authors
- Qiang Liu (7 shared papers)Yujie Wang (4 shared papers)Yibiao Li (3 shared papers)Guang Yu (9 shared papers)Daren Yu (9 shared papers)Yu Lan (3 shared papers)Shihan Liu (3 shared papers)Jike Wang (9 shared papers)
- Journals
- Scientometrics (9 papers)IEEE Access (4 papers)Forests (4 papers)PLoS ONE (2 papers)Journal of Medicinal Chemistry (2 papers)
- Partner nations
- ChinaUnited StatesUnited Kingdom
In The Last Decade
Mingyang Wang
93 papers receiving 1.8k citations
Peers
Comparison fields: 5 of 146
- Process Chemistry and Technology 112
- Inorganic Chemistry 486
- Statistics, Probability and Uncertainty 166
- Computational Theory and Mathematics 338
- Safety, Risk, Reliability and Quality 122
Countries citing papers authored by Mingyang Wang
This map shows the geographic impact of Mingyang 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 Mingyang Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mingyang Wang more than expected).
Fields of papers citing papers by Mingyang Wang
This network shows the impact of papers produced by Mingyang 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 Mingyang Wang. The network helps show where Mingyang Wang may publish in the future.
Co-authors
The 25 scholars most cited alongside Mingyang 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 107 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 240 | |
| 2 | 2020 | 150 | |
| 3 | 2021 | 134 | |
| 4 | 2021 | 89 | |
| 5 | 2024 | 72 | |
| 6 | 2022 | 67 | |
| 7 | 2022 | 67 | |
| 8 | 2022 | 64 | |
| 9 | 2008 | 48 | |
| 10 | 2023 | 47 | |
| 11 | 2011 | 37 | |
| 12 | 2019 | 35 | |
| 13 | 2020 | 35 | |
| 14 | 2019 | 32 | |
| 15 | 2009 | 30 | |
| 16 | 2013 | 29 | |
| 17 | 2016 | 28 | |
| 18 | 2009 | 27 | |
| 19 | 2024 | 26 | |
| 20 | 2006 | 25 |
About Mingyang Wang
Mingyang Wang is a scholar working on Artificial Intelligence, Computational Theory and Mathematics, Molecular Biology, Computer Vision and Pattern Recognition and Information Systems, having authored 107 papers that have together received 1.8k indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (15 papers), Complex Network Analysis Techniques (10 papers), Asymmetric Hydrogenation and Catalysis (8 papers), Machine Learning in Materials Science (8 papers), Topic Modeling (7 papers), Rough Sets and Fuzzy Logic (7 papers), Data Mining Algorithms and Applications (7 papers) and Advanced Text Analysis Techniques (7 papers). The work is most often cited by research in Process Chemistry and Technology (112 citations), Inorganic Chemistry (486 citations), Statistics, Probability and Uncertainty (166 citations), Computational Theory and Mathematics (338 citations) and Safety, Risk, Reliability and Quality (122 citations). Mingyang Wang has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Qiang Liu, Yujie Wang, Yibiao Li, Guang Yu, Daren Yu, Yu Lan, Shihan Liu, Jike Wang, Tingjun Hou and Dongsheng Cao. Their work appears in journals such as Scientometrics, IEEE Access, Forests, PLoS ONE and Journal of Medicinal 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.