Mu Gao

58 papers receiving 3.6k citations

Hit Papers

AF2Complex predicts direct physical interactions in multimeric proteins with deep learning 2022 · 152 citations
1522001202620092017250500750

Peers

Mu Gao
Comparison fields: 5 of 152
  • Immunology and Allergy 302
  • Structural Biology 65
  • Molecular Biology 2.4k
  • Computational Theory and Mathematics 535
  • Cell Biology 534
Replace Kurt S. Thorn with:
Kurt S. Thorn United States
Özlem Keskin Türkiye
Zenon Grabarek United States
Grzegorz Nawrocki United States
Sarah Rauscher Canada
Nagarajan Vaidehi United States
Tanja Kortemme United States
Birthe B. Kragelund Denmark
Ting Ran China
Nathalie Reuter Norway
Mu Gao relative to Kurt S. Thorn United States Kurt S. Thorn's profile →
Citations per field
00.5×1.5×1.9×
Kurt S. Thorn · 1×
Citations per year

Countries citing papers authored by Mu Gao

Since Specialization
Citations

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

Fields of papers citing papers by Mu Gao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Mu Gao, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Mu Gao Line = papers co-authored together Mu Gao links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20243
2 20246
3 20233
4 20232
5 202214
6 20213
7 201823
8 201621
9 201590
10 20149
11 2013102
12 201132
13 201070
14 200927
15 2007184
16 200474
17 200485
18 2002138
19 200139
20
Steered molecular dynamics and mechanical functions of proteins
Hit paper breakdown →
2001803

About Mu Gao

Mu Gao is a scholar working on Aging, Cell Biology, Immunology and Allergy, Molecular Biology and Computational Theory and Mathematics, having authored 58 papers that have together received 3.6k indexed citations. Recurring topics across this work include Protein Structure and Dynamics (25 papers), RNA and protein synthesis mechanisms (20 papers), Enzyme Structure and Function (12 papers), Force Microscopy Techniques and Applications (10 papers), Machine Learning in Bioinformatics (10 papers), Computational Drug Discovery Methods (8 papers), Cellular Mechanics and Interactions (7 papers) and Genomics and Phylogenetic Studies (6 papers). The work is most often cited by research in Immunology and Allergy (302 citations), Structural Biology (65 citations), Molecular Biology (2.4k citations), Computational Theory and Mathematics (535 citations) and Cell Biology (534 citations). Mu Gao has collaborated with scholars based in United States, Switzerland and Germany. Frequent co-authors include Jeffrey Skolnick, Klaus Schulten, Barry Isralewitz, Hongyi Zhou, Viola Vogel, David W. Craig, Matthias Wilmanns, Suresh B. Singh, Jerry M. Parks and Hui Lü. Their work appears in journals such as Proceedings of the National Academy of Sciences, Bioinformatics, Structure, Scientific Reports and PLoS Computational Biology.

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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