Contested result

MPC and MutFormer: Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML) and An openly licensed benchmark and per-gene calibration map for missense pathogenicity predictors on activating cancer drivers disagree

Low severity
Not yet reviewed
two-study-conflict
unknown

Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML) ranks MutFormer above MPC in 7 of 7 comparisons; An openly licensed benchmark and per-gene calibration map for missense pathogenicity predictors on activating cancer drivers ranks MPC above MutFormer in 6 of 6 comparisons. No other study gives a verdict, so neither is the outlier.

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 MPC above MutFormer in 6 of their 13 comparisons and MutFormer higher in 7. Of those papers, Livesey and Marsh 2025 ranks MutFormer higher on its own (7 of 7).

Rank MPC higher (1): An openly licensed benchmark…
Rank MutFormer higher (1): Livesey and Marsh 2025
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.

MPC higherTieMutFormer higherNumbers are counts of comparisons; the outlined bar is the paper that disagrees.

Papers compared (2)
Paper	Role	Ranks MPC higher	Ranks MutFormer higher	Ties
Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML) and 1 more record · DOI	Agrees	0	7	0
An openly licensed benchmark and per-gene calibration map for missense pathogenicity predictors on activating cancer drivers and 1 more record · DOI	Agrees	6	0	0

Comparisons in Livesey and Marsh 2025 (7)

Protocol	Dataset	Metric	MPC	MutFormer	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	64	MutFormer
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	53.6	71.4	MutFormer
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	11.6	17.2	MutFormer
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	57.2	74.7	MutFormer
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	50.7	68.9	MutFormer
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	53.6	71.4	MutFormer
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	54.8	70.5	MutFormer

Comparisons in An openly licensed benchmark… (6)

Protocol	Dataset	Metric	MPC	MutFormer	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	0.823	0.693	MPC
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	0.613	0.446	MPC
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	0.842	0.768	MPC
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	0.716	0.582	MPC
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	0.873	0.655	MPC
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	0.626	0.286	MPC

What disagrees

studies: 2

Compared: MPC, MutFormer
Study that disagrees
Other studies
Rule: two-study-conflict; unknown
Model identity: Matched across studies partly through links not yet reviewed

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.

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