Daniel S. Evans

10.2k total citations
55 papers, 1.3k citations indexed

About

Daniel S. Evans is a scholar working on Molecular Biology, Physiology and Endocrine and Autonomic Systems. According to data from OpenAlex, Daniel S. Evans has authored 55 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Molecular Biology, 17 papers in Physiology and 9 papers in Endocrine and Autonomic Systems. Recurrent topics in Daniel S. Evans's work include Genetics, Aging, and Longevity in Model Organisms (7 papers), Circadian rhythm and melatonin (7 papers) and Sleep and related disorders (6 papers). Daniel S. Evans is often cited by papers focused on Genetics, Aging, and Longevity in Model Organisms (7 papers), Circadian rhythm and melatonin (7 papers) and Sleep and related disorders (6 papers). Daniel S. Evans collaborates with scholars based in United States, Australia and United Kingdom. Daniel S. Evans's co-authors include Sonia Ancoli‐Israel, Wen‐Chi Hsueh, Gregory J. Tranah, Terri Blackwell, Lutz Kockel, Pankaj Kapahi, Steven R. Cummings, Sanjay R. Patel, Yun Kwok Wing and Katie L Stone and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nucleic Acids Research and SHILAP Revista de lepidopterología.

In The Last Decade

Daniel S. Evans

54 papers receiving 1.3k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Daniel S. Evans United States 21 414 296 250 218 171 55 1.3k
Sarah Voisin Australia 24 800 1.9× 533 1.8× 118 0.5× 153 0.7× 484 2.8× 56 1.8k
Yusuke Tanahashi Japan 17 202 0.5× 275 0.9× 150 0.6× 727 3.3× 115 0.7× 48 1.4k
C Alexandre France 22 204 0.5× 320 1.1× 174 0.7× 234 1.1× 61 0.4× 55 1.7k
Anna Karin Hedström Sweden 28 323 0.8× 153 0.5× 108 0.4× 146 0.7× 192 1.1× 87 3.0k
Laura Matthews United Kingdom 22 484 1.2× 594 2.0× 111 0.4× 793 3.6× 236 1.4× 40 2.0k
Genevieve Neal‐Perry United States 24 203 0.5× 137 0.5× 116 0.5× 227 1.0× 672 3.9× 63 2.4k
G. Mazzotta Italy 24 325 0.8× 274 0.9× 85 0.3× 630 2.9× 128 0.7× 76 1.7k
Б. В. Моруков Russia 17 176 0.4× 960 3.2× 68 0.3× 44 0.2× 255 1.5× 73 1.4k
Zila Shen‐Orr Israel 22 435 1.1× 322 1.1× 125 0.5× 375 1.7× 126 0.7× 32 1.8k
David Simon United States 27 1.0k 2.5× 373 1.3× 22 0.1× 84 0.4× 99 0.6× 45 3.5k

Countries citing papers authored by Daniel S. Evans

Since Specialization
Citations

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

Fields of papers citing papers by Daniel S. Evans

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel S. Evans

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel S. Evans. A scholar is included among the top collaborators of Daniel S. Evans 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 S. Evans. Daniel S. Evans 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.
Evans, Daniel S., et al.. (2025). Somatic mutation as an explanation for epigenetic aging. Nature Aging. 5(4). 709–719. 14 indexed citations
2.
Adolfi, Adriana, Angelika Sturm, Daniel S. Evans, et al.. (2025). An accessible 3D HepG2/C3A liver spheroid model supporting the complete intrahepatocytic lifecycle of Plasmodium falciparum. Parasitology. 152(11). 1127–1134. 1 indexed citations
3.
Seo, Da Hea, Maripat Corr, Sheena Patel, et al.. (2024). Chemokine CXCL9, a marker of inflammaging, is associated with changes of muscle strength and mortality in older men. Osteoporosis International. 35(10). 1789–1796. 7 indexed citations
4.
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6.
Tranah, Gregory J., Daniel S. Evans, Paul M. Coen, et al.. (2024). Higher expression of denervation‐responsive genes is negatively associated with muscle volume and performance traits in the study of muscle, mobility, and aging (SOMMA). Aging Cell. 23(6). e14115–e14115. 6 indexed citations
7.
Evans, Daniel S., Toshiko Tanaka, Nathan Basisty, et al.. (2023). Proteomic Analysis of the Senescence-Associated Secretory Phenotype: GDF-15, IGFBP-2, and Cystatin-C Are Associated With Multiple Aging Traits. The Journals of Gerontology Series A. 79(3). 29 indexed citations
8.
Alliston, Tamara, et al.. (2023). Genetic and Gene Expression Resources for Osteoporosis and Bone Biology Research. Current Osteoporosis Reports. 21(6). 637–649. 4 indexed citations
9.
Kim, Kyoung Min, Daniel S. Evans, Jessica Jacobson, et al.. (2022). Rapid prediction of in-hospital mortality among adults with COVID-19 disease. PLoS ONE. 17(7). e0269813–e0269813. 7 indexed citations
10.
Chun, Sung, Sebastian Akle, Athanasios Teodosiadis, et al.. (2022). Leveraging pleiotropy to discover and interpret GWAS results for sleep-associated traits. PLoS Genetics. 18(12). e1010557–e1010557. 8 indexed citations
11.
Evans, Daniel S.. (2022). Target Discovery for Drug Development Using Mendelian Randomization. Methods in molecular biology. 2547. 1–20. 12 indexed citations
12.
Peppard, Paul E., Erika W. Hagen, Katie L. Stone, et al.. (2021). Genetic risk for subjective reports of insomnia associates only weakly with polygraphic measures of insomnia in 2,770 adults. Journal of Clinical Sleep Medicine. 18(1). 21–29. 2 indexed citations
13.
Bonham, Luke W., Daniel S. Evans, Ching‐Ti Liu, et al.. (2018). Neurotransmitter Pathway Genes in Cognitive Decline During Aging: Evidence for GNG4 and KCNQ2 Genes. American Journal of Alzheimer s Disease & Other Dementias®. 33(3). 153–165. 19 indexed citations
14.
Nettiksimmons, Jasmine, Gregory J. Tranah, Daniel S. Evans, Jennifer S. Yokoyama, & Kristine Yaffe. (2016). Gene-based aggregate SNP associations between candidate AD genes and cognitive decline. AGE. 38(2). 41–41. 52 indexed citations
15.
Evans, Daniel S. & Thomas W. Cline. (2013). Drosophila switch gene Sex-lethal can bypass its switch-gene target transformer to regulate aspects of female behavior. Proceedings of the National Academy of Sciences. 110(47). E4474–81. 26 indexed citations
16.
Tranah, Gregory J., Ernest T. Lam, Shana M. Katzman, et al.. (2012). Mitochondrial DNA sequence variation is associated with free-living activity energy expenditure in the elderly. Biochimica et Biophysica Acta (BBA) - Bioenergetics. 1817(9). 1691–1700. 12 indexed citations
17.
Campos, Guilherme M., et al.. (2010). Weight Loss after Roux-en-Y Gastric Bypass in Obese Patients Heterozygous for MC4R Mutations. Obesity Surgery. 21(7). 930–934. 59 indexed citations
18.
Kraemer, William J., Maren S. Fragala, Greig Watson, et al.. (2007). Hormonal responses to a 160-km race across frozen Alaska. British Journal of Sports Medicine. 42(2). 116–120. 47 indexed citations
19.
Evans, Daniel S.. (1999). THE LIGHT OF YOUR LIFE: IN-PAVEMENT LEDS STOP CARS AND SAVE LIVES. Traffic Technology International.
20.
Chapman, Ralph E., et al.. (1992). Body composition testing of athletes in the field using bioelectric impedance analysis.. PubMed. 34(2). 87–90, 95. 4 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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