What is he best known for?
The fields of study he is best known for:
- Gene
- Internal medicine
- DNA
His primary scientific interests are in Computational biology, Molecular biology, Genetics, Proteomics and DNA microarray.
His study in Computational biology is interdisciplinary in nature, drawing from both Genome, Oligonucleotide and Human proteome project.
Many of his research projects under Genetics are closely connected to Origin recognition complex with Origin recognition complex, tying the diverse disciplines of science together.
His Proteomics research incorporates elements of Proteome, Gene expression, Gene expression profiling, Protein Array Analysis and Cell biology.
His work carried out in the field of Proteome brings together such families of science as Alternative splicing, Tissue microarray and NeXtProt.
He has included themes like Expressed sequence tag, Regulation of gene expression and Antibody in his DNA microarray study.
His most cited work include:
- Tissue-based map of the human proteome (5369 citations)
- Genome-Wide Association Analysis Identifies Loci for Type 2 Diabetes and Triglyceride Levels (2462 citations)
- Analysis of the human tissue-specific expression by genome-wide integration of transcriptomics and antibody-based proteomics (1489 citations)
What are the main themes of his work throughout his whole career to date?
Peter Nilsson spends much of his time researching Antibody, Immunology, Internal medicine, Computational biology and Proteomics.
His Antibody research is multidisciplinary, incorporating perspectives in Blood proteins, Biochemistry, Molecular biology and Antigen.
His Internal medicine research is multidisciplinary, incorporating elements of Diabetes mellitus, Endocrinology and Oncology.
Peter Nilsson interconnects Profiling, DNA microarray, Protein Array Analysis and Human proteome project in the investigation of issues within Computational biology.
His DNA microarray study introduces a deeper knowledge of Genetics.
Proteomics and Proteome are frequently intertwined in his study.
He most often published in these fields:
- Antibody (19.26%)
- Immunology (18.33%)
- Internal medicine (17.40%)
What were the highlights of his more recent work (between 2018-2021)?
- Immunology (18.33%)
- Antibody (19.26%)
- Internal medicine (17.40%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Immunology, Antibody, Internal medicine, Autoantibody and Disease.
The study incorporates disciplines such as Saliva and Protein microarray in addition to Immunology.
His Antibody study integrates concerns from other disciplines, such as Bead array and Antigen.
Peter Nilsson has included themes like Sick leave, Oncology and Cardiology in his Internal medicine study.
His studies deal with areas such as Blood proteins and Proteomics as well as Cohort.
Peter Nilsson usually deals with Proteomics and limits it to topics linked to Histidine-rich glycoprotein and All cause mortality.
Between 2018 and 2021, his most popular works were:
- SARS-CoV-2 exposure, symptoms and seroprevalence in healthcare workers in Sweden. (63 citations)
- The human secretome (52 citations)
- Recognition of Amino Acid Motifs, Rather Than Specific Proteins, by Human Plasma Cell-Derived Monoclonal Antibodies to Posttranslationally Modified Proteins in Rheumatoid Arthritis. (49 citations)
In his most recent research, the most cited papers focused on:
- Gene
- Internal medicine
- DNA
His scientific interests lie mostly in Immunology, Antibody, Disease, Computational biology and Cerebrospinal fluid.
His Immunology research is multidisciplinary, incorporating elements of Biomarker and Central nervous system.
The concepts of his Antibody study are interwoven with issues in Antigen and Arthritis.
Peter Nilsson interconnects Deflazacort, Duchenne muscular dystrophy and Oncology in the investigation of issues within Disease.
His Computational biology study combines topics in areas such as Precision medicine, Proteomics, Identification and Metabolomics.
His Protein profiling study, which is part of a larger body of work in Proteomics, is frequently linked to Sample processing, bridging the gap between disciplines.
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