Network-guided prediction of aromatase inhibitor response in breast cancer
Fig 2
(a) Leave-one-out cross-validation results for prediction of non-response to all aromatase inhibitors, using random forests and probabilistic support vector machines. (b) Feature importance from the random forest cross-validation results, showing which constructed features contribute most to the random forest fit. Features prefixed with “Min.” denote elementwise minimum of pairs of matrices, e.g. smoothed (“sm.”) drug targets of Arimidex and smoothed binary differential expression as shown in the first feature listed. “sm. {ESR1, ESR2}” denotes network proximity to the ESR1 and ESR2 genes. Sample×gene matrices are collapsed across genes in various ways to produce feature values for samples: mean or standard deviation across all genes, or through PCA decomposition. Categorical clinical features are represented with one-hot encoding, and are shown as “feature name_column name”, e.g. “er_cell_percentage_90-99%”.