The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models
By Leming Shi, G. Campbell, W. Jones, F. Campagne, Z. Wen, S. Walker, Z. Su, T. Chu, F. Goodsaid, L. Pusztai and 191 more. Nature Biotechnology, 2010
549 citations recorded by the source corpus.
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.
Subfields: Biophysics, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/6cf8956c67ee096df2ef22e9a93fd72266ad9f50
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- The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classification (2023, background): “Actually, as shown by the MACQ Consortium [57], if we introduce the prevalence p = TP + FN TP + TN + FP + FN as the ratio of the actual positive samples over the total number of samples, by definition specificity (TNR) and sensitivity (TPR) do not depend on p , while MCC does.”