Guy Nadav
Impact in
- Health Informatics top 2%
- Artificial Intelligence in Healthcare and Education
- Genetics top 10%
- Genomics and Rare Diseases
- Glioma Diagnosis and Treatment
- Genetics and Neurodevelopmental Disorders
Papers in
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- Advanced MRI Techniques and Applications 5
- MRI in cancer diagnosis 2
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- Advanced Neural Network Applications 1
- Face recognition and analysis 1
- Co-authors
- Peter Michael Krawitz (2 shared papers)Nicole Fleischer (2 shared papers)Susanne B. Kamphausen (1 shared paper)Omri Bar (1 shared paper)Karen Webster Gripp (1 shared paper)Lina Basel‐Salmon (1 shared paper)Yair Hanani (1 shared paper)Yaron Gurovich (1 shared paper)
- Journals
- Journal of Neuro-Oncology (2 papers)Magnetic Resonance Imaging (2 papers)Nature Medicine (1 paper)Journal of Magnetic Resonance Imaging (1 paper)NAR Genomics and Bioinformatics (1 paper)
- Partner nations
- IsraelGermanyUnited States
In The Last Decade
Guy Nadav
10 papers receiving 694 citations
Guy Nadav's Hit Papers
Peers
Comparison fields: 5 of 89
- Health Informatics 137
- Genetics 234
- Computer Vision and Pattern Recognition 161
- Genetics 57
- Health Information Management 55
Countries citing papers authored by Guy Nadav
This map shows the geographic impact of Guy Nadav'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 Guy Nadav with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Guy Nadav more than expected).
Fields of papers citing papers by Guy Nadav
This network shows the impact of papers produced by Guy Nadav. 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 Guy Nadav. The network helps show where Guy Nadav may publish in the future.
Co-authors
The 25 scholars most cited alongside Guy Nadav, 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 | Identifying facial phenotypes of genetic disorders using deep learning Hit paper breakdown → | 2018 | 526 |
| 2 | 2019 | 65 | |
| 3 | 2014 | 35 | |
| 4 | 2016 | 29 | |
| 5 | 2021 | 25 | |
| 6 | 2015 | 20 | |
| 7 | 2015 | 11 | |
| 8 | 2016 | 7 | |
| 9 | 2023 | 5 | |
| 10 | 2016 | 3 |
About Guy Nadav
Guy Nadav is a scholar working on Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition, Genetics, Neurology and Genetics, having authored 10 papers that have together received 726 indexed citations. Recurring topics across this work include Advanced MRI Techniques and Applications (5 papers), Genomics and Rare Diseases (3 papers), MRI in cancer diagnosis (2 papers), Brain Tumor Detection and Classification (1 paper), Advanced Neural Network Applications (1 paper), Domain Adaptation and Few-Shot Learning (1 paper), Biomedical Text Mining and Ontologies (1 paper) and Face recognition and analysis (1 paper). The work is most often cited by research in Health Informatics (137 citations), Genetics (234 citations), Computer Vision and Pattern Recognition (161 citations), Genetics (57 citations) and Health Information Management (55 citations). Guy Nadav has collaborated with scholars based in Israel, Germany and United States. Frequent co-authors include Peter Michael Krawitz, Nicole Fleischer, Susanne B. Kamphausen, Omri Bar, Karen Webster Gripp, Lina Basel‐Salmon, Yair Hanani, Yaron Gurovich, Lynne M. Bird and Martin Zenker. Their work appears in journals such as Journal of Neuro-Oncology, Magnetic Resonance Imaging, Nature Medicine, Journal of Magnetic Resonance Imaging and NAR Genomics and Bioinformatics.
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.