Nicolas Mathis
Impact in
-
- Innovation and Socioeconomic Development
- Aging top 10%
- Genetics, Aging, and Longevity in Model Organisms
Papers in
-
- CRISPR and Genetic Engineering 9
- RNA and protein synthesis mechanisms 5
- RNA regulation and disease 4
- Genetics 4
- Virus-based gene therapy research 3
- Co-authors
- Gerald Schwank (9 shared papers)Lukas Schmidheini (6 shared papers)Lucas Kissling (6 shared papers)Tanja Rothgangl (5 shared papers)Kim Fabiano Marquart (7 shared papers)Desirée Böck (4 shared papers)Ahmed Allam (5 shared papers)Michael Krauthammer (5 shared papers)
- Journals
- Nature Biotechnology (2 papers)Nature Methods (1 paper)Science Translational Medicine (1 paper)Nature Communications (1 paper)Molecular Therapy — Nucleic Acids (1 paper)
- Partner nations
- SwitzerlandUnited StatesGermany
In The Last Decade
Nicolas Mathis
11 papers receiving 360 citations
Nicolas Mathis's Hit Papers
Peers
Comparison fields: 5 of 51
- Business and International Management 24
- Aging 20
- Molecular Biology 321
- Genetics 117
- Virology 5
Countries citing papers authored by Nicolas Mathis
This map shows the geographic impact of Nicolas Mathis'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 Nicolas Mathis with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nicolas Mathis more than expected).
Fields of papers citing papers by Nicolas Mathis
This network shows the impact of papers produced by Nicolas Mathis. 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 Nicolas Mathis. The network helps show where Nicolas Mathis may publish in the future.
Co-authors
The 25 scholars most cited alongside Nicolas Mathis, 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 | In vivo prime editing of a metabolic liver disease in mice Hit paper breakdown → | 2022 | 130 |
| 2 | Predicting prime editing efficiency and product purity by deep learning Hit paper breakdown → | 2023 | 84 |
| 3 | 2020 | 43 | |
| 4 | 2024 | 27 | |
| 5 | 2023 | 24 | |
| 6 | 2022 | 16 | |
| 7 | 2024 | 12 | |
| 8 | 2021 | 12 | |
| 9 | 2024 | 8 | |
| 10 | 2017 | 5 | |
| 11 | 2025 | 1 | |
| 12 | 2025 | 0 |
About Nicolas Mathis
Nicolas Mathis is a scholar working on Molecular Biology, Genetics, Hematology, Genetics and Cardiology and Cardiovascular Medicine, having authored 12 papers that have together received 362 indexed citations. Recurring topics across this work include CRISPR and Genetic Engineering (9 papers), RNA and protein synthesis mechanisms (5 papers), RNA regulation and disease (4 papers), Virus-based gene therapy research (3 papers), Hematopoietic Stem Cell Transplantation (2 papers), Innovation and Socioeconomic Development (1 paper), Cytomegalovirus and herpesvirus research (1 paper) and Cancer Genomics and Diagnostics (1 paper). The work is most often cited by research in Business and International Management (24 citations), Aging (20 citations), Molecular Biology (321 citations), Genetics (117 citations) and Virology (5 citations). Nicolas Mathis has collaborated with scholars based in Switzerland, United States and Germany. Frequent co-authors include Gerald Schwank, Lukas Schmidheini, Lucas Kissling, Tanja Rothgangl, Kim Fabiano Marquart, Desirée Böck, Ahmed Allam, Michael Krauthammer, Lukas Villiger and Zsolt Balázs. Their work appears in journals such as Nature Biotechnology, Nature Methods, Science Translational Medicine, Nature Communications and Molecular Therapy — Nucleic Acids.
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.