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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 1: Pre- and Post-analytical Factors in Biomarker Discovery
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Mentioned by

2 tweeters


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20 Mendeley
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Chapter title
Pre- and Post-analytical Factors in Biomarker Discovery
Chapter number 1
Book title
Current Topics in Behavioral Neurosciences
Published in
Current topics in behavioral neurosciences, March 2019
DOI 10.1007/978-1-4939-9164-8_1
Pubmed ID
Book ISBNs
978-1-4939-9163-1, 978-1-4939-9164-8

Frank Klont, Peter Horvatovich, Natalia Govorukhina, Rainer Bischoff, Klont, Frank, Horvatovich, Peter, Govorukhina, Natalia, Bischoff, Rainer

Twitter Demographics

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

Mendeley readers

The data shown below were compiled from readership statistics for 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 20 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 4 20%
Student > Master 3 15%
Researcher 3 15%
Student > Ph. D. Student 2 10%
Professor 2 10%
Other 2 10%
Unknown 4 20%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 7 35%
Neuroscience 2 10%
Medicine and Dentistry 2 10%
Agricultural and Biological Sciences 1 5%
Pharmacology, Toxicology and Pharmaceutical Science 1 5%
Other 1 5%
Unknown 6 30%

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 11 March 2019.
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So far Altmetric has tracked 350 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.5. This one is in the 13th percentile – i.e., 13% of its peers scored the same or lower than it.
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We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them