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Conference Paper: Gene selection in microarray data analysis for brain cancer classification

TitleGene selection in microarray data analysis for brain cancer classification
Authors
Issue Date2006
PublisherIEEE.
Citation
2006 Ieee International Workshop On Genomic Signal Processing And Statistics, Gensips 2006, 2006, p. 99-100 How to Cite?
AbstractCancer classification has been one of the most challenging tasks in clinical diagnosis. At present cancer classification is done mainly by looking through the cells' morphological differences, which do not always give a clear distinction of cancer subtypes. Unfortunately, this may have a significant impact on the final outcome of whether a patient could be cured effectively. Microarray technology can play an important role on diagnosing which type of disease one is carrying. The gene selection process is critical for developing gene markers for faster and more accurate diagnosis. In this paper, we develop a method using pairwise data comparisons instead of the one-over-the-rest approach used nowadays. Results are evaluated using available clustering techniques including hierarchical clustering and k-means clustering. Using pairwise comparison, the best accuracy achieved is 95% while it is only 83% when using one-over-the-rest approach. ©2006 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/45886
ISBN
References

 

DC FieldValueLanguage
dc.contributor.authorLeung, YYen_HK
dc.contributor.authorChang, CQen_HK
dc.contributor.authorHung, YSen_HK
dc.contributor.authorFung, PCWen_HK
dc.date.accessioned2007-10-30T06:37:45Z-
dc.date.available2007-10-30T06:37:45Z-
dc.date.issued2006en_HK
dc.identifier.citation2006 Ieee International Workshop On Genomic Signal Processing And Statistics, Gensips 2006, 2006, p. 99-100en_HK
dc.identifier.isbn1-4244-0385-5en_HK
dc.identifier.urihttp://hdl.handle.net/10722/45886-
dc.description.abstractCancer classification has been one of the most challenging tasks in clinical diagnosis. At present cancer classification is done mainly by looking through the cells' morphological differences, which do not always give a clear distinction of cancer subtypes. Unfortunately, this may have a significant impact on the final outcome of whether a patient could be cured effectively. Microarray technology can play an important role on diagnosing which type of disease one is carrying. The gene selection process is critical for developing gene markers for faster and more accurate diagnosis. In this paper, we develop a method using pairwise data comparisons instead of the one-over-the-rest approach used nowadays. Results are evaluated using available clustering techniques including hierarchical clustering and k-means clustering. Using pairwise comparison, the best accuracy achieved is 95% while it is only 83% when using one-over-the-rest approach. ©2006 IEEE.en_HK
dc.format.extent147088 bytes-
dc.format.extent4066 bytes-
dc.format.extent3485 bytes-
dc.format.extent2762 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypetext/plain-
dc.format.mimetypetext/plain-
dc.format.mimetypetext/plain-
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.relation.ispartof2006 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2006en_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rights©2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_HK
dc.titleGene selection in microarray data analysis for brain cancer classificationen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1-4244-0385-5&volume=&spage=99&epage=100&date=2006&atitle=Gene+selection+in+microarray+data+analysis+for+brain+cancer+classificationen_HK
dc.identifier.emailChang, CQ: cqchang@eee.hku.hken_HK
dc.identifier.emailHung, YS: yshung@hkucc.hku.hken_HK
dc.identifier.authorityChang, CQ=rp00095en_HK
dc.identifier.authorityHung, YS=rp00220en_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/GENSIPS.2006.353175en_HK
dc.identifier.scopuseid_2-s2.0-48649107389en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-48649107389&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage99en_HK
dc.identifier.epage100en_HK
dc.identifier.scopusauthoridLeung, YY=35490882300en_HK
dc.identifier.scopusauthoridChang, CQ=7407033052en_HK
dc.identifier.scopusauthoridHung, YS=8091656200en_HK
dc.identifier.scopusauthoridFung, PCW=7101613315en_HK

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