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NetCore: a network propagation approach using node coreness
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Zeitschriftentitel: | Nucleic Acids Research |
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Personen und Körperschaften: | , |
In: | Nucleic Acids Research, 48, 2020, 17, S. e98-e98 |
Format: | E-Article |
Sprache: | Englisch |
veröffentlicht: |
Oxford University Press (OUP)
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Schlagwörter: |
author_facet |
Barel, Gal Herwig, Ralf Barel, Gal Herwig, Ralf |
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author |
Barel, Gal Herwig, Ralf |
spellingShingle |
Barel, Gal Herwig, Ralf Nucleic Acids Research NetCore: a network propagation approach using node coreness Genetics |
author_sort |
barel, gal |
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 |
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10.1093/nar/gkaa639 |
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Oxford University Press (OUP) |
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Nucleic Acids Research |
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title |
NetCore: a network propagation approach using node coreness |
title_unstemmed |
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 |
publishDate |
2020 |
physical |
e98-e98 |
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> |
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author | Barel, Gal, Herwig, Ralf |
author_facet | Barel, Gal, Herwig, Ralf, Barel, Gal, Herwig, Ralf |
author_sort | barel, gal |
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> |
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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 |
title_unstemmed | NetCore: a network propagation approach using node coreness |
topic | Genetics |
url | http://dx.doi.org/10.1093/nar/gkaa639 |