What is he best known for?
The fields of study he is best known for:
The scientist’s investigation covers issues in Genetics, Genome-wide association study, Gene, Single-nucleotide polymorphism and Genetic association.
Junwen Wang regularly ties together related areas like Computational biology in his Genetics studies.
His Genome-wide association study study integrates concerns from other disciplines, such as Cancer, Genome, Locus and Heritability.
Junwen Wang has included themes like Family aggregation, Genetic correlation and Polymorphism in his Cancer study.
The Genetic association study combines topics in areas such as Human Phenotype Ontology and Allele.
Junwen Wang has researched SNP in several fields, including Genotyping, Attributable risk, Breast cancer and TOX3.
His most cited work include:
- A genome-wide association study identifies alleles in FGFR2 associated with risk of sporadic postmenopausal breast cancer (1364 citations)
- A genome-wide association study identifies alleles in FGFR2 associated with risk of sporadic postmenopausal breast cancer (1364 citations)
- The support of human genetic evidence for approved drug indications (602 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Genetics, Computational biology, Gene, Genome-wide association study and Regulation of gene expression.
His research on Computational biology also deals with topics like
- RNA-Seq together with Long non-coding RNA and Expression data,
- Drug which is related to area like Disease and Precision medicine,
- DNA sequencing that intertwine with fields like SNP.
The concepts of his Gene study are interwoven with issues in Cancer and Inference.
His Genome-wide association study study is focused on Single-nucleotide polymorphism in general.
His work on Genotyping expands to the thematically related Single-nucleotide polymorphism.
His Regulation of gene expression study combines topics in areas such as Molecular biology, Transcription factor and Gene regulatory network.
He most often published in these fields:
- Genetics (38.64%)
- Computational biology (40.91%)
- Gene (27.84%)
What were the highlights of his more recent work (between 2016-2021)?
- Computational biology (40.91%)
- Cancer research (14.77%)
- Gene (27.84%)
In recent papers he was focusing on the following fields of study:
His main research concerns Computational biology, Cancer research, Gene, Regulation of gene expression and Cancer.
His Computational biology research is multidisciplinary, relying on both Functional genomics, RNA-Seq, RNA and Drug.
His Cancer research research includes elements of Wound healing, SOX2, Prostaglandin E, Lung cancer and Adenocarcinoma.
His Regulation of gene expression study is related to the wider topic of Genetics.
His Cancer study also includes fields such as
- Selection most often made with reference to Exome sequencing,
- Precision medicine which intersects with area such as Genomic Profile.
Junwen Wang connects Genome-wide association study with Chemical similarity in his research.
Between 2016 and 2021, his most popular works were:
- Evaluation of tools for highly variable gene discovery from single-cell RNA-seq data. (57 citations)
- Linnorm: improved statistical analysis for single cell RNA-seq expression data (53 citations)
- Linnorm: improved statistical analysis for single cell RNA-seq expression data (53 citations)
In his most recent research, the most cited papers focused on:
His primary areas of investigation include Genome-wide association study, Regulation of gene expression, Computational biology, Gene and RNA-Seq.
His Genome-wide association study research is classified as research in Genetics.
His research integrates issues of Gene knockdown, Transcription factor, microRNA, Induced pluripotent stem cell and Gene regulatory network in his study of Regulation of gene expression.
The various areas that Junwen Wang examines in his Computational biology study include RNA, Long non-coding RNA and Sequence analysis.
Many of his research projects under Gene are closely connected to Variable with Variable, tying the diverse disciplines of science together.
His work deals with themes such as Normalization, Cluster analysis and Expression data, which intersect with RNA-Seq.
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