Nathan Benaich
Impact in
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- MicroRNA in disease regulation
- Cancer-related molecular mechanisms research
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- Cell Adhesion Molecules Research
Papers in
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- RNA modifications and cancer 2
- Cancer-related gene regulation 1
- Protein Degradation and Inhibitors 1
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- Radiomics and Machine Learning in Medical Imaging 1
- Co-authors
- Stephen J. Goldie (2 shared papers)Fiona M. Watt (2 shared papers)Christopher J. Tape (1 shared paper)Yanchao Huang (1 shared paper)Gillian Murphy (1 shared paper)Hang Fai Kwok (1 shared paper)Samuel Woodhouse (1 shared paper)Sven Quist (1 shared paper)
- Journals
- Journal of the Peripheral Nervous System (1 paper)Cancer Letters (1 paper)Cell Reports (1 paper)International Journal of Biological Sciences (1 paper)Journal of the American College of Radiology (1 paper)
- Partner nations
- United KingdomUnited StatesGermany
In The Last Decade
Nathan Benaich
5 papers receiving 147 citations
Peers
Comparison fields: 5 of 46
- Cancer Research 45
- Immunology and Allergy 12
- Oncology 52
- Otorhinolaryngology 8
- Periodontics 5
Countries citing papers authored by Nathan Benaich
This map shows the geographic impact of Nathan Benaich'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 Nathan Benaich with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nathan Benaich more than expected).
Fields of papers citing papers by Nathan Benaich
This network shows the impact of papers produced by Nathan Benaich. 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 Nathan Benaich. The network helps show where Nathan Benaich may publish in the future.
Co-authors
The 21 scholars most cited alongside Nathan Benaich, 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 | 2014 | 50 | |
| 2 | 2014 | 43 | |
| 3 | 2016 | 41 | |
| 4 | 2012 | 12 | |
| 5 | 2023 | 2 |
About Nathan Benaich
Nathan Benaich is a scholar working on Molecular Biology, Radiology, Nuclear Medicine and Imaging, Cell Biology, Oncology and Health Informatics, having authored 5 papers that have together received 148 indexed citations. Recurring topics across this work include RNA modifications and cancer (2 papers), Zebrafish Biomedical Research Applications (1 paper), Artificial Intelligence in Healthcare and Education (1 paper), Radiomics and Machine Learning in Medical Imaging (1 paper), MicroRNA in disease regulation (1 paper), Hippo pathway signaling and YAP/TAZ (1 paper), Cancer-related gene regulation (1 paper) and Protein Degradation and Inhibitors (1 paper). The work is most often cited by research in Cancer Research (45 citations), Immunology and Allergy (12 citations), Oncology (52 citations), Otorhinolaryngology (8 citations) and Periodontics (5 citations). Nathan Benaich has collaborated with scholars based in United Kingdom, United States and Germany. Frequent co-authors include Stephen J. Goldie, Fiona M. Watt, Christopher J. Tape, Yanchao Huang, Gillian Murphy, Hang Fai Kwok, Samuel Woodhouse, Sven Quist, Ajay Mishra and Kalle Sipilä. Their work appears in journals such as Journal of the Peripheral Nervous System, Cancer Letters, Cell Reports, International Journal of Biological Sciences and Journal of the American College of Radiology.
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.