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Functional Proteomics

Overview of attention for book
Cover of 'Functional Proteomics'

Table of Contents

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    Book Overview
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    Chapter 1 A Brief Summary of the Different Types of Mass Spectrometers Used in Proteomics
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    Chapter 2 Experimental Setups and Considerations to Study Microbial Interactions
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    Chapter 3 Plant Proteomics
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    Chapter 4 Methods for Human CD8 + T Lymphocyte Proteome Analysis
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    Chapter 5 Label-Free Proteomics of Serum
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    Chapter 6 Flow Cytometric Analysis of Cell Membrane Microparticles
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    Chapter 7 Exosomes.
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    Chapter 8 Toward a Full Characterization of the Human 20S Proteasome Subunits and Their Isoforms by a Combination of Proteomic Approaches
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    Chapter 9 Free-Flow Electrophoresis of the Human Urinary Proteome
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    Chapter 10 Versatile Screening for Binary Protein-Protein Interactions by Yeast Two-Hybrid Mating
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    Chapter 11 Native fractionation: isolation of native membrane-bound protein complexes from porcine rod outer segments using isopycnic density gradient centrifugation.
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    Chapter 12 Functional Proteomics
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    Chapter 13 Selection of Recombinant Antibodies by Eukaryotic Ribosome Display
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    Chapter 14 Production of Protein Arrays by Cell-Free Systems
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    Chapter 15 Nondenaturing mass spectrometry to study noncovalent protein/protein and protein/ligand complexes: technical aspects and application to the determi..
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    Chapter 16 Protein Processing Characterized by a Gel-Free Proteomics Approach
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    Chapter 17 Identification and Characterization of N-Glycosylated Proteins Using Proteomics
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    Chapter 18 Data Standards and Controlled Vocabularies for Proteomics
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    Chapter 19 The PRIDE Proteomics Identifications Database: Data Submission, Query, and Dataset Comparison
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    Chapter 20 Searching the Protein Interaction Space Through the MINT Database
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    Chapter 21 PepSeeker: mining information from proteomic data.
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    Chapter 22 Toward High-Throughput and Reliable Peptide Identification via MS/MS Spectra
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    Chapter 23 MassSorter: Peptide Mass Fingerprinting Data Analysis
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    Chapter 24 Database Similarity Searches
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    Chapter 25 Protein Multiple Sequence Alignment
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    Chapter 26 Discovering Biomedical Knowledge from the Literature
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    Chapter 27 Protein Subcellular Localization Prediction Using Artificial Intelligence Technology
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    Chapter 28 Protein Functional Annotation by Homology
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    Chapter 29 Designability and Disease
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    Chapter 30 Prism: Protein-Protein Interaction Prediction by Structural Matching
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    Chapter 31 Prediction of Protein Interaction Based on Similarity of Phylogenetic Trees
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    Chapter 32 Large Multiprotein Structures Modeling and Simulation: The Need for Mesoscopic Models
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    Chapter 33 Dynamic Pathway Modeling of Signal Transduction Networks: A Domain-Oriented Approach
Attention for Chapter 7: Exosomes.
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Chapter title
Exosomes.
Chapter number 7
Book title
Functional Proteomics
Published in
Methods in molecular biology, July 2008
DOI 10.1007/978-1-59745-398-1_7
Pubmed ID
Book ISBNs
978-1-58829-971-0, 978-1-59745-398-1
Authors

Hegmans JP, Gerber PJ, Lambrecht BN, Joost P. J. J. Hegmans, Peter J. Gerber, Bart N. Lambrecht, Hegmans, Joost P. J. J., Gerber, Peter J., Lambrecht, Bart N.

Abstract

Exosomes are small natural membrane vesicles released by a wide variety of cell types into the extracellular compartment by exocytosis. The biological functions of exosomes are only slowly unveiled, but it is clear that they serve to remove unnecessary cellular proteins (e.g., during reticulocyte maturation) and act as intercellular messengers because they fuse easily with the membranes of neighboring cells, delivering membrane and cytoplasmic proteins from one cell to another. Recent findings suggests that cell-derived vesicles (exosomes are also named membranous vesicles or microvesicles) could also induce immune tolerance, suppression of natural killer cell function, T cell apoptosis, or metastasis. For example, by secreting exosomes, tumors may be able to accomplish the loss of those antigens that may be immunogenic and capable of signaling to immune cells as well as inducing dysfunction or death of immune effector cells. On the other hand, dendritic cell-derived exosomes have the potential to be an attractive powerful immunotherapeutic tool combining the antitumor activity of dendritic cells with the advantages of a cell-free vehicle. Although the full understanding of the significance of exosomes requires additional studies, these membrane vesicles could become a new important component in orchestrating responses between cells.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
South Africa 1 2%
Unknown 42 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 21%
Researcher 9 21%
Professor > Associate Professor 6 14%
Student > Bachelor 4 9%
Student > Master 3 7%
Other 8 19%
Unknown 4 9%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 12 28%
Agricultural and Biological Sciences 11 26%
Medicine and Dentistry 6 14%
Immunology and Microbiology 3 7%
Business, Management and Accounting 2 5%
Other 4 9%
Unknown 5 12%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 30 May 2022.
All research outputs
#17,673,866
of 22,689,790 outputs
Outputs from Methods in molecular biology
#7,153
of 13,045 outputs
Outputs of similar age
#74,851
of 81,686 outputs
Outputs of similar age from Methods in molecular biology
#7
of 8 outputs
Altmetric has tracked 22,689,790 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,045 research outputs from this source. They receive a mean Attention Score of 3.3. This one is in the 39th percentile – i.e., 39% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 81,686 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 7th percentile – i.e., 7% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one.