Matahi Moarii
Impact in
- Health Informatics top 2%
- Artificial Intelligence in Healthcare and Education
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- Radiomics and Machine Learning in Medical Imaging
Papers in
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- Epigenetics and DNA Methylation 2
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- AI in cancer detection 4
- Co-authors
- Pierre Courtiol (5 shared papers)Gilles Wainrib (5 shared papers)Elodie Pronier (5 shared papers)Thomas Clozel (5 shared papers)Mikhail Zaslavskiy (5 shared papers)Meriem Sefta (3 shared papers)Fabien Reyal (5 shared papers)Julien Caldéraro (4 shared papers)
- Journals
- PLoS ONE (2 papers)Nature Medicine (1 paper)Nature Communications (1 paper)Journal of Hepatology (1 paper)Hepatology (1 paper)
- Partner nations
- FranceUnited StatesUnited Kingdom
In The Last Decade
Matahi Moarii
12 papers receiving 1.1k citations
Matahi Moarii's Hit Papers
Peers
Comparison fields: 5 of 91
- Health Informatics 62
- Radiology, Nuclear Medicine and Imaging 349
- Biophysics 97
- Cancer Research 211
- Artificial Intelligence 415
Countries citing papers authored by Matahi Moarii
This map shows the geographic impact of Matahi Moarii'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 Matahi Moarii with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matahi Moarii more than expected).
Fields of papers citing papers by Matahi Moarii
This network shows the impact of papers produced by Matahi Moarii. 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 Matahi Moarii. The network helps show where Matahi Moarii may publish in the future.
Co-authors
The 25 scholars most cited alongside Matahi Moarii, 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 | Deep learning-based classification of mesothelioma improves prediction of patient outcome Hit paper breakdown → | 2019 | 326 |
| 2 | A deep learning model to predict RNA-Seq expression of tumours from whole slide images Hit paper breakdown → | 2020 | 302 |
| 3 | 2020 | 214 | |
| 4 | 2015 | 115 | |
| 5 | 2017 | 49 | |
| 6 | 2017 | 46 | |
| 7 | 2015 | 29 | |
| 8 | 2020 | 15 | |
| 9 | 2015 | 13 | |
| 10 | 2016 | 8 | |
| 11 | 2014 | 6 | |
| 12 | 2020 | 2 |
About Matahi Moarii
Matahi Moarii is a scholar working on Molecular Biology, Artificial Intelligence, Oncology, Radiology, Nuclear Medicine and Imaging and Hematology, having authored 12 papers that have together received 1.1k indexed citations. Recurring topics across this work include AI in cancer detection (4 papers), Acute Myeloid Leukemia Research (2 papers), Radiomics and Machine Learning in Medical Imaging (2 papers), Epigenetics and DNA Methylation (2 papers), Biomarkers in Disease Mechanisms (1 paper), Pancreatic and Hepatic Oncology Research (1 paper), Cancer Genomics and Diagnostics (1 paper) and Chronic Myeloid Leukemia Treatments (1 paper). The work is most often cited by research in Health Informatics (62 citations), Radiology, Nuclear Medicine and Imaging (349 citations), Biophysics (97 citations), Cancer Research (211 citations) and Artificial Intelligence (415 citations). Matahi Moarii has collaborated with scholars based in France, United States and United Kingdom. Frequent co-authors include Pierre Courtiol, Gilles Wainrib, Elodie Pronier, Thomas Clozel, Mikhail Zaslavskiy, Meriem Sefta, Fabien Reyal, Julien Caldéraro, Jean‐Philippe Vert and Charlie Saillard. Their work appears in journals such as PLoS ONE, Nature Medicine, Nature Communications, Journal of Hepatology and Hepatology.
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.