What is he best known for?
The fields of study he is best known for:
John Quackenbush focuses on Genetics, Gene, Genome, Gene expression profiling and DNA microarray.
His research on Gene frequently links to adjacent areas such as Annotation.
His Genome research incorporates elements of Gene mapping, Nucleic acid sequence and DNA sequencing.
His research integrates issues of RNA, Microarray, Human genetics and Bioinformatics in his study of Gene expression profiling.
John Quackenbush works mostly in the field of Microarray, limiting it down to concerns involving Microarray analysis techniques and, occasionally, Data mining.
His DNA microarray study integrates concerns from other disciplines, such as Quantitative trait locus, Computational biology and Candidate gene.
His most cited work include:
- TM4: a free, open-source system for microarray data management and analysis. (4153 citations)
- Minimum information about a microarray experiment (MIAME)-toward standards for microarray data. (3539 citations)
- The complete genome sequence of the gastric pathogen Helicobacter pylori (3247 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Genetics, Gene, Computational biology, Gene expression profiling and Bioinformatics.
His work in Genome, Expressed sequence tag, Genomics, Genome-wide association study and Sequence analysis are all subfields of Genetics research.
His Gene study frequently draws connections to adjacent fields such as Disease.
His study in Computational biology is interdisciplinary in nature, drawing from both Phenotype, Transcriptome and Gene regulatory network.
John Quackenbush has researched Gene expression profiling in several fields, including Microarray, DNA microarray, Cancer research and Pathology.
John Quackenbush interconnects Cancer and Data science in the investigation of issues within Bioinformatics.
He most often published in these fields:
- Genetics (28.40%)
- Gene (29.21%)
- Computational biology (30.02%)
What were the highlights of his more recent work (between 2015-2021)?
- Computational biology (30.02%)
- Gene (29.21%)
- Gene regulatory network (13.39%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Computational biology, Gene, Gene regulatory network, Transcription factor and Gene expression.
His biological study spans a wide range of topics, including Phenotype, Cancer, Regulation of gene expression and Genomics.
His Cancer research is multidisciplinary, incorporating elements of Mutation, Germline mutation, Bioinformatics, Cluster analysis and Epigenetics.
His study on Gene is covered under Genetics.
His work deals with themes such as Network medicine, Internal medicine, Disease, Gene targeting and Colorectal cancer, which intersect with Gene regulatory network.
His research in Transcription factor intersects with topics in Cell culture, Transcriptome and Cell biology.
Between 2015 and 2021, his most popular works were:
- Artificial intelligence in radiology (530 citations)
- Exploratory Study to Identify Radiomics Classifiers for Lung Cancer Histology (174 citations)
- Understanding Tissue-Specific Gene Regulation (147 citations)
In his most recent research, the most cited papers focused on:
His scientific interests lie mostly in Computational biology, Gene, Gene regulatory network, Gene expression and Genetics.
His work on Network medicine as part of general Computational biology study is frequently linked to Computational analysis, bridging the gap between disciplines.
His primary area of study in Gene is in the field of Transcriptional regulation.
Within one scientific family, John Quackenbush focuses on topics pertaining to Bioinformatics under Gene regulatory network, and may sometimes address concerns connected to Exosome, Microvesicles and Breast cancer.
His Gene expression research is multidisciplinary, incorporating perspectives in Transcription factor, Immunology, Bronchitis, Regulation of gene expression and Mitochondrion.
His Microarray study combines topics from a wide range of disciplines, such as Sample and Data mining.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
