Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/40431
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dc.contributor.authorLiu, C.C.en_US
dc.contributor.author陳健尉zh_TW
dc.contributor.authorChen, W.S.E.en_US
dc.contributor.authorLin, C.C.en_US
dc.contributor.authorLiu, H.C.en_US
dc.contributor.authorChen, H.Y.en_US
dc.contributor.authorYang, P.C.en_US
dc.contributor.authorChang, P.C.en_US
dc.contributor.authorChen, J.J.W.en_US
dc.contributor.author劉俊吉zh_TW
dc.date2006zh_TW
dc.date.accessioned2014-06-06T08:03:45Z-
dc.date.available2014-06-06T08:03:45Z-
dc.identifier.issn0305-1048zh_TW
dc.identifier.urihttp://hdl.handle.net/11455/40431-
dc.description.abstractCancer classification is the critical basis for patient-tailored therapy, while pathway analysis is a promising method to discover the underlying molecular mechanisms related to cancer development by using microarray data. However, linking the molecular classification and pathway analysis with gene network approach has not been discussed yet. In this study, we developed a novel framework based on cancer class-specific gene networks for classification and pathway analysis. This framework involves a novel gene network construction, named ordering network, which exhibits the power-law node-degree distribution as seen in correlation networks. The results obtained from five public cancer datasets showed that the gene networks with ordering relationship are better than those with correlation relationship in terms of accuracy and stability of the classification performance. Furthermore, we integrated the ordering networks, classification information and pathway database to develop the topology-based pathway analysis for identifying cancer class-specific pathways, which might be essential in the biological significance of cancer. Our results suggest that the topology-based classification technology can precisely distinguish cancer subclasses and the topology-based pathway analysis can characterize the correspondent biochemical pathways even if there are subtle, but consistent, changes in gene expression, which may provide new insights into the underlying molecular mechanisms of tumorigenesis.en_US
dc.language.isoen_USzh_TW
dc.relationNucleic Acids Researchen_US
dc.relation.ispartofseriesNucleic Acids Research, Volume 34, Issue 14, Page(s) 4069-4080.en_US
dc.relation.urihttp://dx.doi.org/10.1093/nar/gkl583en_US
dc.subjectgene-expression profilesen_US
dc.subjectacute lymphoblastic-leukemiaen_US
dc.subjectlung-canceren_US
dc.subjectregulatory networksen_US
dc.subjectfeature-selectionen_US
dc.subjectprotein networksen_US
dc.subjectrnaen_US
dc.subjectexpressionen_US
dc.subjectadenocarcinomaen_US
dc.subjectmodelen_US
dc.subjectdiscoveryen_US
dc.titleTopology-based cancer classification and related pathway mining using microarray dataen_US
dc.typeJournal Articlezh_TW
dc.identifier.doi10.1093/nar/gkl583zh_TW
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