Csaba Kerepesi
Impact in
- Aging top 0.5%
- Genetics, Aging, and Longevity in Model Organisms
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- Epigenetics and DNA Methylation
- Genomics and Phylogenetic Studies
Papers in
- Aging 11
- Genetics, Aging, and Longevity in Model Organisms 11
-
- Epigenetics and DNA Methylation 8
- Genomics and Phylogenetic Studies 4
- Co-authors
- Vadim N. Gladyshev (10 shared papers)Vince I. Grolmusz (13 shared papers)Alexandre Trapp (5 shared papers)Balázs Szalkai (8 shared papers)Bálint Varga (7 shared papers)Bohan Zhang (6 shared papers)Sun Hee Yim (3 shared papers)Steve Horvath (6 shared papers)
- Journals
- Aging Cell (5 papers)Nature Aging (4 papers)Scientific Reports (2 papers)PLoS ONE (2 papers)Archives of Virology (2 papers)
- Partner nations
- HungaryUnited StatesJapan
In The Last Decade
Csaba Kerepesi
45 papers receiving 1.3k citations
Peers
Comparison fields: 5 of 122
- Aging 262
- Molecular Biology 463
- Cognitive Neuroscience 141
- Physiology 170
- Geriatrics and Gerontology 18
Countries citing papers authored by Csaba Kerepesi
This map shows the geographic impact of Csaba Kerepesi'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 Csaba Kerepesi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Csaba Kerepesi more than expected).
Fields of papers citing papers by Csaba Kerepesi
This network shows the impact of papers produced by Csaba Kerepesi. 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 Csaba Kerepesi. The network helps show where Csaba Kerepesi may publish in the future.
Co-authors
The 25 scholars most cited alongside Csaba Kerepesi, 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 47 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2023 | 159 | |
| 2 | 2020 | 129 | |
| 3 | 2021 | 107 | |
| 4 | 2023 | 80 | |
| 5 | 2021 | 79 | |
| 6 | 2013 | 71 | |
| 7 | 2016 | 55 | |
| 8 | 2021 | 43 | |
| 9 | 2018 | 40 | |
| 10 | 2017 | 39 | |
| 11 | 2022 | 37 | |
| 12 | 2015 | 36 | |
| 13 | 2016 | 35 | |
| 14 | 2021 | 35 | |
| 15 | 2017 | 33 | |
| 16 | 2024 | 28 | |
| 17 | 2016 | 27 | |
| 18 | 2017 | 26 | |
| 19 | 2023 | 23 | |
| 20 | 2017 | 20 |
About Csaba Kerepesi
Csaba Kerepesi is a scholar working on Aging, Molecular Biology, Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging and Oncology, having authored 47 papers that have together received 1.3k indexed citations. Recurring topics across this work include Genetics, Aging, and Longevity in Model Organisms (11 papers), Epigenetics and DNA Methylation (8 papers), Functional Brain Connectivity Studies (7 papers), Advanced Neuroimaging Techniques and Applications (6 papers), Cancer Immunotherapy and Biomarkers (4 papers), Genomics and Phylogenetic Studies (4 papers), Bacteriophages and microbial interactions (2 papers) and COVID-19 Clinical Research Studies (2 papers). The work is most often cited by research in Aging (262 citations), Molecular Biology (463 citations), Cognitive Neuroscience (141 citations), Physiology (170 citations) and Geriatrics and Gerontology (18 citations). Csaba Kerepesi has collaborated with scholars based in Hungary, United States and Japan. Frequent co-authors include Vadim N. Gladyshev, Vince I. Grolmusz, Alexandre Trapp, Balázs Szalkai, Bálint Varga, Bohan Zhang, Sun Hee Yim, Steve Horvath, Anastasia V. Shindyapina and Didac Santesmasses. Their work appears in journals such as Aging Cell, Nature Aging, Scientific Reports, PLoS ONE and Archives of Virology.
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.