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Urine Proteomics in Kidney Disease Biomarker Discovery

Overview of attention for book
Cover of 'Urine Proteomics in Kidney Disease Biomarker Discovery'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 Urine Is a Better Biomarker Source Than Blood Especially for Kidney Diseases
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    Chapter 2 Urine Reflection of Changes in Blood
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    Chapter 3 Urimem Facilitates Kidney Disease Biomarker Research
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    Chapter 4 Human urine proteome: a powerful source for clinical research.
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    Chapter 5 Exosomes in Urine Biomarker Discovery
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    Chapter 6 Urinary Proteins with Post-translational Modifications.
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    Chapter 7 Applications of Peptide retention time in proteomic data analysis.
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    Chapter 8 Urine Sample Preparation in 96-well Filter Plates to Characterize Inflammatory and Infectious Diseases of the Urinary Tract.
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    Chapter 9 Variations of human urinary proteome.
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    Chapter 10 Evolution of the urinary proteome during human renal development and maturation.
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    Chapter 11 Hormone-dependent changes in female urinary proteome.
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    Chapter 12 Effects of exercise on the urinary proteome.
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    Chapter 13 Effects of diuretics on urinary proteins.
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    Chapter 14 Applications of urinary proteomics in renal disease research using animal models.
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    Chapter 15 The application of urinary proteomics for the detection of biomarkers of kidney diseases.
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    Chapter 16 Dynamic changes of urinary proteins in focal segmental glomerulosclerosis model.
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    Chapter 17 Using isolated rat kidney to discover kidney origin biomarkers in urine.
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    Chapter 18 Comparing plasma and urinary proteomes to understand kidney function.
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    Chapter 19 Urinary protein biomarker database: a useful tool for biomarker discovery.
Attention for Chapter 7: Applications of Peptide retention time in proteomic data analysis.
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  • Above-average Attention Score compared to outputs of the same age and source (58th percentile)

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Chapter title
Applications of Peptide retention time in proteomic data analysis.
Chapter number 7
Book title
Urine Proteomics in Kidney Disease Biomarker Discovery
Published in
Advances in experimental medicine and biology, January 2015
DOI 10.1007/978-94-017-9523-4_7
Pubmed ID
Book ISBNs
978-9-40-179522-7, 978-9-40-179523-4
Authors

Chen Shao

Abstract

In proteomic studies, liquid chromatography is commonly used to separate peptide mixtures prior to mass spectrometry (MS) detection. As an independent dimension of information from the information provided by the MS, peptide retention time information has been proven to be able to aid proteomic data analysis in many aspects. So far, some popular software has offered options for this information for MS data acquisition and analysis. This chapter is a brief review of current methodologies of retention time prediction and application in proteomic analysis.

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

Geographical breakdown

Country Count As %
Unknown 6 100%

Demographic breakdown

Readers by professional status Count As %
Professor > Associate Professor 2 33%
Student > Ph. D. Student 2 33%
Other 1 17%
Student > Bachelor 1 17%
Readers by discipline Count As %
Medicine and Dentistry 4 67%
Unspecified 1 17%
Engineering 1 17%

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 18 March 2015.
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#3,459,195
of 4,888,368 outputs
Outputs from Advances in experimental medicine and biology
#858
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Outputs of similar age
#95,116
of 139,616 outputs
Outputs of similar age from Advances in experimental medicine and biology
#22
of 72 outputs
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So far Altmetric has tracked 1,471 research outputs from this source. They receive a mean Attention Score of 2.3. This one is in the 27th percentile – i.e., 27% of its peers scored the same or lower than it.
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We're also able to compare this research output to 72 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 58% of its contemporaries.