Jan Gruber

3.3k total citations
65 papers, 2.4k citations indexed

About

Jan Gruber is a scholar working on Aging, Molecular Biology and Physiology. According to data from OpenAlex, Jan Gruber has authored 65 papers receiving a total of 2.4k indexed citations (citations by other indexed papers that have themselves been cited), including 41 papers in Aging, 36 papers in Molecular Biology and 23 papers in Physiology. Recurrent topics in Jan Gruber's work include Genetics, Aging, and Longevity in Model Organisms (41 papers), Mitochondrial Function and Pathology (23 papers) and Circadian rhythm and melatonin (13 papers). Jan Gruber is often cited by papers focused on Genetics, Aging, and Longevity in Model Organisms (41 papers), Mitochondrial Function and Pathology (23 papers) and Circadian rhythm and melatonin (13 papers). Jan Gruber collaborates with scholars based in Singapore, Switzerland and United States. Jan Gruber's co-authors include Barry Halliwell, Li Fang Ng, Soon Yew Tang, Nicholas S. Tolwinski, Sheng Fong, Rudiyanto Gunawan, Irwin K. Cheah, Sebastian Schaffer, Markus R. Wenk and Suresh Poovathingal and has published in prestigious journals such as Nucleic Acids Research, SHILAP Revista de lepidopterología and PLoS ONE.

In The Last Decade

Jan Gruber

63 papers receiving 2.4k citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Jan Gruber 1.2k 863 615 247 179 65 2.4k
Samuel E. Schriner 1.3k 1.0× 486 0.6× 592 1.0× 112 0.5× 59 0.3× 24 2.0k
Michael Petrascheck 760 0.6× 837 1.0× 477 0.8× 293 1.2× 54 0.3× 38 1.7k
Alba Naudí 2.0k 1.6× 449 0.5× 1.3k 2.2× 84 0.3× 289 1.6× 76 3.3k
Guillermo López‐Lluch 2.4k 2.0× 545 0.6× 1.5k 2.4× 212 0.9× 470 2.6× 112 4.1k
Dieter Schmoll 2.1k 1.8× 251 0.3× 966 1.6× 150 0.6× 267 1.5× 63 4.0k
Ritchie J. Feuers 736 0.6× 590 0.7× 1.0k 1.6× 297 1.2× 65 0.4× 75 2.1k
Victòria Ayala 1.6k 1.3× 374 0.4× 1.2k 1.9× 82 0.3× 236 1.3× 75 3.3k
Kyung Jin Jung 1.1k 0.9× 211 0.2× 655 1.1× 92 0.4× 84 0.5× 57 2.6k
Gabriella Cavallini 865 0.7× 390 0.5× 777 1.3× 94 0.4× 66 0.4× 99 2.4k
Byung P. Yu 803 0.7× 360 0.4× 862 1.4× 247 1.0× 129 0.7× 40 2.1k

Countries citing papers authored by Jan Gruber

Since Specialization
Citations

This map shows the geographic impact of Jan Gruber'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 Jan Gruber with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jan Gruber more than expected).

Fields of papers citing papers by Jan Gruber

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jan Gruber. 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 Jan Gruber. The network helps show where Jan Gruber may publish in the future.

Co-authorship network of co-authors of Jan Gruber

This figure shows the co-authorship network connecting the top 25 collaborators of Jan Gruber. A scholar is included among the top collaborators of Jan Gruber based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Jan Gruber. Jan Gruber is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Fong, Sheng, et al.. (2025). LinAge2: providing actionable insights and benchmarking with epigenetic clocks. PubMed. 11(1). 29–29. 1 indexed citations
2.
Fong, Sheng, Woon‐Puay Koh, & Jan Gruber. (2025). Reframing biological age as risk-equivalent age. Nature Aging. 6(1). 2–5.
3.
Fong, Sheng, et al.. (2024). Principal component-based clinical aging clocks identify signatures of healthy aging and targets for clinical intervention. Nature Aging. 4(8). 1137–1152. 17 indexed citations
4.
Kumar, Sanjay, et al.. (2024). Towards Healthy Longevity: Comprehensive Insights from Molecular Targets and Biomarkers to Biological Clocks. International Journal of Molecular Sciences. 25(12). 6793–6793. 5 indexed citations
5.
Pabis, Kamil, Diogo Barardo, Jan Gruber, et al.. (2024). The impact of short-lived controls on the interpretation of lifespan experiments and progress in geroscience – Through the lens of the “900-day rule”. Ageing Research Reviews. 101. 102512–102512. 7 indexed citations
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Ravi, Sudharshan, Diogo Barardo, Hyung-Seok Kim, et al.. (2019). Metabolic stress is a primary pathogenic event in transgenic Caenorhabditis elegans expressing pan-neuronal human amyloid beta. eLife. 8. 55 indexed citations
9.
Lakshmanan, Lakshmi Narayanan, et al.. (2018). Clonal expansion of mitochondrial DNA deletions is a private mechanism of aging in long‐lived animals. Aging Cell. 17(5). e12814–e12814. 32 indexed citations
10.
Admasu, Tesfahun Dessale, Krishna Chaithanya Batchu, Li Fang Ng, et al.. (2018). Lipid profiling of C. elegans strains administered pro-longevity drugs and drug combinations. Scientific Data. 5(1). 180231–180231. 9 indexed citations
11.
Batchu, Krishna Chaithanya, Diogo Barardo, Amaury Cazenave‐Gassiot, et al.. (2018). A novel vibration-induced exercise paradigm improves fitness and lipid metabolism of Caenorhabditis elegans. Scientific Reports. 8(1). 9420–9420. 7 indexed citations
12.
Admasu, Tesfahun Dessale, Krishna Chaithanya Batchu, Diogo Barardo, et al.. (2018). Drug Synergy Slows Aging and Improves Healthspan through IGF and SREBP Lipid Signaling. Developmental Cell. 47(1). 67–79.e5. 64 indexed citations
13.
Tam, Zhi Yang, Jan Gruber, Barry Halliwell, & Rudiyanto Gunawan. (2015). Context-Dependent Role of Mitochondrial Fusion-Fission in Clonal Expansion of mtDNA Mutations. PLoS Computational Biology. 11(5). e1004183–e1004183. 38 indexed citations
14.
Gruber, Jan, et al.. (2014). Caenorhabditis elegans : What We Can and Cannot Learn from Aging Worms. Antioxidants and Redox Signaling. 23(3). 256–279. 36 indexed citations
15.
Tam, Zhi Yang, Jan Gruber, Lisa F. P. Ng, Barry Halliwell, & Rudiyanto Gunawan. (2014). Effects of Lithium on Age-related Decline in Mitochondrial Turnover and Function in Caenorhabditis elegans. The Journals of Gerontology Series A. 69(7). 810–820. 38 indexed citations
16.
Bello, Segun, et al.. (2014). Lack of blinding of outcome assessors in animal model experiments implies risk of observer bias. Journal of Clinical Epidemiology. 67(9). 973–983. 56 indexed citations
17.
Li, Ling, Jan Gruber, Meng Teng Peh, et al.. (2013). Hydrogen Sulfide Is an Endogenous Regulator of Aging in Caenorhabditis elegans. Antioxidants and Redox Signaling. 20(16). 2621–2630. 75 indexed citations
18.
Gruber, Jan, Sheng Fong, Ce-Belle Chen, et al.. (2012). Mitochondria-targeted antioxidants and metabolic modulators as pharmacological interventions to slow ageing. Biotechnology Advances. 31(5). 563–592. 107 indexed citations
19.
Gruber, Jan, Soon Yew Tang, Andrew M. Jenner, et al.. (2009). Allantoin in Human Plasma, Serum, and Nasal-Lining Fluids as a Biomarker of Oxidative Stress: Avoiding Artifacts and Establishing Real in vivo Concentrations. Antioxidants and Redox Signaling. 11(8). 1767–1776. 51 indexed citations
20.
Poovathingal, Suresh, Jan Gruber, Barry Halliwell, & Rudiyanto Gunawan. (2009). Stochastic Drift in Mitochondrial DNA Point Mutations: A Novel Perspective Ex Silico. PLoS Computational Biology. 5(11). e1000572–e1000572. 27 indexed citations

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.

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