Robert A. Saxton

15 papers receiving 8.3k citations

Hit Papers

mTOR Signaling in Growth, Metabolism, and Disease 2017 · 5.6k citations
5.6k201420262018202210002.0k3.0k4.0k5.0k

Peers

Robert A. Saxton
Comparison fields: 5 of 133
  • Aging 274
  • Cell Biology 1.3k
  • Molecular Biology 5.5k
  • Physiology 354
  • Cancer Research 1.0k
Replace Alejo Efeyan with:
Alejo Efeyan Spain
Andrew R. Tee United Kingdom
Leon O. Murphy United States
Stephan Wullschleger Switzerland
Liron Bar‐Peled United States
Dos D. Sarbassov United States
Kenta Hara Japan
Yong‐Keun Jung South Korea
Zengqiang Yuan China
Kazuyoshi Yonezawa Japan
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Citations per field
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Citations per year

Countries citing papers authored by Robert A. Saxton

Since Specialization
Citations

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

Fields of papers citing papers by Robert A. Saxton

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Robert A. Saxton. 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 Robert A. Saxton. The network helps show where Robert A. Saxton may publish in the future.

Co-authors

The 25 scholars most cited alongside Robert A. Saxton, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Robert A. Saxton Line = papers co-authored together Robert A. Saxton links everyone, so they are left out of the graph.

All Works

15 of 15 papers shown
#Work
1
mTOR Signaling in Growth, Metabolism, and Disease
Hit paper breakdown →
20175566
2
Sestrin2 is a leucine sensor for the mTORC1 pathway
Hit paper breakdown →
2015951
3
The CASTOR Proteins Are Arginine Sensors for the mTORC1 Pathway
Hit paper breakdown →
2016607
4
The Sestrins Interact with GATOR2 to Negatively Regulate the Amino-Acid-Sensing Pathway Upstream of mTORC1
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2014374
5 2015356
6 2016232
7 2022116
8 202250
9 202149
10 202336
11 201624
12 20218
13 20245
14
SAMTOR is an S-adenosylmethionine sensor for the mTORC1 pathway
20172
15
Sestrin2 is a leucine sensor for the mTORC1 pathway
20161

About Robert A. Saxton

Robert A. Saxton is a scholar working on Physiology, Immunology, Immunology and Allergy, Molecular Biology and Endocrine and Autonomic Systems, having authored 15 papers that have together received 8.4k indexed citations. Recurring topics across this work include PI3K/AKT/mTOR signaling in cancer (9 papers), Polyamine Metabolism and Applications (6 papers), Cytokine Signaling Pathways and Interactions (3 papers), Biochemical Analysis and Sensing Techniques (2 papers), Immune Response and Inflammation (2 papers), CRISPR and Genetic Engineering (2 papers), Ubiquitin and proteasome pathways (2 papers) and Advanced biosensing and bioanalysis techniques (1 paper). The work is most often cited by research in Aging (274 citations), Cell Biology (1.3k citations), Molecular Biology (5.5k citations), Physiology (354 citations) and Cancer Research (1.0k citations). Robert A. Saxton has collaborated with scholars based in United States, Germany and Canada. Frequent co-authors include David M. Sabatini, Lynne Chantranupong, Sonia M. Scaria, Rachel L. Wolfson, Kuang Shen, Jason R. Cantor, Timothy C. Wang, Steven P. Gygi, Kevin E. Knockenhauer and Thomas Schwartz. Their work appears in journals such as Science, Nature, Cell, Nature Communications and Immunity.

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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