rewirebio.iobenchmarks
Contested result

CADD and PrimateAI: Comprehensive assessment of computational algorithms in predicting cancer driver mutations reverses the order

  • Low severity
  • Not yet reviewed
  • Ordering reversal
  • Different tasks across studies

Comprehensive assessment of computational algorithms in predicting cancer driver mutations ranks PrimateAI above CADD in 10 of 10 comparisons; other studies rank CADD above PrimateAI in 15 of 17.

An automatic lead, not a finding of error. The results below are shown as published, everywhere on the site, until a reviewer decides otherwise.

Which studies agree

3 papers rank CADD above PrimateAI in 15 of their 18 comparisons and PrimateAI higher in 2; 1 is a tie. Comprehensive assessment… ranks PrimateAI above CADD in all 10 of its comparisons.

Papers grouped by which system they rank higher: CADD or PrimateAIRank CADD higher: Livesey and Marsh 2025, An openly licensed benchmark…. Rank PrimateAI higher: Comprehensive assessment… (disagrees). Mixed: Clinical likelihood ratios and balanced….Rank CADD higherMixedRank PrimateAI higherLivesey and Marsh 2025Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML)An openly licensedbenchmark…An openly licensed benchmark and per-gene calibration map for missense pathogenicity predictors on activating cancer driversClinical likelihoodratios and balanced…Clinical likelihood ratios and balanced accuracy for 44 in silico tools against multiple large-scale functional assays of cancer susceptibility genesComprehensive assessment…Comprehensive assessment of computational algorithms in predicting cancer driver mutations
  • Rank CADD higher (2): Livesey and Marsh 2025, An openly licensed benchmark…
  • Rank PrimateAI higher (1): Comprehensive assessment… (disagrees)
  • Mixed (1): Clinical likelihood ratios and balanced…

Each paper sits where at least two thirds of its comparisons point; Mixed means neither model reaches two thirds. The larger, outlined dot is the paper that disagrees.

Comparisons in which each paper scores CADD or PrimateAI higherComprehensive assessment… (disagrees): CADD higher in 0, PrimateAI higher in 10, 0 ties. Livesey and Marsh 2025: CADD higher in 7, PrimateAI higher in 0, 0 ties. An openly licensed benchmark…: CADD higher in 5, PrimateAI higher in 0, 1 tie. Clinical likelihood ratios and balanced…: CADD higher in 3, PrimateAI higher in 2, 0 ties.Disagrees: Comprehensive assessment…Comprehensive assessment of computational algorithms in predicting cancer driver mutations10 PrimateAI higher10Livesey and Marsh 2025Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML)7 CADD higher7An openly licensed benchmark…An openly licensed benchmark and per-gene calibration map for missense pathogenicity predictors on activating cancer drivers5 CADD higher51 tie1Clinical likelihood ratios and balanced…Clinical likelihood ratios and balanced accuracy for 44 in silico tools against multiple large-scale functional assays of cancer susceptibility genes3 CADD higher32 PrimateAI higher2
CADD higherTiePrimateAI higherNumbers are counts of comparisons; the outlined bar is the paper that disagrees.
Papers compared (4)
PaperRoleRanks CADD higherRanks PrimateAI higherTies
Comprehensive assessment of computational algorithms in predicting cancer driver mutations and 4 more records · DOIDisagrees0100
Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML) and 1 more record · DOIAgrees700
An openly licensed benchmark and per-gene calibration map for missense pathogenicity predictors on activating cancer drivers and 1 more record · DOIAgrees501
Clinical likelihood ratios and balanced accuracy for 44 in silico tools against multiple large-scale functional assays of cancer susceptibility genes and 1 more record · DOIAgrees320
Comparisons in Comprehensive assessment… (10)
ProtocolDatasetMetricCADDPrimateAIHigher
OncoKB benchmark (positive set not stated), median-score threshold (Chen et al. 2020 Additional file 9)OncoKB-annotated somatic missense mutations (oncogenic, likely oncogenic and likely neutral)Accuracy (OncoKB benchmark (positive set not stated), median-score threshold), fraction0.640.66PrimateAI
OncoKB benchmark (positive set not stated), median-score threshold (Chen et al. 2020 Additional file 9)OncoKB-annotated somatic missense mutations (oncogenic, likely oncogenic and likely neutral)Negative predictive value (OncoKB benchmark (positive set not stated), median-score threshold), fraction0.640.66PrimateAI
OncoKB benchmark (positive set not stated), median-score threshold (Chen et al. 2020 Additional file 9)OncoKB-annotated somatic missense mutations (oncogenic, likely oncogenic and likely neutral)Precision (OncoKB benchmark (positive set not stated), median-score threshold), fraction0.640.66PrimateAI
OncoKB benchmark (positive set not stated), median-score threshold (Chen et al. 2020 Additional file 9)OncoKB-annotated somatic missense mutations (oncogenic, likely oncogenic and likely neutral)Recall (OncoKB benchmark (positive set not stated), median-score threshold), fraction0.640.66PrimateAI
OncoKB benchmark (positive set not stated), median-score threshold (Chen et al. 2020 Additional file 9)OncoKB-annotated somatic missense mutations (oncogenic, likely oncogenic and likely neutral)Specificity (OncoKB benchmark (positive set not stated), median-score threshold), fraction0.640.66PrimateAI
Cell viability drivers versus neutral, median-score threshold (Chen et al. 2020 Additional file 21)Missense mutations with Ba/F3 and MCF10A cell viability calls (published and new)Accuracy (cell viability drivers versus neutral, median-score threshold), fraction0.590.7PrimateAI
Cell viability drivers versus neutral, median-score threshold (Chen et al. 2020 Additional file 21)Missense mutations with Ba/F3 and MCF10A cell viability calls (published and new)Negative predictive value (cell viability drivers versus neutral, median-score threshold), fraction0.590.7PrimateAI
Cell viability drivers versus neutral, median-score threshold (Chen et al. 2020 Additional file 21)Missense mutations with Ba/F3 and MCF10A cell viability calls (published and new)Precision (cell viability drivers versus neutral, median-score threshold), fraction0.590.7PrimateAI
Cell viability drivers versus neutral, median-score threshold (Chen et al. 2020 Additional file 21)Missense mutations with Ba/F3 and MCF10A cell viability calls (published and new)Recall (cell viability drivers versus neutral, median-score threshold), fraction0.590.7PrimateAI
Cell viability drivers versus neutral, median-score threshold (Chen et al. 2020 Additional file 21)Missense mutations with Ba/F3 and MCF10A cell viability calls (published and new)Specificity (cell viability drivers versus neutral, median-score threshold), fraction0.590.7PrimateAI
Comparisons in Livesey and Marsh 2025 (7)
ProtocolDatasetMetricCADDPrimateAIHigher
Livesey and Marsh 2025 clinical benchmark: pairwise AUROC win rate, ClinVar pathogenic versus gnomAD missenseLivesey and Marsh 2025 clinical set: ClinVar pathogenic versus gnomAD v4.1 missense (819 proteins)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), percent52.131.6CADD
Livesey and Marsh 2025 DMS benchmark: pairwise |Spearman| win rate, all 36 DMS setsLivesey and Marsh 2025 human DMS benchmark set (36 proteins)Pairwise win rate (average pairwise win rate against all other VEPs, |Spearman| with DMS on shared variants, 36 human DMS sets), percent40.319.9CADD
Livesey and Marsh 2025 DMS benchmark: pairwise |Spearman| win rate, all 36 DMS setsLivesey and Marsh 2025 human DMS benchmark set (36 proteins)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), percent2.891.22CADD
Livesey and Marsh 2025 DMS benchmark: pairwise |Spearman| win rate, direct assays onlyLivesey and Marsh 2025 human DMS benchmark set (36 proteins)Pairwise win rate (average pairwise win rate against all other VEPs, |Spearman| with DMS on shared variants, direct DMS assays only), percent3917.5CADD
Livesey and Marsh 2025 DMS benchmark: pairwise |Spearman| win rate, indirect assays onlyLivesey and Marsh 2025 human DMS benchmark set (36 proteins)Pairwise win rate (average pairwise win rate against all other VEPs, |Spearman| with DMS on shared variants, indirect DMS assays only), percent41.221.7CADD
Livesey and Marsh 2025 DMS benchmark: pairwise |Spearman| win rate, SNV-accessible substitutions onlyLivesey and Marsh 2025 human DMS benchmark set (36 proteins)Pairwise win rate (average pairwise win rate against all other VEPs, |Spearman| with DMS on shared variants, SNV-accessible substitutions only), percent40.319.9CADD
Livesey and Marsh 2025 DMS benchmark: pairwise |Spearman| win rate, SNV-accessible substitutions with gaps filledLivesey and Marsh 2025 human DMS benchmark set (36 proteins)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), percent4221CADD
Comparisons in An openly licensed benchmark… (6)
ProtocolDatasetMetricCADDPrimateAIHigher
CGC missense oncogenicity, all genes (Lee 2026, OncoCal tool_performance.tsv)Missense variants in 768 Cancer Gene Census genes with open oncogenicity labelsAUROC (CGC missense, all genes), unitless0.8260.797CADD
CGC missense oncogenicity, all genes (Lee 2026, OncoCal tool_performance.tsv)Missense variants in 768 Cancer Gene Census genes with open oncogenicity labelsAverage precision (CGC missense, all genes), unitless0.5960.508CADD
CGC missense oncogenicity, oncogenes (gain of function) (Lee 2026, OncoCal tool_performance.tsv)Missense variants in 768 Cancer Gene Census genes with open oncogenicity labelsAUROC (CGC missense, oncogenes (gain of function)), unitless0.8210.817Tie
CGC missense oncogenicity, oncogenes (gain of function) (Lee 2026, OncoCal tool_performance.tsv)Missense variants in 768 Cancer Gene Census genes with open oncogenicity labelsAverage precision (CGC missense, oncogenes (gain of function)), unitless0.6420.618CADD
CGC missense oncogenicity, tumour suppressors (loss of function) (Lee 2026, OncoCal tool_performance.tsv)Missense variants in 768 Cancer Gene Census genes with open oncogenicity labelsAUROC (CGC missense, tumour suppressors (loss of function)), unitless0.8940.872CADD
CGC missense oncogenicity, tumour suppressors (loss of function) (Lee 2026, OncoCal tool_performance.tsv)Missense variants in 768 Cancer Gene Census genes with open oncogenicity labelsAverage precision (CGC missense, tumour suppressors (loss of function)), unitless0.6460.505CADD
Comparisons in Clinical likelihood ratios and balanced… (5)
ProtocolDatasetMetricCADDPrimateAIHigher
BRCA1 functional truth set, binary in silico calls (Cubuk et al. 2021)BRCA1 functional truth set: 1641 missense variants classed deleterious or tolerated by HAP1 cell survival after saturation genome editingBenignity likelihood ratio (Source 'negative likelihood ratio' (TNR/FNR): likelihood ratio for benignity of a tolerated call, BRCA1 functional truth set), unitless36.71.2CADD
BRCA1 functional truth set, binary in silico calls (Cubuk et al. 2021)BRCA1 functional truth set: 1641 missense variants classed deleterious or tolerated by HAP1 cell survival after saturation genome editingFalse negatives (Functionally deleterious BRCA1 variants called tolerated), count0184CADD
BRCA1 functional truth set, binary in silico calls (Cubuk et al. 2021)BRCA1 functional truth set: 1641 missense variants classed deleterious or tolerated by HAP1 cell survival after saturation genome editingLikelihood ratio (Positive likelihood ratio of a deleterious call (TPR/FPR), BRCA1 functional truth set), unitless1.113.2PrimateAI
BRCA2 functional truth set, binary in silico calls (Cubuk et al. 2021)BRCA2 functional truth set: 188 missense variants classed deleterious or tolerated by Homology-directed repairFalse negatives (Functionally deleterious BRCA2 variants called tolerated), count032CADD
BRCA2 functional truth set, binary in silico calls (Cubuk et al. 2021)BRCA2 functional truth set: 188 missense variants classed deleterious or tolerated by Homology-directed repairLikelihood ratio (Positive likelihood ratio of a deleterious call (TPR/FPR), BRCA2 functional truth set), unitless1.076.78PrimateAI

What disagrees

Other studies
rank CADD above PrimateAI in 15 of 17 comparisons
This study
ranks PrimateAI above CADD in 10 of 10 comparisons
Compared
CADD, PrimateAI
Study that disagrees
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), Chen et al. 2020, Additional file 21: Performance metrics of 33 algorithms, median-score threshold, benchmark 5 (cell viability), Chen et al. 2020, Additional file 22: Performance metrics of 17 algorithms, default categories, benchmark 5 (cell viability), Chen et al. 2020, Additional file 9: Performance metrics of 33 algorithms, median-score threshold, benchmark 2 (OncoKB)
Other studies
6 studiesClinical 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), 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), Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML), Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8)
Rule
Ordering reversal; different tasks across studies
Model identity
Matched across studies partly through links not yet reviewed

The compared numbers

Each value links to its result record and source. Compare values within one protocol; the rule compares rankings, not raw values across protocols.

In the study that disagrees (20)
StudyToolProtocol or benchmarkMetricValue
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)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 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
In the other studies (35)
StudyToolProtocol or benchmarkMetricValue
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)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)Likelihood ratio (Positive likelihood ratio of a deleterious call (TPR/FPR), BRCA2 functional truth set)6.78
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)CADD_raw 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)0.596
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)CADD_raw rank score, dbNSFP 5.3.1a (Lee 2026)CGC missense oncogenicity, all genes (Lee 2026, OncoCal tool_performance.tsv)AUROC (CGC missense, all genes)0.826
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)CADD_raw 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))0.642
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)CADD_raw 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))0.646
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)CADD_raw 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))0.894
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)Average precision (CGC missense, all genes)0.508
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, tumour suppressors (loss of function) (Lee 2026, OncoCal tool_performance.tsv)Average precision (CGC missense, tumour suppressors (loss of function))0.505
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
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 missensePairwise 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)PrimateAI (Livesey and Marsh 2025)Livesey and Marsh 2025 clinical benchmark: pairwise AUROC win rate, ClinVar pathogenic versus gnomAD missensePairwise 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)31.6
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 setsPairwise 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 setsPairwise 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)PrimateAI (Livesey and Marsh 2025)Livesey and Marsh 2025 DMS benchmark: pairwise |Spearman| win rate, all 36 DMS setsPairwise win rate (average pairwise win rate against all other VEPs, |Spearman| with DMS on shared variants, 36 human DMS sets)19.9
Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML)PrimateAI (Livesey and Marsh 2025)Livesey and Marsh 2025 DMS benchmark: pairwise |Spearman| win rate, all 36 DMS setsPairwise 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)1.22
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 onlyPairwise 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)PrimateAI (Livesey and Marsh 2025)Livesey and Marsh 2025 DMS benchmark: pairwise |Spearman| win rate, direct assays onlyPairwise win rate (average pairwise win rate against all other VEPs, |Spearman| with DMS on shared variants, direct DMS assays only)17.5
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 onlyPairwise 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)PrimateAI (Livesey and Marsh 2025)Livesey and Marsh 2025 DMS benchmark: pairwise |Spearman| win rate, indirect assays onlyPairwise win rate (average pairwise win rate against all other VEPs, |Spearman| with DMS on shared variants, indirect DMS assays only)21.7
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, SNV-accessible substitutions onlyPairwise 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)CADD_1.7 (Livesey and Marsh 2025)Livesey and Marsh 2025 DMS benchmark: pairwise |Spearman| win rate, SNV-accessible substitutions with gaps filledPairwise 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)PrimateAI (Livesey and Marsh 2025)Livesey and Marsh 2025 DMS benchmark: pairwise |Spearman| win rate, SNV-accessible substitutions with gaps filledPairwise 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)21
Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML)PrimateAI (Livesey and Marsh 2025)Livesey and Marsh 2025 DMS benchmark: pairwise |Spearman| win rate, SNV-accessible substitutions onlyPairwise win rate (average pairwise win rate against all other VEPs, |Spearman| with DMS on shared variants, SNV-accessible substitutions only)19.9

Follow-up

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Finding discrepancy-4ff86c73f7766b73 · All contested results · How findings are made and followed up