Dan Han
Impact in
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- Computational Drug Discovery Methods
- Health Informatics top 10%
Papers in
-
- Microbial Metabolic Engineering and Bioproduction 2
- Co-authors
- Jianjun Tan (4 shared papers)Lu Zhang (1 shared paper)Hao Zhu (1 shared paper)Wei Niu (1 shared paper)Long Jiao (1 shared paper)Yuan-Fang Wang (1 shared paper)Jun Gao (1 shared paper)Tao Sun (1 shared paper)
- Journals
- Environmental Pollution (1 paper)Medicinal Chemistry Research (1 paper)Applied Physics Letters (1 paper)Applied Biochemistry and Biotechnology (1 paper)Physical Review Materials (1 paper)
- Partner nations
- ChinaUnited StatesNetherlands
In The Last Decade
Dan Han
17 papers receiving 732 citations
Dan Han's Hit Papers
Peers
Comparison fields: 5 of 141
- Computational Theory and Mathematics 280
- Health Informatics 17
- Computer Vision and Pattern Recognition 118
- Biophysics 23
- Artificial Intelligence 112
Countries citing papers authored by Dan Han
This map shows the geographic impact of Dan 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 Dan Han with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dan Han more than expected).
Fields of papers citing papers by Dan Han
This network shows the impact of papers produced by Dan 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 Dan Han. The network helps show where Dan Han may publish in the future.
Co-authors
The 25 scholars most cited alongside Dan 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 | From machine learning to deep learning: progress in machine intelligence for rational drug discovery Hit paper breakdown → | 2017 | 511 |
| 2 | 2005 | 121 | |
| 3 | 2020 | 35 | |
| 4 | 2017 | 26 | |
| 5 | 2017 | 21 | |
| 6 | 2022 | 10 | |
| 7 | 2016 | 8 | |
| 8 | 2021 | 7 | |
| 9 | 2019 | 5 | |
| 10 | 2020 | 5 | |
| 11 | 2020 | 4 | |
| 12 | 2022 | 3 | |
| 13 | 2022 | 3 | |
| 14 | 2018 | 2 | |
| 15 | 2020 | 1 | |
| 16 | 2013 | 1 | |
| 17 | 2019 | 1 | |
| 18 | 2025 | 0 | |
| 19 | 2023 | 0 | |
| 20 | 2023 | 0 |
About Dan Han
Dan Han is a scholar working on Artificial Intelligence, Molecular Biology, Computational Theory and Mathematics, Biomedical Engineering and Materials Chemistry, having authored 21 papers that have together received 764 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (4 papers), Advanced Condensed Matter Physics (2 papers), Human Pose and Action Recognition (2 papers), HIV/AIDS drug development and treatment (2 papers), Nanomaterials for catalytic reactions (2 papers), Anaerobic Digestion and Biogas Production (2 papers), Microbial Metabolic Engineering and Bioproduction (2 papers) and Biofuel production and bioconversion (2 papers). The work is most often cited by research in Computational Theory and Mathematics (280 citations), Health Informatics (17 citations), Computer Vision and Pattern Recognition (118 citations), Biophysics (23 citations) and Artificial Intelligence (112 citations). Dan Han has collaborated with scholars based in China, United States and Netherlands. Frequent co-authors include Jianjun Tan, Lu Zhang, Hao Zhu, Wei Niu, Long Jiao, Yuan-Fang Wang, Jun Gao, Tao Sun, Shiyou Chen and Hui Liu. Their work appears in journals such as Environmental Pollution, Medicinal Chemistry Research, Applied Physics Letters, Applied Biochemistry and Biotechnology and Physical Review Materials.
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.