Integrative approaches for predicting microRNA function and prioritizing disease-related microRNA using biological interaction networks.

Zeng, X; Zhang, X; Zou, Q

Integrative approaches for predicting microRNA function and prioritizing disease-related microRNA using biological interaction networks.

Zeng, X Zhang, X

Zou, Q

Permalink

Publisher:: OXFORD UNIV PRESS
Publication Type:: Journal Article
Citation:: Brief Bioinform, 2016, 17, (2), pp. 193-203
Issue Date:: 2016-03

Closed Access

	Filename	Description	Size
	bbv033.pdf	Published version	833.76 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Zeng, X
dc.contributor.author	Zhang, X https://orcid.org/0000-0002-3089-9809
dc.contributor.author	Zou, Q
dc.date.accessioned	2022-10-24T05:18:56Z
dc.date.available	2022-10-24T05:18:56Z
dc.date.issued	2016-03
dc.identifier.citation	Brief Bioinform, 2016, 17, (2), pp. 193-203
dc.identifier.issn	1467-5463
dc.identifier.issn	1477-4054
dc.identifier.uri	http://hdl.handle.net/10453/162630
dc.description.abstract	MicroRNAs (miRNA) play critical roles in regulating gene expressions at the posttranscriptional levels. The prediction of disease-related miRNA is vital to the further investigation of miRNA's involvement in the pathogenesis of disease. In previous years, biological experimentation is the main method used to identify whether miRNA was associated with a given disease. With increasing biological information and the appearance of new miRNAs every year, experimental identification of disease-related miRNAs poses considerable difficulties (e.g. time-consumption and high cost). Because of the limitations of experimental methods in determining the relationship between miRNAs and diseases, computational methods have been proposed. A key to predict potential disease-related miRNA based on networks is the calculation of similarity among diseases and miRNA over the networks. Different strategies lead to different results. In this review, we summarize the existing computational approaches and present the confronted difficulties that help understand the research status. We also discuss the principles, efficiency and differences among these methods. The comprehensive comparison and discussion elucidated in this work provide constructive insights into the matter.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	OXFORD UNIV PRESS
dc.relation.ispartof	Brief Bioinform
dc.relation.isbasedon	10.1093/bib/bbv033
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0601 Biochemistry and Cell Biology, 0802 Computation Theory and Mathematics, 0899 Other Information and Computing Sciences
dc.subject.classification	Bioinformatics
dc.subject.mesh	Computer Simulation
dc.subject.mesh	Data Mining
dc.subject.mesh	Databases, Genetic
dc.subject.mesh	Gene Expression Regulation
dc.subject.mesh	Genetic Predisposition to Disease
dc.subject.mesh	Humans
dc.subject.mesh	MicroRNAs
dc.subject.mesh	Models, Genetic
dc.subject.mesh	Protein Interaction Maps
dc.subject.mesh	Proteome
dc.subject.mesh	Sequence Analysis, RNA
dc.subject.mesh	Humans
dc.subject.mesh	Genetic Predisposition to Disease
dc.subject.mesh	Proteome
dc.subject.mesh	MicroRNAs
dc.subject.mesh	Sequence Analysis, RNA
dc.subject.mesh	Gene Expression Regulation
dc.subject.mesh	Models, Genetic
dc.subject.mesh	Computer Simulation
dc.subject.mesh	Databases, Genetic
dc.subject.mesh	Data Mining
dc.subject.mesh	Protein Interaction Maps
dc.title	Integrative approaches for predicting microRNA function and prioritizing disease-related microRNA using biological interaction networks.
dc.type	Journal Article
utslib.citation.volume	17
utslib.location.activity	England
utslib.for	0601 Biochemistry and Cell Biology
utslib.for	0802 Computation Theory and Mathematics
utslib.for	0899 Other Information and Computing Sciences
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
dc.date.updated	2022-10-24T05:18:55Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	17
utslib.citation.issue	2

Abstract:

MicroRNAs (miRNA) play critical roles in regulating gene expressions at the posttranscriptional levels. The prediction of disease-related miRNA is vital to the further investigation of miRNA's involvement in the pathogenesis of disease. In previous years, biological experimentation is the main method used to identify whether miRNA was associated with a given disease. With increasing biological information and the appearance of new miRNAs every year, experimental identification of disease-related miRNAs poses considerable difficulties (e.g. time-consumption and high cost). Because of the limitations of experimental methods in determining the relationship between miRNAs and diseases, computational methods have been proposed. A key to predict potential disease-related miRNA based on networks is the calculation of similarity among diseases and miRNA over the networks. Different strategies lead to different results. In this review, we summarize the existing computational approaches and present the confronted difficulties that help understand the research status. We also discuss the principles, efficiency and differences among these methods. The comprehensive comparison and discussion elucidated in this work provide constructive insights into the matter.

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

http://hdl.handle.net/10453/162630