What is she best known for?
The fields of study she is best known for:
Aviv Regev mainly focuses on Genetics, Computational biology, Single-cell analysis, Gene and Gene expression profiling.
Her study in the fields of Systems biology under the domain of Computational biology overlaps with other disciplines such as N6-Methyladenosine.
Aviv Regev has researched Single-cell analysis in several fields, including Tumor microenvironment, Transcriptome, Cell type and Cell biology.
Her work deals with themes such as ORFS and Function, which intersect with Gene.
The Gene expression profiling study combines topics in areas such as Phenotype, Regulation of gene expression, RNA-Seq and Epigenetics.
Aviv Regev combines subjects such as Cancer research and In situ hybridization with her study of Cell.
Her most cited work include:
- Full-length transcriptome assembly from RNA-Seq data without a reference genome. (11720 citations)
- De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis (4622 citations)
- Chromatin signature reveals over a thousand highly conserved large non-coding RNAs in mammals (3197 citations)
What are the main themes of her work throughout her whole career to date?
Aviv Regev mostly deals with Computational biology, Cell, Cell biology, Genetics and Cancer research.
Her research investigates the link between Computational biology and topics such as Cell type that cross with problems in Neuroscience.
Her study in Cell is interdisciplinary in nature, drawing from both RNA, RNA-Seq, Glioma and Tumor microenvironment.
Her Cell biology study combines topics in areas such as T cell, Transcription factor and Cellular differentiation.
Genetics is a component of her Gene expression profiling, Genome, Regulation of gene expression, Single-cell analysis and Gene duplication studies.
Her Cancer research research includes themes of Cancer cell, Cancer, CD8, Immune system and Cytotoxic T cell.
She most often published in these fields:
- Computational biology (57.99%)
- Cell (51.40%)
- Cell biology (41.34%)
What were the highlights of her more recent work (between 2020-2021)?
- Cell (51.40%)
- Transcriptome (32.85%)
- Computational biology (57.99%)
In recent papers she was focusing on the following fields of study:
Cell, Transcriptome, Computational biology, Immune system and Cancer research are her primary areas of study.
The various areas that Aviv Regev examines in her Cell study include Gene expression, Cancer cell, Genome and Stem cell, Cell biology.
Her studies in Cell biology integrate themes in fields like T cell, Transcription factor and Innate lymphoid cell.
Aviv Regev has included themes like RNA, Regulation of gene expression and Gene regulatory network in her Transcriptome study.
She interconnects Chromatin, Epigenomics, DNA methylation and Cell type in the investigation of issues within Computational biology.
In her research on the topic of Cancer research, Immune checkpoint is strongly related with Cytotoxic T cell.
Between 2020 and 2021, her most popular works were:
- COVID-19 tissue atlases reveal SARS-CoV-2 pathology and cellular targets. (49 citations)
- Expansion sequencing: Spatially precise in situ transcriptomics in intact biological systems. (36 citations)
- Expansion sequencing: Spatially precise in situ transcriptomics in intact biological systems. (36 citations)
In her most recent research, the most cited papers focused on:
Her main research concerns Transcriptome, Computational biology, Cell, Cell biology and Cell type.
Gene covers Aviv Regev research in Transcriptome.
Her studies deal with areas such as Chromatin, Epigenomics and RNA as well as Computational biology.
Her Cell research is multidisciplinary, relying on both Cancer, Immunotherapy, Gene expression, Gene regulatory network and Cytotoxic T cell.
Her Cell biology research incorporates themes from Innate lymphoid cell, Cell fate determination and Epigenome.
Her Cancer research research is multidisciplinary, incorporating elements of Mesenchymal stem cell, Synovial sarcoma and Single-cell analysis.
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