Daniela Bakula
Impact in
- Aging top 2%
- Genetics, Aging, and Longevity in Model Organisms
- Epidemiology top 5%
- Autophagy in Disease and Therapy
Papers in
- Aging 12
- Genetics, Aging, and Longevity in Model Organisms 12
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- Autophagy in Disease and Therapy 9
- Co-authors
- Morten Scheibye‐Knudsen (13 shared papers)Tassula Proikas‐Cezanne (8 shared papers)Guido Keijzers (5 shared papers)Horst Robenek (3 shared papers)Mario P. Tschan (2 shared papers)Tancred Frickey (2 shared papers)Daniel Brigger (2 shared papers)Garik V. Mkrtchyan (5 shared papers)
- Journals
- Aging (6 papers)Biochemical Society Transactions (2 papers)Ageing Research Reviews (2 papers)Nature Communications (1 paper)Nature Aging (1 paper)
- Partner nations
- DenmarkGermanyUnited States
In The Last Decade
Daniela Bakula
26 papers receiving 1.1k citations
Daniela Bakula's Hit Papers
Peers
Comparison fields: 5 of 91
- Aging 131
- Epidemiology 447
- Physiology 145
- Geriatrics and Gerontology 20
- Cell Biology 62
Countries citing papers authored by Daniela Bakula
This map shows the geographic impact of Daniela Bakula'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 Daniela Bakula with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniela Bakula more than expected).
Fields of papers citing papers by Daniela Bakula
This network shows the impact of papers produced by Daniela Bakula. 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 Daniela Bakula. The network helps show where Daniela Bakula may publish in the future.
Co-authors
The 25 scholars most cited alongside Daniela Bakula, 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 27 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2017 | 167 | |
| 2 | Nuclear morphology is a deep learning biomarker of cellular senescence Hit paper breakdown → | 2022 | 156 |
| 3 | 2020 | 124 | |
| 4 | 2010 | 115 | |
| 5 | 2013 | 95 | |
| 6 | 2012 | 84 | |
| 7 | 2018 | 58 | |
| 8 | 2014 | 55 | |
| 9 | 2017 | 54 | |
| 10 | 2020 | 48 | |
| 11 | 2022 | 36 | |
| 12 | 2024 | 28 | |
| 13 | 2018 | 27 | |
| 14 | 2012 | 26 | |
| 15 | 2019 | 22 | |
| 16 | 2013 | 13 | |
| 17 | 2020 | 11 | |
| 18 | 2018 | 9 | |
| 19 | 2022 | 9 | |
| 20 | 2014 | 8 |
About Daniela Bakula
Daniela Bakula is a scholar working on Aging, Epidemiology, Genetics, Neuropsychology and Physiological Psychology and Physiology, having authored 27 papers that have together received 1.2k indexed citations. Recurring topics across this work include Genetics, Aging, and Longevity in Model Organisms (12 papers), Autophagy in Disease and Therapy (9 papers), DNA Repair Mechanisms (3 papers), Telomeres, Telomerase, and Senescence (2 papers), Aging and Gerontology Research (1 paper), Nuclear Structure and Function (1 paper), High Altitude and Hypoxia (1 paper) and Genetic Neurodegenerative Diseases (1 paper). The work is most often cited by research in Aging (131 citations), Epidemiology (447 citations), Physiology (145 citations), Geriatrics and Gerontology (20 citations) and Cell Biology (62 citations). Daniela Bakula has collaborated with scholars based in Denmark, Germany and United States. Frequent co-authors include Morten Scheibye‐Knudsen, Tassula Proikas‐Cezanne, Guido Keijzers, Horst Robenek, Mario P. Tschan, Tancred Frickey, Daniel Brigger, Garik V. Mkrtchyan, Brenna Osborne and Zsuzsanna Takács. Their work appears in journals such as Aging, Biochemical Society Transactions, Ageing Research Reviews, Nature Communications and Nature Aging.
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.