Contested result

LRT and MetaSVM: 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 LRT above MetaSVM in 16 of 20 comparisons; other studies rank MetaSVM above LRT in 12 of 13.

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 MetaSVM above LRT in 12 of their 13 comparisons and LRT higher in 1. Comprehensive assessment… ranks LRT above MetaSVM in 16 of its 20 comparisons and MetaSVM higher in 4.

Rank MetaSVM higher (2): Livesey and Marsh 2025, Clinical likelihood ratios and balanced…
Rank LRT higher (1): Comprehensive assessment… (disagrees)
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.

MetaSVM higherTieLRT higherNumbers are counts of comparisons; the outlined bar is the paper that disagrees.

Papers compared (3)
Paper	Role	Ranks MetaSVM higher	Ranks LRT higher
Comprehensive assessment of computational algorithms in predicting cancer driver mutations and 4 more records · DOI	Disagrees	4	16
Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML) and 1 more record · DOI	Agrees	7	0
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	5	1

Comparisons in Comprehensive assessment… (20)

Protocol	Dataset	Metric	MetaSVM	LRT	Higher
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), fraction	0.6	0.66	LRT
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), fraction	0.62	0.86	LRT
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), fraction	0.59	0.6	LRT
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), fraction	0.68	0.93	LRT
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), fraction	0.52	0.38	MetaSVM
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	0.6	0.65	LRT
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	0.6	0.67	LRT
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	0.6	0.63	LRT
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	0.6	0.73	LRT
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	0.6	0.57	MetaSVM
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), fraction	0.56	0.58	LRT
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), fraction	0.55	0.71	LRT
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), fraction	0.57	0.56	MetaSVM
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), fraction	0.51	0.93	LRT
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), fraction	0.61	0.2	MetaSVM
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	0.55	0.65	LRT
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	0.55	0.68	LRT
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	0.55	0.63	LRT
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	0.55	0.74	LRT
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	0.55	0.56	LRT

Comparisons in Livesey and Marsh 2025 (7)

Protocol	Dataset	Metric	MetaSVM	LRT	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	45.5	18.8	MetaSVM
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	39	13.6	MetaSVM
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	8.13	1.22	MetaSVM
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	39.4	15.4	MetaSVM
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	38.5	12.3	MetaSVM
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	39	13.6	MetaSVM
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	39.9	14.3	MetaSVM

Comparisons in Clinical likelihood ratios and balanced… (6)

Protocol	Dataset	Metric	MetaSVM	LRT	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	Benignity likelihood ratio (Source 'negative likelihood ratio' (TNR/FNR): likelihood ratio for benignity of a tolerated call, BRCA1 functional truth set), unitless	7.85	2.99	MetaSVM
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	21	85	MetaSVM
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	1.7	2.46	LRT
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	Benignity likelihood ratio (Source 'negative likelihood ratio' (TNR/FNR): likelihood ratio for benignity of a tolerated call, BRCA2 functional truth set), unitless	23.4	9.62	MetaSVM
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	1	2	MetaSVM
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	1.55	1.38	MetaSVM

What disagrees

Other studies: rank MetaSVM above LRT in 12 of 13 comparisons
This study: ranks LRT above MetaSVM in 16 of 20 comparisons

Compared: LRT, MetaSVM
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: 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), 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 other studies (26)
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)	LRT (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)	2.99
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)	LRT (Cubuk et al. 2021)	BRCA1 functional truth set, binary in silico calls (Cubuk et al. 2021)	False negatives (Functionally deleterious BRCA1 variants called tolerated)	85
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)	LRT (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)	2.46
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)	LRT (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)	9.62
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)	LRT (Cubuk et al. 2021)	BRCA2 functional truth set, binary in silico calls (Cubuk et al. 2021)	False negatives (Functionally deleterious BRCA2 variants called tolerated)	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)	LRT (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)	1.38
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)	MetaSVM (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)	7.85
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)	MetaSVM (Cubuk et al. 2021)	BRCA1 functional truth set, binary in silico calls (Cubuk et al. 2021)	False negatives (Functionally deleterious BRCA1 variants called tolerated)	21
Clinical likelihood ratios and balanced accuracy for 44 in silico tools against multiple large-scale functional assays of cancer susceptibility genes; Cubuk et al. 2021, Supplementary tables 1-13 (41436_2021_1265_MOESM3_ESM.xlsx)	MetaSVM (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)	1.7
Clinical likelihood ratios and balanced accuracy for 44 in silico tools against multiple large-scale functional assays of cancer susceptibility genes; Cubuk et al. 2021, Supplementary tables 1-13 (41436_2021_1265_MOESM3_ESM.xlsx)	MetaSVM (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)	23.4
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)	MetaSVM (Cubuk et al. 2021)	BRCA2 functional truth set, binary in silico calls (Cubuk et al. 2021)	False negatives (Functionally deleterious BRCA2 variants called tolerated)	1
Clinical likelihood ratios and balanced accuracy for 44 in silico tools against multiple large-scale functional assays of cancer susceptibility genes; Cubuk et al. 2021, Supplementary tables 1-13 (41436_2021_1265_MOESM3_ESM.xlsx)	MetaSVM (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)	1.55
Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML)	LRT (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)	18.8
Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML)	MetaSVM (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)	45.5
Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML)	LRT (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)	13.6
Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML)	LRT (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)	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)	MetaSVM (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)	39
Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML)	MetaSVM (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)	8.13
Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML)	LRT (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)	15.4
Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML)	MetaSVM (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)	39.4
Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML)	LRT (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)	12.3
Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML)	MetaSVM (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)	38.5
Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML)	LRT (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)	14.3
Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML)	MetaSVM (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)	39.9
Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML)	LRT (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)	13.6
Livesey and Marsh 2025, Supplementary Material 2 (Tables S1-S8); Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML)	MetaSVM (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)	39

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-6cb8e22989b027fb · All contested results · How findings are made and followed up

Study	Tool	Protocol or benchmark	Metric	Value
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)	LRT (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)	LRT (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)	0.86
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)	LRT (Chen et al. 2020)	OncoKB benchmark (positive set not stated), default categorical calls (Chen et al. 2020 Additional file 10)	Precision (OncoKB benchmark (positive set not stated), default categorical calls)	0.6
Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 10: Performance metrics of 17 algorithms, default categories, benchmark 2 (OncoKB)	LRT (Chen et al. 2020)	OncoKB benchmark (positive set not stated), default categorical calls (Chen et al. 2020 Additional file 10)	Recall (OncoKB benchmark (positive set not stated), default categorical calls)	0.93
Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 10: Performance metrics of 17 algorithms, default categories, benchmark 2 (OncoKB)	LRT (Chen et al. 2020)	OncoKB benchmark (positive set not stated), default categorical calls (Chen et al. 2020 Additional file 10)	Specificity (OncoKB benchmark (positive set not stated), default categorical calls)	0.38
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)	LRT (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)	LRT (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)	0.67
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)	LRT (Chen et al. 2020)	OncoKB benchmark (positive set not stated), median-score threshold (Chen et al. 2020 Additional file 9)	Precision (OncoKB benchmark (positive set not stated), median-score threshold)	0.63
Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 9: Performance metrics of 33 algorithms, median-score threshold, benchmark 2 (OncoKB)	LRT (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)	0.73
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)	LRT (Chen et al. 2020)	OncoKB benchmark (positive set not stated), median-score threshold (Chen et al. 2020 Additional file 9)	Specificity (OncoKB benchmark (positive set not stated), median-score threshold)	0.57
Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 22: Performance metrics of 17 algorithms, default categories, benchmark 5 (cell viability)	LRT (Chen et al. 2020)	Cell viability drivers versus neutral, default categorical calls (Chen et al. 2020 Additional file 22)	Accuracy (cell viability drivers versus neutral, default categorical calls)	0.58
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)	LRT (Chen et al. 2020)	Cell viability drivers versus neutral, default categorical calls (Chen et al. 2020 Additional file 22)	Negative predictive value (cell viability drivers versus neutral, default categorical calls)	0.71
Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 22: Performance metrics of 17 algorithms, default categories, benchmark 5 (cell viability)	LRT (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)	LRT (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)	0.93
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)	LRT (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)	0.2
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)	LRT (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)	0.65
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)	LRT (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)	0.68
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)	LRT (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)	LRT (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)	0.74
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)	LRT (Chen et al. 2020)	Cell viability drivers versus neutral, median-score threshold (Chen et al. 2020 Additional file 21)	Specificity (cell viability drivers versus neutral, median-score threshold)	0.56
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)	MetaSVM (Chen et al. 2020)	OncoKB benchmark (positive set not stated), default categorical calls (Chen et al. 2020 Additional file 10)	Accuracy (OncoKB benchmark (positive set not stated), default categorical calls)	0.6
Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 10: Performance metrics of 17 algorithms, default categories, benchmark 2 (OncoKB)	MetaSVM (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)	0.62
Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 10: Performance metrics of 17 algorithms, default categories, benchmark 2 (OncoKB)	MetaSVM (Chen et al. 2020)	OncoKB benchmark (positive set not stated), default categorical calls (Chen et al. 2020 Additional file 10)	Precision (OncoKB benchmark (positive set not stated), default categorical calls)	0.59
Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 10: Performance metrics of 17 algorithms, default categories, benchmark 2 (OncoKB)	MetaSVM (Chen et al. 2020)	OncoKB benchmark (positive set not stated), default categorical calls (Chen et al. 2020 Additional file 10)	Recall (OncoKB benchmark (positive set not stated), default categorical calls)	0.68
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)	MetaSVM (Chen et al. 2020)	OncoKB benchmark (positive set not stated), default categorical calls (Chen et al. 2020 Additional file 10)	Specificity (OncoKB benchmark (positive set not stated), default categorical calls)	0.52
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)	MetaSVM (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)	0.6
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)	MetaSVM (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)	0.6
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)	MetaSVM (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)	0.6
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)	MetaSVM (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)	0.6
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)	MetaSVM (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)	0.6
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)	MetaSVM (Chen et al. 2020)	Cell viability drivers versus neutral, default categorical calls (Chen et al. 2020 Additional file 22)	Accuracy (cell viability drivers versus neutral, default categorical calls)	0.56
Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 22: Performance metrics of 17 algorithms, default categories, benchmark 5 (cell viability)	MetaSVM (Chen et al. 2020)	Cell viability drivers versus neutral, default categorical calls (Chen et al. 2020 Additional file 22)	Negative predictive value (cell viability drivers versus neutral, default categorical calls)	0.55
Comprehensive assessment of computational algorithms in predicting cancer driver mutations; Chen et al. 2020, Additional file 22: Performance metrics of 17 algorithms, default categories, benchmark 5 (cell viability)	MetaSVM (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)	MetaSVM (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)	0.51
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)	MetaSVM (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)	0.61
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)	MetaSVM (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)	0.55
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)	MetaSVM (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)	0.55
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)	MetaSVM (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)	0.55
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)	MetaSVM (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)	0.55
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)	MetaSVM (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)	0.55