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Statistical Analysis in Proteomics

Overview of attention for book
Cover of 'Statistical Analysis in Proteomics'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 Introduction to Proteomics Technologies.
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    Chapter 2 Topics in Study Design and Analysis for Multistage Clinical Proteomics Studies
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    Chapter 3 Preprocessing and Analysis of LC-MS-Based Proteomic Data.
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    Chapter 4 Statistical Analysis in Proteomics
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    Chapter 5 Phenylimidazole-based homoleptic iridium(III) compounds for blue phosphorescent organic light-emitting diodes with high efficiency and long lifetime
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    Chapter 6 Visualization and Differential Analysis of Protein Expression Data Using R.
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    Chapter 7 False Discovery Rate Estimation in Proteomics.
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    Chapter 8 A Nonparametric Bayesian Model for Nested Clustering
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    Chapter 9 Set-Based Test Procedures for the Functional Analysis of Protein Lists from Differential Analysis.
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    Chapter 10 Classification of Samples with Order-Restricted Discriminant Rules.
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    Chapter 11 Application of Discriminant Analysis and Cross-Validation on Proteomics Data.
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    Chapter 12 Protein Sequence Analysis by Proximities
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    Chapter 13 Statistical Method for Integrative Platform Analysis: Application to Integration of Proteomic and Microarray Data.
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    Chapter 14 Data Fusion in Metabolomics and Proteomics for Biomarker Discovery.
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    Chapter 15 Reconstruction of Protein Networks Using Reverse-Phase Protein Array Data
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    Chapter 16 Detection of Unknown Amino Acid Substitutions Using Error-Tolerant Database Search
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    Chapter 17 Data Analysis Strategies for Protein Modification Identification.
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    Chapter 18 Dissecting the iTRAQ Data Analysis.
  20. Altmetric Badge
    Chapter 19 Statistical Aspects in Proteomic Biomarker Discovery.
Attention for Chapter 1: Introduction to Proteomics Technologies.
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Chapter title
Introduction to Proteomics Technologies.
Chapter number 1
Book title
Statistical Analysis in Proteomics
Published in
Methods in molecular biology, January 2016
DOI 10.1007/978-1-4939-3106-4_1
Pubmed ID
Book ISBNs
978-1-4939-3105-7, 978-1-4939-3106-4
Authors

Lenz, Christof, Dihazi, Hassan, Christof Lenz, Hassan Dihazi

Abstract

Compared to genomics or transcriptomics, proteomics is often regarded as an "emerging technology," i.e., as not having reached the same level of maturity. While the successful implementation of proteomics workflows and technology still requires significant levels of expertise and specialization, great strides have been made to make the technology more powerful, streamlined and accessible. In 2014, two landmark studies published the first draft versions of the human proteome.We aim to provide an introduction specifically into the background of mass spectrometry (MS)-based proteomics. Within the field, mass spectrometry has emerged as a core technology. Coupled to increasingly powerful separations and data processing and bioinformatics solution, it allows the quantitative analysis of whole proteomes within a matter of days, a timescale that has made global comparative proteome studies feasible at last. We present and discuss the basic concepts behind proteomics mass spectrometry and the accompanying topic of protein and peptide separations, with a focus on the properties of datasets emerging from such studies.

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The data shown below were collected from the profiles of 4 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 69 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 69 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 13 19%
Student > Ph. D. Student 12 17%
Student > Master 10 14%
Student > Doctoral Student 5 7%
Researcher 3 4%
Other 8 12%
Unknown 18 26%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 17 25%
Agricultural and Biological Sciences 11 16%
Pharmacology, Toxicology and Pharmaceutical Science 4 6%
Medicine and Dentistry 4 6%
Computer Science 2 3%
Other 9 13%
Unknown 22 32%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 25 May 2016.
All research outputs
#14,558,031
of 23,314,015 outputs
Outputs from Methods in molecular biology
#4,303
of 13,320 outputs
Outputs of similar age
#208,865
of 396,039 outputs
Outputs of similar age from Methods in molecular biology
#420
of 1,473 outputs
Altmetric has tracked 23,314,015 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,320 research outputs from this source. They receive a mean Attention Score of 3.4. This one has gotten more attention than average, scoring higher than 64% of its peers.
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We're also able to compare this research output to 1,473 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 68% of its contemporaries.