What is she best known for?
The fields of study she is best known for:
Elena R. Alvarez-Buylla spends much of her time researching Ecology, Genetics, Gene, Botany and Cecropia obtusifolia.
Her Genetics study which covers Biological system that intersects with Attractor and Boolean model.
Her research in Gene intersects with topics in Sepal and Petal.
Her Botany study integrates concerns from other disciplines, such as Arabidopsis and Internal transcribed spacer.
Elena R. Alvarez-Buylla interconnects Regulation of gene expression and Computational biology in the investigation of issues within Arabidopsis.
The concepts of her Cecropia obtusifolia study are interwoven with issues in Seed dispersal and Pioneer species.
Her most cited work include:
- An ancestral MADS-box gene duplication occurred before the divergence of plants and animals. (386 citations)
- A Gene Regulatory Network Model for Cell-Fate Determination during Arabidopsis thaliana Flower Development That Is Robust and Recovers Experimental Gene Expression Profiles (358 citations)
- MADS-box gene evolution beyond flowers: expression in pollen, endosperm, guard cells, roots and trichomes (295 citations)
What are the main themes of her work throughout her whole career to date?
Elena R. Alvarez-Buylla mainly investigates Gene regulatory network, Arabidopsis, Botany, Cell biology and Arabidopsis thaliana.
Her research on Gene regulatory network concerns the broader Gene.
Gene is a subfield of Genetics that Elena R. Alvarez-Buylla investigates.
Her Arabidopsis study combines topics in areas such as Meristem, Epidermis and Root hair.
Her Cell biology research incorporates elements of Phenotype, Transcription factor and Cell growth.
Her studies in Arabidopsis thaliana integrate themes in fields like Lateral root and Cell fate determination.
She most often published in these fields:
- Gene regulatory network (35.20%)
- Arabidopsis (25.14%)
- Botany (25.14%)
What were the highlights of her more recent work (between 2017-2021)?
- Cell biology (21.79%)
- Gene regulatory network (35.20%)
- Arabidopsis (25.14%)
In recent papers she was focusing on the following fields of study:
Her primary areas of study are Cell biology, Gene regulatory network, Arabidopsis, Cellular differentiation and Transcription factor.
Elena R. Alvarez-Buylla combines subjects such as Phenotype, Arabidopsis thaliana, Cell and Cell growth with her study of Cell biology.
Her Gene regulatory network study deals with the bigger picture of Gene.
Her study on Gene is covered under Genetics.
Her Arabidopsis research is multidisciplinary, incorporating elements of Evolutionary biology, Gene expression and Meristem.
Her Cellular differentiation research includes themes of Computational biology and Systems biology.
Between 2017 and 2021, her most popular works were:
- MADS-box genes are key components of genetic regulatory networks involved in abiotic stress and plastic developmental responses in plants (30 citations)
- Role of Cytokine Combinations on CD4+ T Cell Differentiation, Partial Polarization, and Plasticity: Continuous Network Modeling Approach. (15 citations)
- Maize yield in Mexico under climate change (13 citations)
In her most recent research, the most cited papers focused on:
Her primary areas of study are Gene, Transcription factor, Arabidopsis, Genetics and Gene regulatory network.
Elena R. Alvarez-Buylla brings together Gene and Streptomyces to produce work in her papers.
The study incorporates disciplines such as Arabidopsis thaliana, Meristem, Botany and Root hair in addition to Arabidopsis.
Elena R. Alvarez-Buylla has researched Botany in several fields, including Cell Maturation, Morphogenesis and Cell type.
The Genome, Genomics and Secondary metabolite research Elena R. Alvarez-Buylla does as part of her general Genetics study is frequently linked to other disciplines of science, such as Streptomyces scabrisporus, therefore creating a link between diverse domains of science.
Her biological study spans a wide range of topics, including Evolutionary biology, MADS-box and Epigenetics.
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.
