Daniel Promislow

13.4k total citations · 2 hit papers
185 papers, 9.0k citations indexed

About

Daniel Promislow is a scholar working on Aging, Genetics and Molecular Biology. According to data from OpenAlex, Daniel Promislow has authored 185 papers receiving a total of 9.0k indexed citations (citations by other indexed papers that have themselves been cited), including 68 papers in Aging, 60 papers in Genetics and 38 papers in Molecular Biology. Recurrent topics in Daniel Promislow's work include Genetics, Aging, and Longevity in Model Organisms (68 papers), Animal Behavior and Reproduction (33 papers) and Physiological and biochemical adaptations (24 papers). Daniel Promislow is often cited by papers focused on Genetics, Aging, and Longevity in Model Organisms (68 papers), Animal Behavior and Reproduction (33 papers) and Physiological and biochemical adaptations (24 papers). Daniel Promislow collaborates with scholars based in United States, United Kingdom and China. Daniel Promislow's co-authors include Paul Harvey, Kate E. Creevy, Stephen R. Proulx, Paul E. M. Phillips, Marc Tatar, Jessica M. Hoffman, John C. Avise, Judith E. Mank, Matt Kaeberlein and Jacob A. Moorad and has published in prestigious journals such as Science, Proceedings of the National Academy of Sciences and Nature Communications.

In The Last Decade

Daniel Promislow

181 papers receiving 8.7k citations

Hit Papers

Living fast and dying you... 1990 2026 2002 2014 1990 2005 250 500 750

Author Peers

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

Author Last Decade Papers Cites
Daniel Promislow 2.5k 2.2k 2.2k 1.9k 1.7k 185 9.0k
Steven N. Austad 1.7k 0.7× 2.6k 1.2× 3.5k 1.6× 2.1k 1.1× 2.6k 1.5× 177 11.2k
Michael R. Rose 5.7k 2.3× 4.6k 2.1× 4.2k 1.9× 4.4k 2.3× 2.2k 1.3× 213 13.7k
Marc Tatar 2.2k 0.9× 1.9k 0.8× 5.4k 2.4× 1.8k 1.0× 3.0k 1.7× 133 12.4k
Thomas Flatt 2.2k 0.9× 1.7k 0.7× 1.1k 0.5× 1.2k 0.6× 960 0.6× 86 5.7k
Simon Verhulst 1.3k 0.5× 6.6k 2.9× 975 0.4× 5.9k 3.1× 496 0.3× 199 11.9k
Daniel H. Nussey 1.9k 0.8× 3.8k 1.7× 834 0.4× 3.7k 2.0× 362 0.2× 97 7.8k
Bas J. Zwaan 2.6k 1.1× 3.0k 1.3× 929 0.4× 1.8k 0.9× 1.1k 0.7× 171 8.0k
Patrick C. Phillips 4.8k 1.9× 2.7k 1.2× 1.3k 0.6× 1.1k 0.6× 2.1k 1.2× 133 8.1k
Eviatar Nevo 4.0k 1.6× 3.2k 1.4× 465 0.2× 3.6k 1.9× 3.9k 2.3× 350 13.1k
James R. Carey 839 0.3× 2.0k 0.9× 1.2k 0.5× 2.0k 1.0× 793 0.5× 195 7.6k

Countries citing papers authored by Daniel Promislow

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Promislow

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel Promislow

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Promislow. A scholar is included among the top collaborators of Daniel Promislow 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 Daniel Promislow. Daniel Promislow 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.
Promislow, Daniel, et al.. (2025). Lessons for Responsible Geroscience From the History of Longevity. The AMA Journal of Ethic. 27(12). E866–872.
2.
Harrison, Benjamin R., Danijel Djukovic, Matthew D. Dunbar, et al.. (2025). Protein Catabolites as Blood‐Based Biomarkers of Aging Physiology: Findings From the Dog Aging Project. Aging Cell. 24(11). e70226–e70226.
3.
Pallos, Judit, et al.. (2024). Natural variation in age-related dopamine neuron degeneration is glutathione dependent and linked to life span. Proceedings of the National Academy of Sciences. 121(42). e2403450121–e2403450121. 3 indexed citations
4.
Harrison, Benjamin R., Mitchell Lee, K. N. Han, et al.. (2024). Wide‐ranging genetic variation in sensitivity to rapamycin in Drosophila melanogaster. Aging Cell. 23(11). e14292–e14292. 1 indexed citations
5.
Jin, Kelly, Victor Tkachev, Alex Chitsazan, et al.. (2024). DNA methylation and chromatin accessibility predict age in the domestic dog. Aging Cell. 23(4). e14079–e14079. 7 indexed citations
6.
Bray, Emily E., Stephanie McGrath, Gene E. Alexander, et al.. (2024). Characterizing dog cognitive aging using spontaneous problem-solving measures: development of a battery of tests from the Dog Aging Project. GeroScience. 47(1). 23–43. 1 indexed citations
7.
Kaeberlein, Matt, et al.. (2024). Clippers are superior to scissors in the collection of hair for chemical analysis in companion dogs: a Dog Aging Project preliminary study. American Journal of Veterinary Research. 85(5). 1 indexed citations
8.
Zinkgraf, Matthew, et al.. (2023). Using Drosophila to identify naturally occurring genetic modifiers of amyloid beta 42- and tau-induced toxicity. G3 Genes Genomes Genetics. 13(9). 4 indexed citations
9.
Promislow, Daniel, et al.. (2023). Lifetime prevalence of owner-reported medical conditions in the 25 most common dog breeds in the Dog Aging Project pack. Frontiers in Veterinary Science. 10. 1140417–1140417. 7 indexed citations
10.
Zhang, Xinyu, Daniel Raftery, Haiwei Gu, et al.. (2022). Predictive Modeling of Alzheimer’s and Parkinson’s Disease Using Metabolomic and Lipidomic Profiles from Cerebrospinal Fluid. Metabolites. 12(4). 277–277. 13 indexed citations
11.
Kraus, Cornelia, Noah Snyder‐Mackler, & Daniel Promislow. (2022). How size and genetic diversity shape lifespan across breeds of purebred dogs. GeroScience. 45(2). 627–643. 21 indexed citations
12.
Kim, Matthew, Di Ren, Zhibin He, et al.. (2022). Age‐Independent Cardiac Protection by Pharmacological Activation of Beclin‐1 During Endotoxemia and Its Association With Energy Metabolic Reprograming in Myocardium—A Targeted Metabolomics Study. Journal of the American Heart Association. 11(14). e025310–e025310. 6 indexed citations
13.
Zhang, Xinyu, Daniel Raftery, Haiwei Gu, et al.. (2021). A Metabolomic Aging Clock Using Human Cerebrospinal Fluid. The Journals of Gerontology Series A. 77(4). 744–754. 29 indexed citations
15.
Lucia, Claudio de, Lu Wang, Fausto Carnevale Neto, et al.. (2020). Effects of myocardial ischemia/reperfusion injury on plasma metabolomic profile during aging. Aging Cell. 20(1). e13284–e13284. 9 indexed citations
16.
Nelson, Paul, Daniel Promislow, & Joanna Masel. (2019). Biomarkers for Aging Identified in Cross-sectional Studies Tend to Be Non-causative. The Journals of Gerontology Series A. 75(3). 466–472. 40 indexed citations
17.
Hoffman, Jessica M., et al.. (2018). Canine hyperadrenocorticism associations with signalment, selected comorbidities and mortality within North American veterinary teaching hospitals. Journal of Small Animal Practice. 59(11). 681–690. 25 indexed citations
18.
McCormick, Mark A. & Daniel Promislow. (2017). Recent Advances in the Systems Biology of Aging. Antioxidants and Redox Signaling. 29(10). 973–984. 13 indexed citations
19.
Masel, Joanna & Daniel Promislow. (2016). Answering evolutionary questions: A guide for mechanistic biologists. BioEssays. 38(7). 704–711. 4 indexed citations
20.
Charmantier, Anne, Amber J. Keyser, & Daniel Promislow. (2007). First evidence for heritable variation in cooperative breeding behaviour. Proceedings of the Royal Society B Biological Sciences. 274(1619). 1757–1761. 38 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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