| 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) | M-CAP (Cubuk et al. 2021) | BRCA1 functional truth set, binary in silico calls (Cubuk et al. 2021) | False negatives (Functionally deleterious BRCA1 variants called tolerated) | 0 |
|---|
| 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) | M-CAP (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 |
|---|
| 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) | MVP (Cubuk et al. 2021) | BRCA1 functional truth set, binary in silico calls (Cubuk et al. 2021) | False negatives (Functionally deleterious BRCA1 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) | MVP (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) | 1.22 |
|---|
| 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) | M-CAP (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) | 0.56 |
|---|
| 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) | M-CAP (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) | 0.56 |
|---|
| 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) | M-CAP (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) | 0.56 |
|---|
| 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) | M-CAP (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) | 0.56 |
|---|
| 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) | M-CAP (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) | 0.56 |
|---|
| 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) | M-CAP (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) | 0.58 |
|---|
| 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) | M-CAP (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) | 0.58 |
|---|
| 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) | M-CAP (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) | 0.58 |
|---|
| 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) | M-CAP (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) | 0.58 |
|---|
| 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) | M-CAP (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) | 0.58 |
|---|
| 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) | MVP (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) | 0.65 |
|---|
| 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) | MVP (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) | 0.65 |
|---|
| 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) | MVP (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) | 0.65 |
|---|
| 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) | MVP (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) | 0.65 |
|---|
| 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) | MVP (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) | 0.65 |
|---|
| 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) | MVP (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) | 0.64 |
|---|
| 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) | MVP (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) | 0.64 |
|---|
| 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) | MVP (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) | 0.64 |
|---|
| 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) | MVP (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) | 0.64 |
|---|
| 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) | MVP (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) | 0.64 |
|---|
| 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) | M-CAP rank score, dbNSFP 5.3.1a (Lee 2026) | CGC missense oncogenicity, all genes (Lee 2026, OncoCal tool_performance.tsv) | Average precision (CGC missense, all genes) | 0.481 |
|---|
| 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) | M-CAP rank score, dbNSFP 5.3.1a (Lee 2026) | CGC missense oncogenicity, all genes (Lee 2026, OncoCal tool_performance.tsv) | AUROC (CGC missense, all genes) | 0.758 |
|---|
| 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) | M-CAP 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)) | 0.544 |
|---|
| 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) | M-CAP 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)) | 0.775 |
|---|
| 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) | M-CAP rank score, dbNSFP 5.3.1a (Lee 2026) | CGC missense oncogenicity, tumour suppressors (loss of function) (Lee 2026, OncoCal tool_performance.tsv) | Average precision (CGC missense, tumour suppressors (loss of function)) | 0.478 |
|---|
| 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) | MVP rank score, dbNSFP 5.3.1a (Lee 2026) | CGC missense oncogenicity, all genes (Lee 2026, OncoCal tool_performance.tsv) | Average precision (CGC missense, all genes) | 0.57 |
|---|
| 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) | MVP rank score, dbNSFP 5.3.1a (Lee 2026) | CGC missense oncogenicity, all genes (Lee 2026, OncoCal tool_performance.tsv) | AUROC (CGC missense, all genes) | 0.807 |
|---|
| 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) | MVP 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)) | 0.657 |
|---|
| 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) | MVP 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)) | 0.815 |
|---|
| 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) | MVP rank score, dbNSFP 5.3.1a (Lee 2026) | CGC missense oncogenicity, tumour suppressors (loss of function) (Lee 2026, OncoCal tool_performance.tsv) | Average precision (CGC missense, tumour suppressors (loss of function)) | 0.502 |
|---|