Further processing options
online resource

NetCore: a network propagation approach using node coreness

Bibliographic Details
Journal Title: Nucleic Acids Research
Authors and Corporations: Barel, Gal, Herwig, Ralf
In: Nucleic Acids Research, 48, 2020, 17, p. e98-e98
Type of Resource: E-Article
Language: English
published:
Oxford University Press (OUP)
Subjects:
finc.format ElectronicArticle
finc.mega_collection Oxford University Press (OUP) (CrossRef)
finc.id ai-49-aHR0cDovL2R4LmRvaS5vcmcvMTAuMTA5My9uYXIvZ2thYTYzOQ
finc.source_id 49
ris.type EJOUR
rft.atitle NetCore: a network propagation approach using node coreness
rft.epage 0
rft.genre article
rft.issn 0305-1048
1362-4962
rft.issue 17
rft.jtitle Nucleic Acids Research
rft.tpages 0
rft.pages e98-e98
rft.pub Oxford University Press (OUP)
rft.date 2020-09-25
x.date 2020-09-25T00:00:00Z
rft.spage 0
rft.volume 48
abstract <jats:title>Abstract</jats:title> <jats:p>We present NetCore, a novel network propagation approach based on node coreness, for phenotype–genotype associations and module identification. NetCore addresses the node degree bias in PPI networks by using node coreness in the random walk with restart procedure, and achieves improved re-ranking of genes after propagation. Furthermore, NetCore implements a semi-supervised approach to identify phenotype-associated network modules, which anchors the identification of novel candidate genes at known genes associated with the phenotype. We evaluated NetCore on gene sets from 11 different GWAS traits and showed improved performance compared to the standard degree-based network propagation using cross-validation. Furthermore, we applied NetCore to identify disease genes and modules for Schizophrenia GWAS data and pan-cancer mutation data. We compared the novel approach to existing network propagation approaches and showed the benefits of using NetCore in comparison to those. We provide an easy-to-use implementation, together with a high confidence PPI network extracted from ConsensusPathDB, which can be applied to various types of genomics data in order to obtain a re-ranking of genes and functionally relevant network modules.</jats:p>
authors Array ( [rft.aulast] => Barel [rft.aufirst] => Gal )
Array ( [rft.aulast] => Herwig [rft.aufirst] => Ralf )
doi 10.1093/nar/gkaa639
languages eng
url http://dx.doi.org/10.1093/nar/gkaa639
version 0.9
x.subjects Genetics
x.type journal-article
x.oa 1
openURL url_ver=Z39.88-2004&ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fwww.ub.uni-leipzig.de%3Agenerator&rft.title=NetCore%3A+a+network+propagation+approach+using+node+coreness&rft.date=2020-09-25&genre=article&issn=1362-4962&volume=48&issue=17&pages=e98-e98&jtitle=Nucleic+Acids+Research&atitle=NetCore%3A+a+network+propagation+approach+using+node+coreness&aulast=Herwig&aufirst=Ralf&rft_id=info%3Adoi%2F10.1093%2Fnar%2Fgkaa639&rft.language%5B0%5D=eng
SOLR
_version_ 1710344153345294341
access_facet Electronic Resources
author Barel, Gal, Herwig, Ralf
author_facet Barel, Gal, Herwig, Ralf, Barel, Gal, Herwig, Ralf
author_sort barel, gal
branch_nrw Electronic Resources
container_issue 17
container_start_page 0
container_title Nucleic Acids Research
container_volume 48
description <jats:title>Abstract</jats:title> <jats:p>We present NetCore, a novel network propagation approach based on node coreness, for phenotype–genotype associations and module identification. NetCore addresses the node degree bias in PPI networks by using node coreness in the random walk with restart procedure, and achieves improved re-ranking of genes after propagation. Furthermore, NetCore implements a semi-supervised approach to identify phenotype-associated network modules, which anchors the identification of novel candidate genes at known genes associated with the phenotype. We evaluated NetCore on gene sets from 11 different GWAS traits and showed improved performance compared to the standard degree-based network propagation using cross-validation. Furthermore, we applied NetCore to identify disease genes and modules for Schizophrenia GWAS data and pan-cancer mutation data. We compared the novel approach to existing network propagation approaches and showed the benefits of using NetCore in comparison to those. We provide an easy-to-use implementation, together with a high confidence PPI network extracted from ConsensusPathDB, which can be applied to various types of genomics data in order to obtain a re-ranking of genes and functionally relevant network modules.</jats:p>
facet_avail Online, Free
finc_class_facet Biologie
format ElectronicArticle
format_de105 Article, E-Article
format_de14 Article, E-Article
format_de15 Article, E-Article
format_de520 Article, E-Article
format_de540 Article, E-Article
format_dech1 Article, E-Article
format_ded117 Article, E-Article
format_degla1 E-Article
format_del152 Buch
format_del189 Article, E-Article
format_dezi4 Article
format_dezwi2 Article, E-Article
format_finc Article, E-Article
format_nrw Article, E-Article
geogr_code not assigned
geogr_code_person not assigned
id ai-49-aHR0cDovL2R4LmRvaS5vcmcvMTAuMTA5My9uYXIvZ2thYTYzOQ
imprint Oxford University Press (OUP), 2020
imprint_str_mv Oxford University Press (OUP), 2020
institution DE-D161, DE-Ch1, DE-Bn3, DE-Pl11, DE-Zwi2, DE-15, DE-Brt1, DE-L229, DE-Gla1, DE-82, DE-105, DE-Zi4, DE-14, DE-D275, DE-Rs1
issn 0305-1048, 1362-4962
language English
last_indexed 2021-09-08T14:28:11.189Z
mega_collection Oxford University Press (OUP) (CrossRef)
physical e98-e98
publishDate 2020
publishDateSort 2020
publisher Oxford University Press (OUP)
recordtype ai
score 18,694372
series Nucleic Acids Research
source_id 49
spelling Barel, Gal Herwig, Ralf 0305-1048 1362-4962 Oxford University Press (OUP) Genetics http://dx.doi.org/10.1093/nar/gkaa639 <jats:title>Abstract</jats:title> <jats:p>We present NetCore, a novel network propagation approach based on node coreness, for phenotype–genotype associations and module identification. NetCore addresses the node degree bias in PPI networks by using node coreness in the random walk with restart procedure, and achieves improved re-ranking of genes after propagation. Furthermore, NetCore implements a semi-supervised approach to identify phenotype-associated network modules, which anchors the identification of novel candidate genes at known genes associated with the phenotype. We evaluated NetCore on gene sets from 11 different GWAS traits and showed improved performance compared to the standard degree-based network propagation using cross-validation. Furthermore, we applied NetCore to identify disease genes and modules for Schizophrenia GWAS data and pan-cancer mutation data. We compared the novel approach to existing network propagation approaches and showed the benefits of using NetCore in comparison to those. We provide an easy-to-use implementation, together with a high confidence PPI network extracted from ConsensusPathDB, which can be applied to various types of genomics data in order to obtain a re-ranking of genes and functionally relevant network modules.</jats:p> NetCore: a network propagation approach using node coreness Nucleic Acids Research
spellingShingle Barel, Gal, Herwig, Ralf, Nucleic Acids Research, NetCore: a network propagation approach using node coreness, Genetics
title NetCore: a network propagation approach using node coreness
title_full NetCore: a network propagation approach using node coreness
title_fullStr NetCore: a network propagation approach using node coreness
title_full_unstemmed NetCore: a network propagation approach using node coreness
title_short NetCore: a network propagation approach using node coreness
title_sort netcore: a network propagation approach using node coreness
topic Genetics
url http://dx.doi.org/10.1093/nar/gkaa639