Microarray data mining: selecting trustworthy genes with gene feature ranking

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dc.contributor.author Ubaudi, Franco en_US
dc.contributor.author Kennedy, Paul en_US
dc.contributor.author Catchpoole, Daniel en_US
dc.contributor.author Guo, Dachuan en_US
dc.contributor.author Simoff, Simeon en_US
dc.contributor.editor en_US
dc.date.accessioned 2010-05-28T09:38:06Z
dc.date.available 2010-05-28T09:38:06Z
dc.date.issued 2009 en_US
dc.identifier 2008001288 en_US
dc.identifier.citation Ubaudi Franco et al. 2009, 'Microarray data mining: selecting trustworthy genes with gene feature ranking', in http://dx.doi.org/10.1007/978-0-387-79420-4_11 (ed.), Springer-Verlag, Berlin Heidelberg, pp. 159-168. en_US
dc.identifier.issn 978-0-387-79419-8 en_US
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. en_US
dc.language en_US
dc.publisher Springer en_US
dc.relation.hasversion Accepted manuscript version
dc.relation.isbasedon http://dx.doi.org/10.1007/978-0-387-79420-4_11 en_US
dc.rights The original publication is available at www.springerlink.com
dc.title Microarray data mining: selecting trustworthy genes with gene feature ranking en_US
dc.parent Data Mining for Business Applications en_US
dc.journal.volume en_US
dc.journal.number en_US
dc.publocation New York, USA en_US
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 en_US
dc.personcode 99018905 en_US
dc.personcode 990679 en_US
dc.personcode 996701 en_US
dc.personcode 0000047575 en_US
dc.personcode 000716 en_US
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.custom en_US
dc.date.activity en_US
dc.location.activity en_US
dc.description.keywords NZ en_US
dc.staffid 000716 en_US


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