What is she best known for?
The fields of study she is best known for:
Luciana Correia de Almeida Regitano spends much of her time researching Beef cattle, Genetics, Single-nucleotide polymorphism, Candidate gene and Quantitative trait locus.
She combines subjects such as Veterinary medicine, Zebu, Meat tenderness, Breed and Feed conversion ratio with her study of Beef cattle.
Her Breed research is multidisciplinary, incorporating perspectives in Birth weight and Genetic association.
The Genetics study combines topics in areas such as Zoology and Residual feed intake.
Her Single-nucleotide polymorphism study incorporates themes from Genome size and Bovine genome.
As part of one scientific family, she deals mainly with the area of Candidate gene, narrowing it down to issues related to the Intramuscular fat, and often Fatty acid.
Her most cited work include:
- Worldwide Patterns of Ancestry, Divergence, and Admixture in Domesticated Cattle (233 citations)
- Analysis of copy number variations among diverse cattle breeds (221 citations)
- Genotypic and allelic frequencies of gene polymorphisms associated with meat tenderness in Nellore beef cattle. (99 citations)
What are the main themes of her work throughout her whole career to date?
Her primary areas of investigation include Genetics, Beef cattle, Gene, Single-nucleotide polymorphism and Quantitative trait locus.
Candidate gene, Microsatellite, Allele, Genotype and Linkage disequilibrium are the core of her Genetics study.
Her Beef cattle study combines topics from a wide range of disciplines, such as Breed, Veterinary medicine, Zebu and Feed conversion ratio.
The study incorporates disciplines such as Genotyping and Haplotype in addition to Single-nucleotide polymorphism.
Her research in Quantitative trait locus intersects with topics in Genetic marker, Genome, Genetic variation and Marbled meat.
Her studies deal with areas such as Weight gain and Residual feed intake as well as Animal science.
She most often published in these fields:
- Genetics (50.70%)
- Beef cattle (38.03%)
- Gene (26.06%)
What were the highlights of her more recent work (between 2018-2021)?
- Gene (26.06%)
- Gene expression (14.79%)
- Fatty acid (4.93%)
In recent papers she was focusing on the following fields of study:
Her primary scientific interests are in Gene, Gene expression, Fatty acid, Beef cattle and Genetics.
Her study on Candidate gene and Transcription factor is often connected to AMPK and TCF4 as part of broader study in Gene.
Her Candidate gene research includes themes of Plant disease resistance and Tick.
The various areas that Luciana Correia de Almeida Regitano examines in her Gene expression study include Phenotype, microRNA, Regulation of gene expression, Computational biology and Tenderness.
Her Beef cattle research incorporates themes from Feces, Archaea, Bacteria, Methane and Haplotype.
Her work in the fields of Single-nucleotide polymorphism and Gene expression profiling overlaps with other areas such as Leukocyte chemotaxis.
Between 2018 and 2021, her most popular works were:
- Allele-specific expression is widespread in Bos indicus muscle and affects meat quality candidate genes. (42 citations)
- Co-expression networks reveal potential regulatory roles of miRNAs in fatty acid of nelore cattle (30 citations)
- Detection of Co-expressed Pathway Modules Associated With Mineral Concentration and Meat Quality in Nelore Cattle. (12 citations)
In her most recent research, the most cited papers focused on:
Her main research concerns Gene, Candidate gene, Fatty acid, microRNA and Nelore cattle.
She usually deals with Gene and limits it to topics linked to Tenderness and Protein metabolism and Lipid metabolism.
Her research on Candidate gene concerns the broader Genetics.
Her Fatty acid research integrates issues from Phenotype, Oleic acid and Beef cattle.
Her research integrates issues of Skeletal muscle, Meat tenderness, Messenger RNA and Cell biology in her study of microRNA.
Luciana Correia de Almeida Regitano has researched Nelore cattle in several fields, including Weighted correlation network analysis, DNA microarray, Carcass weight and Proteomics.
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.
