FUNMarker: Fusion network-based method to identify prognostic and heterogeneous breast cancer biomarkers.

Li, X; Xiang, J; Wang, J; Li, J; Wu, F-X; Li, M

FUNMarker: Fusion network-based method to identify prognostic and heterogeneous breast cancer biomarkers.

Li, X Xiang, J Wang, J Li, J

Wu, F-X Li, M

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE/ACM transactions on computational biology and bioinformatics, 2020, PP, (99), pp. 1-1
Issue Date:: 2020-02-13

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Field	Value	Language
dc.contributor.author	Li, X
dc.contributor.author	Xiang, J
dc.contributor.author	Wang, J
dc.contributor.author	Li, J https://orcid.org/0000-0003-1833-7413
dc.contributor.author	Wu, F-X
dc.contributor.author	Li, M
dc.date.accessioned	2021-04-26T04:58:43Z
dc.date.available	2021-04-26T04:58:43Z
dc.date.issued	2020-02-13
dc.identifier.citation	IEEE/ACM transactions on computational biology and bioinformatics, 2020, PP, (99), pp. 1-1
dc.identifier.issn	1545-5963
dc.identifier.issn	1557-9964
dc.identifier.uri	http://hdl.handle.net/10453/148377
dc.description.abstract	Breast cancer is a heterogeneous disease with many clinically distinguishable molecular subtypes each corresponding to a cluster of patients. Identification of prognostic and heterogeneous biomarkers for breast cancer is to detect cluster-specific gene biomarkers which can be used for accurate survival prediction of breast cancer outcomes. In this study, we proposed a FUsion Network-based method (FUNMarker) to identify prognostic and heterogeneous breast cancer biomarkers by considering the heterogeneity of patient samples and biological information from multiple sources. To reduce the affect of heterogeneity of patients, samples were first clustered using the K-means algorithm based on the principal components of gene expression. For each cluster, to comprehensively evaluate the influence of genes on breast cancer, genes were weighted from three aspects: biological function, prognostic ability and correlation with known disease genes. Then they were ranked via a label propagation model on a fusion network that combined physical protein interactions from seven types of networks and thus could reduce the impact of incompleteness of interactome. We compared FUNMarker with three state-of-the-art methods and the results showed that biomarkers identified by FUNMarker were biological interpretable and had stronger discriminative power than the existing methods in differentiating patients with different prognostic outcomes.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE/ACM transactions on computational biology and bioinformatics
dc.relation.isbasedon	10.1109/tcbb.2020.2973148
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	01 Mathematical Sciences, 06 Biological Sciences, 08 Information and Computing Sciences
dc.subject.classification	Bioinformatics
dc.title	FUNMarker: Fusion network-based method to identify prognostic and heterogeneous breast cancer biomarkers.
dc.type	Journal Article
utslib.citation.volume	PP
utslib.location.activity	United States
utslib.for	01 Mathematical Sciences
utslib.for	06 Biological Sciences
utslib.for	08 Information and Computing Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/A/DRsch The Data Science Institute
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2022-02-28T00:00:00+1000Z
dc.date.updated	2021-04-26T04:58:41Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

Breast cancer is a heterogeneous disease with many clinically distinguishable molecular subtypes each corresponding to a cluster of patients. Identification of prognostic and heterogeneous biomarkers for breast cancer is to detect cluster-specific gene biomarkers which can be used for accurate survival prediction of breast cancer outcomes. In this study, we proposed a FUsion Network-based method (FUNMarker) to identify prognostic and heterogeneous breast cancer biomarkers by considering the heterogeneity of patient samples and biological information from multiple sources. To reduce the affect of heterogeneity of patients, samples were first clustered using the K-means algorithm based on the principal components of gene expression. For each cluster, to comprehensively evaluate the influence of genes on breast cancer, genes were weighted from three aspects: biological function, prognostic ability and correlation with known disease genes. Then they were ranked via a label propagation model on a fusion network that combined physical protein interactions from seven types of networks and thus could reduce the impact of incompleteness of interactome. We compared FUNMarker with three state-of-the-art methods and the results showed that biomarkers identified by FUNMarker were biological interpretable and had stronger discriminative power than the existing methods in differentiating patients with different prognostic outcomes.

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http://hdl.handle.net/10453/148377