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Nature Research, Nature Biotechnology, 8(28), p. 827-838, 2010

DOI: 10.1038/nbt.1665

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The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models

Journal article published in 2010 by Symmans Wf, Leming Shi, Gregory Campbell, Wendell D. Jones, Jones Wd, Fabien Campagne, Walker Sj, Zhining Wen, Stephen J. Walker, Zhenqiang Su, Chu Tm, Tzu-Ming Chu, Federico M. Goodsaid, John D. Shaughnessy, Lajos Pusztai and other authors.
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Abstract

Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.