What is he best known for?
The fields of study he is best known for:
His primary areas of study are Genetics, Gene, Robustness, Evolutionary biology and Genome.
His Computational biology research extends to Genetics, which is thematically connected.
His studies deal with areas such as Phenotype, Genotype, Evolvability and Biological evolution as well as Robustness.
His work deals with themes such as Epistasis, Natural selection, Molecular evolution, Evolutionary dynamics and Genetic systems, which intersect with Evolutionary biology.
Andreas Wagner has researched Natural selection in several fields, including Evolutionary developmental biology and Living systems.
His Molecular evolution study incorporates themes from Ecology, Biological network and Neutral mutation.
His most cited work include:
- The small world inside large metabolic networks (841 citations)
- Robustness and Evolvability in Living Systems (819 citations)
- Perspective: Evolution and detection of genetic robustness. (584 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Genetics, Evolutionary biology, Gene, Phenotype and Robustness.
His biological study deals with issues like Computational biology, which deal with fields such as Organism.
The Evolutionary biology study combines topics in areas such as Natural selection, Molecular evolution, Tetrahymena, Adaptation and Genetic variation.
His is doing research in Gene duplication, Gene regulatory network, Regulation of gene expression, Gene expression and Transcription factor, both of which are found in Gene.
His Phenotype research incorporates elements of Mutation, Epistasis, RNA, Genotype and Stabilizing selection.
His Robustness research includes elements of Evolvability and Protein folding.
He most often published in these fields:
- Genetics (92.53%)
- Evolutionary biology (65.84%)
- Gene (51.78%)
What were the highlights of his more recent work (between 2018-2021)?
- Evolutionary biology (65.84%)
- Phenotype (47.33%)
- Tetrahymena (11.21%)
In recent papers he was focusing on the following fields of study:
Evolutionary biology, Phenotype, Tetrahymena, Gene and Genetic variation are his primary areas of study.
The Mosaic evolution research Andreas Wagner does as part of his general Evolutionary biology study is frequently linked to other disciplines of science, such as Trait, therefore creating a link between diverse domains of science.
The concepts of his Phenotype study are interwoven with issues in Directed evolution, Mutant, Directed Molecular Evolution, Genotype and Evolvability.
His Evolvability study is focused on Genetics in general.
The study of Genetics is intertwined with the study of Social evolution in a number of ways.
In his study, which falls under the umbrella issue of Robustness, Natural selection is strongly linked to Yellow fluorescent protein.
Between 2018 and 2021, his most popular works were:
- The causes of evolvability and their evolution. (86 citations)
- Cryptic genetic variation accelerates evolution by opening access to diverse adaptive peaks. (34 citations)
- Cryptic genetic variation accelerates evolution by opening access to diverse adaptive peaks. (34 citations)
In his most recent research, the most cited papers focused on:
Andreas Wagner mainly investigates Evolutionary biology, Phenotype, Biological system, Gene expression and Directed evolution.
Andreas Wagner has included themes like Fitness landscape, Tetrahymena and Robustness in his Evolutionary biology study.
His Fitness landscape study combines topics in areas such as Adaptation, Genetic variation, Mutation and Variation.
Andreas Wagner interconnects Natural selection, Neoplasm genetics, Genetic systems and Yellow fluorescent protein in the investigation of issues within Robustness.
His Phenotype study integrates concerns from other disciplines, such as Strain, Human evolutionary genetics and Regulon, Escherichia coli.
His Gene expression research is multidisciplinary, relying on both Transcription factor, Fixation and DNA.
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.
