Peifu Han
Impact in
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- Computational Drug Discovery Methods
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- Protein Structure and Dynamics
- Machine Learning in Bioinformatics
- Bioinformatics and Genomic Networks
Papers in
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- Protein Structure and Dynamics 7
- Machine Learning in Bioinformatics 4
- Bioinformatics and Genomic Networks 3
- Biomedical Text Mining and Ontologies 2
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- Computational Drug Discovery Methods 7
- Co-authors
- Tao Song (11 shared papers)Michael F. Rettig (4 shared papers)T. P. Das (4 shared papers)Wenqi Chen (4 shared papers)Shuang Wang (4 shared papers)Shuang Wang (5 shared papers)Alfonso Rodríguez‐Patón (3 shared papers)Xue Li (3 shared papers)
- Journals
- Theoretical Chemistry Accounts (3 papers)Journal of Chemical Information and Modeling (3 papers)Methods (2 papers)BMC Genomics (2 papers)Bioinformatics (2 papers)
- Partner nations
- ChinaUnited StatesSouth Korea
In The Last Decade
Peifu Han
16 papers receiving 281 citations
Peers
Comparison fields: 5 of 58
- Computational Theory and Mathematics 86
- Molecular Biology 176
- Biophysics 13
- Materials Chemistry 70
- Orthodontics 4
Countries citing papers authored by Peifu Han
This map shows the geographic impact of Peifu Han'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 Peifu Han with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Peifu Han more than expected).
Fields of papers citing papers by Peifu Han
This network shows the impact of papers produced by Peifu Han. 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 Peifu Han. The network helps show where Peifu Han may publish in the future.
Co-authors
The 25 scholars most cited alongside Peifu Han, 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 21 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2022 | 60 | |
| 2 | 2022 | 41 | |
| 3 | 2023 | 27 | |
| 4 | 2022 | 27 | |
| 5 | 1968 | 21 | |
| 6 | 1970 | 20 | |
| 7 | 2023 | 20 | |
| 8 | 2022 | 13 | |
| 9 | 1972 | 12 | |
| 10 | 1971 | 11 | |
| 11 | 2024 | 10 | |
| 12 | 2023 | 9 | |
| 13 | 2022 | 8 | |
| 14 | 2025 | 2 | |
| 15 | 2025 | 2 | |
| 16 | 2022 | 2 | |
| 17 | 2025 | 0 | |
| 18 | 2025 | 0 | |
| 19 | 2025 | 0 | |
| 20 | 2025 | 0 |
About Peifu Han
Peifu Han is a scholar working on Molecular Biology, Computational Theory and Mathematics, Materials Chemistry, Artificial Intelligence and Cell Biology, having authored 21 papers that have together received 285 indexed citations. Recurring topics across this work include Protein Structure and Dynamics (7 papers), Computational Drug Discovery Methods (7 papers), Porphyrin and Phthalocyanine Chemistry (4 papers), Machine Learning in Bioinformatics (4 papers), Bioinformatics and Genomic Networks (3 papers), Machine Learning in Materials Science (3 papers), Hemoglobin structure and function (2 papers) and Biomedical Text Mining and Ontologies (2 papers). The work is most often cited by research in Computational Theory and Mathematics (86 citations), Molecular Biology (176 citations), Biophysics (13 citations), Materials Chemistry (70 citations) and Orthodontics (4 citations). Peifu Han has collaborated with scholars based in China, United States and South Korea. Frequent co-authors include Tao Song, Michael F. Rettig, T. P. Das, Wenqi Chen, Shuang Wang, Shuang Wang, Alfonso Rodríguez‐Patón, Xue Li, Xun Wang and Pan Zheng. Their work appears in journals such as Theoretical Chemistry Accounts, Journal of Chemical Information and Modeling, Methods, BMC Genomics and Bioinformatics.
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.