Number of residues lost (stop-gain baseline)

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Evaluations and results

3 evaluations · 3 results. Different protocols are not a single leaderboard.

Protocol: ClinVar stop-gain pathogenicity by NMD status, ROC-AUC (Brandes et al. 2023 Fig. 6d, Supplementary Table 2)

Sorted by AUROC (ROC-AUC, pathogenic versus benign ClinVar all stop-gains; zero-shot) (higher is better). The best value in each column is highlighted. Decimals are rounded for display; each value links to the printed value and its source.

Tested configuration	Protocol and dataset	AUROC (ROC-AUC, pathogenic versus benign ClinVar all stop-gains; zero-shot)	AUROC (ROC-AUC, pathogenic versus benign ClinVar stop-gains expected to undergo NMD (50 bp rule); zero-shot)	AUROC (ROC-AUC, pathogenic versus benign ClinVar stop-gains not expected to undergo NMD (50 bp rule); zero-shot)
Number of residues lost (stop-gain baseline)Evaluation	ClinVar stop-gain variants, pathogenic vs benign (Brandes et al. 2023)	0.695	Not reported	Not reported
Number of residues lost (stop-gain baseline)Evaluation	ClinVar stop-gains expected to undergo NMD (Brandes et al. 2023)	Not reported	0.59	Not reported
Number of residues lost (stop-gain baseline)Evaluation	ClinVar stop-gains not expected to undergo NMD (Brandes et al. 2023)	Not reported	Not reported	0.65

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Exact evaluated configurations and original reported results
Tested configuration	Protocol and dataset	Finding	Evidence and details
Method: Number of residues lost (stop-gain baseline)	Protocol: ClinVar stop-gain pathogenicity by NMD status, ROC-AUC (Brandes et al. 2023 Fig. 6d, Supplementary Table 2) Dataset subset: ClinVar stop-gains expected to undergo NMD (Brandes et al. 2023)	0.59 AUROC (ROC-AUC, pathogenic versus benign ClinVar stop-gains expected to undergo NMD (50 bp rule); zero-shot) unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source num. AA lost on stop-gains expected to undergo NMD (50 bp rule) (Brandes et al. 2023) gfm-guides-20261014-esm1b-protocol-clinvar-stop-gains Aggregation: Not reported Brandes et al. 2023 Supplementary Table 2: full benchmark evaluations of ESM1b and all other methods · Supplementary Table 2 sheet 'Table_of_results_stop_gains' cell C3, row 'num. AA lost', column 'AUC_NMD'
Method: Number of residues lost (stop-gain baseline)	Protocol: ClinVar stop-gain pathogenicity by NMD status, ROC-AUC (Brandes et al. 2023 Fig. 6d, Supplementary Table 2) Dataset subset: ClinVar stop-gains not expected to undergo NMD (Brandes et al. 2023)	0.65 AUROC (ROC-AUC, pathogenic versus benign ClinVar stop-gains not expected to undergo NMD (50 bp rule); zero-shot) unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source num. AA lost on stop-gains not expected to undergo NMD (50 bp rule) (Brandes et al. 2023) gfm-guides-20261014-esm1b-protocol-clinvar-stop-gains Aggregation: Not reported Brandes et al. 2023 Supplementary Table 2: full benchmark evaluations of ESM1b and all other methods · Supplementary Table 2 sheet 'Table_of_results_stop_gains' cell B3, row 'num. AA lost', column 'AUC_no_NMD'
Method: Number of residues lost (stop-gain baseline)	Protocol: ClinVar stop-gain pathogenicity by NMD status, ROC-AUC (Brandes et al. 2023 Fig. 6d, Supplementary Table 2) Dataset: ClinVar stop-gain variants, pathogenic vs benign (Brandes et al. 2023)	0.695 AUROC (ROC-AUC, pathogenic versus benign ClinVar all stop-gains; zero-shot) unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source num. AA lost on all stop-gains (Brandes et al. 2023) gfm-guides-20261014-esm1b-protocol-clinvar-stop-gains Aggregation: Not reported Brandes et al. 2023 Supplementary Table 2: full benchmark evaluations of ESM1b and all other methods · Supplementary Table 2 sheet 'Table_of_results_stop_gains' cell D3, row 'num. AA lost', column 'AUC_ALL'

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Release 2026-10-22-1b7cf027e4f6 · Record review: source checked

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Genome-wide prediction of disease variant effects with a deep protein language model (Brandes et al., Nature Genetics 2023) · Original source · Version of record, Nature Genetics 55, 1512-1522 (published 2023-08-10)

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Technical metadata and extraction receipts

Stable ID: gfm-guides-20261014-esm1b-method-residues-lost-count

areas: protein-function
contexts: clinical_research
method types: conventional_pipeline
reported name: num. AA lost
entity level: method
source locator: Methods 'Baseline scores of indel and stop-gain variant effects'
missing metadata: version: reason: inapplicable; note: Baseline defined in the article's Methods

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