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Proteome Bioinformatics

Overview of attention for book
Cover of 'Proteome Bioinformatics'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 An Introduction to Proteome Bioinformatics.
  3. Altmetric Badge
    Chapter 2 Proteomic Data Storage and Sharing.
  4. Altmetric Badge
    Chapter 3 Choosing an Optimal Database for Protein Identification from Tandem Mass Spectrometry Data.
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    Chapter 4 Label-Based and Label-Free Strategies for Protein Quantitation.
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    Chapter 5 TMT One-Stop Shop: From Reliable Sample Preparation to Computational Analysis Platform.
  7. Altmetric Badge
    Chapter 6 Unassigned MS/MS Spectra: Who Am I?
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    Chapter 7 Methods to Calculate Spectrum Similarity.
  9. Altmetric Badge
    Chapter 8 Proteotypic Peptides and Their Applications.
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    Chapter 9 Statistical Evaluation of Labeled Comparative Profiling Proteomics Experiments Using Permutation Test.
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    Chapter 10 De Novo Peptide Sequencing: Deep Mining of High-Resolution Mass Spectrometry Data.
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    Chapter 11 Phylogenetic Analysis Using Protein Mass Spectrometry.
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    Chapter 12 Bioinformatics Methods to Deduce Biological Interpretation from Proteomics Data.
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    Chapter 13 A Systematic Bioinformatics Approach to Identify High Quality Mass Spectrometry Data and Functionally Annotate Proteins and Proteomes.
  15. Altmetric Badge
    Chapter 14 Network Tools for the Analysis of Proteomic Data.
  16. Altmetric Badge
    Chapter 15 Determining the Significance of Protein Network Features and Attributes Using Permutation Testing.
  17. Altmetric Badge
    Chapter 16 Bioinformatics Tools and Resources for Analyzing Protein Structures.
  18. Altmetric Badge
    Chapter 17 In Silico Approach to Identify Potential Inhibitors for Axl-Gas6 Signaling.
Attention for Chapter 15: Determining the Significance of Protein Network Features and Attributes Using Permutation Testing.
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Chapter title
Determining the Significance of Protein Network Features and Attributes Using Permutation Testing.
Chapter number 15
Book title
Proteome Bioinformatics
Published in
Methods in molecular biology, January 2017
DOI 10.1007/978-1-4939-6740-7_15
Pubmed ID
27975293
Book ISBNs
978-1-4939-6738-4, 978-1-4939-6740-7
Authors

Joseph Cursons, Melissa J. Davis

Editors

Shivakumar Keerthikumar, Suresh Mathivanan

Abstract

Network analysis methods are increasing in popularity. An approach commonly applied to analyze proteomics data involves the use of protein-protein interaction (PPI) networks to explore the systems-level cooperation between proteins identified in a study. In this context, protein interaction networks can be used alongside the statistical analysis of proteomics data and traditional functional enrichment or pathway enrichment analyses. In network analysis it is possible to adjust for some of the complexities that arise due to the known, explicit interdependence between the measured quantities, in particular, differences in the number of interactions between proteins. Here we describe a method for calculating robust empirical p-values for protein interaction networks. We also provide a worked example with python code demonstrating the implementation of this methodology.

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X Demographics

The data shown below were collected from the profile of 1 X user 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 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 %
Researcher 3 50%
Professor > Associate Professor 1 17%
Unknown 2 33%
Readers by discipline Count As %
Computer Science 1 17%
Agricultural and Biological Sciences 1 17%
Social Sciences 1 17%
Unknown 3 50%
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 18 December 2016.
All research outputs
#20,365,559
of 22,914,829 outputs
Outputs from Methods in molecular biology
#9,921
of 13,131 outputs
Outputs of similar age
#355,358
of 420,479 outputs
Outputs of similar age from Methods in molecular biology
#842
of 1,074 outputs
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So far Altmetric has tracked 13,131 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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