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
- Galectins and Cancer Biology 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)Orly Dym (1 shared paper)Jeanne Chiaravalli (1 shared paper)Shir Marciano (1 shared paper)
- Journals
- iScience (1 paper)PLoS Pathogens (1 paper)ACS Biomaterials Science & Engineering (1 paper)FEBS Journal (1 paper)Journal of Molecular Biology (1 paper)
- Partner nations
- IsraelAustraliaUnited Kingdom
In The Last Decade
Maya Shemesh
10 papers receiving 485 citations
Hit Papers
Peers
Comparison fields: 5 of 75
- Infectious Diseases 287
- Sensory Systems 35
- Immunology 113
- Modeling and Simulation 15
- Animal Science and Zoology 29
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 | 230 |
| 2 | 2021 | 84 | |
| 3 | 2015 | 44 | |
| 4 | 2021 | 43 | |
| 5 | 2014 | 25 | |
| 6 | 2019 | 24 | |
| 7 | The effect of inhibition of prostaglandin synthesis on free water and osmolar clearances in patients with hereditary nephrogenic diabetes insipidus. | 1980 | 17 |
| 8 | 2022 | 10 | |
| 9 | 2022 | 6 | |
| 10 | 2021 | 5 |
About Maya Shemesh
Maya Shemesh is a scholar working on Sensory Systems, Immunology, Infectious Diseases, Oncology and Experimental and Cognitive Psychology, having authored 10 papers that have together received 488 indexed citations. Recurring topics across this work include interferon and immune responses (3 papers), Cytokine Signaling Pathways and Interactions (3 papers), SARS-CoV-2 and COVID-19 Research (3 papers), Evolutionary Psychology and Human Behavior (1 paper), Infection Control and Ventilation (1 paper), Galectins and Cancer Biology (1 paper), Neurobiology and Insect Physiology Research (1 paper) and Electrolyte and hormonal disorders (1 paper). The work is most often cited by research in Infectious Diseases (287 citations), Sensory Systems (35 citations), Immunology (113 citations), Modeling and Simulation (15 citations) and Animal Science and Zoology (29 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, Orly Dym, Jeanne Chiaravalli, Shir Marciano, Hanné Andersen and Daniel C. Douek. Their work appears in journals such as iScience, PLoS Pathogens, ACS Biomaterials Science & Engineering, FEBS Journal and Journal of Molecular Biology.
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.