Kaifu Gao

2.1k total citations
30 papers, 1.3k citations indexed

About

Kaifu Gao is a scholar working on Molecular Biology, Computational Theory and Mathematics and Infectious Diseases. According to data from OpenAlex, Kaifu Gao has authored 30 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Molecular Biology, 13 papers in Computational Theory and Mathematics and 9 papers in Infectious Diseases. Recurrent topics in Kaifu Gao's work include Computational Drug Discovery Methods (13 papers), Protein Structure and Dynamics (8 papers) and SARS-CoV-2 and COVID-19 Research (7 papers). Kaifu Gao is often cited by papers focused on Computational Drug Discovery Methods (13 papers), Protein Structure and Dynamics (8 papers) and SARS-CoV-2 and COVID-19 Research (7 papers). Kaifu Gao collaborates with scholars based in United States, China and Canada. Kaifu Gao's co-authors include Guo‐Wei Wei, Rui Wang, Duc Duy Nguyen, Jiahui Chen, Guo‐Wei Wei, Meihua Tu, Menglun Wang, Dong Chen, Yuta Hozumi and Changchuan Yin and has published in prestigious journals such as Chemical Reviews, Nature Communications and Journal of Molecular Biology.

In The Last Decade

Kaifu Gao

29 papers receiving 1.3k citations

Peers

Kaifu Gao
Comparison fields: 5 of 124
  • Computational Theory and Mathematics 682
  • Molecular Biology 659
  • Infectious Diseases 459
  • Materials Chemistry 257
  • Organic Chemistry 105
Replace Anh‐Tien Ton with:
Anh‐Tien Ton Canada
Francesco Gentile Canada
Michael Hsing Canada
Kimberley M. Zorn United States
Thomas R. Lane United States
Andrea Zaliani Germany
Alexander L. Perryman United States
Adrià Cereto‐Massagué Spain
Ana C. Puhl United States
Duc Duy Nguyen United States
Anh‐Tien Ton Canada View profile →
Citations per field, relative to Kaifu Gao
Kaifu Gao · 1×
Citations per year, relative to Kaifu Gao
Kaifu Gao · 1×

Countries citing papers authored by Kaifu Gao

Since Specialization
Citations

This map shows the geographic impact of Kaifu Gao'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 Kaifu Gao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kaifu Gao more than expected).

Fields of papers citing papers by Kaifu Gao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kaifu Gao. 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 Kaifu Gao. The network helps show where Kaifu Gao may publish in the future.

Co-authorship network of co-authors of Kaifu Gao

This figure shows the co-authorship network connecting the top 25 collaborators of Kaifu Gao. A scholar is included among the top collaborators of Kaifu Gao 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 Kaifu Gao. Kaifu Gao is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
# Work Indexed citations
1 16
2 55
3 81
4 126
5 117
6 10
7 49
8 128
9 14
10 85
11 79
12 75
13 14
14 5
15 0
16 19
17 10
18 1
19 12
20 5

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.

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