| 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) | CADD_phred_a (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) | 36.7 |
|---|
| 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) | CADD_phred_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) | 1.1 |
|---|
| 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) | CADD_phred_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) | 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) | CADD_phred_a (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) | 1.07 |
|---|
| 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) | CADD_phred_b (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) | CADD_phred_b (Cubuk et al. 2021) | BRCA2 functional truth set, binary in silico calls (Cubuk et al. 2021) | False negatives (Functionally deleterious BRCA2 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) | Eigen-PC_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) | 14 |
|---|
| 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) | Eigen-PC_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) | 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) | Eigen-PC_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) | 13.6 |
|---|
| 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) | Eigen-PC_b (Cubuk et al. 2021) | BRCA1 functional truth set, binary in silico calls (Cubuk et al. 2021) | False negatives (Functionally deleterious BRCA1 variants called tolerated) | 14 |
|---|
| 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) | Eigen-PC_b (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) | 2.73 |
|---|
| 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) | Eigen-PC_b (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) | Eigen-PC_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) | 1.43 |
|---|
| 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) | CADD (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) | 0.64 |
|---|
| 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) | CADD (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) | 0.64 |
|---|
| 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) | CADD (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) | 0.64 |
|---|
| 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) | CADD (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) | 0.64 |
|---|
| 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) | CADD (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) | 0.64 |
|---|
| 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) | CADD (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) | 0.59 |
|---|
| 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) | CADD (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) | 0.59 |
|---|
| 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) | CADD (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) | 0.59 |
|---|
| 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) | CADD (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) | 0.59 |
|---|
| 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) | CADD (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) | 0.59 |
|---|
| 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) | Eigen (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) | Eigen (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) | Eigen (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) | Eigen (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) | Eigen (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 9: Performance metrics of 33 algorithms, median-score threshold, benchmark 2 (OncoKB) | Eigen-PC (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) | Eigen-PC (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) | Eigen-PC (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) | Eigen-PC (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) | Eigen-PC (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 21: Performance metrics of 33 algorithms, median-score threshold, benchmark 5 (cell viability) | Eigen-PC (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) | 0.63 |
|---|
| 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) | Eigen-PC (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) | 0.63 |
|---|
| 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) | Eigen-PC (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) | 0.63 |
|---|
| 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) | Eigen-PC (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) | 0.63 |
|---|
| 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) | Eigen-PC (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) | 0.63 |
|---|
| Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML) | CADD_1.7 (Livesey and Marsh 2025) | Livesey and Marsh 2025 clinical benchmark: pairwise AUROC win rate, ClinVar pathogenic versus gnomAD missense | Pairwise win rate (average pairwise win rate against all other VEPs, per-protein AUROC on shared variants, ClinVar P/LP versus gnomAD v4.1 missense, proteins with at least 10 of each) | 52.1 |
|---|
| Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML) | Eigen (Livesey and Marsh 2025) | Livesey and Marsh 2025 clinical benchmark: pairwise AUROC win rate, ClinVar pathogenic versus gnomAD missense | Pairwise win rate (average pairwise win rate against all other VEPs, per-protein AUROC on shared variants, ClinVar P/LP versus gnomAD v4.1 missense, proteins with at least 10 of each) | 54.6 |
|---|
| Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML) | CADD_1.7 (Livesey and Marsh 2025) | Livesey and Marsh 2025 DMS benchmark: pairwise |Spearman| win rate, all 36 DMS sets | Pairwise win rate (average pairwise win rate against all other VEPs, |Spearman| with DMS on shared variants, 36 human DMS sets) | 40.3 |
|---|
| Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML) | CADD_1.7 (Livesey and Marsh 2025) | Livesey and Marsh 2025 DMS benchmark: pairwise |Spearman| win rate, all 36 DMS sets | Pairwise win rate (average pairwise win rate against the five top-ranked VEPs (column top_5_winrate; the five are not listed in the table), |Spearman| with DMS on shared variants, 36 human DMS sets) | 2.89 |
|---|
| Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML) | Eigen (Livesey and Marsh 2025) | Livesey and Marsh 2025 DMS benchmark: pairwise |Spearman| win rate, all 36 DMS sets | Pairwise win rate (average pairwise win rate against all other VEPs, |Spearman| with DMS on shared variants, 36 human DMS sets) | 52.4 |
|---|
| Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML) | Eigen (Livesey and Marsh 2025) | Livesey and Marsh 2025 DMS benchmark: pairwise |Spearman| win rate, all 36 DMS sets | Pairwise win rate (average pairwise win rate against the five top-ranked VEPs (column top_5_winrate; the five are not listed in the table), |Spearman| with DMS on shared variants, 36 human DMS sets) | 4.83 |
|---|
| Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML) | CADD_1.7 (Livesey and Marsh 2025) | Livesey and Marsh 2025 DMS benchmark: pairwise |Spearman| win rate, direct assays only | Pairwise win rate (average pairwise win rate against all other VEPs, |Spearman| with DMS on shared variants, direct DMS assays only) | 39 |
|---|
| Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML) | Eigen (Livesey and Marsh 2025) | Livesey and Marsh 2025 DMS benchmark: pairwise |Spearman| win rate, direct assays only | Pairwise win rate (average pairwise win rate against all other VEPs, |Spearman| with DMS on shared variants, direct DMS assays only) | 53.7 |
|---|
| Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML) | CADD_1.7 (Livesey and Marsh 2025) | Livesey and Marsh 2025 DMS benchmark: pairwise |Spearman| win rate, indirect assays only | Pairwise win rate (average pairwise win rate against all other VEPs, |Spearman| with DMS on shared variants, indirect DMS assays only) | 41.2 |
|---|
| Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML) | Eigen (Livesey and Marsh 2025) | Livesey and Marsh 2025 DMS benchmark: pairwise |Spearman| win rate, indirect assays only | Pairwise win rate (average pairwise win rate against all other VEPs, |Spearman| with DMS on shared variants, indirect DMS assays only) | 51.3 |
|---|
| Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML) | CADD_1.7 (Livesey and Marsh 2025) | Livesey and Marsh 2025 DMS benchmark: pairwise |Spearman| win rate, SNV-accessible substitutions only | Pairwise win rate (average pairwise win rate against all other VEPs, |Spearman| with DMS on shared variants, SNV-accessible substitutions only) | 40.3 |
|---|
| Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML) | Eigen (Livesey and Marsh 2025) | Livesey and Marsh 2025 DMS benchmark: pairwise |Spearman| win rate, SNV-accessible substitutions only | Pairwise win rate (average pairwise win rate against all other VEPs, |Spearman| with DMS on shared variants, SNV-accessible substitutions only) | 52.4 |
|---|
| Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML) | CADD_1.7 (Livesey and Marsh 2025) | Livesey and Marsh 2025 DMS benchmark: pairwise |Spearman| win rate, SNV-accessible substitutions with gaps filled | Pairwise win rate (average pairwise win rate against all other VEPs, |Spearman| with DMS, SNV-accessible substitutions, missing predictions filled with the VEP's most benign score) | 42 |
|---|
| Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML) | Eigen (Livesey and Marsh 2025) | Livesey and Marsh 2025 DMS benchmark: pairwise |Spearman| win rate, SNV-accessible substitutions with gaps filled | Pairwise win rate (average pairwise win rate against all other VEPs, |Spearman| with DMS, SNV-accessible substitutions, missing predictions filled with the VEP's most benign score) | 53.9 |
|---|