A balanced iterative random forest for gene selection from microarray data

Anaissi, AH; Kennedy, P; Goyal, M; Catchpoole, D

A balanced iterative random forest for gene selection from microarray data

Anaissi, AH Kennedy, P Goyal, M Catchpoole, D

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Publisher:: Biomed Central Ltd
Publication Type:: Journal Article
Citation:: Anaissi, Ali Hassan et al. 2013, 'A balanced iterative random forest for gene selection from microarray data', BMC Bioinformatics, vol. 14, no. 1, pp. 261-1-261-10.
Issue Date:: 2013

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Field	Value	Language
dc.contributor.author	Anaissi, AH	en_US
dc.contributor.author	Kennedy, P	en_US
dc.contributor.author	Goyal, M	en_US
dc.contributor.author	Catchpoole, D	en_US
dc.date.accessioned	2014-04-14T02:53:49Z
dc.date.available	2014-04-14T02:53:49Z
dc.date.issued	2013	en_US
dc.identifier	2013001058	en_US
dc.identifier.citation	Anaissi, Ali Hassan et al. 2013, 'A balanced iterative random forest for gene selection from microarray data', BMC Bioinformatics, vol. 14, no. 1, pp. 261-1-261-10.	en_US
dc.identifier.issn	1471-2105	en_US
dc.identifier.other	C1	en_US
dc.identifier.uri	http://hdl.handle.net/10453/27173
dc.description.abstract	Background: The wealth of gene expression values being generated by high throughput microarray technologies leads to complex high dimensional datasets. Moreover, many cohorts have the problem of imbalanced classes where the number of patients belonging t	en_US
dc.publisher	Biomed Central Ltd	en_US
dc.relation.ispartof	Verified OK	en_US
dc.relation.ispartof	BMC Bioinformatics	en_US
dc.relation.isbasedon	http://dx.doi.org/10.1186/1471-2105-14-261	en_US
dc.subject	Acute Lymphoblastic-Leukemia; Support Vector Machines; Naive Bayes Classifier; Cancer Classification; Clustering Analysis; Expression Data; Recognition; Patterns	en_US
dc.subject.classification	Computer Software	en_US
dc.title	A balanced iterative random forest for gene selection from microarray data	en_US
dc.type	Journal article
utslib.citation.volume	14	en_US
utslib.citation.number	1	en_US
utslib.location	London	en_US
utslib.citation.startpage	261-1	en_US
utslib.citation.endpage	261-10	en_US
utslib.identifier.org	FEIT.Faculty of Engineering & Information Technology	en_US
utslib.for	080300	en_US
utslib.percentage	100	en_US
utslib.copyright.status	open_access

Abstract:

Background: The wealth of gene expression values being generated by high throughput microarray technologies leads to complex high dimensional datasets. Moreover, many cohorts have the problem of imbalanced classes where the number of patients belonging t

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http://hdl.handle.net/10453/27173