John Karanicolas
- Molecular Biology top 2%
- Protein Structure and Dynamics 23
- RNA and protein synthesis mechanisms 9
- RNA Research and Splicing 9
- RNA modifications and cancer 7
- Viral Infectious Diseases and Gene Expression in Insects 5
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- Computational Drug Discovery Methods 16
- Materials Chemistry top 5%
- Enzyme Structure and Function 10
- Physiology top 5%
- Spectroscopy top 5%
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- Monoclonal and Polyclonal Antibodies Research 10
- Co-authors
- Charles L. BrooksMichael FeigDavid BakerStuart A. SieversDavid EisenbergRhiju DasDavid K. JohnsonMichael J. Thompson
- Journals
- Proceedings of the National Academy of Sciences (4 papers)Journal of Chemical Information and Modeling (4 papers)PLoS ONE (4 papers)
- Partner nations
- United StatesChinaRussia
In The Last Decade
John Karanicolas
62 papers receiving 4.3k citations
Hit Papers
Peers
Comparison fields: 5 of 140
- Molecular Biology 3.6k
- Computational Theory and Mathematics 463
- Materials Chemistry 988
- Physiology 457
- Spectroscopy 284
Countries citing papers authored by John Karanicolas
This map shows the geographic impact of John Karanicolas'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 John Karanicolas with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John Karanicolas more than expected).
Fields of papers citing papers by John Karanicolas
This network shows the impact of papers produced by John Karanicolas. 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 John Karanicolas. The network helps show where John Karanicolas may publish in the future.
Co-authorship network
The 25 scholars most cited alongside John Karanicolas, 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 | 2023 | 11 | |
| 2 | 2023 | 0 | |
| 3 | 2022 | 9 | |
| 4 | 2022 | 36 | |
| 5 | 2022 | 9 | |
| 6 | 2021 | 7 | |
| 7 | 2021 | 93 | |
| 8 | 2020 | 138 | |
| 9 | 2020 | 15 | |
| 10 | 2020 | 10 | |
| 11 | 2020 | 80 | |
| 12 | 2020 | 10 | |
| 13 | 2020 | 22 | |
| 14 | 2019 | 35 | |
| 15 | 2019 | 15 | |
| 16 | 2019 | 96 | |
| 17 | 2018 | 6 | |
| 18 | 2018 | 81 | |
| 19 | 2017 | 4 | |
| 20 | 2016 | 38 |
About John Karanicolas
John Karanicolas is a scholar working on Computational Theory and Mathematics, Molecular Biology, Biotechnology, Radiology, Nuclear Medicine and Imaging and Biophysics, having authored 66 papers that have together received 4.3k indexed citations. Recurring topics across this work include Protein Structure and Dynamics (23 papers), Computational Drug Discovery Methods (16 papers), Monoclonal and Polyclonal Antibodies Research (10 papers), Enzyme Structure and Function (10 papers), RNA and protein synthesis mechanisms (9 papers), RNA Research and Splicing (9 papers), RNA modifications and cancer (7 papers) and Viral Infectious Diseases and Gene Expression in Insects (5 papers). The work is most often cited by research in Molecular Biology (3.6k citations), Computational Theory and Mathematics (463 citations), Materials Chemistry (988 citations), Physiology (457 citations) and Spectroscopy (284 citations). John Karanicolas has collaborated with scholars based in United States, China and Russia. Frequent co-authors include Charles L. Brooks, Michael Feig, David Baker, Stuart A. Sievers, David Eisenberg, Rhiju Das, David K. Johnson, Michael J. Thompson, Magdalena I. Ivanova and Eric J. Deeds. Their work appears in journals such as Proceedings of the National Academy of Sciences, Journal of Chemical Information and Modeling, PLoS ONE, Journal of Medicinal Chemistry and Cancer Research.
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.