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Consistency of predictive signature genes and classifiers generated using different microarray platforms.


ABSTRACT: Microarray-based classifiers and associated signature genes generated from various platforms are abundantly reported in the literature; however, the utility of the classifiers and signature genes in cross-platform prediction applications remains largely uncertain. As part of the MicroArray Quality Control Phase II (MAQC-II) project, we show in this study 80-90% cross-platform prediction consistency using a large toxicogenomics data set by illustrating that: (1) the signature genes of a classifier generated from one platform can be directly applied to another platform to develop a predictive classifier; (2) a classifier developed using data generated from one platform can accurately predict samples that were profiled using a different platform. The results suggest the potential utility of using published signature genes in cross-platform applications and the possible adoption of the published classifiers for a variety of applications. The study reveals an opportunity for possible translation of biomarkers identified using microarrays to clinically validated non-array gene expression assays.

SUBMITTER: Fan X 

PROVIDER: S-EPMC2920073 | biostudies-literature | 2010 Aug

REPOSITORIES: biostudies-literature

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Consistency of predictive signature genes and classifiers generated using different microarray platforms.

Fan X X   Lobenhofer E K EK   Chen M M   Shi W W   Huang J J   Luo J J   Zhang J J   Walker S J SJ   Chu T-M TM   Li L L   Wolfinger R R   Bao W W   Paules R S RS   Bushel P R PR   Li J J   Shi T T   Nikolskaya T T   Nikolsky Y Y   Hong H H   Deng Y Y   Cheng Y Y   Fang H H   Shi L L   Tong W W  

The pharmacogenomics journal 20100801 4


Microarray-based classifiers and associated signature genes generated from various platforms are abundantly reported in the literature; however, the utility of the classifiers and signature genes in cross-platform prediction applications remains largely uncertain. As part of the MicroArray Quality Control Phase II (MAQC-II) project, we show in this study 80-90% cross-platform prediction consistency using a large toxicogenomics data set by illustrating that: (1) the signature genes of a classifie  ...[more]

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