Gary S. Shaw
Impact in
- Molecular Biology top 1%
- Ubiquitin and proteasome pathways
- S100 Proteins and Annexins
- Mitochondrial Function and Pathology
- Protein Degradation and Inhibitors
- Neurology top 2%
- Parkinson's Disease Mechanisms and Treatments
Papers in
-
- Ubiquitin and proteasome pathways 45
- S100 Proteins and Annexins 39
- Protein Structure and Dynamics 12
- Protein Degradation and Inhibitors 12
- RNA and protein synthesis mechanisms 11
- Co-authors
- Anne C. Rintala‐DempseyKathryn R. BarberHelen WaldenSteven P. SmithBrian D. SykesDonald E. SprattRobert S. HodgesViduth K. Chaugule
- Journals
- Biochemistry (19 papers)Journal of Biological Chemistry (15 papers)Protein Science (10 papers)Journal of the American Chemical Society (9 papers)Biochemical Journal (7 papers)
- Partner nations
- CanadaUnited KingdomUnited States
In The Last Decade
Gary S. Shaw
149 papers receiving 5.4k citations
Peers
Comparison fields: 5 of 131
- Molecular Biology 4.2k
- Neurology 736
- Cancer Research 496
- Epidemiology 1.0k
- Cell Biology 430
Countries citing papers authored by Gary S. Shaw
This map shows the geographic impact of Gary S. Shaw'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 Gary S. Shaw with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gary S. Shaw more than expected).
Fields of papers citing papers by Gary S. Shaw
This network shows the impact of papers produced by Gary S. Shaw. 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 Gary S. Shaw. The network helps show where Gary S. Shaw may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Gary S. Shaw, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 3 | |
| 2 | 2025 | 0 | |
| 3 | 2024 | 6 | |
| 4 | 2023 | 17 | |
| 5 | 2021 | 16 | |
| 6 | 2021 | 10 | |
| 7 | 2017 | 78 | |
| 8 | 2015 | 7 | |
| 9 | 2014 | 10 | |
| 10 | 2014 | 64 | |
| 11 | 2012 | 41 | |
| 12 | 2011 | 132 | |
| 13 | Unique S100 target protein interactions. | 2009 | 40 |
| 14 | 2008 | 20 | |
| 15 | 2007 | 7 | |
| 16 | 2003 | 2 | |
| 17 | 2001 | 147 | |
| 18 | 2000 | 10 | |
| 19 | 1998 | 56 | |
| 20 | 1995 | 14 |
About Gary S. Shaw
Gary S. Shaw is a scholar working on Molecular Biology, Microbiology, Neurology, Spectroscopy and Pharmaceutical Science, having authored 153 papers that have together received 5.5k indexed citations. Recurring topics across this work include Ubiquitin and proteasome pathways (45 papers), S100 Proteins and Annexins (39 papers), Autophagy in Disease and Therapy (24 papers), Cancer-related Molecular Pathways (13 papers), Protein Structure and Dynamics (12 papers), Protein Degradation and Inhibitors (12 papers), RNA and protein synthesis mechanisms (11 papers) and Parkinson's Disease Mechanisms and Treatments (11 papers). The work is most often cited by research in Molecular Biology (4.2k citations), Neurology (736 citations), Cancer Research (496 citations), Epidemiology (1.0k citations) and Cell Biology (430 citations). Gary S. Shaw has collaborated with scholars based in Canada, United Kingdom and United States. Frequent co-authors include Anne C. Rintala‐Dempsey, Kathryn R. Barber, Helen Walden, Steven P. Smith, Brian D. Sykes, Donald E. Spratt, Robert S. Hodges, Viduth K. Chaugule, Steven Beasley and Ventzislava A. Hristova. Their work appears in journals such as Biochemistry, Journal of Biological Chemistry, Protein Science, Journal of the American Chemical Society and Biochemical Journal.
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.