Darrell E. Hurt
- Microbiology top 0.5%
- Antimicrobial Peptides and Activities 8
- Cancer Research top 2%
- Molecular Biology top 5%
- Biochemical and Structural Characterization 6
- Glycosylation and Glycoproteins Research 4
- vaccines and immunoinformatics approaches 4
- RNA and protein synthesis mechanisms 4
- Immunology top 10%
- Virology top 10%
- HIV Research and Treatment 7
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- Tuberculosis Research and Epidemiology 10
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- COVID-19 diagnosis using AI 8
- Co-authors
- Tomoshige KinoGeorge P. ChrousosNader D. NaderTakamasa IchijoAlex RosenthalAndrei GabrielianThomas L. LetoTakehiko Ueyama
- Journals
- The Journal of Clinical Endocrinology & Metabolism (4 papers)PLoS ONE (3 papers)Nucleic Acids Research (2 papers)
- Partner nations
- United StatesGreeceGeorgia
In The Last Decade
Darrell E. Hurt
49 papers receiving 3.3k citations
Hit Papers
Peers
Comparison fields: 5 of 170
- Microbiology 573
- Cancer Research 907
- Molecular Biology 2.1k
- Immunology 386
- Virology 63
Countries citing papers authored by Darrell E. Hurt
This map shows the geographic impact of Darrell E. Hurt'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 Darrell E. Hurt with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Darrell E. Hurt more than expected).
Fields of papers citing papers by Darrell E. Hurt
This network shows the impact of papers produced by Darrell E. Hurt. 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 Darrell E. Hurt. The network helps show where Darrell E. Hurt may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Darrell E. Hurt, 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 | 2024 | 4 | |
| 2 | 2024 | 0 | |
| 3 | 2023 | 1 | |
| 4 | 2023 | 3 | |
| 5 | 2022 | 26 | |
| 6 | 2022 | 25 | |
| 7 | 2021 | 11 | |
| 8 | 2021 | 17 | |
| 9 | DBAASP v3: database of antimicrobial/cytotoxic activity and structure of peptides as a resource for development of new therapeuticsbreakdown → | 2020 | 390 |
| 10 | 2020 | 5 | |
| 11 | 2017 | 107 | |
| 12 | 2016 | 12 | |
| 13 | 2014 | 24 | |
| 14 | 2014 | 62 | |
| 15 | 2013 | 0 | |
| 16 | 2011 | 25 | |
| 17 | 2011 | 15 | |
| 18 | 2010 | 93 | |
| 19 | 2008 | 14 | |
| 20 | 2006 | 36 |
About Darrell E. Hurt
Darrell E. Hurt is a scholar working on Virology, Microbiology and Structural Biology, having authored 51 papers that have together received 3.3k indexed citations. Recurring topics across this work include Tuberculosis Research and Epidemiology (10 papers), Antimicrobial Peptides and Activities (8 papers), COVID-19 diagnosis using AI (8 papers), HIV Research and Treatment (7 papers), Biochemical and Structural Characterization (6 papers), Glycosylation and Glycoproteins Research (4 papers), vaccines and immunoinformatics approaches (4 papers) and RNA and protein synthesis mechanisms (4 papers). The work is most often cited by research in Microbiology (573 citations), Cancer Research (907 citations) and Molecular Biology (2.1k citations). Darrell E. Hurt has collaborated with scholars based in United States, Greece and Georgia. Frequent co-authors include Tomoshige Kino, George P. Chrousos, Nader D. Nader, Takamasa Ichijo, Alex Rosenthal, Andrei Gabrielian, Thomas L. Leto, Takehiko Ueyama, Stanislas Morand and Michael Tartakovsky. Their work appears in journals such as The Journal of Clinical Endocrinology & Metabolism, PLoS ONE, Nucleic Acids Research, BMC Bioinformatics and Journal of Visualized Experiments.
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.