Structure-informed variant-effect evaluation compares ranking and binary classification against experimental protein measurements.
Explanatory profile: limited source coverage · Automated source review, 2026-09-16. Review applies to the cited claims; unresolved fields are listed below. Numerical results retain their own review status.
| Property | Description and evidence |
|---|---|
| Datasets | DeepSequence-derived missense-variant DMS datasets, excluding tRNA and viral-family sets; available family sequences and structures support adaptation.SourcesStructure-Informed Protein Language Models are Robust Predictors for Variant Effects (reviewed HTML snapshot) · Results: Structure-Informed pLMs Predict Variant Fitness Robustly; Tables 2 and 4; Methods: Datasets, Mutation effect datasets, Competing methods |
| Splits | A labeled validation subset selects the structural weighting and model; labels are not used to fit the model parameters. The exact held-out membership after selection remains unextracted.SourcesStructure-Informed Protein Language Models are Robust Predictors for Variant Effects (reviewed HTML snapshot) · Results: Structure-Informed pLMs Predict Variant Fitness Robustly; Tables 2 and 4; Methods: Datasets, Mutation effect datasets, Competing methods |
| Metrics | Spearman correlation evaluates ranking; AUROC and AUPRC evaluate high/low labels thresholded relative to the wild type. Table 4 compares sequence, structure and combined score variants.SourcesStructure-Informed Protein Language Models are Robust Predictors for Variant Effects (reviewed HTML snapshot) · Results: Structure-Informed pLMs Predict Variant Fitness Robustly; Tables 2 and 4; Methods: Datasets, Mutation effect datasets, Competing methods |
| Baselines | PSSM, EVmutation, DeepSequence, Wavenet and several protein language models; competing scores are obtained from the ESM-1v repository.SourcesStructure-Informed Protein Language Models are Robust Predictors for Variant Effects (reviewed HTML snapshot) · Results: Structure-Informed pLMs Predict Variant Fitness Robustly; Tables 2 and 4; Methods: Datasets, Mutation effect datasets, Competing methods |
| Leakage controls | Family-specific adaptation and validation-label model selection are part of the protocol; it should not be represented as selection without labeled data.SourcesStructure-Informed Protein Language Models are Robust Predictors for Variant Effects (reviewed HTML snapshot) · Results: Structure-Informed pLMs Predict Variant Fitness Robustly; Tables 2 and 4; Methods: Datasets, Mutation effect datasets, Competing methods |
| Uncertainty | One-sided paired Wilcoxon signed-rank comparisons across assay performances are reported for the main method comparison.SourcesStructure-Informed Protein Language Models are Robust Predictors for Variant Effects (reviewed HTML snapshot) · Results: Structure-Informed pLMs Predict Variant Fitness Robustly; Tables 2 and 4; Methods: Datasets, Mutation effect datasets, Competing methods |
| Entity type | Paper-specific computational evaluation protocol.SourcesStructure-Informed Protein Language Models are Robust Predictors for Variant Effects (reviewed HTML snapshot) · Results: Structure-Informed pLMs Predict Variant Fitness Robustly; Tables 2 and 4; Methods: Datasets, Mutation effect datasets, Competing methods |
| Organisms | Nonviral protein families in the selected DMS collection.SourcesStructure-Informed Protein Language Models are Robust Predictors for Variant Effects (reviewed HTML snapshot) · Results: Structure-Informed pLMs Predict Variant Fitness Robustly; Tables 2 and 4; Methods: Datasets, Mutation effect datasets, Competing methods |
| Assays | DMS missense-variant measurements.SourcesStructure-Informed Protein Language Models are Robust Predictors for Variant Effects (reviewed HTML snapshot) · Results: Structure-Informed pLMs Predict Variant Fitness Robustly; Tables 2 and 4; Methods: Datasets, Mutation effect datasets, Competing methods |
| Allowed inputs | Protein sequence; available family sequences/structures are used during adaptation.SourcesStructure-Informed Protein Language Models are Robust Predictors for Variant Effects (reviewed HTML snapshot) · Results: Structure-Informed pLMs Predict Variant Fitness Robustly; Tables 2 and 4; Methods: Datasets, Mutation effect datasets, Competing methods |
| Adaptation | Unsupervised family adaptation plus labeled-validation model selection; no fitness-label fitting of model parameters.SourcesStructure-Informed Protein Language Models are Robust Predictors for Variant Effects (reviewed HTML snapshot) · Results: Structure-Informed pLMs Predict Variant Fitness Robustly; Tables 2 and 4; Methods: Datasets, Mutation effect datasets, Competing methods |
Conceptual summary of the cited evaluation; exact task configuration and source version remain part of the protocol.
DeepSequence-derived missense-variant DMS datasets, excluding tRNA and viral-family sets; available family sequences and structures support adaptation. A labeled validation subset selects the structural weighting and model; labels are not used to fit the model parameters. The exact held-out membership after selection remains unextracted. Spearman correlation evaluates ranking; AUROC and AUPRC evaluate high/low labels thresholded relative to the wild type. Table 4 compares sequence, structure and combined score variants. PSSM, EVmutation, DeepSequence, Wavenet and several protein language models; competing scores are obtained from the ESM-1v repository. Family-specific adaptation and validation-label model selection are part of the protocol; it should not be represented as selection without labeled data. One-sided paired Wilcoxon signed-rank comparisons across assay performances are reported for the main method comparison.
Each evaluation records what was tested and under which conditions.
Release 2026-09-17-d277315f7d76 · 1 evaluation · 1 metric row. Different protocols are not a single leaderboard.
| Metric and finding | Coverage and uncertainty | Evidence |
|---|---|---|
| structure-informed pLM: protein variant-effect classification Configuration: structure-informed pLMTask: protein variant-effect classificationDataset: variant-effects benchmark combined amino-acid, secondary structure, solvent accessibility and contact-map scoring Author-reported evaluation · Evaluation metadata: needs review | ||
| .803 AUROC Unit: fraction · Direction: unknown | Uncertainty: not reported in legacy extract Scored: Not reported · Eligible: Not reported | source checkedStructure-Informed Protein Language Models are Robust Predictors for Variant Effects · PMC12068927 HTML, Table4, AA+SS+RSA+CM row, AUROC column Source checking is not independent reproduction. |
Last literature check: 2026-09-17. Primary-source discovery and table/protocol screening; source checked is not independently reproduced. Raw acquisitions not automatically numerical publication approval.
| Paper or primary resource | Version | Reference |
|---|---|---|
| Structure-informed protein language models are robust predictors for variant effects - PMC | Human Genetics 2025 journal article (online 2024) | Read source |
source found structured extraction pending
Task-specific computational methodology and field context checked in the cited primary-source artifact. Source-backed fields, inapplicable evaluator dimensions and unresolved details are distinguished. Numerical results were not reproduced.
Stable record: reported-task-83be0998084c91Trace each statement to its source and review. A context-only reference supports the record generally; it does not verify an individual field. Source checking does not reproduce an experiment.
One row per statement and cited source. Multiple citations are not independent evaluations. Shared locators are labelled explicitly.
18 evidence rows matching the loaded filters
| Property and statement | Original source and location | Review and provenance |
|---|---|---|
| Diagram caption Conceptual summary of the cited evaluation; exact task configuration and source version remain part of the protocol. Individual claims | Structure-Informed Protein Language Models are Robust Predictors for Variant Effects (reviewed HTML snapshot) Results: Structure-Informed pLMs Predict Variant Fitness Robustly; Tables 2 and 4; Methods: Datasets, Mutation effect datasets, Competing methods Version: Human Genetics 2025 journal article (online 2024) | source checked automated source review · 2026-09-16 Audit detailsTask-specific computational methodology and field context checked in the cited primary-source artifact. Source-backed fields, inapplicable evaluator dimensions and unresolved details are distinguished. Numerical results were not reproduced. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Diagram steps ["Input: Protein sequence; available family sequences/structures are used during adaptation.","Evaluation: Unsupervised family adaptation plus labeled-validation model selection; no fitness-label fitting of model parameters.","Readout: Spearman correlation evaluates ranking; AUROC and AUPRC evaluate high/low labels thresholded relative to the wild type. Table 4 compares sequence, structure and combined score variants."] Individual claims | Structure-Informed Protein Language Models are Robust Predictors for Variant Effects (reviewed HTML snapshot) Results: Structure-Informed pLMs Predict Variant Fitness Robustly; Tables 2 and 4; Methods: Datasets, Mutation effect datasets, Competing methods Version: Human Genetics 2025 journal article (online 2024) | source checked automated source review · 2026-09-16 Audit detailsTask-specific computational methodology and field context checked in the cited primary-source artifact. Source-backed fields, inapplicable evaluator dimensions and unresolved details are distinguished. Numerical results were not reproduced. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Diagram title Computational evaluation flow Individual claims | Structure-Informed Protein Language Models are Robust Predictors for Variant Effects (reviewed HTML snapshot) Results: Structure-Informed pLMs Predict Variant Fitness Robustly; Tables 2 and 4; Methods: Datasets, Mutation effect datasets, Competing methods Version: Human Genetics 2025 journal article (online 2024) | source checked automated source review · 2026-09-16 Audit detailsTask-specific computational methodology and field context checked in the cited primary-source artifact. Source-backed fields, inapplicable evaluator dimensions and unresolved details are distinguished. Numerical results were not reproduced. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Datasets DeepSequence-derived missense-variant DMS datasets, excluding tRNA and viral-family sets; available family sequences and structures support adaptation. Individual claims | Structure-Informed Protein Language Models are Robust Predictors for Variant Effects (reviewed HTML snapshot) Results: Structure-Informed pLMs Predict Variant Fitness Robustly; Tables 2 and 4; Methods: Datasets, Mutation effect datasets, Competing methods Version: Human Genetics 2025 journal article (online 2024) | source checked automated source review · 2026-09-16 Audit detailsTask-specific computational methodology and field context checked in the cited primary-source artifact. Source-backed fields, inapplicable evaluator dimensions and unresolved details are distinguished. Numerical results were not reproduced. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Splits A labeled validation subset selects the structural weighting and model; labels are not used to fit the model parameters. The exact held-out membership after selection remains unextracted. Individual claims | Structure-Informed Protein Language Models are Robust Predictors for Variant Effects (reviewed HTML snapshot) Results: Structure-Informed pLMs Predict Variant Fitness Robustly; Tables 2 and 4; Methods: Datasets, Mutation effect datasets, Competing methods Version: Human Genetics 2025 journal article (online 2024) | source checked automated source review · 2026-09-16 Audit detailsTask-specific computational methodology and field context checked in the cited primary-source artifact. Source-backed fields, inapplicable evaluator dimensions and unresolved details are distinguished. Numerical results were not reproduced. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Adaptation Unsupervised family adaptation plus labeled-validation model selection; no fitness-label fitting of model parameters. Individual claims | Structure-Informed Protein Language Models are Robust Predictors for Variant Effects (reviewed HTML snapshot) Results: Structure-Informed pLMs Predict Variant Fitness Robustly; Tables 2 and 4; Methods: Datasets, Mutation effect datasets, Competing methods Version: Human Genetics 2025 journal article (online 2024) | source checked automated source review · 2026-09-16 Audit detailsTask-specific computational methodology and field context checked in the cited primary-source artifact. Source-backed fields, inapplicable evaluator dimensions and unresolved details are distinguished. Numerical results were not reproduced. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Metrics Spearman correlation evaluates ranking; AUROC and AUPRC evaluate high/low labels thresholded relative to the wild type. Table 4 compares sequence, structure and combined score variants. Individual claims | Structure-Informed Protein Language Models are Robust Predictors for Variant Effects (reviewed HTML snapshot) Results: Structure-Informed pLMs Predict Variant Fitness Robustly; Tables 2 and 4; Methods: Datasets, Mutation effect datasets, Competing methods Version: Human Genetics 2025 journal article (online 2024) | source checked automated source review · 2026-09-16 Audit detailsTask-specific computational methodology and field context checked in the cited primary-source artifact. Source-backed fields, inapplicable evaluator dimensions and unresolved details are distinguished. Numerical results were not reproduced. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Baselines PSSM, EVmutation, DeepSequence, Wavenet and several protein language models; competing scores are obtained from the ESM-1v repository. Individual claims | Structure-Informed Protein Language Models are Robust Predictors for Variant Effects (reviewed HTML snapshot) Results: Structure-Informed pLMs Predict Variant Fitness Robustly; Tables 2 and 4; Methods: Datasets, Mutation effect datasets, Competing methods Version: Human Genetics 2025 journal article (online 2024) | source checked automated source review · 2026-09-16 Audit detailsTask-specific computational methodology and field context checked in the cited primary-source artifact. Source-backed fields, inapplicable evaluator dimensions and unresolved details are distinguished. Numerical results were not reproduced. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Leakage controls Family-specific adaptation and validation-label model selection are part of the protocol; it should not be represented as selection without labeled data. Individual claims | Structure-Informed Protein Language Models are Robust Predictors for Variant Effects (reviewed HTML snapshot) Results: Structure-Informed pLMs Predict Variant Fitness Robustly; Tables 2 and 4; Methods: Datasets, Mutation effect datasets, Competing methods Version: Human Genetics 2025 journal article (online 2024) | source checked automated source review · 2026-09-16 Audit detailsTask-specific computational methodology and field context checked in the cited primary-source artifact. Source-backed fields, inapplicable evaluator dimensions and unresolved details are distinguished. Numerical results were not reproduced. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Uncertainty One-sided paired Wilcoxon signed-rank comparisons across assay performances are reported for the main method comparison. Individual claims | Structure-Informed Protein Language Models are Robust Predictors for Variant Effects (reviewed HTML snapshot) Results: Structure-Informed pLMs Predict Variant Fitness Robustly; Tables 2 and 4; Methods: Datasets, Mutation effect datasets, Competing methods Version: Human Genetics 2025 journal article (online 2024) | source checked automated source review · 2026-09-16 Audit detailsTask-specific computational methodology and field context checked in the cited primary-source artifact. Source-backed fields, inapplicable evaluator dimensions and unresolved details are distinguished. Numerical results were not reproduced. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
Release 2026-09-17-d277315f7d76 · Record review: needs review
Stable ID: reported-task-83be0998084c91