Guang Song
- Molecular Biology top 10%
- Materials Chemistry top 10%
- Computer Vision and Pattern Recognition top 5%
- Atomic and Molecular Physics, and Optics top 10%
- Control and Systems Engineering top 5%
- Co-authors
- Nancy M. AmatoRobert L. JerniganLei YangO. Burçhan BayazıtKen A. DillAlicia CarriquiryShawna ThomasAndreas Klappenecker
- Topics
- Protein Structure and Dynamics (43 papers)Enzyme Structure and Function (24 papers)RNA and protein synthesis mechanisms (8 papers)
- Partner nations
- United StatesChinaTaiwan
In The Last Decade
Guang Song
67 papers receiving 1.7k citations
Peers
Comparison fields: 5 of 111
- Molecular Biology 1.2k
- Materials Chemistry 535
- Computer Vision and Pattern Recognition 314
- Atomic and Molecular Physics, and Optics 201
- Control and Systems Engineering 185
Countries citing papers authored by Guang Song
This map shows the geographic impact of Guang Song'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 Guang Song with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Guang Song more than expected).
Fields of papers citing papers by Guang Song
This network shows the impact of papers produced by Guang Song. 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 Guang Song. The network helps show where Guang Song may publish in the future.
Co-authorship network of co-authors of Guang Song
This figure shows the co-authorship network connecting the top 25 collaborators of Guang Song. A scholar is included among the top collaborators of Guang Song 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 Guang Song. Guang Song is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 0 | |
| 3 | 3 | |
| 4 | 2 | |
| 5 | 7 | |
| 6 | 8 | |
| 7 | 3 | |
| 8 | 6 | |
| 9 | 3 | |
| 10 | 0 | |
| 11 | 11 | |
| 12 | 10 | |
| 13 | 5 | |
| 14 | 34 | |
| 15 | 165 | |
| 16 | 62 | |
| 17 | 36 | |
| 18 | A Motion Planning Approach to Folding: From Paper Craft to Protein Structure Prediction | 9 |
| 19 | How Does It Fold? Searching for Folding Pathways using A Motion Planning Approach | 1 |
| 20 | 1 |
About Guang Song
Guang Song is a scholar working on Software, Spectroscopy and Molecular Biology, having authored 71 papers that have together received 1.7k indexed citations. Recurring topics across this work include Protein Structure and Dynamics (43 papers), Enzyme Structure and Function (24 papers) and RNA and protein synthesis mechanisms (8 papers). The work is most often cited by research in Molecular Biology (1.2k citations), Computer Vision and Pattern Recognition (314 citations) and Materials Chemistry (535 citations). Guang Song has collaborated with scholars based in United States, China and Taiwan. Frequent co-authors include Nancy M. Amato, Robert L. Jernigan, Lei Yang, O. Burçhan Bayazıt, Ken A. Dill, Alicia Carriquiry, Shawna Thomas, Andreas Klappenecker, S.L. Miller and Dinghui Yang. Their work appears in journals such as Proceedings of the National Academy of Sciences, PLoS ONE and Journal of Molecular 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.