Greg M. Findlay

2.5k total citations · 1 hit paper
30 papers, 2.0k citations indexed

About

Greg M. Findlay is a scholar working on Molecular Biology, Genetics and Cell Biology. According to data from OpenAlex, Greg M. Findlay has authored 30 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Molecular Biology, 7 papers in Genetics and 4 papers in Cell Biology. Recurrent topics in Greg M. Findlay's work include Pluripotent Stem Cells Research (9 papers), CRISPR and Genetic Engineering (9 papers) and Genetics and Neurodevelopmental Disorders (7 papers). Greg M. Findlay is often cited by papers focused on Pluripotent Stem Cells Research (9 papers), CRISPR and Genetic Engineering (9 papers) and Genetics and Neurodevelopmental Disorders (7 papers). Greg M. Findlay collaborates with scholars based in United Kingdom, United States and Canada. Greg M. Findlay's co-authors include Richard F. Lamb, Laura Harrington, Peter R. Shepherd, Alexander Gray, Т. А. Толкачева, Simon Wigfield, Nicholas R. Leslie, Ivan Gout, C. Peter Downes and Heike Rebholz and has published in prestigious journals such as Cell, Proceedings of the National Academy of Sciences and Journal of Biological Chemistry.

In The Last Decade

Greg M. Findlay

28 papers receiving 1.9k citations

Hit Papers

The TSC1-2 tumor suppressor controls insulin–PI3K signali... 2004 2026 2011 2018 2004 250 500 750

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Greg M. Findlay United Kingdom 15 1.7k 261 228 189 185 30 2.0k
Laura R. Pearce United Kingdom 8 1.6k 1.0× 190 0.7× 309 1.4× 188 1.0× 162 0.9× 9 2.1k
Deanna M. Stevens United States 7 1.4k 0.8× 175 0.7× 254 1.1× 141 0.7× 214 1.2× 7 1.6k
Charles Betz Switzerland 10 1.5k 0.9× 224 0.9× 411 1.8× 138 0.7× 137 0.7× 11 1.9k
Mimi Tamamori‐Adachi Japan 21 1.0k 0.6× 169 0.6× 171 0.8× 306 1.6× 155 0.8× 41 1.5k
Ariel F. Castro United States 20 1.3k 0.8× 206 0.8× 243 1.1× 364 1.9× 139 0.8× 46 1.7k
Satoru Torii Japan 22 1.5k 0.9× 121 0.5× 345 1.5× 213 1.1× 215 1.2× 48 2.0k
Jee‐Yin Ahn South Korea 26 1.2k 0.7× 130 0.5× 159 0.7× 297 1.6× 309 1.7× 77 2.0k
Emily Foulstone United Kingdom 18 1.2k 0.7× 208 0.8× 212 0.9× 153 0.8× 77 0.4× 30 1.7k
Chang‐Wook Lee United States 14 1.9k 1.1× 277 1.1× 436 1.9× 292 1.5× 230 1.2× 21 2.3k
Hanying Chen United States 23 1.8k 1.1× 106 0.4× 234 1.0× 157 0.8× 146 0.8× 38 2.4k

Countries citing papers authored by Greg M. Findlay

Since Specialization
Citations

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

Fields of papers citing papers by Greg M. Findlay

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Greg M. Findlay. 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 Greg M. Findlay. The network helps show where Greg M. Findlay may publish in the future.

Co-authorship network of co-authors of Greg M. Findlay

This figure shows the co-authorship network connecting the top 25 collaborators of Greg M. Findlay. A scholar is included among the top collaborators of Greg M. Findlay 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 Greg M. Findlay. Greg M. Findlay 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.
Brenes, Alejandro J., Eva Griesser, Linda V. Sinclair, et al.. (2024). Proteomic and functional comparison between human induced and embryonic stem cells. eLife. 13. 1 indexed citations
2.
May, Danielle G., Kyle J. Roux, Jeroen Demmers, et al.. (2024). Chromatin targeting of the RNF12/RLIM E3 ubiquitin ligase controls transcriptional responses. Life Science Alliance. 7(3). e202302282–e202302282. 1 indexed citations
4.
Brenes, Alejandro J., Eva Griesser, Linda V. Sinclair, et al.. (2024). Proteomic and functional comparison between human induced and embryonic stem cells. eLife. 13. 2 indexed citations
5.
Wang, Feng, Yeonsoo Yoon, Mary C. Wallingford, et al.. (2023). Roles of the Rlim–Rex1 axis during X chromosome inactivation in mice. Proceedings of the National Academy of Sciences. 120(52). e2313200120–e2313200120. 4 indexed citations
6.
Bustos, Francisco, Houjiang Zhou, Feng Wang, et al.. (2022). An RNF12-USP26 amplification loop drives germ cell specification and is disrupted by disease-associated mutations. Science Signaling. 15(742). eabm5995–eabm5995. 6 indexed citations
7.
Bustos, Francisco, Sunil Mathur, Rachel Toth, et al.. (2022). Activity-based probe profiling of RNF12 E3 ubiquitin ligase function in Tonne-Kalscheuer syndrome. Life Science Alliance. 5(11). e202101248–e202101248. 5 indexed citations
8.
Bustos, Francisco, Rachel Toth, Alison Eaton, et al.. (2021). A novel RLIM/RNF12 variant disrupts protein stability and function to cause severe Tonne–Kalscheuer syndrome. Scientific Reports. 11(1). 9560–9560. 7 indexed citations
9.
Mullin, Nicholas P., Joby Varghese, Douglas Colby, et al.. (2020). Phosphorylation of NANOG by casein kinase I regulates embryonic stem cell self‐renewal. FEBS Letters. 595(1). 14–25. 6 indexed citations
11.
Bustos, Francisco, Viduth K. Chaugule, Lennart Brandenburg, et al.. (2018). RNF12 X-Linked Intellectual Disability Mutations Disrupt E3 Ligase Activity and Neural Differentiation. Cell Reports. 23(6). 1599–1611. 29 indexed citations
12.
Williams, Charles A.C., Nathanael S. Gray, & Greg M. Findlay. (2017). A Simple Method to Identify Kinases That Regulate Embryonic Stem Cell Pluripotency by High-throughput Inhibitor Screening. Journal of Visualized Experiments. 2 indexed citations
13.
Davidson, L. S. P., Jens Hukelmann, Michael Zengerle, et al.. (2017). Brd4‐Brd2 isoform switching coordinates pluripotent exit and Smad2‐dependent lineage specification. EMBO Reports. 18(7). 1108–1122. 21 indexed citations
14.
Bustos, Francisco, et al.. (2017). Protein Kinases in Pluripotency—Beyond the Usual Suspects. Journal of Molecular Biology. 429(10). 1504–1520. 14 indexed citations
15.
Yasui, N., Greg M. Findlay, Gerald Gish, et al.. (2014). Directed Network Wiring Identifies a Key Protein Interaction in Embryonic Stem Cell Differentiation. Molecular Cell. 54(6). 1034–1041. 29 indexed citations
16.
Findlay, Greg M., Matthew J. Smith, Fredrik Lanner, et al.. (2013). Interaction Domains of Sos1/Grb2 Are Finely Tuned for Cooperative Control of Embryonic Stem Cell Fate. Cell. 152(5). 1008–1020. 54 indexed citations
17.
Yan, Lijun, Virginie Mieulet, Darren J. Burgess, et al.. (2010). PP2AT61ɛ Is an Inhibitor of MAP4K3 in Nutrient Signaling to mTOR. Molecular Cell. 37(5). 633–642. 95 indexed citations
18.
Yan, Lijun, et al.. (2006). Hyperactivation of Mammalian Target of Rapamycin (mTOR) Signaling by a Gain-of-Function Mutant of the Rheb GTPase. Journal of Biological Chemistry. 281(29). 19793–19797. 58 indexed citations
19.
Findlay, Greg M., Laura Harrington, & Richard F. Lamb. (2004). TSC1-2 tumour suppressor and regulation of mTOR signalling: linking cell growth and proliferation?. Current Opinion in Genetics & Development. 15(1). 69–76. 35 indexed citations
20.
Harrington, Laura, Greg M. Findlay, & Richard F. Lamb. (2004). Restraining PI3K: mTOR signalling goes back to the membrane. Trends in Biochemical Sciences. 30(1). 35–42. 313 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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