Gareth E. Lim

2.5k total citations
35 papers, 1.8k citations indexed

About

Gareth E. Lim is a scholar working on Molecular Biology, Surgery and Biochemistry. According to data from OpenAlex, Gareth E. Lim has authored 35 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Molecular Biology, 16 papers in Surgery and 8 papers in Biochemistry. Recurrent topics in Gareth E. Lim's work include Pancreatic function and diabetes (15 papers), 14-3-3 protein interactions (12 papers) and Ubiquitin and proteasome pathways (10 papers). Gareth E. Lim is often cited by papers focused on Pancreatic function and diabetes (15 papers), 14-3-3 protein interactions (12 papers) and Ubiquitin and proteasome pathways (10 papers). Gareth E. Lim collaborates with scholars based in Canada, United States and Australia. Gareth E. Lim's co-authors include Patricia L. Brubaker, James D. Johnson, Nicole M. Templeman, Alison C. Holloway, Jim Petrik, Warren G. Foster, Ali Asadi, Xiaoke Hu, Timothy J. Kieffer and Arya E. Mehran and has published in prestigious journals such as Journal of Biological Chemistry, Nature Communications and PLoS ONE.

In The Last Decade

Gareth E. Lim

34 papers receiving 1.8k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Gareth E. Lim Canada 20 723 635 585 549 224 35 1.8k
Jamileh Movassat France 29 670 0.9× 929 1.5× 379 0.6× 624 1.1× 105 0.5× 67 1.9k
Viviane Delghingaro‐Augusto Australia 20 588 0.8× 873 1.4× 460 0.8× 560 1.0× 115 0.5× 37 1.6k
Daniela Gašperíková Slovakia 24 728 1.0× 485 0.8× 970 1.7× 424 0.8× 132 0.6× 96 2.4k
Kinsuke Tsuda Japan 27 786 1.1× 759 1.2× 507 0.9× 936 1.7× 132 0.6× 83 2.2k
Ruth Gutiérrez‐Aguilar Mexico 18 624 0.9× 459 0.7× 478 0.8× 486 0.9× 256 1.1× 35 1.6k
Patricia Serradas France 23 715 1.0× 1.1k 1.7× 518 0.9× 836 1.5× 218 1.0× 48 2.2k
Juraj Koška United States 26 483 0.7× 299 0.5× 560 1.0× 907 1.7× 162 0.7× 55 2.0k
Giovanni Ceccarini Italy 24 484 0.7× 187 0.3× 428 0.7× 731 1.3× 368 1.6× 73 1.8k
Toyoyoshi Uchida Japan 20 659 0.9× 1.1k 1.8× 387 0.7× 744 1.4× 87 0.4× 55 2.4k
Nathan Qi United States 27 897 1.2× 199 0.3× 1.1k 1.9× 277 0.5× 185 0.8× 51 2.3k

Countries citing papers authored by Gareth E. Lim

Since Specialization
Citations

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

Fields of papers citing papers by Gareth E. Lim

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Gareth E. Lim. 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 Gareth E. Lim. The network helps show where Gareth E. Lim may publish in the future.

Co-authorship network of co-authors of Gareth E. Lim

This figure shows the co-authorship network connecting the top 25 collaborators of Gareth E. Lim. A scholar is included among the top collaborators of Gareth E. Lim 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 Gareth E. Lim. Gareth E. Lim 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.
You, Zhipeng, Frédéric Paré, Geneviève Lavoie, et al.. (2025). 14-3-3ζ allows for adipogenesis by modulating chromatin accessibility during the early stages of adipocyte differentiation. Molecular Metabolism. 97. 102159–102159.
2.
3.
Mugabo, Yves, et al.. (2024). Plakoglobin regulates adipocyte differentiation independently of the Wnt/β-catenin signaling pathway. Biochimica et Biophysica Acta (BBA) - Molecular Cell Research. 1871(4). 119690–119690. 1 indexed citations
4.
Mugabo, Yves, Cheng Zhao, Anindya Ghosh, et al.. (2022). 14-3-3ζ Constrains insulin secretion by regulating mitochondrial function in pancreatic β cells. JCI Insight. 7(8). 17 indexed citations
5.
Lim, Gareth E., et al.. (2021). Metabolic Contributions of Wnt Signaling: More Than Controlling Flight. Frontiers in Cell and Developmental Biology. 9. 709823–709823. 20 indexed citations
6.
Botezelli, José Diego, Su Wang, Gareth E. Lim, et al.. (2020). Adipose depot-specific upregulation of Ucp1 or mitochondrial oxidative complex proteins are early consequences of genetic insulin reduction in mice. American Journal of Physiology-Endocrinology and Metabolism. 319(3). E529–E539. 11 indexed citations
7.
Frayne, Isabelle Robillard, et al.. (2020). Reducing 14-3-3ζ expression influences adipocyte maturity and impairs function. American Journal of Physiology-Endocrinology and Metabolism. 319(1). E117–E132. 4 indexed citations
8.
Dussault, Sylvie, Christophe Noll, Angel F. López, et al.. (2020). 14-3-3ζ mediates an alternative, non-thermogenic mechanism in male mice to reduce heat loss and improve cold tolerance. Molecular Metabolism. 41. 101052–101052. 3 indexed citations
9.
Lim, Gareth E., et al.. (2018). Can 14-3-3 proteins serve as therapeutic targets for the treatment of metabolic diseases?. Pharmacological Research. 139. 199–206. 26 indexed citations
10.
Templeman, Nicole M., Stéphane Flibotte, Jenny Chik, et al.. (2017). Reduced Circulating Insulin Enhances Insulin Sensitivity in Old Mice and Extends Lifespan. Cell Reports. 20(2). 451–463. 101 indexed citations
11.
Page, Melissa M., Haoning Howard Cen, Amy P. Chiu, et al.. (2017). Reducing insulin via conditional partial gene ablation in adults reverses diet‐induced weight gain. The FASEB Journal. 32(3). 1196–1206. 35 indexed citations
12.
Boothe, Tobias, Gareth E. Lim, Haoning Howard Cen, et al.. (2016). Inter-domain tagging implicates caveolin-1 in insulin receptor trafficking and Erk signaling bias in pancreatic beta-cells. Molecular Metabolism. 5(5). 366–378. 34 indexed citations
13.
Lim, Gareth E., Tobias Albrecht, Micah Piske, et al.. (2015). 14-3-3ζ coordinates adipogenesis of visceral fat. Nature Communications. 6(1). 7671–7671. 48 indexed citations
14.
Chan, Michelle, Gareth E. Lim, Søs Skovsø, et al.. (2014). Effects of insulin on human pancreatic cancer progression modeled in vitro. BMC Cancer. 14(1). 814–814. 31 indexed citations
15.
Wang, Minghu, Jiaxu Li, Gareth E. Lim, & James D. Johnson. (2013). Is Dynamic Autocrine Insulin Signaling Possible? A Mathematical Model Predicts Picomolar Concentrations of Extracellular Monomeric Insulin within Human Pancreatic Islets. PLoS ONE. 8(6). e64860–e64860. 28 indexed citations
16.
Lim, Gareth E., Micah Piske, & James D. Johnson. (2013). 14-3-3 proteins are essential signalling hubs for beta cell survival. Diabetologia. 56(4). 825–837. 30 indexed citations
17.
Gao, Yujing, Alison C. Holloway, Zhaohua Zeng, et al.. (2005). Prenatal Exposure to Nicotine Causes Postnatal Obesity and Altered Perivascular Adipose Tissue Function. Obesity Research. 13(4). 687–692. 154 indexed citations
18.
Holloway, Alison C., Gareth E. Lim, Jim Petrik, et al.. (2005). Fetal and neonatal exposure to nicotine in Wistar rats results in increased beta cell apoptosis at birth and postnatal endocrine and metabolic changes associated with type 2 diabetes. Diabetologia. 48(12). 2661–2666. 113 indexed citations
19.
20.
Lim, Gareth E., et al.. (2003). Interactions of grapefruit juice and cardiovascular medications: A potential risk of toxicity.. PubMed. 8(2). 99–107. 11 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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