Fig. 4: Performance of molecular prediction on 40 datasets across 10 cancer types.

a Overall Performance of Gene Mutation Prediction on 23 datasets. b Performance of Mutation Prediction on 18 held-out datasets. c Overall Performance of Immunohistochemistry (IHC) Biomarker Prediction on 10 datasets. d Performance of IHC Biomarker Prediction on 4 independent datasets. e Overall Performance of Molecular Subtyping on 7 datasets. f Performance of Molecular Subtyping on 4 held-out datasets. In subfigures b, d and f, the minima and maxima represent the minimum and maximum performance among corresponding datasets, respectively, while the center and the bound of box represent the mean performance, 25% and 75% percentiles, respectively. The red lines and the values reported at the top of figures (a–f) refer to the averaged performance across datasets. Each point represents a dataset, with the size of the point indicating the standard deviation. g Positive and Negative Ratios of gene mutation for every mutation dataset, including genes with high-frequency mutations highlighted in green and genes related to FDA-approved therapies highlighted in red. h–j Internal (In) v.s. External (Ext) Evaluation. h Performance of Mutation Prediction on 5 internal and 5 external datasets. i Performance of IHC Biomarker Prediction on 3 internal and 3 external datasets. j Performance of Molecular Subtyping on 3 internal and 3 external datasets. Error bars represent standard errors across datasets for all bar plots in h-j. P-value for every group of experiments is given through one-sided Wilcoxon signed-rank test between mSTAR and the second-best FM. * represents P < 0.05, ** means P < 0.01 and *** indicates P < 0.001. Detailed performances of every dataset spanning 10 cancer types are presented in Supplementary Fig. 3 and Supplementary Table 8--11. Source data are provided as a Source Data file.