What is he best known for?
The fields of study he is best known for:
Ken Chen mainly investigates Genetics, Computational biology, Genomics, Human genome and Mutation.
His studies in Point mutation, Gene, Allele frequency, Gene expression profiling and Germline mutation are all subfields of Genetics research.
Ken Chen interconnects Exome sequencing, Genome-wide association study, Genetic variation and Whole genome sequencing in the investigation of issues within Computational biology.
His research integrates issues of Structural variation, Reference genome and 1000 Genomes Project in his study of Genomics.
While the research belongs to areas of 1000 Genomes Project, Ken Chen spends his time largely on the problem of Human genetic variation, intersecting his research to questions surrounding Evolutionary biology.
His Mutation research is multidisciplinary, incorporating perspectives in Cancer, Myeloid, Cancer research, Myeloid leukemia and Leukemia.
His most cited work include:
- A global reference for human genetic variation. (7825 citations)
- Comprehensive genomic characterization defines human glioblastoma genes and core pathways (5484 citations)
- Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia (2799 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Computational biology, Genetics, Cancer research, Gene and Cancer.
Within one scientific family, he focuses on topics pertaining to Genomics under Computational biology, and may sometimes address concerns connected to 1000 Genomes Project.
His Genome, Human genome, DNA sequencing, Point mutation and Structural variation study are his primary interests in Genetics.
In his work, Transcriptome is strongly intertwined with Cell, which is a subfield of Cancer research.
Gene is often connected to Disease in his work.
His work deals with themes such as Mutation, Oncology and Bioinformatics, which intersect with Cancer.
He most often published in these fields:
- Computational biology (27.31%)
- Genetics (24.72%)
- Cancer research (19.93%)
What were the highlights of his more recent work (between 2019-2021)?
- Computational biology (27.31%)
- Cancer research (19.93%)
- Cell (7.38%)
In recent papers he was focusing on the following fields of study:
Ken Chen mostly deals with Computational biology, Cancer research, Cell, Cancer and Gene.
Ken Chen has researched Computational biology in several fields, including RNA-Seq, Mass cytometry, Genome, Genomics and Chronic lymphocytic leukemia.
His Genome research is multidisciplinary, relying on both Germline, DNA sequencing and Somatic cell.
His Genomics research includes themes of Interactive visualization, Visualization and Single cell sequencing.
His research in Cell intersects with topics in Tumor microenvironment, Transcriptome and Breast cancer.
His study in the field of Phylogenetics, IRF1 and Epigenetics is also linked to topics like 2019-20 coronavirus outbreak.
Between 2019 and 2021, his most popular works were:
- Pan-cancer analysis of whole genomes (538 citations)
- Pan-cancer analysis of whole genomes identifies driver rearrangements promoted by LINE-1 retrotransposition. (77 citations)
- A robust benchmark for detection of germline large deletions and insertions. (54 citations)
In his most recent research, the most cited papers focused on:
His scientific interests lie mostly in Cancer research, Computational biology, Genome, Gene and Cell.
His studies in Computational biology integrate themes in fields like RNA-Seq, Whole genome sequencing, Point mutation, Genomics and Disease.
His work carried out in the field of Genome brings together such families of science as Chromothripsis, Gene rearrangement and Germline.
His research investigates the connection between Gene and topics such as Cancer that intersect with problems in Function and DNA sequencing.
His Cell research is multidisciplinary, incorporating elements of Lineage, Breast cancer, Immune system and Transcriptome.
His Carcinogenesis study is associated with Genetics.
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