Dan Han
Impact in
-
- Computational Drug Discovery Methods
- Health Informatics top 10%
Papers in
-
- Computational Drug Discovery Methods 4
- Co-authors
- Jianjun Tan (4 shared papers)Lu Zhang (1 shared paper)Hao Zhu (1 shared paper)Long Jiao (1 shared paper)Wei Niu (1 shared paper)Yuan-Fang Wang (1 shared paper)Jixuan Wu (1 shared paper)Shiyou Chen (1 shared paper)
- Journals
- Environmental Pollution (1 paper)RSC Advances (1 paper)Frontiers in Genetics (1 paper)Environmental Technology (1 paper)Applied Biochemistry and Biotechnology (1 paper)
- Partner nations
- ChinaUnited StatesSouth Korea
In The Last Decade
Dan Han
20 papers receiving 829 citations
Dan Han's Hit Papers
Peers
Comparison fields: 5 of 140
- Computational Theory and Mathematics 317
- Health Informatics 22
- Computer Vision and Pattern Recognition 132
- Health Information Management 21
- Biophysics 27
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 | 590 |
| 2 | 2005 | 135 | |
| 3 | 2020 | 37 | |
| 4 | 2017 | 28 | |
| 5 | 2017 | 22 | |
| 6 | 2022 | 11 | |
| 7 | 2021 | 8 | |
| 8 | 2016 | 8 | |
| 9 | 2020 | 5 | |
| 10 | 2019 | 5 | |
| 11 | 2020 | 4 | |
| 12 | 2022 | 3 | |
| 13 | 2022 | 3 | |
| 14 | 2018 | 2 | |
| 15 | 2020 | 1 | |
| 16 | 2025 | 1 | |
| 17 | 2023 | 1 | |
| 18 | 2013 | 1 | |
| 19 | 2023 | 1 | |
| 20 | 2021 | 1 |
About Dan Han
Dan Han is a scholar working on Computational Theory and Mathematics, Artificial Intelligence, Computer Vision and Pattern Recognition, Condensed Matter Physics and Building and Construction, having authored 21 papers that have together received 867 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (4 papers), Advanced Condensed Matter Physics (2 papers), HIV/AIDS drug development and treatment (2 papers), Nanomaterials for catalytic reactions (2 papers), Biofuel production and bioconversion (2 papers), Microbial Metabolic Engineering and Bioproduction (2 papers), Anaerobic Digestion and Biogas Production (2 papers) and Face recognition and analysis (2 papers). The work is most often cited by research in Computational Theory and Mathematics (317 citations), Health Informatics (22 citations), Computer Vision and Pattern Recognition (132 citations), Health Information Management (21 citations) and Biophysics (27 citations). Dan Han has collaborated with scholars based in China, United States and South Korea. Frequent co-authors include Jianjun Tan, Lu Zhang, Hao Zhu, Long Jiao, Wei Niu, Yuan-Fang Wang, Jixuan Wu, Shiyou Chen, Wenjing Yang and Xiangwei Jiang. Their work appears in journals such as Environmental Pollution, RSC Advances, Frontiers in Genetics, Environmental Technology and Applied Biochemistry and Biotechnology.
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.