What is he best known for?
The fields of study Ying-Hui Fu is best known for:
His work often combines Genetics and Mutant studies.
Borrowing concepts from Transcription factor, Ying-Hui Fu weaves in ideas under Gene.
He frequently studies issues relating to Genetics and Transcription factor.
He merges many fields, such as Mutation and Locus (genetics), in his writings.
His work often combines Locus (genetics) and Mutation studies.
Internal medicine is frequently linked to Dyskinesia in his study.
His research on Dyskinesia often connects related areas such as Internal medicine.
He carries out multidisciplinary research, doing studies in Missense mutation and Phenotype.
His multidisciplinary approach integrates Phenotype and Missense mutation in his work.
His most cited work include:
- Identification of a gene (FMR-1) containing a CGG repeat coincident with a breakpoint cluster region exhibiting length variation in fragile X syndrome (3133 citations)
- Candidate Gene for the Chromosome 1 Familial Alzheimer's Disease Locus (2330 citations)
- Variation of the CGG repeat at the fragile X site results in genetic instability: Resolution of the Sherman paradox (1891 citations)
What are the main themes of his work throughout his whole career to date
In his work, Internal medicine is strongly intertwined with Disease, which is a subfield of Pathology.
His Internal medicine study often links to related topics such as Disease.
In his articles, Ying-Hui Fu combines various disciplines, including Genetics and Mutation.
He merges many fields, such as Gene and Molecular biology, in his writings.
He combines Molecular biology and Gene in his research.
He connects Neuroscience with Cell biology in his study.
His work blends Cell biology and Neuroscience studies together.
In his works, he conducts interdisciplinary research on Circadian rhythm and Circadian clock.
Ying-Hui Fu applies his multidisciplinary studies on Circadian clock and Circadian rhythm in his research.
Ying-Hui Fu most often published in these fields:
- Genetics (76.70%)
- Gene (70.87%)
- Neuroscience (49.51%)
What were the highlights of his more recent work (between 2017-2022)?
- Operating system (84.62%)
- Sleep (system call) (84.62%)
- Neuroscience (76.92%)
In recent works Ying-Hui Fu was focusing on the following fields of study:
Ying-Hui Fu incorporates Operating system and Sleep (system call) in his studies.
In his study, he carries out multidisciplinary Sleep (system call) and Operating system research.
He combines Neuroscience and Behavioural genetics in his research.
In his works, he performs multidisciplinary study on Behavioural genetics and Molecular genetics.
He connects Molecular genetics with Human genetics in his research.
He combines Genetics and Cell biology in his studies.
Ying-Hui Fu combines Cell biology and Gene in his studies.
He performs integrative study on Gene and Evolutionary biology.
He combines Evolutionary biology and Genetics in his research.
Between 2017 and 2022, his most popular works were:
- Genetics of the human circadian clock and sleep homeostat (55 citations)
- TIMELESS mutation alters phase responsiveness and causes advanced sleep phase (41 citations)
- A Rare Mutation of β1-Adrenergic Receptor Affects Sleep/Wake Behaviors (40 citations)
In his most recent research, the most cited works focused on:
- Circadian rhythm
- Circadian clock
- PER2
Ying-Hui Fu integrates many fields, such as Operating system and Sleep (system call), in his works.
In his works, he performs multidisciplinary study on Sleep (system call) and Operating system.
His study connects Adrenergic receptor and Genetics.
As part of his studies on Adrenergic receptor, he often connects relevant areas like Receptor.
His Genetics research extends to the thematically linked field of Receptor.
He regularly ties together related areas like Circadian rhythm in his Neuroscience studies.
He integrates several fields in his works, including Circadian rhythm and Chronotype.
His research is interdisciplinary, bridging the disciplines of Neuroscience and Chronotype.
Ying-Hui Fu brings together Circadian clock and Cryptochrome to produce work in his papers.
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.
