What is he best known for?
The fields of study he is best known for:
- Gene
- Internal medicine
- Cancer
Gilbert S. Omenn spends much of his time researching Proteome, Internal medicine, Computational biology, Bioinformatics and Proteomics.
His Proteome research includes elements of Blood proteins, Chromatography, Biomarker and Human proteome project.
The Internal medicine study combines topics in areas such as Gastroenterology, Endocrinology and Surgery.
His studies examine the connections between Computational biology and genetics, as well as such issues in PeptideAtlas, with regards to Proteomics Standards Initiative.
His research integrates issues of Cancer research, Gene expression profiling, Genetic risk, Disease and Coaching in his study of Bioinformatics.
While the research belongs to areas of Lung cancer, he spends his time largely on the problem of beta-Carotene, intersecting his research to questions surrounding Retinol.
His most cited work include:
- A Quantitative Assessment of Plasma Homocysteine as a Risk Factor for Vascular Disease. Probable Benefits of Increasing Folic Acid Intakes (3327 citations)
- EFFECTS OF A COMBINATION OF BETA CAROTENE AND VITAMIN A ON LUNG CANCER AND CARDIOVASCULAR DISEASE (3031 citations)
- ProteomeXchange provides globally coordinated proteomics data submission and dissemination (1926 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Computational biology, Internal medicine, Proteomics, Human proteome project and Proteome.
His biological study spans a wide range of topics, including Gene, Bioinformatics and Genomics.
His Internal medicine research incorporates themes from Endocrinology and Oncology.
His study in Blood proteins extends to Proteomics with its themes.
His study in NeXtProt and PeptideAtlas falls within the category of Human proteome project.
His studies deal with areas such as Caret, Asbestos, Surgery and Cancer as well as Lung cancer.
He most often published in these fields:
- Computational biology (21.21%)
- Internal medicine (17.54%)
- Proteomics (16.27%)
What were the highlights of his more recent work (between 2015-2021)?
- Computational biology (21.21%)
- Human proteome project (16.27%)
- Proteomics (16.27%)
In recent papers he was focusing on the following fields of study:
Gilbert S. Omenn mainly investigates Computational biology, Human proteome project, Proteomics, Proteome and NeXtProt.
His research in Computational biology intersects with topics in Bioinformatics, Function, Genomics, Gene and Chromosome.
His Bioinformatics study combines topics from a wide range of disciplines, such as Genome-wide association study and Genetic association.
His work in the fields of Human proteome project, such as PeptideAtlas, overlaps with other areas such as Data interpretation.
Gilbert S. Omenn interconnects Blood proteins, Proteogenomics, Metabolomics and Blot in the investigation of issues within Proteomics.
His Disease research is under the purview of Internal medicine.
Between 2015 and 2021, his most popular works were:
- A wellness study of 108 individuals using personal, dense, dynamic data clouds. (200 citations)
- A wellness study of 108 individuals using personal, dense, dynamic data clouds. (200 citations)
- miRmine: a database of human miRNA expression profiles. (108 citations)
In his most recent research, the most cited papers focused on:
- Gene
- Internal medicine
- Cancer
Computational biology, Human proteome project, Proteomics, Proteome and NeXtProt are his primary areas of study.
He has included themes like Bioinformatics, Gene, Alternative splicing, Gene isoform and Genomics in his Computational biology study.
Gilbert S. Omenn studies Human proteome project, namely PeptideAtlas.
His Proteomics research integrates issues from Blood proteins, Proteogenomics and Pharmacology.
His Proteome research is multidisciplinary, relying on both Human genome, Data science and Data-independent acquisition.
His research investigates the connection between NeXtProt and topics such as Human Protein Atlas that intersect with problems in International working group.
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.
