Network-based methods for gene function prediction.

Chen, Q; Li, Y; Tan, K; Qiao, Y; Pan, S; Jiang, T; Chen, Y-PP

Network-based methods for gene function prediction.

Chen, Q Li, Y Tan, K Qiao, Y Pan, S

Jiang, T Chen, Y-PP

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Publisher:: Oxford University Press (OUP)
Publication Type:: Journal Article
Citation:: Brief Funct Genomics, 2021, 20, (4), pp. 249-257
Issue Date:: 2021-07-17

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Field	Value	Language
dc.contributor.author	Chen, Q
dc.contributor.author	Li, Y
dc.contributor.author	Tan, K
dc.contributor.author	Qiao, Y
dc.contributor.author	Pan, S https://orcid.org/0000-0003-0794-527X
dc.contributor.author	Jiang, T
dc.contributor.author	Chen, Y-PP
dc.date.accessioned	2022-05-18T04:31:05Z
dc.date.available	2021-01-26
dc.date.available	2022-05-18T04:31:05Z
dc.date.issued	2021-07-17
dc.identifier.citation	Brief Funct Genomics, 2021, 20, (4), pp. 249-257
dc.identifier.issn	2041-2649
dc.identifier.issn	2041-2657
dc.identifier.uri	http://hdl.handle.net/10453/157488
dc.description.abstract	The rapid development of high-throughput technology has generated a large number of biological networks. Network-based methods are able to provide rich information for inferring gene function. This is composed of analyzing the topological characteristics of genes in related networks, integrating biological information, and considering data from different data sources. To promote network biology and related biotechnology research, this article provides a survey for the state of the art of advanced methods of network-based gene function prediction and discusses the potential challenges.
dc.format	Print
dc.language	eng
dc.publisher	Oxford University Press (OUP)
dc.relation.ispartof	Brief Funct Genomics
dc.relation.isbasedon	10.1093/bfgp/elab006
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0604 Genetics
dc.subject.mesh	Computational Biology
dc.subject.mesh	Gene Regulatory Networks
dc.subject.mesh	Phenotype
dc.subject.mesh	Computational Biology
dc.subject.mesh	Phenotype
dc.subject.mesh	Gene Regulatory Networks
dc.title	Network-based methods for gene function prediction.
dc.type	Journal Article
utslib.citation.volume	20
utslib.location.activity	England
utslib.for	0604 Genetics
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
dc.date.updated	2022-05-18T04:31:04Z
pubs.issue	4
pubs.publication-status	Published
pubs.volume	20
utslib.citation.issue	4

Abstract:

The rapid development of high-throughput technology has generated a large number of biological networks. Network-based methods are able to provide rich information for inferring gene function. This is composed of analyzing the topological characteristics of genes in related networks, integrating biological information, and considering data from different data sources. To promote network biology and related biotechnology research, this article provides a survey for the state of the art of advanced methods of network-based gene function prediction and discusses the potential challenges.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/157488