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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.
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    Chapter 2 Proteomic Data Storage and Sharing.
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    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.
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    Chapter 6 Unassigned MS/MS Spectra: Who Am I?
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    Chapter 7 Methods to Calculate Spectrum Similarity.
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    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.
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    Chapter 14 Network Tools for the Analysis of Proteomic Data.
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    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 14: Network Tools for the Analysis of Proteomic Data.
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Chapter title
Network Tools for the Analysis of Proteomic Data.
Chapter number 14
Book title
Proteome Bioinformatics
Published in
Methods in molecular biology, January 2017
DOI 10.1007/978-1-4939-6740-7_14
Pubmed ID
27975292
Book ISBNs
978-1-4939-6738-4, 978-1-4939-6740-7
Authors

David Chisanga, Shivakumar Keerthikumar, Suresh Mathivanan, Naveen Chilamkurti, Chisanga, David, Keerthikumar, Shivakumar, Mathivanan, Suresh, Chilamkurti, Naveen

Editors

Shivakumar Keerthikumar, Suresh Mathivanan

Abstract

Recent advancements in high-throughput technologies such as mass spectrometry have led to an increase in the rate at which data is generated and accumulated. As a result, standard statistical methods no longer suffice as a way of analyzing such gigantic amounts of data. Network analysis, the evaluation of how nodes relate to one another, has over the years become an integral tool for analyzing high throughput proteomic data as they provide a structure that helps reduce the complexity of the underlying data.Computational tools, including pathway databases and network building tools, have therefore been developed to store, analyze, interpret, and learn from proteomics data. These tools enable the visualization of proteins as networks of signaling, regulatory, and biochemical interactions. In this chapter, we provide an overview of networks and network theory fundamentals for the analysis of proteomics data. We further provide an overview of interaction databases and network tools which are frequently used for analyzing proteomics data.

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

Geographical breakdown

Country Count As %
United Kingdom 1 3%
Unknown 33 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 21%
Student > Bachelor 5 15%
Researcher 5 15%
Student > Master 3 9%
Student > Postgraduate 2 6%
Other 4 12%
Unknown 8 24%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 10 29%
Agricultural and Biological Sciences 4 12%
Social Sciences 3 9%
Computer Science 3 9%
Chemical Engineering 2 6%
Other 3 9%
Unknown 9 26%
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 19 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
Altmetric has tracked 22,914,829 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
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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We're also able to compare this research output to 1,074 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.

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