What is he best known for?
The fields of study he is best known for:
His primary areas of study are Cell biology, Biochemistry, Hippo signaling pathway, PI3K/AKT/mTOR pathway and Signal transduction.
His Cell biology research includes elements of Autophagy, Transcription factor and Cell growth.
He combines subjects such as Tissue homeostasis, WWTR1, Nuclear protein and Cell polarity with his study of Hippo signaling pathway.
In his research, TSC1 is intimately related to Cancer research, which falls under the overarching field of PI3K/AKT/mTOR pathway.
His research integrates issues of Gene expression and Cellular differentiation in his study of Signal transduction.
He has researched Phosphorylation in several fields, including Transcription and Kinase.
His most cited work include:
- Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition) (4170 citations)
- AMPK and mTOR regulate autophagy through direct phosphorylation of Ulk1 (3674 citations)
- Guidelines for the use and interpretation of assays for monitoring autophagy (3242 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Cell biology, Biochemistry, Signal transduction, Hippo signaling pathway and Phosphorylation.
His Cell biology study combines topics from a wide range of disciplines, such as Transcription factor and Cell growth.
His study focuses on the intersection of Biochemistry and fields such as Molecular biology with connections in the field of c-Raf and Isocitrate dehydrogenase.
His Signal transduction study frequently draws parallels with other fields, such as HEK 293 cells.
Kun-Liang Guan has included themes like Cancer research, Tissue homeostasis and WWTR1 in his Hippo signaling pathway study.
His Cancer research research incorporates elements of Carcinogenesis and Suppressor.
He most often published in these fields:
- Cell biology (67.98%)
- Biochemistry (34.38%)
- Signal transduction (26.52%)
What were the highlights of his more recent work (between 2015-2021)?
- Cell biology (67.98%)
- Hippo signaling pathway (23.97%)
- Cancer research (22.99%)
In recent papers he was focusing on the following fields of study:
Kun-Liang Guan mainly focuses on Cell biology, Hippo signaling pathway, Cancer research, Cell growth and Phosphorylation.
His Cell biology study integrates concerns from other disciplines, such as Cell, Tissue homeostasis and Transcription factor.
His studies in Hippo signaling pathway integrate themes in fields like Carcinogenesis and YAP1.
His Cancer research research includes themes of Cancer, Suppressor, Signal transducing adaptor protein, Cancer immunotherapy and RHOA.
His research in Cell growth intersects with topics in Receptor, G protein-coupled receptor, Actin cytoskeleton, Ubiquitin ligase and Cell polarity.
His mTORC1 study is concerned with the larger field of Biochemistry.
Between 2015 and 2021, his most popular works were:
- Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition) (4170 citations)
- Mechanisms of Hippo pathway regulation (680 citations)
- mTOR as a central hub of nutrient signalling and cell growth. (285 citations)
In his most recent research, the most cited papers focused on:
The scientist’s investigation covers issues in Cell biology, Hippo signaling pathway, Cell growth, Transcription factor and Tissue homeostasis.
His work deals with themes such as Regulator, Biochemistry and DNA damage, which intersect with Cell biology.
The study incorporates disciplines such as Cell and Cancer research in addition to Hippo signaling pathway.
The concepts of his Cell growth study are interwoven with issues in Oxidative stress, Cancer, Actin cytoskeleton, Stem cell and Programmed cell death.
His studies examine the connections between Transcription factor and genetics, as well as such issues in HEK 293 cells, with regards to Extracellular matrix, RHOA, Interactome, Regulation of gene expression and WWTR1.
His Phosphorylation study combines topics in areas such as Cytoplasm and BECN1.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
