rewirebio.iobenchmarks
Contested result

MutPred 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 MutPred in 9 of 10 comparisons; other studies rank MutPred above PrimateAI in 16 of 20.

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

4 papers rank MutPred above PrimateAI in 16 of their 20 comparisons and PrimateAI higher in 4. Of those papers, Clinical likelihood ratios and balanced… ranks PrimateAI higher on its own (4 of 6). Comprehensive assessment… ranks PrimateAI above MutPred in 9 of its 10 comparisons and MutPred higher in 1.

Papers grouped by which system they rank higher: MutPred or PrimateAIRank MutPred higher: Livesey and Marsh 2025, An openly licensed benchmark…, ProteinGym AMFR assay-level Spearman…. Rank PrimateAI higher: Comprehensive assessment… (disagrees), Clinical likelihood ratios and balanced…. Mixed: none.Rank MutPred 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 driversProteinGym AMFRassay-level Spearman…ProteinGym AMFR assay-level Spearman comparisonComprehensive assessment…Comprehensive assessment of computational algorithms in predicting cancer driver mutationsClinical likelihoodratios and balanced…Clinical likelihood ratios and balanced accuracy for 44 in silico tools against multiple large-scale functional assays of cancer susceptibility genes
  • Rank MutPred higher (3): Livesey and Marsh 2025, An openly licensed benchmark…, ProteinGym AMFR assay-level Spearman…
  • Rank PrimateAI higher (2): Comprehensive assessment… (disagrees), Clinical likelihood ratios and balanced…
  • Mixed (0): none

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 MutPred or PrimateAI higherComprehensive assessment… (disagrees): MutPred higher in 1, PrimateAI higher in 9, 0 ties. Livesey and Marsh 2025: MutPred higher in 7, PrimateAI higher in 0, 0 ties. Clinical likelihood ratios and balanced…: MutPred higher in 2, PrimateAI higher in 4, 0 ties. An openly licensed benchmark…: MutPred higher in 6, PrimateAI higher in 0, 0 ties. ProteinGym AMFR assay-level Spearman…: MutPred higher in 1, PrimateAI higher in 0, 0 ties.Disagrees: Comprehensive assessment…Comprehensive assessment of computational algorithms in predicting cancer driver mutations1 MutPred higher19 PrimateAI higher9Livesey and Marsh 2025Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML)7 MutPred higher7Clinical likelihood ratios and balanced…Clinical likelihood ratios and balanced accuracy for 44 in silico tools against multiple large-scale functional assays of cancer susceptibility genes2 MutPred higher24 PrimateAI higher4An openly licensed benchmark…An openly licensed benchmark and per-gene calibration map for missense pathogenicity predictors on activating cancer drivers6 MutPred higher6ProteinGym AMFR assay-level Spearman…ProteinGym AMFR assay-level Spearman comparison1 MutPred higher1
MutPred higherTiePrimateAI higherNumbers are counts of comparisons; the outlined bar is the paper that disagrees.
Comparisons in Comprehensive assessment… (10)
ProtocolDatasetMetricMutPredPrimateAIHigher
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), fraction0.530.66PrimateAI
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), fraction0.540.66PrimateAI
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), fraction0.520.66PrimateAI
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), fraction0.690.66MutPred
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), fraction0.360.66PrimateAI
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), fraction0.660.7PrimateAI
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), fraction0.660.7PrimateAI
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), fraction0.660.7PrimateAI
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), fraction0.660.7PrimateAI
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), fraction0.660.7PrimateAI
Comparisons in Livesey and Marsh 2025 (7)
ProtocolDatasetMetricMutPredPrimateAIHigher
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), percent65.131.6MutPred
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), percent47.919.9MutPred
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), percent14.41.22MutPred
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), percent46.517.5MutPred
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), percent4921.7MutPred
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), percent47.919.9MutPred
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), percent49.221MutPred
Comparisons in Clinical likelihood ratios and balanced… (6)
ProtocolDatasetMetricMutPredPrimateAIHigher
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), unitless1.181.2PrimateAI
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), count258184PrimateAI
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), unitless4.1813.2PrimateAI
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 repairBenignity likelihood ratio (Source 'negative likelihood ratio' (TNR/FNR): likelihood ratio for benignity of a tolerated call, BRCA2 functional truth set), unitless9.351.23MutPred
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), count332MutPred
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), unitless2.676.78PrimateAI
Comparisons in An openly licensed benchmark… (6)
ProtocolDatasetMetricMutPredPrimateAIHigher
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), unitless0.8460.797MutPred
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), unitless0.6640.508MutPred
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)), unitless0.8760.817MutPred
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)), unitless0.7730.618MutPred
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)), unitless0.8960.872MutPred
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)), unitless0.6610.505MutPred
Comparisons in ProteinGym AMFR assay-level Spearman… (1)
ProtocolDatasetMetricMutPredPrimateAIHigher
ProteinGym clinical substitutions, zero-shot, average AUC (commit 144fe22)ProteinGym clinical substitution benchmark at commit 144fe22AUROC (ProteinGym clinical substitutions, zero-shot, Average_AUC over proteins), unitless0.8750.855MutPred

What disagrees

Other studies
rank MutPred above PrimateAI in 16 of 20 comparisons
This study
ranks PrimateAI above MutPred in 9 of 10 comparisons
Compared
MutPred, 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
11 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), ProteinGym AMFR assay-level Spearman comparison, Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML), Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8), ProteinGym clinical supervised substitutions AUC summary at commit 144fe22, ProteinGym clinical zero-shot indels AUC summary at commit 144fe22, ProteinGym clinical zero-shot substitutions AUC summary at commit 144fe22, ProteinGym zero-shot DMS substitutions Spearman summary at commit 144fe22
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)MutPred (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)0.53
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)MutPred (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)0.54
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)MutPred (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)0.52
Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 9: Performance metrics of 33 algorithms, median-score threshold, benchmark 2 (OncoKB)MutPred (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)0.69
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)MutPred (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)0.36
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)MutPred (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)0.66
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)MutPred (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)0.66
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)MutPred (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)0.66
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)MutPred (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)0.66
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)MutPred (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)0.66
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)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 (40)
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)MutPred_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)4.18
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)MutPred_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)2.67
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)MutPred_c (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)1.18
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)MutPred_c (Cubuk et al. 2021)BRCA1 functional truth set, binary in silico calls (Cubuk et al. 2021)False negatives (Functionally deleterious BRCA1 variants called tolerated)258
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)MutPred_c (Cubuk et al. 2021)BRCA2 functional truth set, binary in silico calls (Cubuk et al. 2021)Benignity likelihood ratio (Source 'negative likelihood ratio' (TNR/FNR): likelihood ratio for benignity of a tolerated call, BRCA2 functional truth set)9.35
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)MutPred_c (Cubuk et al. 2021)BRCA2 functional truth set, binary in silico calls (Cubuk et al. 2021)False negatives (Functionally deleterious BRCA2 variants called tolerated)3
Clinical likelihood ratios and balanced accuracy for 44 in silico tools against multiple large-scale functional assays of cancer susceptibility genes; Cubuk et al. 2021, Supplementary tables 1-13 (41436_2021_1265_MOESM3_ESM.xlsx)primateAI_a (Cubuk et al. 2021)BRCA1 functional truth set, binary in silico calls (Cubuk et al. 2021)False negatives (Functionally deleterious BRCA1 variants called tolerated)184
Clinical likelihood ratios and balanced accuracy for 44 in silico tools against multiple large-scale functional assays of cancer susceptibility genes; Cubuk et al. 2021, Supplementary tables 1-13 (41436_2021_1265_MOESM3_ESM.xlsx)primateAI_a (Cubuk et al. 2021)BRCA1 functional truth set, binary in silico calls (Cubuk et al. 2021)Likelihood ratio (Positive likelihood ratio of a deleterious call (TPR/FPR), BRCA1 functional truth set)13.2
Clinical likelihood ratios and balanced accuracy for 44 in silico tools against multiple large-scale functional assays of cancer susceptibility genes; Cubuk et al. 2021, Supplementary tables 1-13 (41436_2021_1265_MOESM3_ESM.xlsx)primateAI_a (Cubuk et al. 2021)BRCA2 functional truth set, binary in silico calls (Cubuk et al. 2021)False negatives (Functionally deleterious BRCA2 variants called tolerated)32
Clinical likelihood ratios and balanced accuracy for 44 in silico tools against multiple large-scale functional assays of cancer susceptibility genes; Cubuk et al. 2021, Supplementary tables 1-13 (41436_2021_1265_MOESM3_ESM.xlsx)primateAI_b (Cubuk et al. 2021)BRCA1 functional truth set, binary in silico calls (Cubuk et al. 2021)Benignity likelihood ratio (Source 'negative likelihood ratio' (TNR/FNR): likelihood ratio for benignity of a tolerated call, BRCA1 functional truth set)1.2
Clinical likelihood ratios and balanced accuracy for 44 in silico tools against multiple large-scale functional assays of cancer susceptibility genes; Cubuk et al. 2021, Supplementary tables 1-13 (41436_2021_1265_MOESM3_ESM.xlsx)primateAI_b (Cubuk et al. 2021)BRCA2 functional truth set, binary in silico calls (Cubuk et al. 2021)Benignity likelihood ratio (Source 'negative likelihood ratio' (TNR/FNR): likelihood ratio for benignity of a tolerated call, BRCA2 functional truth set)1.23
Clinical likelihood ratios and balanced accuracy for 44 in silico tools against multiple large-scale functional assays of cancer susceptibility genes; Cubuk et al. 2021, Supplementary tables 1-13 (41436_2021_1265_MOESM3_ESM.xlsx)primateAI_b (Cubuk et al. 2021)BRCA2 functional truth set, binary in silico calls (Cubuk et al. 2021)Likelihood ratio (Positive likelihood ratio of a deleterious call (TPR/FPR), BRCA2 functional truth set)6.78
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)MutPred2 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)0.664
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)MutPred2 rank score, dbNSFP 5.3.1a (Lee 2026)CGC missense oncogenicity, all genes (Lee 2026, OncoCal tool_performance.tsv)AUROC (CGC missense, all genes)0.846
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)MutPred2 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))0.773
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)MutPred2 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))0.876
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)MutPred2 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))0.661
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)MutPred2 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))0.896
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, oncogenes (gain of function) (Lee 2026, OncoCal tool_performance.tsv)AUROC (CGC missense, oncogenes (gain of function))0.817
An openly licensed benchmark and per-gene calibration map for missense pathogenicity predictors on activating cancer drivers; OncoCal repository, tables/tool_performance.tsv (per-tool AUROC and AUPRC by gene role)PrimateAI rank score, dbNSFP 5.3.1a (Lee 2026)CGC missense oncogenicity, tumour suppressors (loss of function) (Lee 2026, OncoCal tool_performance.tsv)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)MutPred (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)65.1
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)MutPred (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)47.9
Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML)MutPred (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)14.4
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)MutPred (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)46.5
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)MutPred (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)49
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)MutPred (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)49.2
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, 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)MutPred (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)47.9
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, 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
ProteinGym clinical zero-shot substitutions AUC summary at commit 144fe22MutPred [ProteinGym 144fe22 clinical summary]ProteinGym clinical substitutions, zero-shot, average AUC (commit 144fe22)AUROC (ProteinGym clinical substitutions, zero-shot, Average_AUC over proteins)0.875
ProteinGym clinical zero-shot substitutions AUC summary at commit 144fe22PrimateAI [ProteinGym 144fe22 clinical summary]ProteinGym clinical substitutions, zero-shot, average AUC (commit 144fe22)AUROC (ProteinGym clinical substitutions, zero-shot, Average_AUC over proteins)0.855

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Finding discrepancy-605fd20ed6a27cf4 · All contested results · How findings are made and followed up