Vladimir N. Uversky
- Molecular Biology top 0.01%
- Protein Structure and Dynamics 373
- RNA and protein synthesis mechanisms 124
- RNA Research and Splicing 80
- Machine Learning in Bioinformatics 62
- Neurology top 0.05%
- Parkinson's Disease Mechanisms and Treatments 77
- Cell Biology top 0.02%
- Physiology top 0.05%
- Alzheimer's disease research and treatments 109
- Materials Chemistry top 0.1%
- Enzyme Structure and Function 213
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- SARS-CoV-2 and COVID-19 Research 63
- Co-authors
- A. Keith DunkerAnthony L. FinkChristopher J. OldfieldBin XueJie LiMarc S. CorteseJ. R. GillespieIrina М. Kuznetsova
- Journals
- Chemical Reviews (9 papers)Proceedings of the National Academy of Sciences (4 papers)Journal of the American Chemical Society (2 papers)
- Partner nations
- United StatesRussiaSaudi Arabia
In The Last Decade
Vladimir N. Uversky
1.1k papers receiving 72.6k citations
Hit Papers
Peers
Comparison fields: 5 of 212
- Molecular Biology 51.9k
- Neurology 7.8k
- Cell Biology 7.2k
- Physiology 10.8k
- Materials Chemistry 13.9k
Countries citing papers authored by Vladimir N. Uversky
This map shows the geographic impact of Vladimir N. Uversky'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 Vladimir N. Uversky with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Vladimir N. Uversky more than expected).
Fields of papers citing papers by Vladimir N. Uversky
This network shows the impact of papers produced by Vladimir N. Uversky. 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 Vladimir N. Uversky. The network helps show where Vladimir N. Uversky may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Vladimir N. Uversky, 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 | 0 | |
| 2 | 2024 | 5 | |
| 3 | 2024 | 7 | |
| 4 | 2024 | 0 | |
| 5 | 2024 | 4 | |
| 6 | 2023 | 6 | |
| 7 | 2023 | 4 | |
| 8 | 2023 | 5 | |
| 9 | 2023 | 4 | |
| 10 | 2023 | 4 | |
| 11 | 2023 | 6 | |
| 12 | 2023 | 0 | |
| 13 | 2023 | 5 | |
| 14 | 2023 | 7 | |
| 15 | 2021 | 11 | |
| 16 | 2021 | 6 | |
| 17 | 2021 | 57 | |
| 18 | 2019 | 0 | |
| 19 | 2007 | 50 | |
| 20 | 2003 | 389 |
About Vladimir N. Uversky
Vladimir N. Uversky is a scholar working on Filtration and Separation, Molecular Biology and Cell Biology, having authored 1.1k papers that have together received 73.4k indexed citations. Recurring topics across this work include Protein Structure and Dynamics (373 papers), Enzyme Structure and Function (213 papers), RNA and protein synthesis mechanisms (124 papers), Alzheimer's disease research and treatments (109 papers), RNA Research and Splicing (80 papers), Parkinson's Disease Mechanisms and Treatments (77 papers), SARS-CoV-2 and COVID-19 Research (63 papers) and Machine Learning in Bioinformatics (62 papers). The work is most often cited by research in Molecular Biology (51.9k citations), Neurology (7.8k citations) and Cell Biology (7.2k citations). Vladimir N. Uversky has collaborated with scholars based in United States, Russia and Saudi Arabia. Frequent co-authors include A. Keith Dunker, Anthony L. Fink, Christopher J. Oldfield, Bin Xue, Jie Li, Marc S. Cortese, J. R. Gillespie, Irina М. Kuznetsova, Konstantin К. Turoverov and Pedro Romero. Their work appears in journals such as Chemical Reviews, Proceedings of the National Academy of Sciences and Journal of the American Chemical Society.
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.