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Title:
Determining the Significance of Protein Network Features and Attributes Using Permutation Testing | SpringerLink
Description:
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...
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month download article/chapter convergent bio-nano science calculating exact p-values protein–protein interaction protein interaction networks protein network features privacy choices/manage cookies device instant download accurate p-values signaling network characteristics proteome bioinformatics permutation p-values author information authors la trobe institute australian nbcf cdf-14-043 protocol cursons human molecular subgroups european economic area traditional functional enrichment python code demonstrating sleeping beauty mutagenesis system level dynamics post-translational modifications comput struct biotechnol o'toole sa mining interactome modules eliza hall institute 1g royal parade integrating spatial proteomics permutation testing protocol conditions privacy policy systems-level cooperation systems biology laboratory approach commonly applied pathway enrichment analyses la trobe university journal finder publish bioinformatics division accepting optional cookies stimulus-dependent differences project number ce140100036 australia melissa main content log network analysis bmc genomics 11 protocol usd 49 medical research permissions reprints social media stat sci 19
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ScholarlyArticle:
headline:Determining the Significance of Protein Network Features and Attributes Using Permutation Testing
pageEnd:208
pageStart:199
image:https://media.springernature.com/w153/springer-static/cover/book/978-1-4939-6740-7.jpg
genre:
Springer Protocols
isPartOf:
name:Proteome Bioinformatics
isbn:
978-1-4939-6740-7
978-1-4939-6738-4
type:Book
publisher:
name:Springer New York
logo:
url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
type:ImageObject
type:Organization
author:
name:Joseph Cursons
affiliation:
name:The University of Melbourne
address:
name:Systems Biology Laboratory, Melbourne School of Engineering, The University of Melbourne, Parkville, Australia
type:PostalAddress
type:Organization
name:The University of Melbourne
address:
name:ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, Melbourne School of Engineering, The University of Melbourne, Parkville, Australia
type:PostalAddress
type:Organization
name:Walter and Eliza Hall Institute of Medical Research
address:
name:Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Parkville, Australia
type:PostalAddress
type:Organization
type:Person
name:Melissa J. Davis
affiliation:
name:The University of Melbourne
address:
name:Systems Biology Laboratory, Melbourne School of Engineering, The University of Melbourne, Parkville, Australia
type:PostalAddress
type:Organization
name:Walter and Eliza Hall Institute of Medical Research
address:
name:Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Parkville, Australia
type:PostalAddress
type:Organization
name:The University of Melbourne
address:
name:Faculty of Medicine, Dentistry and Health Science, Department of Biochemistry and Molecular Biology, The University of Melbourne, Parkville, Australia
type:PostalAddress
type:Organization
type:Person
keywords:PPI, Protein interaction, Network structure, Permutation testing, Computational systems biology, PROSPERITI, Proteomics
description: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.
datePublished:2017
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type:PostalAddress
name:Walter and Eliza Hall Institute of Medical Research
address:
name:Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Parkville, Australia
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name:The University of Melbourne
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name:Systems Biology Laboratory, Melbourne School of Engineering, The University of Melbourne, Parkville, Australia
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name:Walter and Eliza Hall Institute of Medical Research
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name:Walter and Eliza Hall Institute of Medical Research
address:
name:Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Parkville, Australia
type:PostalAddress
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name:Melissa J. Davis
affiliation:
name:The University of Melbourne
address:
name:Systems Biology Laboratory, Melbourne School of Engineering, The University of Melbourne, Parkville, Australia
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name:Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Parkville, Australia
name:Faculty of Medicine, Dentistry and Health Science, Department of Biochemistry and Molecular Biology, The University of Melbourne, Parkville, Australia
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