| 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) | fathmm-MKL_coding (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) | 14.5 |
|---|
| 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) | fathmm-MKL_coding (Cubuk et al. 2021) | BRCA1 functional truth set, binary in silico calls (Cubuk et al. 2021) | False negatives (Functionally deleterious BRCA1 variants called tolerated) | 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) | fathmm-MKL_coding (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) | fathmm-MKL_coding (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) | 1.21 |
|---|
| 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) | MetaLR (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) | 6.05 |
|---|
| 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) | MetaLR (Cubuk et al. 2021) | BRCA1 functional truth set, binary in silico calls (Cubuk et al. 2021) | False negatives (Functionally deleterious BRCA1 variants called tolerated) | 18 |
|---|
| 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) | MetaLR (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) | MetaLR (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) | 1.49 |
|---|
| 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) | FATHMM-MKL (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) | FATHMM-MKL (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) | 0.93 |
|---|
| 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) | FATHMM-MKL (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) | 0.98 |
|---|
| 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) | FATHMM-MKL (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) | 0.21 |
|---|
| 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) | FATHMM-MKL (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) | 0.63 |
|---|
| 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) | FATHMM-MKL (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) | 0.63 |
|---|
| 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) | FATHMM-MKL (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) | 0.63 |
|---|
| 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) | FATHMM-MKL (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) | 0.63 |
|---|
| 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) | FATHMM-MKL (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) | 0.63 |
|---|
| 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) | FATHMM-MKL (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) | 0.53 |
|---|
| 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) | FATHMM-MKL (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) | 0.71 |
|---|
| 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) | FATHMM-MKL (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) | 0.52 |
|---|
| 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) | FATHMM-MKL (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) | 0.97 |
|---|
| 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) | FATHMM-MKL (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) | 0.08 |
|---|
| 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) | FATHMM-MKL (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) | 0.62 |
|---|
| 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) | FATHMM-MKL (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) | 0.62 |
|---|
| 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) | FATHMM-MKL (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) | 0.62 |
|---|
| 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) | FATHMM-MKL (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) | 0.62 |
|---|
| 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) | FATHMM-MKL (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) | 0.62 |
|---|
| 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) | MetaLR (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) | 0.57 |
|---|
| 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) | MetaLR (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) | 0.59 |
|---|
| 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) | MetaLR (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) | 0.67 |
|---|
| 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) | MetaLR (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) | 0.47 |
|---|
| 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) | MetaLR (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) | 0.57 |
|---|
| 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) | MetaLR (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) | 0.57 |
|---|
| 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) | MetaLR (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) | 0.57 |
|---|
| 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) | MetaLR (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) | 0.57 |
|---|
| 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) | MetaLR (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) | 0.57 |
|---|
| 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) | MetaLR (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) | MetaLR (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) | MetaLR (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) | 0.56 |
|---|
| 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) | MetaLR (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) | 0.53 |
|---|
| 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) | MetaLR (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) | 0.59 |
|---|
| 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) | MetaLR (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) | 0.56 |
|---|
| 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) | MetaLR (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) | 0.56 |
|---|
| 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) | MetaLR (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) | 0.56 |
|---|
| 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) | MetaLR (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) | 0.56 |
|---|
| 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) | MetaLR (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) | 0.56 |
|---|