Maya Shemesh
Impact in
- Infectious Diseases top 5%
- SARS-CoV-2 and COVID-19 Research
- COVID-19 Clinical Research Studies
- SARS-CoV-2 detection and testing
- Viral gastroenteritis research and epidemiology
- Sensory Systems top 10%
- Olfactory and Sensory Function Studies
Papers in
-
- interferon and immune responses 3
-
- SARS-CoV-2 and COVID-19 Research 3
- COVID-19 Clinical Research Studies 1
- Co-authors
- Gideon Schreiber (6 shared papers)Daniel Harari (3 shared papers)Yinon Rudich (2 shared papers)Chunlin Li (2 shared papers)Ira Marton (2 shared papers)Bjoern Meyer (1 shared paper)Robert A. Seder (1 shared paper)Jiří Zahradník (2 shared papers)
- Journals
- iScience (1 paper)PLoS Pathogens (1 paper)FEBS Journal (1 paper)ACS Biomaterials Science & Engineering (1 paper)Science Signaling (1 paper)
- Partner nations
- IsraelAustraliaUnited Kingdom
In The Last Decade
Maya Shemesh
10 papers receiving 575 citations
Maya Shemesh's Hit Papers
Peers
Comparison fields: 5 of 77
- Infectious Diseases 328
- Sensory Systems 40
- Immunology 123
- Animal Science and Zoology 44
- Modeling and Simulation 14
Countries citing papers authored by Maya Shemesh
This map shows the geographic impact of Maya Shemesh'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 Maya Shemesh with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Maya Shemesh more than expected).
Fields of papers citing papers by Maya Shemesh
This network shows the impact of papers produced by Maya Shemesh. 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 Maya Shemesh. The network helps show where Maya Shemesh may publish in the future.
Co-authors
The 25 scholars most cited alongside Maya Shemesh, 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 | SARS-CoV-2 variant prediction and antiviral drug design are enabled by RBD in vitro evolution Hit paper breakdown → | 2021 | 282 |
| 2 | 2021 | 92 | |
| 3 | 2021 | 57 | |
| 4 | 2015 | 53 | |
| 5 | 2014 | 26 | |
| 6 | 2019 | 26 | |
| 7 | The effect of inhibition of prostaglandin synthesis on free water and osmolar clearances in patients with hereditary nephrogenic diabetes insipidus. | 1980 | 18 |
| 8 | 2022 | 15 | |
| 9 | 2022 | 7 | |
| 10 | 2021 | 5 |
About Maya Shemesh
Maya Shemesh is a scholar working on Immunology, Infectious Diseases, Oncology, Sensory Systems and Pulmonary and Respiratory Medicine, having authored 10 papers that have together received 581 indexed citations. Recurring topics across this work include interferon and immune responses (3 papers), SARS-CoV-2 and COVID-19 Research (3 papers), Cytokine Signaling Pathways and Interactions (3 papers), Infection Control and Ventilation (1 paper), COVID-19 Clinical Research Studies (1 paper), CRISPR and Genetic Engineering (1 paper), Olfactory and Sensory Function Studies (1 paper) and Inhalation and Respiratory Drug Delivery (1 paper). The work is most often cited by research in Infectious Diseases (328 citations), Sensory Systems (40 citations), Immunology (123 citations), Animal Science and Zoology (44 citations) and Modeling and Simulation (14 citations). Maya Shemesh has collaborated with scholars based in Israel, Australia and United Kingdom. Frequent co-authors include Gideon Schreiber, Daniel Harari, Yinon Rudich, Chunlin Li, Ira Marton, Bjoern Meyer, Robert A. Seder, Jiří Zahradník, Mark G. Lewis and Orly Dym. Their work appears in journals such as iScience, PLoS Pathogens, FEBS Journal, ACS Biomaterials Science & Engineering and Science Signaling.
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.