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Proteomics for Biomarker Discovery

Overview of attention for book
Cover of 'Proteomics for Biomarker Discovery'

Table of Contents

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    Book Overview
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    Chapter 1 Pre- and Post-analytical Factors in Biomarker Discovery
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    Chapter 2 Pre-fractionation of Noncirculating Biological Fluids to Improve Discovery of Clinically Relevant Protein Biomarkers
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    Chapter 3 Serum Exosome Isolation by Size-Exclusion Chromatography for the Discovery and Validation of Preeclampsia-Associated Biomarkers
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    Chapter 4 Protein Biomarker Discovery Using Human Blood Plasma Microparticles
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    Chapter 5 A Standardized and Reproducible Proteomics Protocol for Bottom-Up Quantitative Analysis of Protein Samples Using SP3 and Mass Spectrometry
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    Chapter 6 Analyzing Cerebrospinal Fluid Proteomes to Characterize Central Nervous System Disorders: A Highly Automated Mass Spectrometry-Based Pipeline for Biomarker Discovery
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    Chapter 7 Lys-C/Trypsin Tandem-Digestion Protocol for Gel-Free Proteomic Analysis of Colon Biopsies
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    Chapter 8 Tube-Gel: A Fast and Effective Sample Preparation Method for High-Throughput Quantitative Proteomics
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    Chapter 9 Protein Biomarker Discovery in Non-depleted Serum by Spectral Library-Based Data-Independent Acquisition Mass Spectrometry
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    Chapter 10 Discovering Protein Biomarkers from Clinical Peripheral Blood Mononuclear Cells Using Data-Independent Acquisition Mass Spectrometry
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    Chapter 11 Intact Protein Analysis by LC-MS for Characterizing Biomarkers in Cerebrospinal Fluid
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    Chapter 12 Detection of Proteoforms Using Top-Down Mass Spectrometry and Diagnostic Ions
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    Chapter 13 Development of a Highly Multiplexed SRM Assay for Biomarker Discovery in Formalin-Fixed Paraffin-Embedded Tissues
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    Chapter 14 Development and Validation of Multiple Reaction Monitoring (MRM) Assays for Clinical Applications
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    Chapter 15 Protein-Level Statistical Analysis of Quantitative Label-Free Proteomics Data with ProStaR
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    Chapter 16 Computation and Selection of Optimal Biomarker Combinations by Integrative ROC Analysis Using CombiROC
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    Chapter 17 PanelomiX for the Combination of Biomarkers
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    Chapter 18 Designing an In Silico Strategy to Select Tissue-Leakage Biomarkers Using the Galaxy Framework
Attention for Chapter 9: Protein Biomarker Discovery in Non-depleted Serum by Spectral Library-Based Data-Independent Acquisition Mass Spectrometry
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (79th percentile)
  • High Attention Score compared to outputs of the same age and source (86th percentile)

Mentioned by

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1 news outlet
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2 X users

Citations

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4 Dimensions

Readers on

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14 Mendeley
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Chapter title
Protein Biomarker Discovery in Non-depleted Serum by Spectral Library-Based Data-Independent Acquisition Mass Spectrometry
Chapter number 9
Book title
Current Topics in Behavioral Neurosciences
Published in
Current topics in behavioral neurosciences, March 2019
DOI 10.1007/978-1-4939-9164-8_9
Pubmed ID
Book ISBNs
978-1-4939-9163-1, 978-1-4939-9164-8
Authors

Alexandra Kraut, Mathilde Louwagie, Christophe Bruley, Christophe Masselon, Yohann Couté, Virginie Brun, Anne-Marie Hesse, Kraut, Alexandra, Louwagie, Mathilde, Bruley, Christophe, Masselon, Christophe, Couté, Yohann, Brun, Virginie, Hesse, Anne-Marie

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 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 14 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 29%
Unspecified 1 7%
Professor > Associate Professor 1 7%
Student > Master 1 7%
Unknown 7 50%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 2 14%
Agricultural and Biological Sciences 2 14%
Unspecified 1 7%
Engineering 1 7%
Unknown 8 57%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 07 January 2020.
All research outputs
#3,122,146
of 23,133,982 outputs
Outputs from Current topics in behavioral neurosciences
#106
of 500 outputs
Outputs of similar age
#71,299
of 352,174 outputs
Outputs of similar age from Current topics in behavioral neurosciences
#2
of 15 outputs
Altmetric has tracked 23,133,982 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 500 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.0. This one has done well, scoring higher than 78% of its peers.
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 352,174 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 79% of its contemporaries.
We're also able to compare this research output to 15 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 86% of its contemporaries.