NetCore: a network propagation approach using node coreness.

Abstract:

: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.

journal_name

Nucleic Acids Res

journal_title

Nucleic acids research

authors

Barel G,Herwig R

doi

10.1093/nar/gkaa639

subject

Has Abstract

pub_date

2020-09-25 00:00:00

pages

e98

issue

17

eissn

0305-1048

issn

1362-4962

pii

5879427

journal_volume

48

pub_type

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