The ability to classify patients based on gene-expression data varies by algorithm and performance metric
Fig 7
Relative classification performance per combination of feature-selection and classification algorithm.
For each combination of dataset and class variable, we averaged area under receiver operating characteristic curve (AUROC) values across all Monte Carlo cross-validation iterations. Then for each classification algorithm, we ranked the feature-selection algorithms based on AUROC scores across all datasets and class variables. Lower ranks indicate better performance. Dark-red boxes indicate cases where a particular feature-selection algorithm was especially effective for a particular classification algorithm. The opposite was true for dark-blue boxes.