Alex Renn
Impact in
- Health Informatics top 2%
- Artificial Intelligence in Healthcare and Education
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- SARS-CoV-2 and COVID-19 Research
- COVID-19 Clinical Research Studies
- SARS-CoV-2 detection and testing
- Viral gastroenteritis research and epidemiology
Papers in
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- Toxin Mechanisms and Immunotoxins 2
- Co-authors
- Matthew D. Hall (3 shared papers)Ying Fu (1 shared paper)Anton Simeonov (1 shared paper)Xin Hu (1 shared paper)William J. Mack (1 shared paper)Jonathan Dallas (1 shared paper)Gabriel Zada (3 shared papers)Vincent Nguyen (1 shared paper)
- Journals
- Journal of neurosurgery (3 papers)Trends in Pharmacological Sciences (1 paper)International Immunopharmacology (1 paper)Molecular Cancer Therapeutics (1 paper)Wiley Interdisciplinary Reviews Computational Molecular Science (1 paper)
- Partner nations
- United StatesChinaTaiwan
In The Last Decade
Alex Renn
8 papers receiving 181 citations
Peers
Comparison fields: 5 of 47
- Health Informatics 56
- Infectious Diseases 64
- Immunology 30
- Radiology, Nuclear Medicine and Imaging 29
- Applied Microbiology and Biotechnology 2
Countries citing papers authored by Alex Renn
This map shows the geographic impact of Alex Renn'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 Alex Renn with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Alex Renn more than expected).
Fields of papers citing papers by Alex Renn
This network shows the impact of papers produced by Alex Renn. 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 Alex Renn. The network helps show where Alex Renn may publish in the future.
Co-authors
The 25 scholars most cited alongside Alex Renn, 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 | 2020 | 79 | |
| 2 | 2023 | 61 | |
| 3 | 2018 | 18 | |
| 4 | 2022 | 8 | |
| 5 | 2020 | 7 | |
| 6 | 2022 | 6 | |
| 7 | 2025 | 4 | |
| 8 | 2025 | 1 |
About Alex Renn
Alex Renn is a scholar working on Immunology, Molecular Biology, Infectious Diseases, Surgery and Neurology, having authored 8 papers that have together received 184 indexed citations. Recurring topics across this work include Toxin Mechanisms and Immunotoxins (2 papers), Pharmacogenetics and Drug Metabolism (1 paper), Radiomics and Machine Learning in Medical Imaging (1 paper), Medical Imaging and Analysis (1 paper), Meningioma and schwannoma management (1 paper), Neuroblastoma Research and Treatments (1 paper), Artificial Intelligence in Healthcare and Education (1 paper) and COVID-19 Clinical Research Studies (1 paper). The work is most often cited by research in Health Informatics (56 citations), Infectious Diseases (64 citations), Immunology (30 citations), Radiology, Nuclear Medicine and Imaging (29 citations) and Applied Microbiology and Biotechnology (2 citations). Alex Renn has collaborated with scholars based in United States, China and Taiwan. Frequent co-authors include Matthew D. Hall, Ying Fu, Anton Simeonov, Xin Hu, William J. Mack, Jonathan Dallas, Gabriel Zada, Vincent Nguyen, Benjamin S. Hopkins and Pavlos Texakalidis. Their work appears in journals such as Journal of neurosurgery, Trends in Pharmacological Sciences, International Immunopharmacology, Molecular Cancer Therapeutics and Wiley Interdisciplinary Reviews Computational Molecular Science.
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.