Rahul Gokarn

1.6k total citations · 1 hit paper
8 papers, 1.2k citations indexed

About

Rahul Gokarn is a scholar working on Physiology, Aging and Molecular Biology. According to data from OpenAlex, Rahul Gokarn has authored 8 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Physiology, 3 papers in Aging and 2 papers in Molecular Biology. Recurrent topics in Rahul Gokarn's work include Adipose Tissue and Metabolism (6 papers), Diet and metabolism studies (4 papers) and Genetics, Aging, and Longevity in Model Organisms (3 papers). Rahul Gokarn is often cited by papers focused on Adipose Tissue and Metabolism (6 papers), Diet and metabolism studies (4 papers) and Genetics, Aging, and Longevity in Model Organisms (3 papers). Rahul Gokarn collaborates with scholars based in Australia, United States and Finland. Rahul Gokarn's co-authors include David Raubenheimer, David G. Le Couteur, Samantha M. Solon‐Biet, Stephen J. Simpson, Victoria C. Cogger, Aisling C. McMahon, Alessandra Warren, Kari Ruohonen, Gregory J. Cooney and J. William O. Ballard and has published in prestigious journals such as Cell Metabolism, Cell Reports and The Journals of Gerontology Series A.

In The Last Decade

Rahul Gokarn

8 papers receiving 1.2k citations

Hit Papers

The Ratio of Macronutrients, Not Caloric Intake, Dictates... 2014 2026 2018 2022 2014 200 400 600

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Rahul Gokarn Australia 8 638 372 315 155 151 8 1.2k
Mamdouh Khalil Australia 5 395 0.6× 209 0.6× 226 0.7× 100 0.6× 102 0.7× 6 772
Nicole M. Templeman Canada 14 439 0.7× 325 0.9× 135 0.4× 169 1.1× 65 0.4× 19 1.0k
April M. Handy United States 9 712 1.1× 287 0.8× 520 1.7× 206 1.3× 50 0.3× 9 1.2k
Christina Cruzen United States 4 984 1.5× 577 1.6× 705 2.2× 269 1.7× 70 0.5× 4 1.8k
B.J. Merry United Kingdom 21 881 1.4× 487 1.3× 697 2.2× 217 1.4× 52 0.3× 34 1.6k
Dennis E. Barnard United States 7 555 0.9× 252 0.7× 406 1.3× 148 1.0× 47 0.3× 9 973
Brad A. Rikke United States 18 643 1.0× 432 1.2× 705 2.2× 259 1.7× 32 0.2× 29 1.3k
Susan M. Krzysik-Walker United States 16 489 0.8× 337 0.9× 181 0.6× 202 1.3× 29 0.2× 19 1.3k
Thomas Laeger Germany 16 575 0.9× 746 2.0× 77 0.2× 180 1.2× 79 0.5× 23 1.5k
Viveca Sapin United States 10 342 0.5× 565 1.5× 697 2.2× 263 1.7× 21 0.1× 12 1.4k

Countries citing papers authored by Rahul Gokarn

Since Specialization
Citations

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

Fields of papers citing papers by Rahul Gokarn

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Rahul Gokarn

This figure shows the co-authorship network connecting the top 25 collaborators of Rahul Gokarn. A scholar is included among the top collaborators of Rahul Gokarn 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 Rahul Gokarn. Rahul Gokarn is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

8 of 8 papers shown
1.
Couteur, David G. Le, Samantha M. Solon‐Biet, Benjamin L. Parker, et al.. (2021). Nutritional reprogramming of mouse liver proteome is dampened by metformin, resveratrol, and rapamycin. Cell Metabolism. 33(12). 2367–2379.e4. 47 indexed citations
2.
Wahl, Devin, Rahul Gokarn, Sarah J. Mitchell, et al.. (2019). Central nervous system SIRT1 expression is required for cued and contextual fear conditioning memory responses in aging mice. PubMed. 5(2). 111–117. 7 indexed citations
3.
Gokarn, Rahul, Samantha M. Solon‐Biet, Victoria C. Cogger, et al.. (2018). Long-term Dietary Macronutrients and Hepatic Gene Expression in Aging Mice. The Journals of Gerontology Series A. 73(12). 1618–1625. 22 indexed citations
4.
Gokarn, Rahul, Samantha M. Solon‐Biet, Neil A. Youngson, et al.. (2017). The Relationship Between Dietary Macronutrients and Hepatic Telomere Length in Aging Mice. The Journals of Gerontology Series A. 73(4). 446–449. 31 indexed citations
5.
Wahl, Devin, Victoria C. Cogger, Samantha M. Solon‐Biet, et al.. (2016). Nutritional strategies to optimise cognitive function in the aging brain. Ageing Research Reviews. 31. 80–92. 74 indexed citations
6.
Solon‐Biet, Samantha M., Victoria C. Cogger, Tamara Pulpitel, et al.. (2016). Defining the Nutritional and Metabolic Context of FGF21 Using the Geometric Framework. Cell Metabolism. 24(4). 555–565. 166 indexed citations
7.
Solon‐Biet, Samantha M., Sarah J. Mitchell, Sean C. P. Coogan, et al.. (2015). Dietary Protein to Carbohydrate Ratio and Caloric Restriction: Comparing Metabolic Outcomes in Mice. Cell Reports. 11(10). 1529–1534. 145 indexed citations
8.
Solon‐Biet, Samantha M., Aisling C. McMahon, J. William O. Ballard, et al.. (2014). The Ratio of Macronutrients, Not Caloric Intake, Dictates Cardiometabolic Health, Aging, and Longevity in Ad Libitum-Fed Mice. Cell Metabolism. 19(3). 418–430. 692 indexed citations breakdown →

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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