Contested result

DANN and M-CAP: 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 DANN above M-CAP in 5 of 5 comparisons; other studies rank M-CAP above DANN in 12 of 15.

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 M-CAP above DANN in 12 of their 17 comparisons and DANN higher in 3; 2 are ties. Of those papers, Clinical likelihood ratios and balanced… ranks DANN higher on its own (2 of 4). Comprehensive assessment… ranks DANN above M-CAP in all 5 comparisons that are not ties (5 are ties).

Rank M-CAP higher (2): Livesey and Marsh 2025, An openly licensed benchmark…
Rank DANN 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.

M-CAP higherTieDANN higherNumbers are counts of comparisons; the outlined bar is the paper that disagrees.

Papers compared (4)
Paper	Role	Ranks M-CAP higher	Ranks DANN higher	Ties
Comprehensive assessment of computational algorithms in predicting cancer driver mutations and 4 more records · DOI	Disagrees	0	5	5
Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML) and 1 more record · DOI	Agrees	7	0	0
An openly licensed benchmark and per-gene calibration map for missense pathogenicity predictors on activating cancer drivers and 1 more record · DOI	Agrees	4	1	1
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 · DOI	Agrees	1	2	1

Comparisons in Comprehensive assessment… (10)

Protocol	Dataset	Metric	M-CAP	DANN	Higher
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), fraction	0.56	0.59	DANN
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), fraction	0.56	0.59	DANN
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), fraction	0.56	0.59	DANN
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), fraction	0.56	0.59	DANN
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), fraction	0.56	0.59	DANN
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), fraction	0.58	0.58	Tie
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), fraction	0.58	0.58	Tie
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), fraction	0.58	0.58	Tie
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), fraction	0.58	0.58	Tie
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), fraction	0.58	0.58	Tie

Comparisons in Livesey and Marsh 2025 (7)

Protocol	Dataset	Metric	M-CAP	DANN	Higher
Livesey and Marsh 2025 clinical benchmark: pairwise AUROC win rate, ClinVar pathogenic versus gnomAD missense	Livesey 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), percent	47.9	27.9	M-CAP
Livesey and Marsh 2025 DMS benchmark: pairwise \|Spearman\| win rate, all 36 DMS sets	Livesey 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), percent	40	13.4	M-CAP
Livesey and Marsh 2025 DMS benchmark: pairwise \|Spearman\| win rate, all 36 DMS sets	Livesey 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), percent	7.71	0.667	M-CAP
Livesey and Marsh 2025 DMS benchmark: pairwise \|Spearman\| win rate, direct assays only	Livesey 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), percent	38.3	14.5	M-CAP
Livesey and Marsh 2025 DMS benchmark: pairwise \|Spearman\| win rate, indirect assays only	Livesey 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), percent	41.2	12.6	M-CAP
Livesey and Marsh 2025 DMS benchmark: pairwise \|Spearman\| win rate, SNV-accessible substitutions only	Livesey 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), percent	40	13.4	M-CAP
Livesey and Marsh 2025 DMS benchmark: pairwise \|Spearman\| win rate, SNV-accessible substitutions with gaps filled	Livesey 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), percent	39.7	14.5	M-CAP

Comparisons in An openly licensed benchmark… (6)

Protocol	Dataset	Metric	M-CAP	DANN	Higher
CGC missense oncogenicity, all genes (Lee 2026, OncoCal tool_performance.tsv)	Missense variants in 768 Cancer Gene Census genes with open oncogenicity labels	AUROC (CGC missense, all genes), unitless	0.758	0.762	Tie
CGC missense oncogenicity, all genes (Lee 2026, OncoCal tool_performance.tsv)	Missense variants in 768 Cancer Gene Census genes with open oncogenicity labels	Average precision (CGC missense, all genes), unitless	0.481	0.389	M-CAP
CGC missense oncogenicity, oncogenes (gain of function) (Lee 2026, OncoCal tool_performance.tsv)	Missense variants in 768 Cancer Gene Census genes with open oncogenicity labels	AUROC (CGC missense, oncogenes (gain of function)), unitless	0.775	0.749	M-CAP
CGC missense oncogenicity, oncogenes (gain of function) (Lee 2026, OncoCal tool_performance.tsv)	Missense variants in 768 Cancer Gene Census genes with open oncogenicity labels	Average precision (CGC missense, oncogenes (gain of function)), unitless	0.544	0.468	M-CAP
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 labels	AUROC (CGC missense, tumour suppressors (loss of function)), unitless	0.803	0.823	DANN
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 labels	Average precision (CGC missense, tumour suppressors (loss of function)), unitless	0.478	0.378	M-CAP

Comparisons in Clinical likelihood ratios and balanced… (4)

Protocol	Dataset	Metric	M-CAP	DANN	Higher
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 editing	False negatives (Functionally deleterious BRCA1 variants called tolerated), count	0	3	M-CAP
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 editing	Likelihood ratio (Positive likelihood ratio of a deleterious call (TPR/FPR), BRCA1 functional truth set), unitless	1	1.47	DANN
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 repair	False negatives (Functionally deleterious BRCA2 variants called tolerated), count	0	0	Tie
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 repair	Likelihood ratio (Positive likelihood ratio of a deleterious call (TPR/FPR), BRCA2 functional truth set), unitless	1	1.12	DANN

What disagrees

Other studies: rank M-CAP above DANN in 12 of 15 comparisons
This study: ranks DANN above M-CAP in 5 of 5 comparisons

Compared: DANN, M-CAP
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 studies
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), 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 (10)
Study	Tool	Protocol or benchmark	Metric	Value
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)	DANN (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)	0.59
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)	DANN (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)	0.59
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)	DANN (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)	0.59
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)	DANN (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)	0.59
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)	DANN (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)	0.59
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)	M-CAP (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)	0.56
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)	M-CAP (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)	0.56
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)	M-CAP (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)	0.56
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)	M-CAP (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)	0.56
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)	M-CAP (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)	0.56

In the other studies (30)
Study	Tool	Protocol or benchmark	Metric	Value
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)	DANN_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)	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)	DANN_c (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)	1.47
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)	DANN_c (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)	1.12
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)	M-CAP (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)	M-CAP (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
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)	M-CAP (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
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)	DANN 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)	0.389
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)	DANN 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))	0.468
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)	DANN 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))	0.749
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)	DANN 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))	0.378
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)	DANN 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))	0.823
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)	M-CAP 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)	0.481
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)	M-CAP 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))	0.544
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)	M-CAP 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))	0.775
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)	M-CAP 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))	0.478
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)	M-CAP 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))	0.803
Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML)	DANN (Livesey and Marsh 2025)	Livesey and Marsh 2025 clinical benchmark: pairwise AUROC win rate, ClinVar pathogenic versus gnomAD missense	Pairwise win rate (average pairwise win rate against all other VEPs, per-protein AUROC on shared variants, ClinVar P/LP versus gnomAD v4.1 missense, proteins with at least 10 of each)	27.9
Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML)	M-CAP (Livesey and Marsh 2025)	Livesey and Marsh 2025 clinical benchmark: pairwise AUROC win rate, ClinVar pathogenic versus gnomAD missense	Pairwise win rate (average pairwise win rate against all other VEPs, per-protein AUROC on shared variants, ClinVar P/LP versus gnomAD v4.1 missense, proteins with at least 10 of each)	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)	DANN (Livesey and Marsh 2025)	Livesey and Marsh 2025 DMS benchmark: pairwise \|Spearman\| win rate, all 36 DMS sets	Pairwise win rate (average pairwise win rate against all other VEPs, \|Spearman\| with DMS on shared variants, 36 human DMS sets)	13.4
Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML)	DANN (Livesey and Marsh 2025)	Livesey and Marsh 2025 DMS benchmark: pairwise \|Spearman\| win rate, all 36 DMS sets	Pairwise win rate (average pairwise win rate against the five top-ranked VEPs (column top_5_winrate; the five are not listed in the table), \|Spearman\| with DMS on shared variants, 36 human DMS sets)	0.667
Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML)	M-CAP (Livesey and Marsh 2025)	Livesey and Marsh 2025 DMS benchmark: pairwise \|Spearman\| win rate, all 36 DMS sets	Pairwise win rate (average pairwise win rate against all other VEPs, \|Spearman\| with DMS on shared variants, 36 human DMS sets)	40
Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML)	M-CAP (Livesey and Marsh 2025)	Livesey and Marsh 2025 DMS benchmark: pairwise \|Spearman\| win rate, all 36 DMS sets	Pairwise win rate (average pairwise win rate against the five top-ranked VEPs (column top_5_winrate; the five are not listed in the table), \|Spearman\| with DMS on shared variants, 36 human DMS sets)	7.71
Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML)	DANN (Livesey and Marsh 2025)	Livesey and Marsh 2025 DMS benchmark: pairwise \|Spearman\| win rate, direct assays only	Pairwise win rate (average pairwise win rate against all other VEPs, \|Spearman\| with DMS on shared variants, direct DMS assays only)	14.5
Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML)	M-CAP (Livesey and Marsh 2025)	Livesey and Marsh 2025 DMS benchmark: pairwise \|Spearman\| win rate, direct assays only	Pairwise win rate (average pairwise win rate against all other VEPs, \|Spearman\| with DMS on shared variants, direct DMS assays only)	38.3
Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML)	DANN (Livesey and Marsh 2025)	Livesey and Marsh 2025 DMS benchmark: pairwise \|Spearman\| win rate, indirect assays only	Pairwise win rate (average pairwise win rate against all other VEPs, \|Spearman\| with DMS on shared variants, indirect DMS assays only)	12.6
Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML)	M-CAP (Livesey and Marsh 2025)	Livesey and Marsh 2025 DMS benchmark: pairwise \|Spearman\| win rate, indirect assays only	Pairwise win rate (average pairwise win rate against all other VEPs, \|Spearman\| with DMS on shared variants, indirect DMS assays only)	41.2
Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML)	DANN (Livesey and Marsh 2025)	Livesey and Marsh 2025 DMS benchmark: pairwise \|Spearman\| win rate, SNV-accessible substitutions only	Pairwise win rate (average pairwise win rate against all other VEPs, \|Spearman\| with DMS on shared variants, SNV-accessible substitutions only)	13.4
Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML)	DANN (Livesey and Marsh 2025)	Livesey and Marsh 2025 DMS benchmark: pairwise \|Spearman\| win rate, SNV-accessible substitutions with gaps filled	Pairwise win rate (average pairwise win rate against all other VEPs, \|Spearman\| with DMS, SNV-accessible substitutions, missing predictions filled with the VEP's most benign score)	14.5
Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML)	M-CAP (Livesey and Marsh 2025)	Livesey and Marsh 2025 DMS benchmark: pairwise \|Spearman\| win rate, SNV-accessible substitutions with gaps filled	Pairwise win rate (average pairwise win rate against all other VEPs, \|Spearman\| with DMS, SNV-accessible substitutions, missing predictions filled with the VEP's most benign score)	39.7
Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML)	M-CAP (Livesey and Marsh 2025)	Livesey and Marsh 2025 DMS benchmark: pairwise \|Spearman\| win rate, SNV-accessible substitutions only	Pairwise win rate (average pairwise win rate against all other VEPs, \|Spearman\| with DMS on shared variants, SNV-accessible substitutions only)	40

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-da69c6affc5ec39e · All contested results · How findings are made and followed up