Dan Zhao
- Molecular Biology top 5%
- Computational Theory and Mathematics top 1%
- Cancer Research top 10%
- Immunology
- Materials Chemistry
- Co-authors
- Jianyang ZengHaitao LiFangping WanShuya LiHantao ShuHan LiYuanyuan LiXiaobing Shi
- Topics
- Computational Drug Discovery Methods (12 papers)Protein Structure and Dynamics (8 papers)Machine Learning in Materials Science (8 papers)
- Journals
- Proceedings of the National Academy of SciencesJournal of the American Chemical SocietyNucleic Acids Research
- Partner nations
- ChinaUnited StatesUnited Kingdom
In The Last Decade
Dan Zhao
124 papers receiving 2.9k citations
Peers
Comparison fields: 5 of 159
- Molecular Biology 1.8k
- Computational Theory and Mathematics 417
- Cancer Research 231
- Immunology 199
- Materials Chemistry 194
Countries citing papers authored by Dan Zhao
This map shows the geographic impact of Dan Zhao'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 Zhao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dan Zhao more than expected).
Fields of papers citing papers by Dan Zhao
This network shows the impact of papers produced by Dan Zhao. 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 Zhao. The network helps show where Dan Zhao may publish in the future.
Co-authorship network of co-authors of Dan Zhao
This figure shows the co-authorship network connecting the top 25 collaborators of Dan Zhao. A scholar is included among the top collaborators of Dan Zhao based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Dan Zhao. Dan Zhao is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 4 | |
| 3 | 0 | |
| 4 | 3 | |
| 5 | 4 | |
| 6 | 16 | |
| 7 | 7 | |
| 8 | 16 | |
| 9 | 61 | |
| 10 | 10 | |
| 11 | 74 | |
| 12 | 23 | |
| 13 | 47 | |
| 14 | 103 | |
| 15 | 24 | |
| 16 | Initial alignment algorithm for SINS based on reconstructed pseudo-Earth frame | 1 |
| 17 | 43 | |
| 18 | 55 | |
| 19 | Analysis of Poisson Traffic Accident Prediction Models and Their Application | 2 |
| 20 | Characteristics Analysis and Model Establishment for Passenger Boarding Time in Urban Rail Transit | 7 |
About Dan Zhao
Dan Zhao is a scholar working on Computational Theory and Mathematics, Molecular Biology and Structural Biology, having authored 134 papers that have together received 2.9k indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (12 papers), Protein Structure and Dynamics (8 papers) and Machine Learning in Materials Science (8 papers). The work is most often cited by research in Computational Theory and Mathematics (417 citations), Molecular Biology (1.8k citations) and Cancer Research (231 citations). Dan Zhao has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Jianyang Zeng, Haitao Li, Fangping Wan, Shuya Li, Hantao Shu, Han Li, Yuanyuan Li, Xiaobing Shi, Hong Wen and Tao Jiang. Their work appears in journals such as Proceedings of the National Academy of Sciences, Journal of the American Chemical Society and Nucleic Acids Research.
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.