What is he best known for?
The fields of study he is best known for:
Proteomics, Computational biology, Bioinformatics, Tandem mass spectrometry and Mass spectrometry are his primary areas of study.
In his research on the topic of Proteomics, Posterior probability and Statistical model is strongly related with Data mining.
His Computational biology study combines topics in areas such as Genetics, Regulation of gene expression, Proteome, Gene and Peptide spectral library.
The various areas that Alexey I. Nesvizhskii examines in his Peptide spectral library study include Mass spectrometry data format and ProteinProphet.
The study incorporates disciplines such as Identification, Quantitative proteomics, Peptide sequence, Artificial intelligence and Pattern recognition in addition to Bioinformatics.
His research investigates the connection with Tandem mass spectrometry and areas like Shotgun proteomics which intersect with concerns in Bayes' theorem, Linear model and Experimental data.
His most cited work include:
- Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. (3993 citations)
- A statistical model for identifying proteins by tandem mass spectrometry. (3625 citations)
- The CRAPome: a contaminant repository for affinity purification–mass spectrometry data (844 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Proteomics, Computational biology, Cell biology, Proteome and Data mining.
His Proteomics research integrates issues from Identification, Tandem mass spectrometry, Mass spectrometry and Bioinformatics.
His Mass spectrometry research includes elements of Artificial intelligence and Pattern recognition.
Alexey I. Nesvizhskii combines subjects such as Proteogenomics, Gene, Protein–protein interaction and Shotgun proteomics with his study of Computational biology.
His studies in Proteome integrate themes in fields like PeptideAtlas and Cancer research.
The Data mining study combines topics in areas such as Database search engine, PeptideProphet, False discovery rate, Interactome and Workflow.
He most often published in these fields:
- Proteomics (31.33%)
- Computational biology (23.69%)
- Cell biology (14.46%)
What were the highlights of his more recent work (between 2017-2021)?
- Computational biology (23.69%)
- Proteomics (31.33%)
- Cell biology (14.46%)
In recent papers he was focusing on the following fields of study:
Alexey I. Nesvizhskii mainly focuses on Computational biology, Proteomics, Cell biology, Cancer research and Mass spectrometry.
His Computational biology study incorporates themes from Gene, Peptide and Protein–protein interaction.
Many of his research projects under Proteomics are closely connected to Sensitivity with Sensitivity, tying the diverse disciplines of science together.
His Cell biology research incorporates themes from Parthenolide and Carboxypeptidase.
His research in Cancer research intersects with topics in Immune checkpoint, Wnt signaling pathway, Tumor microenvironment, Familial partial lipodystrophy and Sarcoma.
His Mass spectrometry research is multidisciplinary, incorporating perspectives in Label-free quantification, Algorithm, Data mining and Missing data.
Between 2017 and 2021, his most popular works were:
- The Landscape of Circular RNA in Cancer. (368 citations)
- Integrated Proteogenomic Characterization of Clear Cell Renal Cell Carcinoma. (96 citations)
- The SysteMHC Atlas project. (75 citations)
In his most recent research, the most cited papers focused on:
Alexey I. Nesvizhskii focuses on Proteomics, Computational biology, Cell biology, Cancer research and Proteogenomics.
Alexey I. Nesvizhskii usually deals with Proteomics and limits it to topics linked to Identification and Data visualization, Artificial intelligence and Visualization.
In his articles, Alexey I. Nesvizhskii combines various disciplines, including Computational biology and Extramural.
His Cell biology research incorporates elements of Cytotoxic T cell, Programmed cell death, Ubiquitin ligase and Ripoptosome assembly.
His Cancer research research also works with subjects such as
- Wnt signaling pathway that connect with fields like Tenascin C, Tumor progression and Extracellular matrix,
- Sarcoma, which have a strong connection to Breast cancer, Metaplastic Breast Carcinoma, Proteome and Epithelial–mesenchymal transition,
- Cancer which intersects with area such as Serous fluid, Regulation of gene expression, Exon, RNase R and RNA.
His work deals with themes such as Druggability, Histone, Endometrial cancer and Phosphoproteomics, which intersect with Proteogenomics.
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