| Clinical likelihood ratios and balanced accuracy for 44 in silico tools against multiple large-scale functional assays of cancer susceptibility genes; Cubuk et al. 2021, Supplementary tables 1-13 (41436_2021_1265_MOESM3_ESM.xlsx) | MetaSVM (Cubuk et al. 2021) | BRCA1 functional truth set, binary in silico calls (Cubuk et al. 2021) | Benignity likelihood ratio (Source 'negative likelihood ratio' (TNR/FNR): likelihood ratio for benignity of a tolerated call, BRCA1 functional truth set) | 7.85 |
|---|
| Clinical likelihood ratios and balanced accuracy for 44 in silico tools against multiple large-scale functional assays of cancer susceptibility genes; Cubuk et al. 2021, Supplementary tables 1-13 (41436_2021_1265_MOESM3_ESM.xlsx) | MetaSVM (Cubuk et al. 2021) | BRCA1 functional truth set, binary in silico calls (Cubuk et al. 2021) | False negatives (Functionally deleterious BRCA1 variants called tolerated) | 21 |
|---|
| Clinical likelihood ratios and balanced accuracy for 44 in silico tools against multiple large-scale functional assays of cancer susceptibility genes; Cubuk et al. 2021, Supplementary tables 1-13 (41436_2021_1265_MOESM3_ESM.xlsx) | MetaSVM (Cubuk et al. 2021) | BRCA1 functional truth set, binary in silico calls (Cubuk et al. 2021) | Likelihood ratio (Positive likelihood ratio of a deleterious call (TPR/FPR), BRCA1 functional truth set) | 1.7 |
|---|
| Clinical likelihood ratios and balanced accuracy for 44 in silico tools against multiple large-scale functional assays of cancer susceptibility genes; Cubuk et al. 2021, Supplementary tables 1-13 (41436_2021_1265_MOESM3_ESM.xlsx) | MetaSVM (Cubuk et al. 2021) | BRCA2 functional truth set, binary in silico calls (Cubuk et al. 2021) | Benignity likelihood ratio (Source 'negative likelihood ratio' (TNR/FNR): likelihood ratio for benignity of a tolerated call, BRCA2 functional truth set) | 23.4 |
|---|
| Clinical likelihood ratios and balanced accuracy for 44 in silico tools against multiple large-scale functional assays of cancer susceptibility genes; Cubuk et al. 2021, Supplementary tables 1-13 (41436_2021_1265_MOESM3_ESM.xlsx) | MetaSVM (Cubuk et al. 2021) | BRCA2 functional truth set, binary in silico calls (Cubuk et al. 2021) | False negatives (Functionally deleterious BRCA2 variants called tolerated) | 1 |
|---|
| Clinical likelihood ratios and balanced accuracy for 44 in silico tools against multiple large-scale functional assays of cancer susceptibility genes; Cubuk et al. 2021, Supplementary tables 1-13 (41436_2021_1265_MOESM3_ESM.xlsx) | MetaSVM (Cubuk et al. 2021) | BRCA2 functional truth set, binary in silico calls (Cubuk et al. 2021) | Likelihood ratio (Positive likelihood ratio of a deleterious call (TPR/FPR), BRCA2 functional truth set) | 1.55 |
|---|
| Clinical likelihood ratios and balanced accuracy for 44 in silico tools against multiple large-scale functional assays of cancer susceptibility genes; Cubuk et al. 2021, Supplementary tables 1-13 (41436_2021_1265_MOESM3_ESM.xlsx) | primateAI_a (Cubuk et al. 2021) | BRCA1 functional truth set, binary in silico calls (Cubuk et al. 2021) | False negatives (Functionally deleterious BRCA1 variants called tolerated) | 184 |
|---|
| Clinical likelihood ratios and balanced accuracy for 44 in silico tools against multiple large-scale functional assays of cancer susceptibility genes; Cubuk et al. 2021, Supplementary tables 1-13 (41436_2021_1265_MOESM3_ESM.xlsx) | primateAI_a (Cubuk et al. 2021) | BRCA1 functional truth set, binary in silico calls (Cubuk et al. 2021) | Likelihood ratio (Positive likelihood ratio of a deleterious call (TPR/FPR), BRCA1 functional truth set) | 13.2 |
|---|
| Clinical likelihood ratios and balanced accuracy for 44 in silico tools against multiple large-scale functional assays of cancer susceptibility genes; Cubuk et al. 2021, Supplementary tables 1-13 (41436_2021_1265_MOESM3_ESM.xlsx) | primateAI_a (Cubuk et al. 2021) | BRCA2 functional truth set, binary in silico calls (Cubuk et al. 2021) | False negatives (Functionally deleterious BRCA2 variants called tolerated) | 32 |
|---|
| Clinical likelihood ratios and balanced accuracy for 44 in silico tools against multiple large-scale functional assays of cancer susceptibility genes; Cubuk et al. 2021, Supplementary tables 1-13 (41436_2021_1265_MOESM3_ESM.xlsx) | primateAI_b (Cubuk et al. 2021) | BRCA1 functional truth set, binary in silico calls (Cubuk et al. 2021) | Benignity likelihood ratio (Source 'negative likelihood ratio' (TNR/FNR): likelihood ratio for benignity of a tolerated call, BRCA1 functional truth set) | 1.2 |
|---|
| Clinical likelihood ratios and balanced accuracy for 44 in silico tools against multiple large-scale functional assays of cancer susceptibility genes; Cubuk et al. 2021, Supplementary tables 1-13 (41436_2021_1265_MOESM3_ESM.xlsx) | primateAI_b (Cubuk et al. 2021) | BRCA2 functional truth set, binary in silico calls (Cubuk et al. 2021) | Benignity likelihood ratio (Source 'negative likelihood ratio' (TNR/FNR): likelihood ratio for benignity of a tolerated call, BRCA2 functional truth set) | 1.23 |
|---|
| Clinical likelihood ratios and balanced accuracy for 44 in silico tools against multiple large-scale functional assays of cancer susceptibility genes; Cubuk et al. 2021, Supplementary tables 1-13 (41436_2021_1265_MOESM3_ESM.xlsx) | primateAI_b (Cubuk et al. 2021) | BRCA2 functional truth set, binary in silico calls (Cubuk et al. 2021) | Likelihood ratio (Positive likelihood ratio of a deleterious call (TPR/FPR), BRCA2 functional truth set) | 6.78 |
|---|
| Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 10: Performance metrics of 17 algorithms, default categories, benchmark 2 (OncoKB) | MetaSVM (Chen et al. 2020) | OncoKB benchmark (positive set not stated), default categorical calls (Chen et al. 2020 Additional file 10) | Accuracy (OncoKB benchmark (positive set not stated), default categorical calls) | 0.6 |
|---|
| Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 10: Performance metrics of 17 algorithms, default categories, benchmark 2 (OncoKB) | MetaSVM (Chen et al. 2020) | OncoKB benchmark (positive set not stated), default categorical calls (Chen et al. 2020 Additional file 10) | Negative predictive value (OncoKB benchmark (positive set not stated), default categorical calls) | 0.62 |
|---|
| Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 10: Performance metrics of 17 algorithms, default categories, benchmark 2 (OncoKB) | MetaSVM (Chen et al. 2020) | OncoKB benchmark (positive set not stated), default categorical calls (Chen et al. 2020 Additional file 10) | Precision (OncoKB benchmark (positive set not stated), default categorical calls) | 0.59 |
|---|
| Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 10: Performance metrics of 17 algorithms, default categories, benchmark 2 (OncoKB) | MetaSVM (Chen et al. 2020) | OncoKB benchmark (positive set not stated), default categorical calls (Chen et al. 2020 Additional file 10) | Recall (OncoKB benchmark (positive set not stated), default categorical calls) | 0.68 |
|---|
| Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 10: Performance metrics of 17 algorithms, default categories, benchmark 2 (OncoKB) | MetaSVM (Chen et al. 2020) | OncoKB benchmark (positive set not stated), default categorical calls (Chen et al. 2020 Additional file 10) | Specificity (OncoKB benchmark (positive set not stated), default categorical calls) | 0.52 |
|---|
| Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 9: Performance metrics of 33 algorithms, median-score threshold, benchmark 2 (OncoKB) | MetaSVM (Chen et al. 2020) | OncoKB benchmark (positive set not stated), median-score threshold (Chen et al. 2020 Additional file 9) | Accuracy (OncoKB benchmark (positive set not stated), median-score threshold) | 0.6 |
|---|
| Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 9: Performance metrics of 33 algorithms, median-score threshold, benchmark 2 (OncoKB) | MetaSVM (Chen et al. 2020) | OncoKB benchmark (positive set not stated), median-score threshold (Chen et al. 2020 Additional file 9) | Negative predictive value (OncoKB benchmark (positive set not stated), median-score threshold) | 0.6 |
|---|
| Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 9: Performance metrics of 33 algorithms, median-score threshold, benchmark 2 (OncoKB) | MetaSVM (Chen et al. 2020) | OncoKB benchmark (positive set not stated), median-score threshold (Chen et al. 2020 Additional file 9) | Precision (OncoKB benchmark (positive set not stated), median-score threshold) | 0.6 |
|---|
| Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 9: Performance metrics of 33 algorithms, median-score threshold, benchmark 2 (OncoKB) | MetaSVM (Chen et al. 2020) | OncoKB benchmark (positive set not stated), median-score threshold (Chen et al. 2020 Additional file 9) | Recall (OncoKB benchmark (positive set not stated), median-score threshold) | 0.6 |
|---|
| Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 9: Performance metrics of 33 algorithms, median-score threshold, benchmark 2 (OncoKB) | MetaSVM (Chen et al. 2020) | OncoKB benchmark (positive set not stated), median-score threshold (Chen et al. 2020 Additional file 9) | Specificity (OncoKB benchmark (positive set not stated), median-score threshold) | 0.6 |
|---|
| Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 22: Performance metrics of 17 algorithms, default categories, benchmark 5 (cell viability) | MetaSVM (Chen et al. 2020) | Cell viability drivers versus neutral, default categorical calls (Chen et al. 2020 Additional file 22) | Accuracy (cell viability drivers versus neutral, default categorical calls) | 0.56 |
|---|
| Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 22: Performance metrics of 17 algorithms, default categories, benchmark 5 (cell viability) | MetaSVM (Chen et al. 2020) | Cell viability drivers versus neutral, default categorical calls (Chen et al. 2020 Additional file 22) | Negative predictive value (cell viability drivers versus neutral, default categorical calls) | 0.55 |
|---|
| Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 22: Performance metrics of 17 algorithms, default categories, benchmark 5 (cell viability) | MetaSVM (Chen et al. 2020) | Cell viability drivers versus neutral, default categorical calls (Chen et al. 2020 Additional file 22) | Precision (cell viability drivers versus neutral, default categorical calls) | 0.57 |
|---|
| Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 22: Performance metrics of 17 algorithms, default categories, benchmark 5 (cell viability) | MetaSVM (Chen et al. 2020) | Cell viability drivers versus neutral, default categorical calls (Chen et al. 2020 Additional file 22) | Recall (cell viability drivers versus neutral, default categorical calls) | 0.51 |
|---|
| Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 22: Performance metrics of 17 algorithms, default categories, benchmark 5 (cell viability) | MetaSVM (Chen et al. 2020) | Cell viability drivers versus neutral, default categorical calls (Chen et al. 2020 Additional file 22) | Specificity (cell viability drivers versus neutral, default categorical calls) | 0.61 |
|---|
| Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 21: Performance metrics of 33 algorithms, median-score threshold, benchmark 5 (cell viability) | MetaSVM (Chen et al. 2020) | Cell viability drivers versus neutral, median-score threshold (Chen et al. 2020 Additional file 21) | Accuracy (cell viability drivers versus neutral, median-score threshold) | 0.55 |
|---|
| Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 21: Performance metrics of 33 algorithms, median-score threshold, benchmark 5 (cell viability) | MetaSVM (Chen et al. 2020) | Cell viability drivers versus neutral, median-score threshold (Chen et al. 2020 Additional file 21) | Negative predictive value (cell viability drivers versus neutral, median-score threshold) | 0.55 |
|---|
| Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 21: Performance metrics of 33 algorithms, median-score threshold, benchmark 5 (cell viability) | MetaSVM (Chen et al. 2020) | Cell viability drivers versus neutral, median-score threshold (Chen et al. 2020 Additional file 21) | Precision (cell viability drivers versus neutral, median-score threshold) | 0.55 |
|---|
| Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 21: Performance metrics of 33 algorithms, median-score threshold, benchmark 5 (cell viability) | MetaSVM (Chen et al. 2020) | Cell viability drivers versus neutral, median-score threshold (Chen et al. 2020 Additional file 21) | Recall (cell viability drivers versus neutral, median-score threshold) | 0.55 |
|---|
| Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 21: Performance metrics of 33 algorithms, median-score threshold, benchmark 5 (cell viability) | MetaSVM (Chen et al. 2020) | Cell viability drivers versus neutral, median-score threshold (Chen et al. 2020 Additional file 21) | Specificity (cell viability drivers versus neutral, median-score threshold) | 0.55 |
|---|
| Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 10: Performance metrics of 17 algorithms, default categories, benchmark 2 (OncoKB) | PrimateAI (Chen et al. 2020) | OncoKB benchmark (positive set not stated), default categorical calls (Chen et al. 2020 Additional file 10) | Accuracy (OncoKB benchmark (positive set not stated), default categorical calls) | 0.66 |
|---|
| Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 10: Performance metrics of 17 algorithms, default categories, benchmark 2 (OncoKB) | PrimateAI (Chen et al. 2020) | OncoKB benchmark (positive set not stated), default categorical calls (Chen et al. 2020 Additional file 10) | Negative predictive value (OncoKB benchmark (positive set not stated), default categorical calls) | 0.71 |
|---|
| Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 10: Performance metrics of 17 algorithms, default categories, benchmark 2 (OncoKB) | PrimateAI (Chen et al. 2020) | OncoKB benchmark (positive set not stated), default categorical calls (Chen et al. 2020 Additional file 10) | Precision (OncoKB benchmark (positive set not stated), default categorical calls) | 0.63 |
|---|
| Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 10: Performance metrics of 17 algorithms, default categories, benchmark 2 (OncoKB) | PrimateAI (Chen et al. 2020) | OncoKB benchmark (positive set not stated), default categorical calls (Chen et al. 2020 Additional file 10) | Recall (OncoKB benchmark (positive set not stated), default categorical calls) | 0.77 |
|---|
| Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 10: Performance metrics of 17 algorithms, default categories, benchmark 2 (OncoKB) | PrimateAI (Chen et al. 2020) | OncoKB benchmark (positive set not stated), default categorical calls (Chen et al. 2020 Additional file 10) | Specificity (OncoKB benchmark (positive set not stated), default categorical calls) | 0.55 |
|---|
| Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 9: Performance metrics of 33 algorithms, median-score threshold, benchmark 2 (OncoKB) | PrimateAI (Chen et al. 2020) | OncoKB benchmark (positive set not stated), median-score threshold (Chen et al. 2020 Additional file 9) | Accuracy (OncoKB benchmark (positive set not stated), median-score threshold) | 0.66 |
|---|
| Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 9: Performance metrics of 33 algorithms, median-score threshold, benchmark 2 (OncoKB) | PrimateAI (Chen et al. 2020) | OncoKB benchmark (positive set not stated), median-score threshold (Chen et al. 2020 Additional file 9) | Negative predictive value (OncoKB benchmark (positive set not stated), median-score threshold) | 0.66 |
|---|
| Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 9: Performance metrics of 33 algorithms, median-score threshold, benchmark 2 (OncoKB) | PrimateAI (Chen et al. 2020) | OncoKB benchmark (positive set not stated), median-score threshold (Chen et al. 2020 Additional file 9) | Precision (OncoKB benchmark (positive set not stated), median-score threshold) | 0.66 |
|---|
| Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 9: Performance metrics of 33 algorithms, median-score threshold, benchmark 2 (OncoKB) | PrimateAI (Chen et al. 2020) | OncoKB benchmark (positive set not stated), median-score threshold (Chen et al. 2020 Additional file 9) | Recall (OncoKB benchmark (positive set not stated), median-score threshold) | 0.66 |
|---|
| Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 9: Performance metrics of 33 algorithms, median-score threshold, benchmark 2 (OncoKB) | PrimateAI (Chen et al. 2020) | OncoKB benchmark (positive set not stated), median-score threshold (Chen et al. 2020 Additional file 9) | Specificity (OncoKB benchmark (positive set not stated), median-score threshold) | 0.66 |
|---|
| Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 22: Performance metrics of 17 algorithms, default categories, benchmark 5 (cell viability) | PrimateAI (Chen et al. 2020) | Cell viability drivers versus neutral, default categorical calls (Chen et al. 2020 Additional file 22) | Accuracy (cell viability drivers versus neutral, default categorical calls) | 0.65 |
|---|
| Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 22: Performance metrics of 17 algorithms, default categories, benchmark 5 (cell viability) | PrimateAI (Chen et al. 2020) | Cell viability drivers versus neutral, default categorical calls (Chen et al. 2020 Additional file 22) | Negative predictive value (cell viability drivers versus neutral, default categorical calls) | 0.7 |
|---|
| Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 22: Performance metrics of 17 algorithms, default categories, benchmark 5 (cell viability) | PrimateAI (Chen et al. 2020) | Cell viability drivers versus neutral, default categorical calls (Chen et al. 2020 Additional file 22) | Precision (cell viability drivers versus neutral, default categorical calls) | 0.63 |
|---|
| Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 22: Performance metrics of 17 algorithms, default categories, benchmark 5 (cell viability) | PrimateAI (Chen et al. 2020) | Cell viability drivers versus neutral, default categorical calls (Chen et al. 2020 Additional file 22) | Recall (cell viability drivers versus neutral, default categorical calls) | 0.79 |
|---|
| Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 22: Performance metrics of 17 algorithms, default categories, benchmark 5 (cell viability) | PrimateAI (Chen et al. 2020) | Cell viability drivers versus neutral, default categorical calls (Chen et al. 2020 Additional file 22) | Specificity (cell viability drivers versus neutral, default categorical calls) | 0.52 |
|---|
| Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 21: Performance metrics of 33 algorithms, median-score threshold, benchmark 5 (cell viability) | PrimateAI (Chen et al. 2020) | Cell viability drivers versus neutral, median-score threshold (Chen et al. 2020 Additional file 21) | Accuracy (cell viability drivers versus neutral, median-score threshold) | 0.7 |
|---|
| Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 21: Performance metrics of 33 algorithms, median-score threshold, benchmark 5 (cell viability) | PrimateAI (Chen et al. 2020) | Cell viability drivers versus neutral, median-score threshold (Chen et al. 2020 Additional file 21) | Negative predictive value (cell viability drivers versus neutral, median-score threshold) | 0.7 |
|---|
| Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 21: Performance metrics of 33 algorithms, median-score threshold, benchmark 5 (cell viability) | PrimateAI (Chen et al. 2020) | Cell viability drivers versus neutral, median-score threshold (Chen et al. 2020 Additional file 21) | Precision (cell viability drivers versus neutral, median-score threshold) | 0.7 |
|---|
| Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 21: Performance metrics of 33 algorithms, median-score threshold, benchmark 5 (cell viability) | PrimateAI (Chen et al. 2020) | Cell viability drivers versus neutral, median-score threshold (Chen et al. 2020 Additional file 21) | Recall (cell viability drivers versus neutral, median-score threshold) | 0.7 |
|---|
| Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 21: Performance metrics of 33 algorithms, median-score threshold, benchmark 5 (cell viability) | PrimateAI (Chen et al. 2020) | Cell viability drivers versus neutral, median-score threshold (Chen et al. 2020 Additional file 21) | Specificity (cell viability drivers versus neutral, median-score threshold) | 0.7 |
|---|
| An openly licensed benchmark and per-gene calibration map for missense pathogenicity predictors on activating cancer drivers; OncoCal repository, tables/tool_performance.tsv (per-tool AUROC and AUPRC by gene role) | MetaSVM rank score, dbNSFP 5.3.1a (Lee 2026) | CGC missense oncogenicity, all genes (Lee 2026, OncoCal tool_performance.tsv) | AUROC (CGC missense, all genes) | 0.762 |
|---|
| An openly licensed benchmark and per-gene calibration map for missense pathogenicity predictors on activating cancer drivers; OncoCal repository, tables/tool_performance.tsv (per-tool AUROC and AUPRC by gene role) | MetaSVM rank score, dbNSFP 5.3.1a (Lee 2026) | CGC missense oncogenicity, oncogenes (gain of function) (Lee 2026, OncoCal tool_performance.tsv) | Average precision (CGC missense, oncogenes (gain of function)) | 0.563 |
|---|
| An openly licensed benchmark and per-gene calibration map for missense pathogenicity predictors on activating cancer drivers; OncoCal repository, tables/tool_performance.tsv (per-tool AUROC and AUPRC by gene role) | MetaSVM rank score, dbNSFP 5.3.1a (Lee 2026) | CGC missense oncogenicity, oncogenes (gain of function) (Lee 2026, OncoCal tool_performance.tsv) | AUROC (CGC missense, oncogenes (gain of function)) | 0.766 |
|---|
| An openly licensed benchmark and per-gene calibration map for missense pathogenicity predictors on activating cancer drivers; OncoCal repository, tables/tool_performance.tsv (per-tool AUROC and AUPRC by gene role) | MetaSVM rank score, dbNSFP 5.3.1a (Lee 2026) | CGC missense oncogenicity, tumour suppressors (loss of function) (Lee 2026, OncoCal tool_performance.tsv) | AUROC (CGC missense, tumour suppressors (loss of function)) | 0.794 |
|---|
| An openly licensed benchmark and per-gene calibration map for missense pathogenicity predictors on activating cancer drivers; OncoCal repository, tables/tool_performance.tsv (per-tool AUROC and AUPRC by gene role) | PrimateAI rank score, dbNSFP 5.3.1a (Lee 2026) | CGC missense oncogenicity, all genes (Lee 2026, OncoCal tool_performance.tsv) | AUROC (CGC missense, all genes) | 0.797 |
|---|
| An openly licensed benchmark and per-gene calibration map for missense pathogenicity predictors on activating cancer drivers; OncoCal repository, tables/tool_performance.tsv (per-tool AUROC and AUPRC by gene role) | PrimateAI rank score, dbNSFP 5.3.1a (Lee 2026) | CGC missense oncogenicity, oncogenes (gain of function) (Lee 2026, OncoCal tool_performance.tsv) | Average precision (CGC missense, oncogenes (gain of function)) | 0.618 |
|---|
| An openly licensed benchmark and per-gene calibration map for missense pathogenicity predictors on activating cancer drivers; OncoCal repository, tables/tool_performance.tsv (per-tool AUROC and AUPRC by gene role) | PrimateAI rank score, dbNSFP 5.3.1a (Lee 2026) | CGC missense oncogenicity, oncogenes (gain of function) (Lee 2026, OncoCal tool_performance.tsv) | AUROC (CGC missense, oncogenes (gain of function)) | 0.817 |
|---|
| An openly licensed benchmark and per-gene calibration map for missense pathogenicity predictors on activating cancer drivers; OncoCal repository, tables/tool_performance.tsv (per-tool AUROC and AUPRC by gene role) | PrimateAI rank score, dbNSFP 5.3.1a (Lee 2026) | CGC missense oncogenicity, tumour suppressors (loss of function) (Lee 2026, OncoCal tool_performance.tsv) | AUROC (CGC missense, tumour suppressors (loss of function)) | 0.872 |
|---|