What is he best known for?
The fields of study he is best known for:
His main research concerns Genetics, Gene, Oryza sativa, Biochemistry and Mutant.
His work on Locus, MADS-box, Sequence analysis and Promoter as part of general Genetics study is frequently connected to Binary number, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
Many of his research projects under Gene are closely connected to Selection with Selection, tying the diverse disciplines of science together.
His studies deal with areas such as Abiotic stress and Gene expression profiling as well as Oryza sativa.
He has included themes like Cell biology, Botany and Genetically modified rice in his Mutant study.
His Botany research is multidisciplinary, relying on both Signal transduction and WRKY protein domain.
His most cited work include:
- T-DNA insertional mutagenesis for functional genomics in rice. (573 citations)
- The Senescence-Induced Staygreen Protein Regulates Chlorophyll Degradation (362 citations)
- Starch biosynthesis in cereal endosperm (258 citations)
What are the main themes of his work throughout his whole career to date?
Gene, Genetics, Mutant, Biochemistry and Oryza sativa are his primary areas of study.
As a part of the same scientific study, Jong-Seong Jeon usually deals with the Gene, concentrating on Molecular biology and frequently concerns with Reporter gene and Proteomics.
The study incorporates disciplines such as Hexokinase, Chlorophyll, Stamen and Cell biology in addition to Mutant.
His work deals with themes such as Gene family, Gene expression profiling, Botany and Genetically modified rice, which intersect with Oryza sativa.
His study focuses on the intersection of Botany and fields such as Abiotic stress with connections in the field of Abiotic component.
His Arabidopsis study integrates concerns from other disciplines, such as Genetically modified crops, Arabidopsis thaliana and Psychological repression.
He most often published in these fields:
- Gene (39.52%)
- Genetics (31.74%)
- Mutant (30.54%)
What were the highlights of his more recent work (between 2018-2021)?
- Gene (39.52%)
- Cell biology (22.75%)
- Transcription factor (7.78%)
In recent papers he was focusing on the following fields of study:
Jong-Seong Jeon mostly deals with Gene, Cell biology, Transcription factor, Mutant and Effector.
Gene is a subfield of Genetics that he explores.
The Cell biology study combines topics in areas such as Genetically modified rice, Promoter, Chromatin immunoprecipitation, Immunity and Chromatin.
His Genetically modified rice research focuses on Xanthomonas oryzae and how it connects with Reactive oxygen species.
Jong-Seong Jeon has researched Transcription factor in several fields, including Gene expression, Ubiquitin ligase, RNA interference, Gene silencing and Transcription.
His studies in Mutant integrate themes in fields like Starch, Sucrose and Carbon metabolism, Cytosol, Enzyme.
Between 2018 and 2021, his most popular works were:
- OsbHLH058 and OsbHLH059 transcription factors positively regulate iron deficiency responses in rice. (20 citations)
- Nicotianamine Synthesis by OsNAS3 Is Important for Mitigating Iron Excess Stress in Rice (14 citations)
- Genome-wide Analysis of Root Hair Preferred RBOH Genes Suggests that Three RBOH Genes are Associated with Auxin-mediated Root Hair Development in Rice (14 citations)
In his most recent research, the most cited papers focused on:
His scientific interests lie mostly in Cell biology, Gene, Arabidopsis, Proteome and Protein degradation.
His study in Cell biology is interdisciplinary in nature, drawing from both Abiotic stress and Chromosomal translocation.
His Gene research includes elements of In vitro and Immune system.
The Arabidopsis study combines topics in areas such as Mitogen-activated protein kinase and Pattern recognition receptor, Immunity.
His Proteome research incorporates themes from Effector, Proteomics and Secondary metabolism.
His study on Protein degradation is mostly dedicated to connecting different topics, such as Computational biology.
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.
