Gao Tu

1.2k citations
23 papers · 997 indexed · 1 hit paper · h-index 13
Topics
Computational Drug Discovery Methods (11 papers)Receptor Mechanisms and Signaling (5 papers)Protein Structure and Dynamics (4 papers)
Partner nations
ChinaMacaoUnited States

In The Last Decade

Gao Tu

22 papers receiving 989 citations

Hit Papers

Therapeutic target database update 2018: enriched resourc...20172026202020232017100200300400

Peers

Gao Tu
Comparison fields: 5 of 102
  • Molecular Biology 684
  • Computational Theory and Mathematics 381
  • Pharmacology 115
  • Pharmacology 95
  • Oncology 86
Replace Xiaoxu Li with:
Xiaoxu Li China
Danfeng Shi China
Brett Lomenick United States
Guoxun Zheng China
Helena Almqvist Sweden
Gilbert M. Rishton United States
Xichen Lian China
Bianca M. Liederer United States
Qiu Sun China
Gao Tu relative to Xiaoxu Li China Xiaoxu Li's profile →
Citations per field
00.5×3.4×
Xiaoxu Li · 1×
Citations per year

Countries citing papers authored by Gao Tu

Since Specialization
Citations

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

Fields of papers citing papers by Gao Tu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Gao Tu

This figure shows the co-authorship network connecting the top 25 collaborators of Gao Tu. A scholar is included among the top collaborators of Gao Tu 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 Gao Tu. Gao Tu 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
#WorkIndexed citations
1 1
2 2
3 9
4 0
5 1
6 10
7 4
8 4
9 13
10 14
11 3
12 8
13 21
14 29
15 101
16 133
17 19
18 107
19 38
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Therapeutic target database update 2018: enriched resource for facilitating bench-to-clinic research of targeted therapeuticsbreakdown →
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About Gao Tu

Gao Tu is a scholar working on Computational Theory and Mathematics, Virology and Biological Psychiatry, having authored 23 papers that have together received 997 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (11 papers), Receptor Mechanisms and Signaling (5 papers) and Protein Structure and Dynamics (4 papers). The work is most often cited by research in Computational Theory and Mathematics (381 citations), Pharmacology (115 citations) and Molecular Biology (684 citations). Gao Tu has collaborated with scholars based in China, Macao and United States. Frequent co-authors include Feng Zhu, Weiwei Xue, Yu Chen, Fengyuan Yang, Xiaojun Yao, Guoxun Zheng, Qingxia Yang, Xuejiao Cui, Jing Tang and Xiaofeng Li. Their work appears in journals such as Nucleic Acids Research, International Journal of Molecular Sciences and Physical Chemistry Chemical Physics.

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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