What is he best known for?
The fields of study he is best known for:
- Gene
- Enzyme
- Internal medicine
His primary areas of study are Cell biology, Lipidomics, Bioinformatics, Lipid metabolism and Biochemistry.
His Cell biology research is multidisciplinary, incorporating perspectives in Proinflammatory cytokine, Molecular biology and Oxidative stress.
His Lipidomics research integrates issues from Sphingolipid, Systems biology and Steatosis.
The Bioinformatics study combines topics in areas such as Cirrhosis, Liver biopsy, Signalling and Data science.
His studies in Lipid metabolism integrate themes in fields like Class, Relational database, Data field, Computational biology and Data retrieval.
His Biochemistry research is multidisciplinary, incorporating elements of Electrospray ionization and Cytokine.
His most cited work include:
- A comprehensive classification system for lipids (1052 citations)
- Update of the LIPID MAPS comprehensive classification system for lipids (894 citations)
- Lipidomics reveals a remarkable diversity of lipids in human plasma (802 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Computational biology, Cell biology, Systems biology, Bioinformatics and Biochemistry.
His Computational biology research integrates issues from Skeletal muscle, Genetics, Proteomics and Gene regulatory network.
The various areas that Shankar Subramaniam examines in his Cell biology study include Cell cycle, Transcriptome, Molecular biology and Lipid metabolism.
His research in Lipidomics and Sphingolipid are components of Biochemistry.
He does research in Signal transduction, focusing on Cell signaling specifically.
He most often published in these fields:
- Computational biology (19.61%)
- Cell biology (16.40%)
- Systems biology (11.25%)
What were the highlights of his more recent work (between 2015-2021)?
- Cell biology (16.40%)
- Computational biology (19.61%)
- Systems biology (11.25%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Cell biology, Computational biology, Systems biology, Immunology and Cancer research.
Signal transduction is the focus of his Cell biology research.
His Computational biology study incorporates themes from MEDLINE, Lipidomics and Chromatin, Histone, Chromatin remodeling.
The Lipidomics study combines topics in areas such as Metabolic labeling, Mass spectrometric and Glycerophospholipids.
His research on Systems biology concerns the broader Bioinformatics.
His work deals with themes such as Phenotype and Metadata, which intersect with Bioinformatics.
Between 2015 and 2021, his most popular works were:
- Metabolomics Workbench: An international repository for metabolomics data and metadata, metabolite standards, protocols, tutorials and training, and analysis tools (216 citations)
- p62, Upregulated during Preneoplasia, Induces Hepatocellular Carcinogenesis by Maintaining Survival of Stressed HCC-Initiating Cells (214 citations)
- Metabolic Phenotypes of Response to Vaccination in Humans (119 citations)
In his most recent research, the most cited papers focused on:
- Gene
- Enzyme
- Internal medicine
The scientist’s investigation covers issues in Cell biology, Computational biology, Regulation of gene expression, Immunology and Bioinformatics.
His Cell biology research includes themes of Inflammation, Cell, Transcriptome and Transcription factor.
He combines subjects such as Lipidomics, Metabolic labeling, Mass spectrometric and Glycerophospholipids with his study of Computational biology.
The study incorporates disciplines such as HCCS, Carcinoma, Downregulation and upregulation, Receptor and Chromatin remodeling in addition to Regulation of gene expression.
His work on Acquired immune system, CD8, Vaccination and Cell adhesion molecule as part of general Immunology research is frequently linked to Herpes Zoster Vaccine, thereby connecting diverse disciplines of science.
His Bioinformatics research is multidisciplinary, relying on both Metadata and Data science.
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