What is he best known for?
The fields of study he is best known for:
John C. Marioni mostly deals with Computational biology, Genetics, Single-cell analysis, Transcriptome and Gene expression profiling.
His Computational biology study combines topics from a wide range of disciplines, such as Cell, DNA methylation, Bioinformatics, RNA and Transcription.
His Single-cell analysis research incorporates themes from Embryonic stem cell and Gene regulatory network.
His Embryonic stem cell research includes elements of Cellular differentiation, Systems biology and Cell biology.
His work carried out in the field of Transcriptome brings together such families of science as Cancer research, Cell Cycle Stage and Cell type.
His Gene expression profiling study integrates concerns from other disciplines, such as Preprocessor and Human genetics.
His most cited work include:
- RNA-seq: An assessment of technical reproducibility and comparison with gene expression arrays (2282 citations)
- Understanding mechanisms underlying human gene expression variation with RNA sequencing (1115 citations)
- Intratumor heterogeneity in human glioblastoma reflects cancer evolutionary dynamics (1036 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Cell biology, Computational biology, Genetics, Gene and Transcriptome.
His studies deal with areas such as Embryonic stem cell and Mesoderm as well as Cell biology.
His work deals with themes such as Cell, Single-cell analysis, RNA-Seq, Gene expression profiling and RNA, which intersect with Computational biology.
His RNA research is multidisciplinary, incorporating perspectives in Normalization and Sequencing data.
His work in Gene addresses subjects such as Evolutionary biology, which are connected to disciplines such as Placozoa and Clade.
The study incorporates disciplines such as Cell type and Cellular differentiation in addition to Transcriptome.
He most often published in these fields:
- Cell biology (83.73%)
- Computational biology (60.85%)
- Genetics (39.15%)
What were the highlights of his more recent work (between 2019-2021)?
- Cell biology (83.73%)
- Cell (42.92%)
- Computational biology (60.85%)
In recent papers he was focusing on the following fields of study:
Cell biology, Cell, Computational biology, Progenitor cell and Induced pluripotent stem cell are his primary areas of study.
His Cell biology research incorporates elements of Transcription factor, Immune system, Epithelial cell differentiation and Ageing.
His Cell research is multidisciplinary, incorporating elements of Pathogenesis, RNA, Cirrhotic liver, Biological system and Generalized linear model.
His research in RNA tackles topics such as Gene expression which are related to areas like Human genetics.
His Computational biology research includes themes of RNA-Seq, Gene expression profiling, Genomics, Locus and Cell fate determination.
His Embryonic stem cell study which covers Mammalian heart that intersects with Transcriptome.
Between 2019 and 2021, his most popular works were:
- Eleven grand challenges in single-cell data science (187 citations)
- Eleven grand challenges in single-cell data science (187 citations)
- Eleven grand challenges in single-cell data science (187 citations)
In his most recent research, the most cited papers focused on:
His scientific interests lie mostly in Cell biology, Computational biology, Cell, Expression quantitative trait loci and Induced pluripotent stem cell.
His research in Cell biology intersects with topics in Transcriptome and Cytokinesis.
His studies in Transcriptome integrate themes in fields like Embryonic stem cell, Lineage, Mouse Heart, Cardiac cell and Mammalian heart.
His biological study spans a wide range of topics, including Key genes and Genomics.
His research in Cell focuses on subjects like Dopaminergic neuron differentiation, which are connected to RNA and Locus.
His Expression quantitative trait loci study combines topics from a wide range of disciplines, such as Gene expression, Gene expression profiling, Single-cell analysis, Quantitative trait locus and Genetic heterogeneity.
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