What is he best known for?
The fields of study he is best known for:
- Gene
- Bioinformatics
- Astronomy
His primary areas of investigation include Proteomics, PeptideAtlas, Computational biology, Proteome and Bioinformatics.
His studies in Proteomics integrate themes in fields like Data access and Peptide library.
His PeptideAtlas research is multidisciplinary, relying on both Trans-Proteomic Pipeline, Data visualization, NeXtProt, Workflow and Mass spectrometry data format.
Eric W. Deutsch has researched Computational biology in several fields, including Genetics, Saccharomyces cerevisiae, Mass spectrometric, Mass spectrometry and Selected reaction monitoring.
The various areas that Eric W. Deutsch examines in his Proteome study include Tandem mass spectrometry and Systems biology.
His study in Bioinformatics is interdisciplinary in nature, drawing from both Information Dissemination, Human proteins, Data science and Human proteome project.
His most cited work include:
- ProteomeXchange provides globally coordinated proteomics data submission and dissemination (1926 citations)
- A cross-platform toolkit for mass spectrometry and proteomics (1281 citations)
- A common open representation of mass spectrometry data and its application to proteomics research (656 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Proteomics, Computational biology, PeptideAtlas, Proteome and Astrophysics.
Eric W. Deutsch interconnects Data science, Genomics, Bioinformatics and Mass spectrometry in the investigation of issues within Proteomics.
His research integrates issues of Proteomics Standards Initiative, Field, Workflow and Big data in his study of Data science.
While the research belongs to areas of Computational biology, Eric W. Deutsch spends his time largely on the problem of Human proteome project, intersecting his research to questions surrounding Human proteins.
The PeptideAtlas study combines topics in areas such as Human plasma and Human genome.
His Proteome research incorporates elements of Molecular biology, Peptide sequence and Tandem mass spectrometry.
He most often published in these fields:
- Proteomics (32.11%)
- Computational biology (26.42%)
- PeptideAtlas (24.39%)
What were the highlights of his more recent work (between 2017-2021)?
- Proteomics (32.11%)
- Computational biology (26.42%)
- Human proteome project (17.48%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Proteomics, Computational biology, Human proteome project, Data science and Proteomics Standards Initiative.
In the field of Proteomics, his study on NeXtProt overlaps with subjects such as Pipeline.
His Computational biology research includes themes of PeptideAtlas, Proteome, Peptide sequence and Sequence database.
His PeptideAtlas research includes elements of False discovery rate and Phosphopeptide.
His research on Human proteome project also deals with topics like
- Human proteins which connect with Human genome,
- Genomics together with World Wide Web.
As part of one scientific family, Eric W. Deutsch deals mainly with the area of Proteomics Standards Initiative, narrowing it down to issues related to the Metadata, and often XML and Web service.
Between 2017 and 2021, his most popular works were:
- The ProteomeXchange consortium in 2020: enabling 'big data' approaches in proteomics. (102 citations)
- The SysteMHC Atlas project. (75 citations)
- Mass Spectrometry-Based Plasma Proteomics: Considerations from Sample Collection to Achieving Translational Data (30 citations)
In his most recent research, the most cited papers focused on:
Eric W. Deutsch mainly investigates Proteomics, Human proteome project, Data science, Computational biology and Proteome.
His work on PeptideAtlas as part of general Proteomics study is frequently linked to Pipeline, bridging the gap between disciplines.
His research in Human proteome project focuses on subjects like Genomics, which are connected to World Wide Web and File format.
His Data science research integrates issues from Proteomics Standards Initiative, Precision medicine, Metadata, Workflow and Big data.
His research in Computational biology intersects with topics in Normal tissue and CD8, Immune system, Antigen.
The Proteome study which covers Data-independent acquisition that intersects with Biomarker discovery.
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