Peng Yin
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- Computational Drug Discovery Methods 11
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- Protein Structure and Dynamics 8
- Machine Learning in Bioinformatics 4
- RNA and protein synthesis mechanisms 3
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- Machine Learning in Materials Science 6
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- Soil Carbon and Nitrogen Dynamics 5
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- Industrial Vision Systems and Defect Detection 3
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- SARS-CoV-2 and COVID-19 Research 3
- Co-authors
- Fan HuJianye ZhangLinbu LiaoYanjie WeiKonda Mani SaravananHaiping ZhangJiaxin JiangMunir Pirmohamed
- Partner nations
- ChinaUnited KingdomSerbia
In The Last Decade
Peng Yin
52 papers receiving 615 citations
Peers
Comparison fields: 5 of 131
- Computational Theory and Mathematics 117
- Computational Mathematics 3
- Geriatrics and Gerontology 18
- Pharmacology 39
- Sensory Systems 15
Countries citing papers authored by Peng Yin
This map shows the geographic impact of Peng Yin'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 Peng Yin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Peng Yin more than expected).
Fields of papers citing papers by Peng Yin
This network shows the impact of papers produced by Peng Yin. 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 Peng Yin. The network helps show where Peng Yin may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Peng Yin, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2024 | 0 | |
| 3 | 2024 | 8 | |
| 4 | 2024 | 1 | |
| 5 | 2023 | 5 | |
| 6 | 2023 | 0 | |
| 7 | 2023 | 16 | |
| 8 | 2023 | 3 | |
| 9 | 2022 | 6 | |
| 10 | 2022 | 1 | |
| 11 | 2021 | 7 | |
| 12 | 2019 | 70 | |
| 13 | 2018 | 32 | |
| 14 | Effects of altitude and growth stage on amorphophallus konjac microecosystem in mount emei. | 2018 | 1 |
| 15 | 2018 | 50 | |
| 16 | 2018 | 6 | |
| 17 | 2016 | 21 | |
| 18 | Difference Parasitic Abilities Comparison of Sclerodermus sichuanensis Reared by Different Hosts | 2013 | 2 |
| 19 | Distribution Pattern of Above-ground Biomass and Culm Form Characteristics of Bambusa blumeana | 2013 | 1 |
| 20 | 2006 | 41 |
About Peng Yin
Peng Yin is a scholar working on Computational Theory and Mathematics, Soil Science and Industrial and Manufacturing Engineering, having authored 58 papers that have together received 624 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (11 papers), Protein Structure and Dynamics (8 papers), Machine Learning in Materials Science (6 papers), Soil Carbon and Nitrogen Dynamics (5 papers), Machine Learning in Bioinformatics (4 papers), RNA and protein synthesis mechanisms (3 papers), Industrial Vision Systems and Defect Detection (3 papers) and SARS-CoV-2 and COVID-19 Research (3 papers). The work is most often cited by research in Computational Theory and Mathematics (117 citations), Computational Mathematics (3 citations) and Geriatrics and Gerontology (18 citations). Peng Yin has collaborated with scholars based in China, United Kingdom and Serbia. Frequent co-authors include Fan Hu, Jianye Zhang, Linbu Liao, Yanjie Wei, Konda Mani Saravanan, Haiping Zhang, Jiaxin Jiang, Munir Pirmohamed, Lin Shi and Andrea Jorgensen. Their work appears in journals such as PLoS ONE, IEEE Access and Frontiers in Immunology.
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.