What is he best known for?
The fields of study he is best known for:
Michael J. MacCoss mostly deals with Proteomics, Computational biology, Biochemistry, Tandem mass spectrometry and Shotgun proteomics.
His Proteomics study combines topics from a wide range of disciplines, such as Integral membrane protein, Proteome, Bioinformatics, Vesicle-associated membrane protein 8 and Chromatography.
His Computational biology research is multidisciplinary, incorporating perspectives in Genetics, Genome, Human genome, ENCODE and DNase-Seq.
As part of one scientific family, he deals mainly with the area of Biochemistry, narrowing it down to issues related to the Cell biology, and often Mitochondrial Degradation and PINK1.
His work on Peptide spectral library as part of general Tandem mass spectrometry study is frequently connected to Data acquisition, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His work in Shotgun proteomics addresses subjects such as Mass spectrum, which are connected to disciplines such as Fourier transform ion cyclotron resonance, Data reduction and Protein mass spectrometry.
His most cited work include:
- An integrated encyclopedia of DNA elements in the human genome (2642 citations)
- Skyline: an open source document editor for creating and analyzing targeted proteomics experiments (2583 citations)
- A cross-platform toolkit for mass spectrometry and proteomics (1281 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Proteomics, Mass spectrometry, Cell biology, Computational biology and Biochemistry.
The Proteomics study combines topics in areas such as Proteome, Chromatography and Tandem mass spectrometry.
His Mass spectrometry study combines topics in areas such as Biological system and Data-independent acquisition.
While the research belongs to areas of Cell biology, Michael J. MacCoss spends his time largely on the problem of Transcription factor, intersecting his research to questions surrounding Identification and Green fluorescent protein.
The study incorporates disciplines such as Molecular biology, Targeted proteomics and Genome, Gene in addition to Computational biology.
His is doing research in Ubiquitin and Protein turnover, both of which are found in Biochemistry.
He most often published in these fields:
- Proteomics (29.85%)
- Mass spectrometry (16.79%)
- Cell biology (16.04%)
What were the highlights of his more recent work (between 2017-2021)?
- Cell biology (16.04%)
- Proteomics (29.85%)
- Computational biology (15.30%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Cell biology, Proteomics, Computational biology, Proteome and Mass spectrometry.
The concepts of his Cell biology study are interwoven with issues in Mutant, Giardia lamblia, Innate immune system, Protein turnover and Potato leafroll virus.
His Proteomics research includes elements of Evolutionary biology, Field, Mating system, Diaphorina citri and Data science.
His Computational biology study incorporates themes from De novo sequencing and Sequence database, Genome, Gene.
Michael J. MacCoss has researched Proteome in several fields, including Tandem mass spectrometry, Chromatin, Nuclear protein, Bromodomain and Peptide.
He combines subjects such as Quantitative proteomics and Data-independent acquisition with his study of Mass spectrometry.
Between 2017 and 2021, his most popular works were:
- How many human proteoforms are there (219 citations)
- The Skyline ecosystem: Informatics for quantitative mass spectrometry proteomics. (125 citations)
- The ProteomeXchange consortium in 2020: enabling 'big data' approaches in proteomics. (102 citations)
In his most recent research, the most cited papers focused on:
Michael J. MacCoss focuses on Cell biology, Proteomics, Skyline, Data science and Mutation.
His Cell biology research is multidisciplinary, relying on both Protein turnover and Biofilm.
His research on Proteomics often connects related areas such as Orbitrap.
His study in Skyline is interdisciplinary in nature, drawing from both Data visualization, Data processing, Small molecule and Metabolomics.
As a member of one scientific family, Michael J. MacCoss mostly works in the field of Data science, focusing on Field and, on occasion, Group method of data handling.
His studies in Mutation integrate themes in fields like Protein aggregation, Microautophagy, Mutant, Glucocerebrosidase and Kinetochore.
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