What is he best known for?
The fields of study he is best known for:
Justin O. Borevitz focuses on Genetics, Allele, Genetic variation, Arabidopsis and Arabidopsis thaliana.
His Genetics research focuses on Gene, Genome, Genetic variability, Genomics and Quantitative trait locus.
His research in Allele intersects with topics in Genetic Fitness, Trade-off and Locus.
His work in Genetic variation addresses subjects such as Genome-wide association study, which are connected to disciplines such as Linkage disequilibrium, Missing heritability problem, Computational biology and Evolutionary biology.
His study in Arabidopsis is interdisciplinary in nature, drawing from both Enhancer, Wild type, MYB and Cauliflower mosaic virus.
His Arabidopsis thaliana research includes elements of Plant disease resistance, Pseudomonas syringae and Cell biology.
His most cited work include:
- Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines (1295 citations)
- Activation Tagging Identifies a Conserved MYB Regulator of Phenylpropanoid Biosynthesis (1083 citations)
- Activation tagging in Arabidopsis. (913 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Genetics, Genetic variation, Arabidopsis, Quantitative trait locus and Gene.
His is doing research in Arabidopsis thaliana, Genome, Allele, Genomics and Epigenetics, both of which are found in Genetics.
His work is dedicated to discovering how Allele, Flowering Locus C are connected with Null allele and other disciplines.
His Genetic variation research incorporates themes from Evolutionary biology, Genome-wide association study, Genetic diversity, Genetic association and Adaptation.
His Arabidopsis study results in a more complete grasp of Mutant.
His Quantitative trait locus study integrates concerns from other disciplines, such as Locus, Inbred strain, Vernalization and Candidate gene.
He most often published in these fields:
- Genetics (65.97%)
- Genetic variation (29.17%)
- Arabidopsis (29.17%)
What were the highlights of his more recent work (between 2016-2021)?
- Genetic diversity (13.89%)
- Ecology (12.50%)
- Genetic variation (29.17%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Genetic diversity, Ecology, Genetic variation, Evolutionary biology and Genetics.
Justin O. Borevitz works mostly in the field of Ecology, limiting it down to topics relating to Isolation by distance and, in certain cases, Local adaptation, as a part of the same area of interest.
Justin O. Borevitz combines subjects such as Population genetics and Genomics with his study of Genetic variation.
His Evolutionary biology study combines topics in areas such as Genetic marker, Population genomics and Adaptation.
Justin O. Borevitz does research in Genetics, focusing on Quantitative trait locus specifically.
His research investigates the connection between Quantitative trait locus and topics such as Photosynthesis that intersect with problems in Genome-wide association study and Arabidopsis.
Between 2016 and 2021, his most popular works were:
- Deep phenotyping: deep learning for temporal phenotype/genotype classification. (49 citations)
- Rapid Recovery Gene Downregulation during Excess-Light Stress and Recovery in Arabidopsis (44 citations)
- kWIP: The k -mer weighted inner product, a de novo estimator of genetic similarity (25 citations)
In his most recent research, the most cited papers focused on:
His primary scientific interests are in Ecology, Reference genome, Genetic diversity, Eucalyptus and Classifier.
His Ecology research includes themes of Gene flow, Isolation by distance, Genetic structure and Repens.
His Reference genome study combines topics from a wide range of disciplines, such as k-mer, Distance matrix, Genetic variation and Comparative genomics.
The concepts of his Genetic diversity study are interwoven with issues in Critically endangered, Reforestation, Eucalyptus melliodora, Climate change and Environmental resource management.
His work investigates the relationship between Computational biology and topics such as Support vector machine that intersect with problems in Genome.
His Genome research entails a greater understanding of Gene.
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