rewirebio.iobenchmarks
Contested result

LIST-S2 and MetaLR: 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 MetaLR above LIST-S2 in 7 of 7 comparisons; An openly licensed benchmark and per-gene calibration map for missense pathogenicity predictors on activating cancer drivers ranks LIST-S2 above MetaLR in 4 of 5 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 LIST-S2 above MetaLR in 4 of their 13 comparisons and MetaLR higher in 8; 1 is a tie. Of those papers, Livesey and Marsh 2025 ranks MetaLR higher on its own (7 of 7).

Papers grouped by which system they rank higher: LIST-S2 or MetaLRRank LIST-S2 higher: An openly licensed benchmark…. Rank MetaLR higher: Livesey and Marsh 2025. Mixed: none.Rank LIST-S2 higherMixedRank MetaLR higherAn openly licensedbenchmark…An openly licensed benchmark and per-gene calibration map for missense pathogenicity predictors on activating cancer driversLivesey and Marsh 2025Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML)
  • Rank LIST-S2 higher (1): An openly licensed benchmark…
  • Rank MetaLR 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.

Comparisons in which each paper scores LIST-S2 or MetaLR higherLivesey and Marsh 2025: LIST-S2 higher in 0, MetaLR higher in 7, 0 ties. An openly licensed benchmark…: LIST-S2 higher in 4, MetaLR higher in 1, 1 tie.Livesey and Marsh 2025Livesey and Marsh 2025, Genome Biology 26:104 (Europe PMC full text XML)7 MetaLR higher7An openly licensed benchmark…An openly licensed benchmark and per-gene calibration map for missense pathogenicity predictors on activating cancer drivers4 LIST-S2 higher41 tie11 MetaLR higher1
LIST-S2 higherTieMetaLR higherNumbers are counts of comparisons; the outlined bar is the paper that disagrees.
Comparisons in Livesey and Marsh 2025 (7)
ProtocolDatasetMetricLIST-S2MetaLRHigher
Livesey and Marsh 2025 clinical benchmark: pairwise AUROC win rate, ClinVar pathogenic versus gnomAD missenseLivesey and Marsh 2025 clinical set: ClinVar pathogenic versus gnomAD v4.1 missense (819 proteins)Pairwise win rate (average pairwise win rate against all other VEPs, per-protein AUROC on shared variants, ClinVar P/LP versus gnomAD v4.1 missense, proteins with at least 10 of each), percent35.349.1MetaLR
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), percent22.145.7MetaLR
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), percent4.48.13MetaLR
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), percent16.742.1MetaLR
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), percent26.248.3MetaLR
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), percent22.145.7MetaLR
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), percent23.546.6MetaLR
Comparisons in An openly licensed benchmark… (6)
ProtocolDatasetMetricLIST-S2MetaLRHigher
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), unitless0.7680.748LIST-S2
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), unitless0.5210.544MetaLR
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)), unitless0.7560.75Tie
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)), unitless0.5620.543LIST-S2
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)), unitless0.8320.788LIST-S2
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)), unitless0.5360.522LIST-S2

What disagrees

studies
2
Compared
LIST-S2, MetaLR
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.

Finding discrepancy-d3fc621f9621b4f1 · All contested results · How findings are made and followed up