Kun‐Liang Guan

131.1k citations
422 papers · 89.1k indexed · 51 hit papers · h-index 148
Topics
Hippo pathway signaling and YAP/TAZ (112 papers)PI3K/AKT/mTOR signaling in cancer (73 papers)Protein Kinase Regulation and GTPase Signaling (54 papers)
Partner nations
United StatesChinaJapan

In The Last Decade

Kun‐Liang Guan

413 papers receiving 88.3k citations

Hit Papers

AMPK and mTOR regulate autophagy thr...19912026200220142011200320072002200810002.0k3.0k4.0k5.0k

Peers

Kun‐Liang Guan
Comparison fields: 5 of 184
  • Molecular Biology 57.9k
  • Cell Biology 32.0k
  • Epidemiology 14.3k
  • Cancer Research 10.2k
  • Oncology 10.0k
Replace Steven P. Gygi with:
Steven P. Gygi United States
David M. Sabatini United States
Randal J. Kaufman United States
David Ron United States
Daniel J. Klionsky United States
Beth Levine United States
Noboru Mizushima Japan
Keiji Tanaka Japan
Paul Tempst United States
Alfred L. Goldberg United States
Kun‐Liang Guan relative to Steven P. Gygi United States Steven P. Gygi's profile →
Citations per field
00.5×1.5×1.8×
Steven P. Gygi · 1×
Citations per year

Countries citing papers authored by Kun‐Liang Guan

Since Specialization
Citations

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

Fields of papers citing papers by Kun‐Liang Guan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kun‐Liang Guan. 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 Kun‐Liang Guan. The network helps show where Kun‐Liang Guan may publish in the future.

Co-authorship network of co-authors of Kun‐Liang Guan

This figure shows the co-authorship network connecting the top 25 collaborators of Kun‐Liang Guan. A scholar is included among the top collaborators of Kun‐Liang Guan 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 Kun‐Liang Guan. Kun‐Liang Guan 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
#WorkIndexed citations
1 1
2 5
3 4
4 147
5 67
6 51
7 31
8 2
9 12
10 22
11 68
12 69
13 100
14 128
15 169
16
ATM signals to TSC2 in the cytoplasm to regulate mTORC1 in response to ROSbreakdown →
576
17 435
18
Glioma-Derived Mutations in IDH1 Dominantly Inhibit IDH1 Catalytic Activity and Induce HIF-1αbreakdown →
896
19 74
20 198

About Kun‐Liang Guan

Kun‐Liang Guan is a scholar working on Cell Biology, Aging and Geriatrics and Gerontology, having authored 422 papers that have together received 89.1k indexed citations. Recurring topics across this work include Hippo pathway signaling and YAP/TAZ (112 papers), PI3K/AKT/mTOR signaling in cancer (73 papers) and Protein Kinase Regulation and GTPase Signaling (54 papers). The work is most often cited by research in Cell Biology (32.0k citations), Aging (1.7k citations) and Molecular Biology (57.9k citations). Kun‐Liang Guan has collaborated with scholars based in United States, China and Japan. Frequent co-authors include Ken Inoki, Joungmok Kim, Bin Zhao, Fa‐Xing Yu, Yue Xiong, Mondira Kundu, Benoı̂t Viollet, Tianqing Zhu, Young Chul Kim and Zhipeng Meng. Their work appears in journals such as Nature, Science and Cell.

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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