What is he best known for?
The fields of study he is best known for:
His main research concerns Genetics, International HapMap Project, Haplotype, Genetic association and Genome-wide association study.
His work in Single-nucleotide polymorphism, Linkage disequilibrium, Haplotype estimation, Imputation and Population stratification are all subfields of Genetics research.
Matthew Stephens has included themes like Statistics, Genotyping and Data mining in his Haplotype estimation study.
His Population stratification research incorporates elements of Effective population size, Human population genetics, Generalized linear mixed model, Linear model and Mixed model.
His studies in International HapMap Project integrate themes in fields like Quantitative trait locus and Expression quantitative trait loci.
His Population genetics study integrates concerns from other disciplines, such as Allele frequency, Population fragmentation and Genetic divergence.
His most cited work include:
- Inference of population structure using multilocus genotype data (24178 citations)
- A new statistical method for haplotype reconstruction from population data (6659 citations)
- Inference of Population Structure Using Multilocus Genotype Data: Linked Loci and Correlated Allele Frequencies (6410 citations)
What are the main themes of his work throughout his whole career to date?
Matthew Stephens focuses on Genetics, Gene, Computational biology, Genome-wide association study and Genetic association.
Quantitative trait locus, Genetic variation, Single-nucleotide polymorphism, International HapMap Project and Haplotype are among the areas of Genetics where he concentrates his study.
His biological study spans a wide range of topics, including Human genetic variation and Population genetics.
His research in Population genetics intersects with topics in Genetic variability and Inference.
His Genome-wide association study study combines topics from a wide range of disciplines, such as Multivariate analysis, Bayes' theorem and Bioinformatics.
His study looks at the relationship between Genetic association and fields such as Bayesian probability, as well as how they intersect with chemical problems.
He most often published in these fields:
- Genetics (34.50%)
- Gene (21.83%)
- Computational biology (20.52%)
What were the highlights of his more recent work (between 2018-2021)?
- Computational biology (20.52%)
- Gene (21.83%)
- Gene expression (11.79%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Computational biology, Gene, Gene expression, Genome-wide association study and Transcriptome.
His Computational biology research is multidisciplinary, incorporating perspectives in Mutation, Genome, Allele and Carcinogenesis.
His study looks at the intersection of Gene and topics like Cell type with Genotype.
Matthew Stephens studied Gene expression and Genetic variation that intersect with Cell biology.
His Genome-wide association study research focuses on subjects like Genetic association, which are linked to Replicate, Multivariate statistics, Multivariate analysis, Mendelian randomization and Bayesian probability.
RNA splicing is a subfield of Genetics that Matthew Stephens investigates.
Between 2018 and 2021, his most popular works were:
- The GTEx Consortium atlas of genetic regulatory effects across human tissues (238 citations)
- The GTEx Consortium atlas of genetic regulatory effects across human tissues (128 citations)
- Cell type specific genetic regulation of gene expression across human tissues (91 citations)
In his most recent research, the most cited papers focused on:
His primary areas of investigation include Computational biology, Transcriptome, Gene, Genome-wide association study and RNA splicing.
Matthew Stephens interconnects Phenotype and Genomic data in the investigation of issues within Computational biology.
His Transcriptome research focuses on Regulation of gene expression and how it connects with Cell type, Cell and Genotype.
His Genome-wide association study study deals with the bigger picture of Genetics.
His work in RNA splicing addresses issues such as Allelic heterogeneity, which are connected to fields such as DECIPHER.
His Genetic variation research includes themes of Expression quantitative trait loci, Single-cell analysis and Gene expression profiling.
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