SubNet: A Java application for subnetwork extraction

Quanwei Zhang; Zhengdong D. Zhang

doi:10.1093/bioinformatics/btt430

SubNet: A Java application for subnetwork extraction

Quanwei Zhang, Zhengdong D. Zhang

Genetics

Research output: Contribution to journal › Article › peer-review

14 Scopus citations

Abstract

Summary: The extraction of targeted subnetworks is a powerful way to identify functional modules and pathways within complex networks. Here, we present SubNet, a Java-based stand-alone program for extracting subnetworks, given a basal network and a set of selected nodes. Designed with a graphical user-friendly interface, SubNet combines four different extraction methods, which offer the possibility to interrogate a biological network according to the question investigated. Of note, we developed a method based on the highly successful Google PageRank algorithm to extract the subnetwork using the node centrality metric, to which possible node weights of the selected genes can be incorporated.

Original language	English (US)
Pages (from-to)	2509-2511
Number of pages	3
Journal	Bioinformatics
Volume	29
Issue number	19
DOIs	https://doi.org/10.1093/bioinformatics/btt430
State	Published - 2013

ASJC Scopus subject areas

Statistics and Probability
Biochemistry
Molecular Biology
Computer Science Applications
Computational Theory and Mathematics
Computational Mathematics

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10.1093/bioinformatics/btt430

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title = "SubNet: A Java application for subnetwork extraction",

abstract = "Summary: The extraction of targeted subnetworks is a powerful way to identify functional modules and pathways within complex networks. Here, we present SubNet, a Java-based stand-alone program for extracting subnetworks, given a basal network and a set of selected nodes. Designed with a graphical user-friendly interface, SubNet combines four different extraction methods, which offer the possibility to interrogate a biological network according to the question investigated. Of note, we developed a method based on the highly successful Google PageRank algorithm to extract the subnetwork using the node centrality metric, to which possible node weights of the selected genes can be incorporated.",

author = "Quanwei Zhang and Zhang, {Zhengdong D.}",

note = "Funding Information: Funding: NIH Pathway to Independence Award from National Library of Medicine (5R00LM009770-06 to Z.D.Z.) and the Ellison Medical Foundation New Scholar in Aging Award (AG-NS-0751-11 to Z.D.Z.).",

year = "2013",

doi = "10.1093/bioinformatics/btt430",

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AU - Zhang, Zhengdong D.

N1 - Funding Information: Funding: NIH Pathway to Independence Award from National Library of Medicine (5R00LM009770-06 to Z.D.Z.) and the Ellison Medical Foundation New Scholar in Aging Award (AG-NS-0751-11 to Z.D.Z.).

PY - 2013

Y1 - 2013

N2 - Summary: The extraction of targeted subnetworks is a powerful way to identify functional modules and pathways within complex networks. Here, we present SubNet, a Java-based stand-alone program for extracting subnetworks, given a basal network and a set of selected nodes. Designed with a graphical user-friendly interface, SubNet combines four different extraction methods, which offer the possibility to interrogate a biological network according to the question investigated. Of note, we developed a method based on the highly successful Google PageRank algorithm to extract the subnetwork using the node centrality metric, to which possible node weights of the selected genes can be incorporated.

AB - Summary: The extraction of targeted subnetworks is a powerful way to identify functional modules and pathways within complex networks. Here, we present SubNet, a Java-based stand-alone program for extracting subnetworks, given a basal network and a set of selected nodes. Designed with a graphical user-friendly interface, SubNet combines four different extraction methods, which offer the possibility to interrogate a biological network according to the question investigated. Of note, we developed a method based on the highly successful Google PageRank algorithm to extract the subnetwork using the node centrality metric, to which possible node weights of the selected genes can be incorporated.

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SubNet: A Java application for subnetwork extraction

Abstract

ASJC Scopus subject areas

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