What is he best known for?
The fields of study he is best known for:
His scientific interests lie mostly in Computational biology, Proteomics, Proteome, Biochemistry and Phosphorylation.
His Computational biology research is multidisciplinary, incorporating perspectives in Genetics, Gene, Bioinformatics and Human Protein Reference Database.
Akhilesh Pandey usually deals with Proteomics and limits it to topics linked to Genomics and Metabolomics.
The study incorporates disciplines such as Proteogenomics, Database, Human proteome project, Mass spectrometry and Gel electrophoresis in addition to Proteome.
His work focuses on many connections between Phosphorylation and other disciplines, such as Signal transduction, that overlap with his field of interest in Kinase.
In his study, Peptide is strongly linked to Chromatography, which falls under the umbrella field of Quantitative proteomics.
His most cited work include:
- Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. (4618 citations)
- Human Protein Reference Database—2009 update (2458 citations)
- ONCOMINE: A Cancer Microarray Database and Integrated Data-Mining Platform (2220 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Proteomics, Cell biology, Proteome, Computational biology and Cancer research.
Proteomics is a primary field of his research addressed under Biochemistry.
His research on Cell biology focuses in particular on Signal transduction.
The various areas that Akhilesh Pandey examines in his Signal transduction study include Receptor and Phosphorylation.
Proteome is closely attributed to Human proteome project in his work.
His Computational biology research incorporates elements of Genetics, Gene and Bioinformatics.
He most often published in these fields:
- Proteomics (23.26%)
- Cell biology (19.02%)
- Proteome (17.32%)
What were the highlights of his more recent work (between 2018-2021)?
- Cell biology (19.02%)
- Cancer research (15.28%)
- Proteome (17.32%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Cell biology, Cancer research, Proteome, Proteomics and Exome sequencing.
The Cancer research study combines topics in areas such as Cancer, Targeted therapy, Gallbladder cancer, Gene silencing and Kinase.
His biological study spans a wide range of topics, including Aneurysm, Cerebral vasospasm, Nuclear lamina, Inner membrane and Heterochromatin.
His study in Proteomics is interdisciplinary in nature, drawing from both Proteogenomics, Computational biology, Stem cell marker and Phosphorylation.
He studied Computational biology and Data-independent acquisition that intersect with Protein identification.
To a larger extent, he studies Genetics with the aim of understanding Exome sequencing.
Between 2018 and 2021, his most popular works were:
- Proteogenomic Analysis of Human Colon Cancer Reveals New Therapeutic Opportunities. (142 citations)
- Quantitative Proteomic Profiling of Cerebrospinal Fluid to Identify Candidate Biomarkers for Alzheimer's Disease. (26 citations)
- Identification of lncRNAs associated with early-stage breast cancer and their prognostic implications. (18 citations)
In his most recent research, the most cited papers focused on:
His primary scientific interests are in Cancer research, Exome sequencing, Proteomics, Genetics and Targeted therapy.
His studies deal with areas such as Cancer, Breast cancer, Gallbladder cancer, Malignancy and Gene silencing as well as Cancer research.
Akhilesh Pandey interconnects Cornea, Proteogenomics, Computational biology and Phosphoproteomics in the investigation of issues within Proteomics.
Akhilesh Pandey integrates many fields in his works, including Computational biology and Core set.
His work on Comparative genomic hybridization, Cytogenetics and Gene mutation as part of general Genetics study is frequently linked to Trigonocephaly and Hypotelorism, therefore connecting diverse disciplines of science.
His Gene study deals with Cell biology intersecting with Proteome.
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