Manipulating cellular microRNAs and analyzing high-dimensional gene expression data using machine learning workflows.

Saini, V; Joglekar, MV; Wong, WKM; Jiang, G; Nassif, NT; Simpson, AM; Ma, RCW; Dalgaard, LT; Hardikar, AA

Manipulating cellular microRNAs and analyzing high-dimensional gene expression data using machine learning workflows.

Saini, V Joglekar, MV Wong, WKM Jiang, G Nassif, NT Simpson, AM Ma, RCW Dalgaard, LT Hardikar, AA

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Publisher:: Elsevier BV
Publication Type:: Journal Article
Citation:: STAR Protoc, 2021, 2, (4), pp. 100910
Issue Date:: 2021-12-17

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Field	Value	Language
dc.contributor.author	Saini, V
dc.contributor.author	Joglekar, MV
dc.contributor.author	Wong, WKM
dc.contributor.author	Jiang, G
dc.contributor.author	Nassif, NT
dc.contributor.author	Simpson, AM
dc.contributor.author	Ma, RCW
dc.contributor.author	Dalgaard, LT
dc.contributor.author	Hardikar, AA
dc.date.accessioned	2022-01-14T06:12:28Z
dc.date.available	2022-01-14T06:12:28Z
dc.date.issued	2021-12-17
dc.identifier.citation	STAR Protoc, 2021, 2, (4), pp. 100910
dc.identifier.issn	2666-1667
dc.identifier.issn	2666-1667
dc.identifier.uri	http://hdl.handle.net/10453/153130
dc.description.abstract	MicroRNAs (miRNAs) are elements of the gene regulatory network and manipulating their abundance is essential toward elucidating their role in patho-physiological conditions. We present a detailed workflow that identifies important miRNAs using a machine learning algorithm. We then provide optimized techniques to validate the identified miRNAs through over-expression/loss-of-function studies. Overall, these protocols apply to any field in biology where high-dimensional data are produced. For complete details on the use and execution of this protocol, please refer to Wong et al. (2021a).
dc.format	Electronic-eCollection
dc.language	eng
dc.publisher	Elsevier BV
dc.relation.ispartof	STAR Protoc
dc.relation.isbasedon	10.1016/j.xpro.2021.100910
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Manipulating cellular microRNAs and analyzing high-dimensional gene expression data using machine learning workflows.
dc.type	Journal Article
utslib.citation.volume	2
utslib.location.activity	United States
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Life Sciences
pubs.organisational-group	/University of Technology Sydney/Centre for Health Technologies (CHT)
utslib.copyright.status	open_access	*
dc.date.updated	2022-01-14T06:12:23Z
pubs.issue	4
pubs.publication-status	Published online
pubs.volume	2
utslib.citation.issue	4

Abstract:

MicroRNAs (miRNAs) are elements of the gene regulatory network and manipulating their abundance is essential toward elucidating their role in patho-physiological conditions. We present a detailed workflow that identifies important miRNAs using a machine learning algorithm. We then provide optimized techniques to validate the identified miRNAs through over-expression/loss-of-function studies. Overall, these protocols apply to any field in biology where high-dimensional data are produced. For complete details on the use and execution of this protocol, please refer to Wong et al. (2021a).

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http://hdl.handle.net/10453/153130