What is he best known for?
The fields of study he is best known for:
Tzumin Lee mainly focuses on Mushroom bodies, Neuroscience, MARCM, Lineage and Neuron.
His study in Mushroom bodies is interdisciplinary in nature, drawing from both Transgene, Ecdysone receptor, Morphogenesis, Cell biology and Regulation of gene expression.
His research in the fields of GABAergic overlaps with other disciplines such as Negative feedback.
Tzumin Lee has researched MARCM in several fields, including Neurogenesis and Neural development.
His Lineage study integrates concerns from other disciplines, such as Anatomy and Neuroblast.
His research in Neuron intersects with topics in Genetics, Drosophila Protein, Neural coding, Odor and Neural Inhibition.
His most cited work include:
- Mosaic analysis with a repressible cell marker (MARCM) for Drosophila neural development (765 citations)
- Optimized CRISPR/Cas tools for efficient germline and somatic genome engineering in Drosophila. (624 citations)
- Genetic mosaic with dual binary transcriptional systems in Drosophila. (396 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Neuroscience, Cell biology, Lineage, Mushroom bodies and Neuroblast.
His biological study spans a wide range of topics, including Anatomy and MARCM.
His Cell biology study combines topics from a wide range of disciplines, such as Genetics, Transcription factor, Drosophila Protein and DSCAM.
Tzumin Lee has included themes like Evolutionary biology, Organism, Drosophila and Notch signaling pathway in his Lineage study.
The various areas that Tzumin Lee examines in his Mushroom bodies study include Transgene, Ecdysone receptor, Axon guidance, Morphogenesis and Regulation of gene expression.
As part of one scientific family, Tzumin Lee deals mainly with the area of Neuroblast, narrowing it down to issues related to the Neural stem cell, and often Developmental biology.
He most often published in these fields:
- Neuroscience (57.29%)
- Cell biology (45.83%)
- Lineage (46.88%)
What were the highlights of his more recent work (between 2018-2021)?
- CRISPR (16.67%)
- Evolutionary biology (14.58%)
- Cas9 (14.58%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in CRISPR, Evolutionary biology, Cas9, Neuroscience and Lineage.
His Evolutionary biology research includes themes of Stem cell, Neural stem cell, Lineage and Drosophila.
His work deals with themes such as Gene targeting, Computational biology, Transgene and Cell type, which intersect with Cas9.
As part of his studies on Neuroscience, Tzumin Lee often connects relevant subjects like Drosophila melanogaster.
The various areas that Tzumin Lee examines in his Lineage study include Algorithm, Linkage and Lineage tracing.
His Gene regulatory network study incorporates themes from Mushroom bodies and Developmental biology.
Between 2018 and 2021, his most popular works were:
- Neurotransmitter identity is acquired in a lineage-restricted manner in the Drosophila CNS (28 citations)
- Temporal control of Drosophila central nervous system development (20 citations)
- Unlimited Genetic Switches for Cell-Type-Specific Manipulation. (16 citations)
In his most recent research, the most cited papers focused on:
Tzumin Lee mainly investigates Neuroscience, Evolutionary biology, Lineage, Central nervous system and Neuroblast.
His Neuroscience study combines topics from a wide range of disciplines, such as Glutamate receptor, Glutamatergic and Acetylcholine.
The concepts of his Evolutionary biology study are interwoven with issues in Stem cell, Neuron, Dna mutation and Drosophila.
His Stem cell study combines topics in areas such as Mushroom bodies and Developmental biology.
His Central nervous system research integrates issues from Drosophila melanogaster, Transcription factor and GABAergic.
Tzumin Lee has researched Neuroblast in several fields, including Regulation of gene expression, Gene expression profiling, Cellular differentiation and Gene regulatory network.
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.
