Simone G. Riva
Impact in
Papers in
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- Single-cell and spatial transcriptomics 9
- Gene expression and cancer classification 5
- Gene Regulatory Network Analysis 4
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- Cancer-related molecular mechanisms research 2
- Cancer Genomics and Diagnostics 2
- Co-authors
- Ana Cvejic (2 shared papers)Andrea Tangherloni (8 shared papers)Brynelle Myers (5 shared papers)Elisa Panada (1 shared paper)Anna Maria Ranzoni (1 shared paper)Ivan Berest (1 shared paper)Irina Mohorianu (1 shared paper)Judith B. Zaugg (1 shared paper)
- Journals
- Cell stem cell (2 papers)Scientific Reports (1 paper)BMC Bioinformatics (1 paper)Journal of Biomedical Informatics (1 paper)Symmetry (1 paper)
- Partner nations
- United KingdomItalyNetherlands
In The Last Decade
Simone G. Riva
13 papers receiving 242 citations
Peers
Comparison fields: 5 of 51
- Biophysics 19
- Hematology 36
- Immunology 61
- Cancer Research 43
- Molecular Biology 179
Countries citing papers authored by Simone G. Riva
This map shows the geographic impact of Simone G. Riva'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 Simone G. Riva with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Simone G. Riva more than expected).
Fields of papers citing papers by Simone G. Riva
This network shows the impact of papers produced by Simone G. Riva. 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 Simone G. Riva. The network helps show where Simone G. Riva may publish in the future.
Co-authors
The 25 scholars most cited alongside Simone G. Riva, 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 | 2020 | 192 | |
| 2 | 2022 | 15 | |
| 3 | 2020 | 8 | |
| 4 | 2022 | 7 | |
| 5 | 2023 | 7 | |
| 6 | 2021 | 4 | |
| 7 | 2021 | 4 | |
| 8 | 2008 | 4 | |
| 9 | 2023 | 2 | |
| 10 | 2025 | 2 | |
| 11 | 2022 | 2 | |
| 12 | 2023 | 1 | |
| 13 | 2022 | 1 | |
| 14 | 2023 | 0 | |
| 15 | 2021 | 0 | |
| 16 | 2022 | 0 |
About Simone G. Riva
Simone G. Riva is a scholar working on Molecular Biology, Cancer Research, Ocean Engineering, Computational Theory and Mathematics and Immunology, having authored 16 papers that have together received 249 indexed citations. Recurring topics across this work include Single-cell and spatial transcriptomics (9 papers), Gene expression and cancer classification (5 papers), Gene Regulatory Network Analysis (4 papers), Cancer-related molecular mechanisms research (2 papers), Cancer Genomics and Diagnostics (2 papers), Reservoir Engineering and Simulation Methods (2 papers), Plant Molecular Biology Research (1 paper) and Cell Image Analysis Techniques (1 paper). The work is most often cited by research in Biophysics (19 citations), Hematology (36 citations), Immunology (61 citations), Cancer Research (43 citations) and Molecular Biology (179 citations). Simone G. Riva has collaborated with scholars based in United Kingdom, Italy and Netherlands. Frequent co-authors include Ana Cvejic, Andrea Tangherloni, Brynelle Myers, Elisa Panada, Anna Maria Ranzoni, Ivan Berest, Irina Mohorianu, Judith B. Zaugg, Paulina M. Strzelecka and T. J. Neep. Their work appears in journals such as Cell stem cell, Scientific Reports, BMC Bioinformatics, Journal of Biomedical Informatics and Symmetry.
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.