Sheng Long
Impact in
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- SARS-CoV-2 and COVID-19 Research
- COVID-19 Clinical Research Studies
- SARS-CoV-2 detection and testing
- Modeling and Simulation top 10%
- COVID-19 epidemiological studies
Papers in
- Genetics 2
- Genomics and Rare Diseases 2
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- Cancer Genomics and Diagnostics 2
- Co-authors
- Randall J. Olsen (4 shared papers)Akanksha Batajoo (1 shared paper)Matthew Ojeda Saavedra (1 shared paper)Jacob Kinskey (1 shared paper)James M. Musser (1 shared paper)Jessica Cambric (1 shared paper)Kristina Reppond (1 shared paper)Paul Christensen (1 shared paper)
- Journals
- Journal of Pathology Informatics (2 papers)American Journal Of Pathology (1 paper)Military Medical Research (1 paper)American Journal of Clinical Pathology (1 paper)Frontiers in Pharmacology (1 paper)
- Partner nations
- United StatesChinaUnited Kingdom
In The Last Decade
Sheng Long
9 papers receiving 155 citations
Hit Papers
Peers
Comparison fields: 5 of 54
- Infectious Diseases 110
- Modeling and Simulation 18
- Neurology 26
- Health 13
- Biological Psychiatry 2
Countries citing papers authored by Sheng Long
This map shows the geographic impact of Sheng Long'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 Sheng Long with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sheng Long more than expected).
Fields of papers citing papers by Sheng Long
This network shows the impact of papers produced by Sheng Long. 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 Sheng Long. The network helps show where Sheng Long may publish in the future.
Co-authors
The 25 scholars most cited alongside Sheng Long, 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 | Signals of Significantly Increased Vaccine Breakthrough, Decreased Hospitalization Rates, and Less Severe Disease in Patients with Coronavirus Disease 2019 Caused by the Omicron Variant of Severe Acute Respiratory Syndrome Coronavirus 2 in Houston, Texas Hit paper breakdown → | 2022 | 127 |
| 2 | 2024 | 12 | |
| 3 | 2017 | 6 | |
| 4 | 2017 | 4 | |
| 5 | 1999 | 4 | |
| 6 | 2018 | 2 | |
| 7 | 2024 | 1 | |
| 8 | 2024 | 1 | |
| 9 | 2020 | 1 | |
| 10 | 2025 | 0 | |
| 11 | 2024 | 0 |
About Sheng Long
Sheng Long is a scholar working on Genetics, Cancer Research, Infectious Diseases, Surgery and Molecular Biology, having authored 11 papers that have together received 158 indexed citations. Recurring topics across this work include Cancer Genomics and Diagnostics (2 papers), Genomics and Rare Diseases (2 papers), COVID-19 diagnosis using AI (1 paper), Oil and Gas Production Techniques (1 paper), Genetic factors in colorectal cancer (1 paper), SARS-CoV-2 and COVID-19 Research (1 paper), Poxvirus research and outbreaks (1 paper) and Genomics and Phylogenetic Studies (1 paper). The work is most often cited by research in Infectious Diseases (110 citations), Modeling and Simulation (18 citations), Neurology (26 citations), Health (13 citations) and Biological Psychiatry (2 citations). Sheng Long has collaborated with scholars based in United States, China and United Kingdom. Frequent co-authors include Randall J. Olsen, Akanksha Batajoo, Matthew Ojeda Saavedra, Jacob Kinskey, James M. Musser, Jessica Cambric, Kristina Reppond, Paul Christensen, Ryan Gadd and Guy Williams. Their work appears in journals such as Journal of Pathology Informatics, American Journal Of Pathology, Military Medical Research, American Journal of Clinical Pathology and Frontiers in Pharmacology.
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.