What is he best known for?
The fields of study he is best known for:
His primary scientific interests are in Biochemistry, Cell biology, Apoptosis, Caspase and Protein structure.
He regularly links together related areas like Biophysics in his Biochemistry studies.
His biological study spans a wide range of topics, including Caenorhabditis elegans and APAF1.
His research investigates the link between Apoptosis and topics such as Mitochondrion that cross with problems in ENDOG, Endonuclease, RNA interference, Cytosol and Tissue homeostasis.
His studies deal with areas such as Inhibitor of apoptosis and Enzyme activator as well as Caspase.
The various areas that he examines in his Protein structure study include Ryanodine receptor, Antiporter, Cryo-electron microscopy, DNA-binding protein and Peptide sequence.
His most cited work include:
- Mechanisms of TGF-β Signaling from Cell Membrane to the Nucleus (4729 citations)
- The Galvanization of Biology: A Growing Appreciation for the Roles of Zinc (1551 citations)
- Molecular mechanisms of caspase regulation during apoptosis (1503 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Cell biology, Biochemistry, Caspase, Biophysics and Protein structure.
His research integrates issues of Apoptosome, Apoptosis, Programmed cell death and RNA splicing, Spliceosome in his study of Cell biology.
Within one scientific family, Yigong Shi focuses on topics pertaining to Stereochemistry under RNA splicing, and may sometimes address concerns connected to Active site.
The concepts of his Caspase study are interwoven with issues in Structural biology, Enzyme activator, Plasma protein binding and Effector.
His Biophysics research focuses on Transmembrane domain and how it connects with Membrane protein.
His studies in Protein structure integrate themes in fields like Peptide sequence and Binding site.
He most often published in these fields:
- Cell biology (50.58%)
- Biochemistry (38.13%)
- Caspase (19.07%)
What were the highlights of his more recent work (between 2015-2021)?
- RNA splicing (25.29%)
- Spliceosome (24.51%)
- Cell biology (50.58%)
In recent papers he was focusing on the following fields of study:
Yigong Shi spends much of his time researching RNA splicing, Spliceosome, Cell biology, Small nuclear RNA and Biophysics.
His RNA splicing research also works with subjects such as
- Exon together with Structural biology,
- Intron which connect with Polypyrimidine tract,
- Stereochemistry that intertwine with fields like Active site.
His Spliceosome research is multidisciplinary, relying on both Molecular biology, snRNP, Minor spliceosome and Helicase.
Yigong Shi specializes in Cell biology, namely SMAD.
His Biophysics study combines topics from a wide range of disciplines, such as Protein subunit, Gamma secretase, Transmembrane domain, Protein structure and Membrane protein.
His Protein structure research is multidisciplinary, incorporating elements of Hydrolase and Proteasome.
Between 2015 and 2021, his most popular works were:
- PPM1A Functions as a Smad Phosphatase to Terminate TGFβ Signaling (377 citations)
- Structure of a yeast activated spliceosome at 3.5 Å resolution (179 citations)
- Mechanistic insights into precursor messenger RNA splicing by the spliceosome (153 citations)
In his most recent research, the most cited papers focused on:
The scientist’s investigation covers issues in RNA splicing, Biophysics, Spliceosome, Exon and Small nuclear RNA.
His work deals with themes such as Messenger RNA, Stereochemistry and Intron, which intersect with RNA splicing.
His Biophysics research includes elements of Gamma secretase, Transmembrane domain, Intramembrane protease, Protein structure and Membrane protein.
His biological study spans a wide range of topics, including Hydrolase, Cell biology, Proteasome and Proteases.
His Spliceosome study combines topics in areas such as Molecular biology, Minor spliceosome, RNA Splicing Factors and snRNP.
Cleavage is a subfield of Biochemistry that Yigong Shi investigates.
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.
