Sheida Nabavi
Impact in
- Health Informatics top 5%
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- Radiomics and Machine Learning in Medical Imaging
- COVID-19 diagnosis using AI
Papers in
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- AI in cancer detection 16
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- Gene expression and cancer classification 9
- Single-cell and spatial transcriptomics 8
- Bioinformatics and Genomic Networks 5
- Co-authors
- Tianyu Wang (10 shared papers)Clifford Yang (11 shared papers)Reda A. Ammar (5 shared papers)Boyang Li (1 shared paper)Craig E. Nelson (1 shared paper)Jun Bai (3 shared papers)Abdelrahman Hosny (3 shared papers)B. V. K. Vijaya Kumar (4 shared papers)
- Journals
- BMC Bioinformatics (7 papers)IEEE Transactions on Magnetics (3 papers)Medical Physics (2 papers)BMC Genomics (2 papers)Bioinformatics (2 papers)
- Partner nations
- United StatesIranCanada
In The Last Decade
Sheida Nabavi
55 papers receiving 1.2k citations
Peers
Comparison fields: 5 of 103
- Health Informatics 34
- Radiology, Nuclear Medicine and Imaging 319
- Artificial Intelligence 429
- Cancer Research 180
- Neurology 101
Countries citing papers authored by Sheida Nabavi
This map shows the geographic impact of Sheida Nabavi'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 Sheida Nabavi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sheida Nabavi more than expected).
Fields of papers citing papers by Sheida Nabavi
This network shows the impact of papers produced by Sheida Nabavi. 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 Sheida Nabavi. The network helps show where Sheida Nabavi may publish in the future.
Co-authors
The 25 scholars most cited alongside Sheida Nabavi, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 57 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2019 | 184 | |
| 2 | 2019 | 183 | |
| 3 | 2021 | 105 | |
| 4 | 2017 | 104 | |
| 5 | 2016 | 59 | |
| 6 | 2007 | 55 | |
| 7 | 2022 | 54 | |
| 8 | 2014 | 49 | |
| 9 | 2015 | 46 | |
| 10 | 2020 | 43 | |
| 11 | 2021 | 38 | |
| 12 | 2019 | 33 | |
| 13 | 2024 | 31 | |
| 14 | 2018 | 30 | |
| 15 | 2020 | 24 | |
| 16 | 2022 | 22 | |
| 17 | 2010 | 19 | |
| 18 | 2022 | 17 | |
| 19 | 2017 | 16 | |
| 20 | 2021 | 16 |
About Sheida Nabavi
Sheida Nabavi is a scholar working on Artificial Intelligence, Molecular Biology, Radiology, Nuclear Medicine and Imaging, Cancer Research and Genetics, having authored 57 papers that have together received 1.3k indexed citations. Recurring topics across this work include AI in cancer detection (16 papers), Gene expression and cancer classification (9 papers), Radiomics and Machine Learning in Medical Imaging (9 papers), Single-cell and spatial transcriptomics (8 papers), Genomic variations and chromosomal abnormalities (8 papers), Cancer Genomics and Diagnostics (5 papers), Bioinformatics and Genomic Networks (5 papers) and Genomics and Rare Diseases (4 papers). The work is most often cited by research in Health Informatics (34 citations), Radiology, Nuclear Medicine and Imaging (319 citations), Artificial Intelligence (429 citations), Cancer Research (180 citations) and Neurology (101 citations). Sheida Nabavi has collaborated with scholars based in United States, Iran and Canada. Frequent co-authors include Tianyu Wang, Clifford Yang, Reda A. Ammar, Boyang Li, Craig E. Nelson, Jun Bai, Abdelrahman Hosny, B. V. K. Vijaya Kumar, Mohammad Madani and Michelle T. Dow. Their work appears in journals such as BMC Bioinformatics, IEEE Transactions on Magnetics, Medical Physics, BMC 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.