Ken Inoki

25.7k total citations · 9 hit papers
92 papers, 17.9k citations indexed

About

Ken Inoki is a scholar working on Molecular Biology, Physiology and Cell Biology. According to data from OpenAlex, Ken Inoki has authored 92 papers receiving a total of 17.9k indexed citations (citations by other indexed papers that have themselves been cited), including 72 papers in Molecular Biology, 24 papers in Physiology and 19 papers in Cell Biology. Recurrent topics in Ken Inoki's work include PI3K/AKT/mTOR signaling in cancer (53 papers), Polyamine Metabolism and Applications (20 papers) and Tuberous Sclerosis Complex Research (15 papers). Ken Inoki is often cited by papers focused on PI3K/AKT/mTOR signaling in cancer (53 papers), Polyamine Metabolism and Applications (20 papers) and Tuberous Sclerosis Complex Research (15 papers). Ken Inoki collaborates with scholars based in United States, Japan and France. Ken Inoki's co-authors include Kun‐Liang Guan, Tianqing Zhu, Yong Li, Jun Wu, Tianquan Zhu, Michael N. Corradetti, Tian Xu, Tsuneo Ikenoue, Qian Yang and Joungmok Kim and has published in prestigious journals such as Science, Cell and Proceedings of the National Academy of Sciences.

In The Last Decade

Ken Inoki

91 papers receiving 17.7k citations

Hit Papers

TSC2 Mediates Cellular Energy Response to Control Cell Gr... 2002 2026 2010 2018 2003 2002 2003 2006 2004 1000 2.0k 3.0k
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. TSC2 Mediates Cellular Energy Response to Control Cell Growth and Survival
    2003 3.1k citations Ken Inoki, Kun‐Liang Guan et al. profile →
  2. TSC2 is phosphorylated and inhibited by Akt and suppresses mTOR signalling
    2002 2.4k citations Ken Inoki, Yong Li et al. profile →
  3. Rheb GTPase is a direct target of TSC2 GAP activity and regulates mTOR signaling
    2003 1.5k citations Ken Inoki, Yong Li et al. profile →
  4. TSC2 Integrates Wnt and Energy Signals via a Coordinated Phosphorylation by AMPK and GSK3 to Regulate Cell Growth
    2006 1.1k citations Ken Inoki, Kun‐Liang Guan et al. profile →
  5. Dysregulation of the TSC-mTOR pathway in human disease
    2004 775 citations Ken Inoki, Kun‐Liang Guan et al. profile →
  6. AMPK and mTOR in Cellular Energy Homeostasis and Drug Targets
    2011 629 citations Ken Inoki, Kun‐Liang Guan et al. profile →
  7. ATM signals to TSC2 in the cytoplasm to regulate mTORC1 in response to ROS
    2010 576 citations Angela Alexander, Jinhee Kim et al. Proceedings of the National Academy of Sciences profile →
  8. Spatial Coupling of mTOR and Autophagy Augments Secretory Phenotypes
    2011 459 citations Masako Narita, Andrew Young et al. Science profile →
  9. mTORC1 activation in podocytes is a critical step in the development of diabetic nephropathy in mice
    2011 438 citations Ken Inoki, Tsukasa Suzuki et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ken Inoki United States 56 12.9k 3.4k 3.0k 2.4k 1.9k 92 17.9k
Mathieu Laplante Canada 36 8.9k 0.7× 3.3k 1.0× 3.8k 1.2× 1.6k 0.7× 1.2k 0.7× 78 15.1k
Tomoichiro Asano Japan 69 10.3k 0.8× 3.0k 0.9× 2.9k 1.0× 2.4k 1.0× 4.1k 2.2× 313 17.8k
George Thomas Switzerland 83 18.4k 1.4× 1.9k 0.6× 3.3k 1.1× 2.9k 1.2× 1.9k 1.0× 162 24.4k
Dos D. Sarbassov United States 26 12.8k 1.0× 1.6k 0.5× 1.6k 0.5× 1.9k 0.8× 1.1k 0.6× 53 16.1k
Sara C. Kozma United States 54 12.0k 0.9× 1.5k 0.4× 2.2k 0.7× 1.7k 0.7× 1.5k 0.8× 97 16.5k
Jorge Moscat United States 76 12.8k 1.0× 4.2k 1.2× 1.4k 0.5× 2.8k 1.2× 1.3k 0.7× 182 18.3k
Boudewijn Burgering Netherlands 64 17.6k 1.4× 1.4k 0.4× 2.3k 0.8× 2.1k 0.9× 1.3k 0.7× 155 22.7k
Joungmok Kim South Korea 27 8.7k 0.7× 6.7k 2.0× 1.6k 0.5× 4.2k 1.8× 1.2k 0.6× 50 15.2k
Stephen R. Farmer United States 59 7.6k 0.6× 3.8k 1.1× 5.3k 1.8× 1.8k 0.7× 1.4k 0.8× 116 13.9k
Satoshi Waguri Japan 47 8.8k 0.7× 8.5k 2.5× 2.3k 0.7× 4.0k 1.7× 870 0.5× 134 16.7k

Countries citing papers authored by Ken Inoki

Since Specialization
Citations

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

Fields of papers citing papers by Ken Inoki

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ken Inoki

This figure shows the co-authorship network connecting the top 25 collaborators of Ken Inoki. A scholar is included among the top collaborators of Ken Inoki 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 Ken Inoki. Ken Inoki 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.
Liu, Suxuan, Yin Zhou, Takao Iwawaki, et al.. (2025). PERK signaling maintains hematopoietic stem cell pool integrity under endoplasmic reticulum stress by promoting proliferation. Blood. 146(7). 806–818. 2 indexed citations
2.
Inoki, Ken, et al.. (2024). mTORC1 signaling and diabetic kidney disease. Diabetology International. 15(4). 707–718. 4 indexed citations
3.
Srivastava, Swayam Prakash, Han Zhou, Begoña Lainez, et al.. (2024). Renal Angptl4 is a key fibrogenic molecule in progressive diabetic kidney disease. Science Advances. 10(49). eadn6068–eadn6068. 8 indexed citations
4.
Meng, Ziyi, Linkang Zhou, Sungki Hong, et al.. (2023). Myeloid-specific ablation of Basp1 ameliorates diet-induced NASH in mice by attenuating pro-inflammatory signaling. Hepatology. 79(2). 409–424. 8 indexed citations
5.
Hua, Rui, Mauricio Torres, Yanan Li, et al.. (2023). Identification of circular dorsal ruffles as signal platforms for the AKT pathway in glomerular podocytes. Journal of Cellular Physiology. 238(5). 1063–1079. 4 indexed citations
6.
Inoki, Ken, et al.. (2022). Nutrient-sensing mTORC1 and AMPK pathways in chronic kidney diseases. Nature Reviews Nephrology. 19(2). 102–122. 69 indexed citations
7.
Liu, Lu, Fengbiao Mao, Guojun Shi, et al.. (2020). ER-associated degradation preserves hematopoietic stem cell quiescence and self-renewal by restricting mTOR activity. Blood. 136(26). 2975–2986. 47 indexed citations
8.
Kazyken, Dubek, Brian Magnuson, Çağrı Bodur, et al.. (2019). AMPK directly activates mTORC2 to promote cell survival during acute energetic stress. Science Signaling. 12(585). 175 indexed citations
9.
Zhang, Honglai, Rork Kuick, Sung Soo Park, et al.. (2018). Loss of AMPKα2 Impairs Hedgehog-Driven Medulloblastoma Tumorigenesis. International Journal of Molecular Sciences. 19(11). 3287–3287. 6 indexed citations
10.
Yao, Yao, E. Yvonne Jones, & Ken Inoki. (2017). Lysosomal Regulation of mTORC1 by Amino Acids in Mammalian Cells. Biomolecules. 7(3). 51–51. 55 indexed citations
11.
Hong, Sungki, Mallory Freeberg, Ting Han, et al.. (2017). LARP1 functions as a molecular switch for mTORC1-mediated translation of an essential class of mRNAs. eLife. 6. 149 indexed citations
12.
Hong, Sungki & Ken Inoki. (2016). Evaluating the mTOR Pathway in Physiological and Pharmacological Settings. Methods in enzymology on CD-ROM/Methods in enzymology. 587. 405–428. 5 indexed citations
13.
Bartholomew, Clinton R., Tsukasa Suzuki, Zhou Du, et al.. (2012). Ume6 transcription factor is part of a signaling cascade that regulates autophagy. Proceedings of the National Academy of Sciences. 109(28). 11206–11210. 88 indexed citations
14.
Henry, Fredrick E., Amber J. McCartney, Ryan Neely, et al.. (2012). Retrograde Changes in Presynaptic Function Driven by Dendritic mTORC1. Journal of Neuroscience. 32(48). 17128–17142. 47 indexed citations
15.
Narita, Masako, Andrew Young, Satoko Arakawa, et al.. (2011). Spatial Coupling of mTOR and Autophagy Augments Secretory Phenotypes. Science. 332(6032). 966–970. 459 indexed citations breakdown →
16.
Alexander, Angela, Jinhee Kim, Adrian Nañez, et al.. (2010). ATM signals to TSC2 in the cytoplasm to regulate mTORC1 in response to ROS. Proceedings of the National Academy of Sciences. 107(9). 4153–4158. 576 indexed citations breakdown →
17.
Lee, Chung‐Han, Ken Inoki, Magdalena Karbowniczek, et al.. (2007). Constitutive mTOR activation in TSC mutants sensitizes cells to energy starvation and genomic damage via p53. The EMBO Journal. 26(23). 4812–4823. 130 indexed citations
18.
Li, Yong, Ken Inoki, Haris G. Vikis, & Kun‐Liang Guan. (2006). Measurements of TSC2 GAP Activity Toward Rheb. Methods in enzymology on CD-ROM/Methods in enzymology. 407. 46–54. 16 indexed citations
19.
Li, Yong, Ken Inoki, & Kun‐Liang Guan. (2004). Biochemical and Functional Characterizations of Small GTPase Rheb and TSC2 GAP Activity. Molecular and Cellular Biology. 24(18). 7965–7975. 198 indexed citations
20.
Li, Yong, Ken Inoki, Panayiotis O. Vacratsis, & Kun‐Liang Guan. (2003). The p38 and MK2 Kinase Cascade Phosphorylates Tuberin, the Tuberous Sclerosis 2 Gene Product, and Enhances Its Interaction with 14-3-3. Journal of Biological Chemistry. 278(16). 13663–13671. 139 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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