Microarray data mining: selecting trustworthy genes with gene feature ranking

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dc.contributor.author Ubaudi, FA
dc.contributor.author Kennedy, PJ
dc.contributor.author Catchpoole, DR
dc.contributor.author Guo, D
dc.contributor.author Simoff, SJ
dc.date.accessioned 2010-05-28T09:38:06Z
dc.date.issued 2009-01
dc.identifier.citation Data Mining for Business Applications, 2009, 1, pp. 159 - 168
dc.identifier.isbn 978-0-387-79419-8
dc.identifier.other B1 en_US
dc.identifier.uri http://hdl.handle.net/10453/7836
dc.description.abstract Gene expression datasets used in biomedical data mining frequently have two characteristics: they have many thousand attributes but only relatively few sample points and the measurements are noisy. In other words, individual expression measurements may be untrustworthy. Gene Feature Ranking (GFR) is a feature selection methodology that addresses these domain specific characteristics by selecting features (i.e. genes) based on two criteria: (i) how well the gene can discriminate between classes of patient and (ii) the trustworthiness of the microarray data associated with the gene. An example from the pediatric cancer domain demonstrates the use of GFR and compares its performance with a feature selection method that does not explicitly address the trustworthiness of the underlying data.
dc.publisher Springer
dc.relation.hasversion Accepted manuscript version
dc.relation.isbasedon 10.1007/978-0-387-79420-4_11
dc.rights The original publication is available at www.springerlink.com
dc.title Microarray data mining: selecting trustworthy genes with gene feature ranking
dc.type Chapter
dc.parent Data Mining for Business Applications
dc.journal.number en_US
dc.publocation New York, USA en_US
dc.publocation New York, USA
dc.publocation New York, USA
dc.identifier.startpage 159 en_US
dc.identifier.endpage 168 en_US
dc.cauo.name FEIT.Faculty of Engineering & Information Technology en_US
dc.conference Verified OK en_US
dc.for 080109 Pattern Recognition and Data Mining
dc.personcode 000716
dc.personcode 990679
dc.personcode 996701
dc.percentage 100 en_US
dc.classification.name Pattern Recognition and Data Mining en_US
dc.classification.type FOR-08 en_US
dc.edition 1 en_US
dc.edition 1
dc.edition 1
dc.custom en_US
dc.date.activity en_US
dc.location.activity en_US
dc.description.keywords NZ en_US
dc.description.keywords NZ
dc.description.keywords NZ
pubs.embargo.period Not known
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/Faculty of Engineering and Information Technology/School of Software
pubs.organisational-group /University of Technology Sydney/Strength - Quantum Computation and Intelligent Systems


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