Graeme Hewitt
Impact in
- Aging top 0.5%
- Genetics, Aging, and Longevity in Model Organisms
- Physiology top 2%
- Telomeres, Telomerase, and Senescence
Papers in
- Aging 2
- Genetics, Aging, and Longevity in Model Organisms 2
-
- DNA Repair Mechanisms 15
- CRISPR and Genetic Engineering 7
- Genomics and Chromatin Dynamics 3
- Co-authors
- João F. PassosClara Correia‐MeloDiana JurkViktor I. KorolchukJelena MannRhys AndersonTimothy HardyMorgan L. Taschuk
- Journals
- Molecular Cell (6 papers)Nature Communications (3 papers)The Journal of Cell Biology (2 papers)Cell Reports (2 papers)Trends in cancer (1 paper)
- Partner nations
- United KingdomUnited StatesGermany
In The Last Decade
Graeme Hewitt
22 papers receiving 2.9k citations
Hit Papers
Peers
Comparison fields: 5 of 117
- Aging 347
- Physiology 1.2k
- Molecular Biology 1.7k
- Immunology 434
- Geriatrics and Gerontology 71
Countries citing papers authored by Graeme Hewitt
This map shows the geographic impact of Graeme Hewitt'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 Graeme Hewitt with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Graeme Hewitt more than expected).
Fields of papers citing papers by Graeme Hewitt
This network shows the impact of papers produced by Graeme Hewitt. 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 Graeme Hewitt. The network helps show where Graeme Hewitt may publish in the future.
Co-authors
The 25 scholars most cited alongside Graeme Hewitt, 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 | 2025 | 0 | |
| 2 | 2023 | 6 | |
| 3 | 2023 | 22 | |
| 4 | 2022 | 23 | |
| 5 | 2022 | 72 | |
| 6 | 2021 | 91 | |
| 7 | 2021 | 38 | |
| 8 | 2021 | 37 | |
| 9 | 2021 | 29 | |
| 10 | 2020 | 28 | |
| 11 | 2020 | 43 | |
| 12 | 2019 | 67 | |
| 13 | 2017 | 50 | |
| 14 | 2016 | 114 | |
| 15 | 2016 | 189 | |
| 16 | 2015 | 117 | |
| 17 | Chronic inflammation induces telomere dysfunction and accelerates ageing in mice Hit paper breakdown → | 2014 | 593 |
| 18 | 2014 | 147 | |
| 19 | 2013 | 11 | |
| 20 | Telomeres are favoured targets of a persistent DNA damage response in ageing and stress-induced senescence Hit paper breakdown → | 2012 | 651 |
About Graeme Hewitt
Graeme Hewitt is a scholar working on Aging, Molecular Biology, Physiology, Oncology and Immunology, having authored 23 papers that have together received 2.9k indexed citations. Recurring topics across this work include DNA Repair Mechanisms (15 papers), Telomeres, Telomerase, and Senescence (7 papers), CRISPR and Genetic Engineering (7 papers), PARP inhibition in cancer therapy (6 papers), Genomics and Chromatin Dynamics (3 papers), Autophagy in Disease and Therapy (3 papers), Neutrophil, Myeloperoxidase and Oxidative Mechanisms (2 papers) and Genetics, Aging, and Longevity in Model Organisms (2 papers). The work is most often cited by research in Aging (347 citations), Physiology (1.2k citations), Molecular Biology (1.7k citations), Immunology (434 citations) and Geriatrics and Gerontology (71 citations). Graeme Hewitt has collaborated with scholars based in United Kingdom, United States and Germany. Frequent co-authors include João F. Passos, Clara Correia‐Melo, Diana Jurk, Viktor I. Korolchuk, Jelena Mann, Rhys Anderson, Timothy Hardy, Morgan L. Taschuk, Simon J. Boulton and Thomas von Zglinicki. Their work appears in journals such as Molecular Cell, Nature Communications, The Journal of Cell Biology, Cell Reports and Trends in cancer.
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.