William M. Keyes

3.8k total citations · 2 hit papers
27 papers, 2.9k citations indexed

About

William M. Keyes is a scholar working on Molecular Biology, Physiology and Oncology. According to data from OpenAlex, William M. Keyes has authored 27 papers receiving a total of 2.9k indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Molecular Biology, 11 papers in Physiology and 6 papers in Oncology. Recurrent topics in William M. Keyes's work include Telomeres, Telomerase, and Senescence (11 papers), Cancer-related Molecular Pathways (6 papers) and Cancer Research and Treatments (5 papers). William M. Keyes is often cited by papers focused on Telomeres, Telomerase, and Senescence (11 papers), Cancer-related Molecular Pathways (6 papers) and Cancer Research and Treatments (5 papers). William M. Keyes collaborates with scholars based in Spain, United States and France. William M. Keyes's co-authors include Mekayla A. Storer, Alea A. Mills, Matteo Pecoraro, Xuecui Guo, Hannes Vogel, Alexandre Robert‐Moreno, Valeria Di Giacomo, Noam Pilpel, Valery Krizhanovsky and James Sharpe and has published in prestigious journals such as Cell, Proceedings of the National Academy of Sciences and Nature Communications.

In The Last Decade

William M. Keyes

27 papers receiving 2.8k citations

Hit Papers

Senescence Is a Developmental Mechanism that Contributes ... 2013 2026 2017 2021 2013 2017 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
William M. Keyes Spain 17 1.6k 1.3k 655 485 353 27 2.9k
Matthew R. Ramsey United States 18 1.9k 1.2× 1.1k 0.9× 735 1.1× 393 0.8× 403 1.1× 25 3.4k
Ana Banito United States 18 2.2k 1.3× 1.2k 1.0× 617 0.9× 706 1.5× 482 1.4× 27 3.5k
Purificacı́on Muñoz Spain 29 2.5k 1.5× 1.2k 1.0× 1.1k 1.7× 237 0.5× 755 2.1× 35 3.8k
André Lechel Germany 25 1.3k 0.8× 755 0.6× 595 0.9× 309 0.6× 378 1.1× 54 2.5k
Shenghui He United States 14 2.4k 1.5× 973 0.8× 709 1.1× 573 1.2× 504 1.4× 20 4.1k
Enrique Samper Spain 23 4.1k 2.5× 3.4k 2.7× 675 1.0× 426 0.9× 616 1.7× 32 6.2k
Manisha Sinha India 15 2.1k 1.3× 575 0.5× 795 1.2× 225 0.5× 411 1.2× 41 3.3k
Sundaresan Venkatachalam United States 15 1.4k 0.9× 394 0.3× 814 1.2× 85 0.2× 380 1.1× 20 2.0k
Timothy Thompson United States 6 1.0k 0.6× 363 0.3× 617 0.9× 113 0.2× 256 0.7× 7 1.8k
Kentaro Hosokawa Japan 17 1.8k 1.1× 445 0.4× 641 1.0× 888 1.8× 449 1.3× 28 3.7k

Countries citing papers authored by William M. Keyes

Since Specialization
Citations

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

Fields of papers citing papers by William M. Keyes

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of William M. Keyes

This figure shows the co-authorship network connecting the top 25 collaborators of William M. Keyes. A scholar is included among the top collaborators of William M. Keyes 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 William M. Keyes. William M. Keyes 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.
Lesnik, Chen, Rachel Kaletsky, Jasmine M. Ashraf, et al.. (2024). Enhanced branched-chain amino acid metabolism improves age-related reproduction in C. elegans. Nature Metabolism. 6(4). 724–740. 6 indexed citations
2.
Rhinn, Muriel, et al.. (2023). Cellular senescence and developmental defects. FEBS Journal. 290(5). 1303–1313. 4 indexed citations
3.
Keyes, William M., et al.. (2022). Endothelial cells give a boost to senescence surveillance. Genes & Development. 36(9-10). 511–513. 3 indexed citations
4.
Riba, Andrea, Attila Oravecz, Matej Durik, et al.. (2022). Cell cycle gene regulation dynamics revealed by RNA velocity and deep-learning. Nature Communications. 13(1). 2865–2865. 41 indexed citations
5.
Plassat, Jean‐Luc, Matej Durik, Hugues Jacobs, et al.. (2020). The senotherapeutic drug ABT-737 disrupts aberrant p21 expression to restore liver regeneration in adult mice. Genes & Development. 34(7-8). 489–494. 73 indexed citations
6.
Giacomo, Viviana di, Tian V. Tian, Matteo Pecoraro, et al.. (2017). ΔNp63α promotes adhesion of metastatic prostate cancer cells to the bone through regulation of CD82. Oncogene. 36(31). 4381–4392. 17 indexed citations
7.
Storer, Mekayla A., Florian Heinzmann, Jennifer P. Morton, et al.. (2017). The senescence-associated secretory phenotype induces cellular plasticity and tissue regeneration. Genes & Development. 31(2). 172–183. 479 indexed citations breakdown →
8.
Storer, Mekayla A. & William M. Keyes. (2016). Detection of Senescence Markers During Mammalian Embryonic Development. Methods in molecular biology. 1534. 199–210. 4 indexed citations
9.
Simboeck, Elisabeth, Arantxa Gutiérrez, Luca Cozzuto, et al.. (2013). DPY30 regulates pathways in cellular senescence through ID protein expression. The EMBO Journal. 32(16). 2217–2230. 29 indexed citations
10.
Storer, Mekayla A., Alexandre Robert‐Moreno, Matteo Pecoraro, et al.. (2013). Senescence Is a Developmental Mechanism that Contributes to Embryonic Growth and Patterning. Cell. 155(5). 1119–1130. 884 indexed citations breakdown →
11.
Morey, Lluís, Antonio Más, Arantxa Gutiérrez, et al.. (2012). ZRF1 controls oncogene-induced senescence through the INK4-ARF locus. Oncogene. 32(17). 2161–2168. 29 indexed citations
12.
Doles, Jason D., Mekayla A. Storer, Luca Cozzuto, Guglielmo Roma, & William M. Keyes. (2012). Age-associated inflammation inhibits epidermal stem cell function. Genes & Development. 26(19). 2144–2153. 125 indexed citations
13.
Keyes, William M., Matteo Pecoraro, Victoria Aranda, et al.. (2011). ΔNp63α Is an Oncogene that Targets Chromatin Remodeler Lsh to Drive Skin Stem Cell Proliferation and Tumorigenesis. Cell stem cell. 8(2). 164–176. 159 indexed citations
14.
Guo, Xuecui, William M. Keyes, Cristian Papazoglu, et al.. (2009). TAp63 induces senescence and suppresses tumorigenesis in vivo. Nature Cell Biology. 11(12). 1451–1457. 206 indexed citations
15.
Mignone, John, José L. Roig-López, Natalia Fedtsova, et al.. (2007). Neural Potential of a Stem Cell Population in the Hair Follicle. Cell Cycle. 6(17). 2161–2170. 64 indexed citations
16.
Keyes, William M. & Alea A. Mills. (2006). p63: A New Link Between Senescence and Aging. Cell Cycle. 5(3). 260–265. 38 indexed citations
17.
Jacobs, W. Bradley, Gregory Govoni, Jasvinder K. Atwal, et al.. (2005). P63 Is an Essential Proapoptotic Protein during Neural Development. Neuron. 48(5). 743–756. 84 indexed citations
18.
Keyes, William M., Ying Wu, Hannes Vogel, et al.. (2005). p63 deficiency activates a program of cellular senescence and leads to accelerated aging. Genes & Development. 19(17). 1986–1999. 237 indexed citations
19.
Keyes, William M. & Alea A. Mills. (2003). Inducible systems see the light. Trends in biotechnology. 21(2). 53–55. 4 indexed citations
20.
Keyes, William M., Cairine Logan, Eve Parker, & Esmond J. Sanders. (2003). Expression and function of bone morphogenetic proteins in the development of the embryonic endocardial cushions. Anatomy and Embryology. 207(2). 135–147. 24 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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