What is she best known for?
The fields of study she is best known for:
Her primary areas of investigation include Genetics, microRNA, Gene, Computational biology and Small RNA.
Her research in the fields of Morphogenesis, Embryonic stem cell and Gene regulatory network overlaps with other disciplines such as Blastoderm and Reaper.
Debora S. Marks has included themes like Psychological repression, Molecular biology, Gene silencing and Regulation of gene expression in her microRNA study.
Her Gene silencing research includes themes of Translational regulation and Gene expression profiling.
Her Gene study frequently links to related topics such as Supplementary data.
Her Computational biology study incorporates themes from Transcriptome, Sequence analysis, DNA, Protein structure and De novo protein structure prediction.
Her most cited work include:
- Human MicroRNA targets. (2996 citations)
- MicroRNA targets in Drosophila (2293 citations)
- The microRNA.org resource: targets and expression (1944 citations)
What are the main themes of her work throughout her whole career to date?
Debora S. Marks focuses on Computational biology, Genetics, Gene, Protein structure and microRNA.
Her Computational biology study combines topics in areas such as RNA, Principle of maximum entropy, Function, Protein structure prediction and Sequence.
Her RNA study combines topics from a wide range of disciplines, such as Sequence analysis and Sequence.
Her Genetics research integrates issues from De novo protein structure prediction and Threading.
Her study in Protein structure is interdisciplinary in nature, drawing from both Amino acid, Plasma protein binding, Transmembrane protein, Protein family and Biological system.
Her microRNA research is multidisciplinary, incorporating elements of Gene expression, Molecular biology, Messenger RNA, Cell biology and Regulation of gene expression.
She most often published in these fields:
- Computational biology (47.10%)
- Genetics (26.81%)
- Gene (23.91%)
What were the highlights of her more recent work (between 2018-2021)?
- Computational biology (47.10%)
- Artificial intelligence (17.39%)
- Sequence (17.39%)
In recent papers she was focusing on the following fields of study:
Her primary areas of study are Computational biology, Artificial intelligence, Sequence, Machine learning and Protein structure.
The Computational biology study combines topics in areas such as RNA, Recombinant DNA, Saccharomyces cerevisiae and Protein–protein interaction.
Her Artificial intelligence study deals with Variable intersecting with Expression, Generative model, Sequence space, Protein design and Leverage.
Her Machine learning research incorporates themes from Network model and Computational model.
Her Protein structure research is multidisciplinary, relying on both Plasma protein binding, Epistasis, Mutant, Sequence and Protein family.
Her biological study focuses on Gene.
Between 2018 and 2021, her most popular works were:
- Inferring protein 3D structure from deep mutation scans. (50 citations)
- Inferring protein 3D structure from deep mutation scans. (50 citations)
- The EVcouplings Python framework for coevolutionary sequence analysis (45 citations)
In her most recent research, the most cited papers focused on:
The scientist’s investigation covers issues in RNA, Computational biology, Epistasis, Autoregressive model and Leverage.
She has researched RNA in several fields, including WW domain, Mutant, Mutation, Sequence and Protein structure.
Her studies deal with areas such as Transmembrane protein, Peptidoglycan, Cell wall, Transmembrane domain and Thermus thermophilus as well as Protein structure.
Her work carried out in the field of Computational biology brings together such families of science as Missense mutation, Complementarity determining region, Generative grammar, Genetic variation and Ribozyme.
Her research on Epistasis concerns the broader Gene.
The concepts of her Leverage study are interwoven with issues in Sequence space and Generative model.
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