What is he best known for?
The fields of study he is best known for:
Michael R. Kanost spends much of his time researching Biochemistry, Manduca sexta, Hemolymph, Serine and Prophenoloxidase.
His Biochemistry study frequently draws connections between adjacent fields such as Cuticle.
The various areas that Michael R. Kanost examines in his Manduca sexta study include Complementary DNA, Molecular biology and Pattern recognition receptor.
His research in Hemolymph tackles topics such as Peptide sequence which are related to areas like Molecular model, Molecule and Globular protein.
Michael R. Kanost interconnects Proteases and Serpin in the investigation of issues within Serine.
Within one scientific family, Michael R. Kanost focuses on topics pertaining to Serine protease under Innate immune system, and may sometimes address concerns connected to Genome, Drosophila melanogaster and Gene.
His most cited work include:
- Insights into social insects from the genome of the honeybee Apis mellifera (1472 citations)
- BIOLOGICAL MEDIATORS OF INSECT IMMUNITY (1114 citations)
- Immune pathways and defence mechanisms in honey bees Apis mellifera (672 citations)
What are the main themes of his work throughout his whole career to date?
Manduca sexta, Biochemistry, Hemolymph, Molecular biology and Cell biology are his primary areas of study.
His Manduca sexta research includes elements of Complementary DNA, Serpin and Innate immune system.
His works in Prophenoloxidase, Serine, Peptide sequence, Serine protease and Proteases are all subjects of inquiry into Biochemistry.
His biological study spans a wide range of topics, including Laminarin, Zymogen and Pattern recognition receptor.
His study focuses on the intersection of Hemolymph and fields such as Hemolin with connections in the field of Hyalophora cecropia.
His work focuses on many connections between Molecular biology and other disciplines, such as cDNA library, that overlap with his field of interest in Expressed sequence tag.
He most often published in these fields:
- Manduca sexta (54.91%)
- Biochemistry (51.79%)
- Hemolymph (35.27%)
What were the highlights of his more recent work (between 2014-2021)?
- Manduca sexta (54.91%)
- Cell biology (20.09%)
- Gene (21.88%)
In recent papers he was focusing on the following fields of study:
Michael R. Kanost mostly deals with Manduca sexta, Cell biology, Gene, Biochemistry and Hemolymph.
His Manduca sexta study combines topics from a wide range of disciplines, such as Serine protease and Genome.
The concepts of his Cell biology study are interwoven with issues in Superoxide dismutase, Gene expression, Serpin, Molecular biology and Peptide.
His research investigates the connection between Gene and topics such as Botany that intersect with issues in Cuticle pigmentation and Bombyx mori.
His work carried out in the field of Hemolymph brings together such families of science as Transferrin and Antimicrobial peptides.
His study in Innate immune system is interdisciplinary in nature, drawing from both Insect and Effector.
Between 2014 and 2021, his most popular works were:
- Clip-domain serine proteases as immune factors in insect hemolymph (115 citations)
- Multifaceted biological insights from a draft genome sequence of the tobacco hornworm moth, Manduca sexta (114 citations)
- Serpins in arthropod biology (68 citations)
In his most recent research, the most cited papers focused on:
Michael R. Kanost focuses on Gene, Biochemistry, Botany, Serine protease and Innate immune system.
His research in Gene intersects with topics in Chitin and Manduca sexta.
Manduca and Computational biology is closely connected to Genome in his research, which is encompassed under the umbrella topic of Manduca sexta.
His Serine protease study combines topics in areas such as Proteases and Prophenoloxidase.
His Prophenoloxidase research incorporates themes from Plasma protein binding, Zymogen, Pattern recognition receptor and Protease.
His research integrates issues of Serpin, Immunity, Function and Virology in his study of Innate immune system.
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.
