Gerald H. Learn
- Virology top 0.05%
- HIV Research and Treatment 74
- Infectious Diseases top 0.2%
- HIV/AIDS drug development and treatment 37
- HIV/AIDS Research and Interventions 32
- Hepatology top 1%
- Hepatitis C virus research 13
- Immunology top 2%
- Immune Cell Function and Interaction 7
- Epidemiology top 2%
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- Genomics and Phylogenetic Studies 12
- vaccines and immunoinformatics approaches 8
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- Mosquito-borne diseases and control 7
- Co-authors
- James I. MullinsBeatrice H. HahnMichael T. CleggAllen G. RodrigoDavid C. NicklePhalguni GuptaBrian R. MortonBrandon S. Gaut
- Journals
- Journal of Virology (31 papers)Proceedings of the National Academy of Sciences (5 papers)PLoS ONE (5 papers)
- Partner nations
- United StatesUnited KingdomFrance
In The Last Decade
Gerald H. Learn
98 papers receiving 6.9k citations
Hit Papers
Peers
Comparison fields: 5 of 129
- Virology 4.5k
- Infectious Diseases 3.1k
- Hepatology 604
- Immunology 1.4k
- Epidemiology 1.5k
Countries citing papers authored by Gerald H. Learn
This map shows the geographic impact of Gerald H. Learn'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 Gerald H. Learn with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gerald H. Learn more than expected).
Fields of papers citing papers by Gerald H. Learn
This network shows the impact of papers produced by Gerald H. Learn. 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 Gerald H. Learn. The network helps show where Gerald H. Learn may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Gerald H. Learn, 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 | 2018 | 73 | |
| 2 | 2016 | 227 | |
| 3 | 2016 | 128 | |
| 4 | 2016 | 74 | |
| 5 | 2016 | 30 | |
| 6 | 2012 | 29 | |
| 7 | 2012 | 36 | |
| 8 | 2012 | 71 | |
| 9 | 2010 | 150 | |
| 10 | 2010 | 64 | |
| 11 | 2010 | 41 | |
| 12 | 2009 | 8 | |
| 13 | Low dose rectal inoculation of rhesus macaques by SIV smE660 or SIVmac251 recapitulates | 2008 | 1 |
| 14 | 2007 | 52 | |
| 15 | 2004 | 156 | |
| 16 | 2004 | 11 | |
| 17 | 2004 | 13 | |
| 18 | 2001 | 16 | |
| 19 | 2000 | 26 | |
| 20 | 1988 | 1 |
About Gerald H. Learn
Gerald H. Learn is a scholar working on Virology, Infectious Diseases, Hepatology, Epidemiology and Immunology, having authored 98 papers that have together received 7.0k indexed citations. Recurring topics across this work include HIV Research and Treatment (74 papers), HIV/AIDS drug development and treatment (37 papers), HIV/AIDS Research and Interventions (32 papers), Hepatitis C virus research (13 papers), Genomics and Phylogenetic Studies (12 papers), vaccines and immunoinformatics approaches (8 papers), Mosquito-borne diseases and control (7 papers) and Immune Cell Function and Interaction (7 papers). The work is most often cited by research in Virology (4.5k citations), Infectious Diseases (3.1k citations), Hepatology (604 citations), Immunology (1.4k citations) and Epidemiology (1.5k citations). Gerald H. Learn has collaborated with scholars based in United States, United Kingdom and France. Frequent co-authors include James I. Mullins, Beatrice H. Hahn, Michael T. Clegg, Allen G. Rodrigo, David C. Nickle, Phalguni Gupta, Brian R. Morton, Brandon S. Gaut, George M. Shaw and Raj Shankarappa. Their work appears in journals such as Journal of Virology, Proceedings of the National Academy of Sciences, PLoS ONE, Cell Host & Microbe and PLoS Pathogens.
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.