What is he best known for?
The fields of study he is best known for:
His scientific interests lie mostly in Database, Gene, Gene regulatory network, Gene expression profiling and Genetics.
The Database study combines topics in areas such as Protein structure, Hypothetical protein, Binding site and Protein folding.
Kengo Kinoshita has included themes like Gene coexpression and Arabidopsis in his Gene regulatory network study.
His Arabidopsis study combines topics in areas such as Biotechnology and Computational biology.
The study incorporates disciplines such as Interaction network, Training set and Support vector machine in addition to Computational biology.
His Gene expression profiling study combines topics in areas such as Animal data and Sequence analysis.
His most cited work include:
- PrDOS: prediction of disordered protein regions from amino acid sequence (487 citations)
- ATTED-II: a database of co-expressed genes and cis elements for identifying co-regulated gene groups in Arabidopsis (337 citations)
- ATTED-II provides coexpressed gene networks for Arabidopsis. (336 citations)
What are the main themes of his work throughout his whole career to date?
Computational biology, Genetics, Gene, Protein structure and Bioinformatics are his primary areas of study.
His research in Computational biology intersects with topics in Biochemistry, Identification, Function, Similarity and Structural genomics.
His studies link Disease with Genetics.
His Gene study frequently intersects with other fields, such as Database.
His biological study spans a wide range of topics, including Crystallography and Peptide sequence, Sequence alignment.
Kengo Kinoshita has included themes like Gene coexpression and Arabidopsis in his Gene regulatory network study.
He most often published in these fields:
- Computational biology (25.76%)
- Genetics (17.80%)
- Gene (14.02%)
What were the highlights of his more recent work (between 2018-2021)?
- Genetics (17.80%)
- Gene (14.02%)
- Cohort study (5.68%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Genetics, Gene, Cohort study, Computational biology and Cohort.
His Genetics research focuses on subjects like Disease, which are linked to Missense mutation.
His Cohort study research incorporates themes from Field, Information retrieval and Gerontology.
His Computational biology study integrates concerns from other disciplines, such as Genotype, Haplotype, Genetic variation and 1000 Genomes Project.
The Cohort study combines topics in areas such as Prospective cohort study, MEDLINE, Omics and Family medicine.
His Transcriptome study incorporates themes from Individual gene, Inference and Database.
Between 2018 and 2021, his most popular works were:
- COXPRESdb v7: a gene coexpression database for 11 animal species supported by 23 coexpression platforms for technical evaluation and evolutionary inference. (45 citations)
- 3.5KJPNv2: an allele frequency panel of 3552 Japanese individuals including the X chromosome. (44 citations)
- Cohort Profile: Tohoku Medical Megabank Project Birth and Three-Generation Cohort Study (TMM BirThree Cohort Study): rationale, progress and perspective. (23 citations)
In his most recent research, the most cited papers focused on:
The scientist’s investigation covers issues in Computational biology, Genetics, Personalized medicine, Cohort study and Cohort.
Kengo Kinoshita merges Computational biology with Single molecule real time sequencing in his study.
His work in Genetics is not limited to one particular discipline; it also encompasses Paget Disease.
His Cohort study research includes themes of Gerontology, MEDLINE, Omics and Family medicine.
His Omics research integrates issues from Prospective cohort study and Incidence.
Gene is closely attributed to Inference in his research.
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