Jonathan Borowsky
Impact in
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- Computational Drug Discovery Methods
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- SARS-CoV-2 and COVID-19 Research
Papers in
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- Protein Structure and Dynamics 2
- Protist diversity and phylogeny 1
- vaccines and immunoinformatics approaches 1
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- Computational Drug Discovery Methods 3
- Co-authors
- Gregory R. Bowman (4 shared papers)Michael D. Ward (4 shared papers)Artur Meller (3 shared papers)Maxwell I. Zimmerman (2 shared papers)Sukrit Singh (2 shared papers)Neha Vithani (2 shared papers)Juan Lavista Ferres (2 shared papers)Felipe Oviedo (2 shared papers)
- Journals
- Biophysical Journal (2 papers)Nature Chemistry (1 paper)BMC Genomics (1 paper)Nature Communications (1 paper)SSRN Electronic Journal (1 paper)
- Partner nations
- United StatesSwitzerlandGermany
In The Last Decade
Jonathan Borowsky
7 papers receiving 388 citations
Peers
Comparison fields: 5 of 75
- Computational Theory and Mathematics 108
- Infectious Diseases 112
- Molecular Biology 287
- Radiology, Nuclear Medicine and Imaging 30
- Spectroscopy 21
Countries citing papers authored by Jonathan Borowsky
This map shows the geographic impact of Jonathan Borowsky'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 Jonathan Borowsky with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jonathan Borowsky more than expected).
Fields of papers citing papers by Jonathan Borowsky
This network shows the impact of papers produced by Jonathan Borowsky. 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 Jonathan Borowsky. The network helps show where Jonathan Borowsky may publish in the future.
Co-authors
The 25 scholars most cited alongside Jonathan Borowsky, 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 | 2021 | 167 | |
| 2 | 2023 | 86 | |
| 3 | 2016 | 66 | |
| 4 | 2021 | 56 | |
| 5 | 2023 | 13 | |
| 6 | 2019 | 2 | |
| 7 | 2022 | 1 |
About Jonathan Borowsky
Jonathan Borowsky is a scholar working on Molecular Biology, Computational Theory and Mathematics, Infectious Diseases, Spectroscopy and Astronomy and Astrophysics, having authored 7 papers that have together received 391 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (3 papers), SARS-CoV-2 and COVID-19 Research (2 papers), Protein Structure and Dynamics (2 papers), Advanced Proteomics Techniques and Applications (2 papers), Fiscal Policy and Economic Growth (1 paper), Protist diversity and phylogeny (1 paper), Ion-surface interactions and analysis (1 paper) and vaccines and immunoinformatics approaches (1 paper). The work is most often cited by research in Computational Theory and Mathematics (108 citations), Infectious Diseases (112 citations), Molecular Biology (287 citations), Radiology, Nuclear Medicine and Imaging (30 citations) and Spectroscopy (21 citations). Jonathan Borowsky has collaborated with scholars based in United States, Switzerland and Germany. Frequent co-authors include Gregory R. Bowman, Michael D. Ward, Artur Meller, Maxwell I. Zimmerman, Sukrit Singh, Neha Vithani, Juan Lavista Ferres, Felipe Oviedo, Jeffrey M. Lotthammer and Meghana Kshirsagar. Their work appears in journals such as Biophysical Journal, Nature Chemistry, BMC Genomics, Nature Communications and SSRN Electronic 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.