rewirebio.iobenchmarks
Contested result

FATHMM-XF and PolyPhen-2: 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 FATHMM-XF above PolyPhen-2 in 14 of 16 comparisons; other studies rank PolyPhen-2 above FATHMM-XF in 9 of 11.

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

2 papers rank PolyPhen-2 above FATHMM-XF in 9 of their 12 comparisons and FATHMM-XF higher in 2; 1 is a tie. Comprehensive assessment… ranks FATHMM-XF above PolyPhen-2 in 14 of its 20 comparisons and PolyPhen-2 higher in 2; 4 are ties.

Papers grouped by which system they rank higher: PolyPhen-2 or FATHMM-XFRank PolyPhen-2 higher: Livesey and Marsh 2025. Rank FATHMM-XF higher: Comprehensive assessment… (disagrees). Mixed: An openly licensed benchmark….Rank PolyPhen-2 higherMixedRank FATHMM-XF 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 driversComprehensive assessment…Comprehensive assessment of computational algorithms in predicting cancer driver mutations
  • Rank PolyPhen-2 higher (1): Livesey and Marsh 2025
  • Rank FATHMM-XF higher (1): Comprehensive assessment… (disagrees)
  • Mixed (1): An openly licensed benchmark…

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 PolyPhen-2 or FATHMM-XF higherComprehensive assessment… (disagrees): PolyPhen-2 higher in 2, FATHMM-XF higher in 14, 4 ties. Livesey and Marsh 2025: PolyPhen-2 higher in 6, FATHMM-XF higher in 0, 0 ties. An openly licensed benchmark…: PolyPhen-2 higher in 3, FATHMM-XF higher in 2, 1 tie.Disagrees: Comprehensive assessment…Comprehensive assessment of computational algorithms in predicting cancer driver mutations2 PolyPhen-2 higher24 tie414 FATHMM-XF higher14Livesey and Marsh 2025Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML)6 PolyPhen-2 higher6An openly licensed benchmark…An openly licensed benchmark and per-gene calibration map for missense pathogenicity predictors on activating cancer drivers3 PolyPhen-2 higher31 tie12 FATHMM-XF higher2
PolyPhen-2 higherTieFATHMM-XF higherNumbers are counts of comparisons; the outlined bar is the paper that disagrees.
Comparisons in Comprehensive assessment… (20)
ProtocolDatasetMetricPolyPhen-2FATHMM-XFHigher
OncoKB benchmark (positive set not stated), default categorical calls (Chen et al. 2020 Additional file 10)OncoKB-annotated somatic missense mutations (oncogenic, likely oncogenic and likely neutral)Accuracy (OncoKB benchmark (positive set not stated), default categorical calls), fraction0.640.66FATHMM-XF
OncoKB benchmark (positive set not stated), default categorical calls (Chen et al. 2020 Additional file 10)OncoKB-annotated somatic missense mutations (oncogenic, likely oncogenic and likely neutral)Negative predictive value (OncoKB benchmark (positive set not stated), default categorical calls), fraction0.790.85FATHMM-XF
OncoKB benchmark (positive set not stated), default categorical calls (Chen et al. 2020 Additional file 10)OncoKB-annotated somatic missense mutations (oncogenic, likely oncogenic and likely neutral)Precision (OncoKB benchmark (positive set not stated), default categorical calls), fraction0.60.6Tie
OncoKB benchmark (positive set not stated), default categorical calls (Chen et al. 2020 Additional file 10)OncoKB-annotated somatic missense mutations (oncogenic, likely oncogenic and likely neutral)Recall (OncoKB benchmark (positive set not stated), default categorical calls), fraction0.930.93Tie
OncoKB benchmark (positive set not stated), default categorical calls (Chen et al. 2020 Additional file 10)OncoKB-annotated somatic missense mutations (oncogenic, likely oncogenic and likely neutral)Specificity (OncoKB benchmark (positive set not stated), default categorical calls), fraction0.390.39Tie
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), fraction0.630.65FATHMM-XF
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), fraction0.630.65FATHMM-XF
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), fraction0.620.65FATHMM-XF
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), fraction0.640.65FATHMM-XF
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), fraction0.620.65FATHMM-XF
Cell viability drivers versus neutral, default categorical calls (Chen et al. 2020 Additional file 22)Missense mutations with Ba/F3 and MCF10A cell viability calls (published and new)Accuracy (cell viability drivers versus neutral, default categorical calls), fraction0.590.59Tie
Cell viability drivers versus neutral, default categorical calls (Chen et al. 2020 Additional file 22)Missense mutations with Ba/F3 and MCF10A cell viability calls (published and new)Negative predictive value (cell viability drivers versus neutral, default categorical calls), fraction0.70.72FATHMM-XF
Cell viability drivers versus neutral, default categorical calls (Chen et al. 2020 Additional file 22)Missense mutations with Ba/F3 and MCF10A cell viability calls (published and new)Precision (cell viability drivers versus neutral, default categorical calls), fraction0.570.56PolyPhen-2
Cell viability drivers versus neutral, default categorical calls (Chen et al. 2020 Additional file 22)Missense mutations with Ba/F3 and MCF10A cell viability calls (published and new)Recall (cell viability drivers versus neutral, default categorical calls), fraction0.880.89FATHMM-XF
Cell viability drivers versus neutral, default categorical calls (Chen et al. 2020 Additional file 22)Missense mutations with Ba/F3 and MCF10A cell viability calls (published and new)Specificity (cell viability drivers versus neutral, default categorical calls), fraction0.380.28PolyPhen-2
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), fraction0.590.63FATHMM-XF
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), fraction0.590.63FATHMM-XF
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), fraction0.590.63FATHMM-XF
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), fraction0.590.63FATHMM-XF
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), fraction0.590.63FATHMM-XF
Comparisons in Livesey and Marsh 2025 (6)
ProtocolDatasetMetricPolyPhen-2FATHMM-XFHigher
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), percent48.324.5PolyPhen-2
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), percent8.471.83PolyPhen-2
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), percent46.227.1PolyPhen-2
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), percent5022.6PolyPhen-2
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), percent48.424.5PolyPhen-2
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), percent49.725.5PolyPhen-2
Comparisons in An openly licensed benchmark… (6)
ProtocolDatasetMetricPolyPhen-2FATHMM-XFHigher
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), unitless0.8260.813PolyPhen-2
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), unitless0.550.568FATHMM-XF
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)), unitless0.820.81PolyPhen-2
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)), unitless0.6020.644FATHMM-XF
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)), unitless0.8520.857Tie
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)), unitless0.4820.476PolyPhen-2

What disagrees

Other studies
rank PolyPhen-2 above FATHMM-XF in 9 of 11 comparisons
This study
ranks FATHMM-XF above PolyPhen-2 in 14 of 16 comparisons
Compared
FATHMM-XF, PolyPhen-2
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
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 (33)
StudyToolProtocol or benchmarkMetricValue
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-XF (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)0.66
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-XF (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)0.85
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-XF (Chen et al. 2020)OncoKB benchmark (positive set not stated), median-score threshold (Chen et al. 2020 Additional file 9)Accuracy (OncoKB benchmark (positive set not stated), median-score threshold)0.65
Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 9: Performance metrics of 33 algorithms, median-score threshold, benchmark 2 (OncoKB)FATHMM-XF (Chen et al. 2020)OncoKB benchmark (positive set not stated), median-score threshold (Chen et al. 2020 Additional file 9)Negative predictive value (OncoKB benchmark (positive set not stated), median-score threshold)0.65
Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 9: Performance metrics of 33 algorithms, median-score threshold, benchmark 2 (OncoKB)FATHMM-XF (Chen et al. 2020)OncoKB benchmark (positive set not stated), median-score threshold (Chen et al. 2020 Additional file 9)Precision (OncoKB benchmark (positive set not stated), median-score threshold)0.65
Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 9: Performance metrics of 33 algorithms, median-score threshold, benchmark 2 (OncoKB)FATHMM-XF (Chen et al. 2020)OncoKB benchmark (positive set not stated), median-score threshold (Chen et al. 2020 Additional file 9)Recall (OncoKB benchmark (positive set not stated), median-score threshold)0.65
Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 9: Performance metrics of 33 algorithms, median-score threshold, benchmark 2 (OncoKB)FATHMM-XF (Chen et al. 2020)OncoKB benchmark (positive set not stated), median-score threshold (Chen et al. 2020 Additional file 9)Specificity (OncoKB benchmark (positive set not stated), median-score threshold)0.65
Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 22: Performance metrics of 17 algorithms, default categories, benchmark 5 (cell viability)FATHMM-XF (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)0.72
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-XF (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)FATHMM-XF (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)0.89
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-XF (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)0.28
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-XF (Chen et al. 2020)Cell viability drivers versus neutral, median-score threshold (Chen et al. 2020 Additional file 21)Accuracy (cell viability drivers versus neutral, median-score threshold)0.63
Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 21: Performance metrics of 33 algorithms, median-score threshold, benchmark 5 (cell viability)FATHMM-XF (Chen et al. 2020)Cell viability drivers versus neutral, median-score threshold (Chen et al. 2020 Additional file 21)Negative predictive value (cell viability drivers versus neutral, median-score threshold)0.63
Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 21: Performance metrics of 33 algorithms, median-score threshold, benchmark 5 (cell viability)FATHMM-XF (Chen et al. 2020)Cell viability drivers versus neutral, median-score threshold (Chen et al. 2020 Additional file 21)Precision (cell viability drivers versus neutral, median-score threshold)0.63
Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 21: Performance metrics of 33 algorithms, median-score threshold, benchmark 5 (cell viability)FATHMM-XF (Chen et al. 2020)Cell viability drivers versus neutral, median-score threshold (Chen et al. 2020 Additional file 21)Recall (cell viability drivers versus neutral, median-score threshold)0.63
Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 21: Performance metrics of 33 algorithms, median-score threshold, benchmark 5 (cell viability)FATHMM-XF (Chen et al. 2020)Cell viability drivers versus neutral, median-score threshold (Chen et al. 2020 Additional file 21)Specificity (cell viability drivers versus neutral, median-score threshold)0.63
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)Polyphen2_HDIV (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)0.79
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)Polyphen2_HDIV (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)Polyphen2_HDIV (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)Polyphen2_HDIV (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)0.62
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)Polyphen2_HDIV (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)Polyphen2_HDIV (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)0.62
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)Polyphen2_HDIV (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)0.7
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)Polyphen2_HDIV (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)0.88
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)Polyphen2_HVAR (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)0.64
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)Polyphen2_HVAR (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)0.62
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)Polyphen2_HVAR (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)0.57
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)Polyphen2_HVAR (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)0.38
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)Polyphen2_HVAR (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)Polyphen2_HVAR (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)Polyphen2_HVAR (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)Polyphen2_HVAR (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)Polyphen2_HVAR (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
In the other studies (22)
StudyToolProtocol or benchmarkMetricValue
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)fathmm-XF_coding 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)0.568
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)fathmm-XF_coding rank score, dbNSFP 5.3.1a (Lee 2026)CGC missense oncogenicity, all genes (Lee 2026, OncoCal tool_performance.tsv)AUROC (CGC missense, all genes)0.813
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)fathmm-XF_coding 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))0.644
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)fathmm-XF_coding 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))0.81
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)fathmm-XF_coding 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))0.476
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)Polyphen2_HVAR 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)0.55
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)Polyphen2_HVAR 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)Polyphen2_HVAR 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))0.602
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)Polyphen2_HVAR 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))0.82
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)Polyphen2_HVAR 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))0.482
Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML)FathmmXF (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)24.5
Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML)FathmmXF (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)1.83
Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML)PolyPhen2_HumVar (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)48.3
Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML)PolyPhen2_HumVar (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)8.47
Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML)FathmmXF (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)27.1
Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML)PolyPhen2_HumVar (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)46.2
Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML)FathmmXF (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)22.6
Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML)PolyPhen2_HumVar (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)50
Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML)FathmmXF (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)24.5
Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML)FathmmXF (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)25.5
Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML)PolyPhen2_HumVar (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)49.7
Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML)PolyPhen2_HumVar (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)48.4

Follow-up

Current state: Not yet reviewed.

No one has reviewed this finding yet.

How to help resolve this

If you know why these studies disagree, for example a different population, metric or preprocessing step, or you have a corrected value, tell us through the contribution form. A reviewer checks every submission against its source before anything changes, and the step is added to the follow-up above.

Finding discrepancy-10ebf525bc518113 · All contested results · How findings are made and followed up