Raphael Petegrosso
Impact in
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- Cell Image Analysis Techniques
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- Cancer-related molecular mechanisms research
- MicroRNA in disease regulation
Papers in
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- Bioinformatics and Genomic Networks 3
- Gene expression and cancer classification 3
- Single-cell and spatial transcriptomics 2
- Machine Learning in Bioinformatics 1
- RNA and protein synthesis mechanisms 1
- Extracellular vesicles in disease 1
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- Tensor decomposition and applications 1
- Co-authors
- Rui Kuang (7 shared papers)Zhuliu Li (4 shared papers)Sunho Park (1 shared paper)Tae Hyun Hwang (1 shared paper)Cindy Eide (1 shared paper)Catherine Lee (1 shared paper)David Bernlohr (1 shared paper)John Garbe (1 shared paper)
- Journals
- Plant Phenomics (1 paper)BMC Genomics (1 paper)PLoS Computational Biology (1 paper)Journal of Biological Chemistry (1 paper)Proteins Structure Function and Bioinformatics (1 paper)
- Partner nations
- United StatesRussiaFrance
In The Last Decade
Raphael Petegrosso
7 papers receiving 262 citations
Peers
Comparison fields: 5 of 79
- Biophysics 22
- Cancer Research 52
- Molecular Biology 186
- Immunology 27
- Neurology 9
Countries citing papers authored by Raphael Petegrosso
This map shows the geographic impact of Raphael Petegrosso'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 Raphael Petegrosso with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Raphael Petegrosso more than expected).
Fields of papers citing papers by Raphael Petegrosso
This network shows the impact of papers produced by Raphael Petegrosso. 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 Raphael Petegrosso. The network helps show where Raphael Petegrosso may publish in the future.
Co-authors
The 22 scholars most cited alongside Raphael Petegrosso, 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 | 2019 | 175 | |
| 2 | 2016 | 31 | |
| 3 | 2018 | 24 | |
| 4 | 2018 | 19 | |
| 5 | 2020 | 7 | |
| 6 | 2019 | 3 | |
| 7 | 2021 | 3 | |
| 8 | 2020 | 0 |
About Raphael Petegrosso
Raphael Petegrosso is a scholar working on Molecular Biology, Computational Mathematics, Artificial Intelligence, Physiology and Genetics, having authored 8 papers that have together received 262 indexed citations. Recurring topics across this work include Bioinformatics and Genomic Networks (3 papers), Gene expression and cancer classification (3 papers), Single-cell and spatial transcriptomics (2 papers), Machine Learning in Bioinformatics (1 paper), Genetic Mapping and Diversity in Plants and Animals (1 paper), Tensor decomposition and applications (1 paper), RNA and protein synthesis mechanisms (1 paper) and Extracellular vesicles in disease (1 paper). The work is most often cited by research in Biophysics (22 citations), Cancer Research (52 citations), Molecular Biology (186 citations), Immunology (27 citations) and Neurology (9 citations). Raphael Petegrosso has collaborated with scholars based in United States, Russia and France. Frequent co-authors include Rui Kuang, Zhuliu Li, Sunho Park, Tae Hyun Hwang, Cindy Eide, Catherine Lee, David Bernlohr, John Garbe, Amy K. Hauck and Yue Chen. Their work appears in journals such as Plant Phenomics, BMC Genomics, PLoS Computational Biology, Journal of Biological Chemistry and Proteins Structure Function 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.