Model type
Study-specific predictive method; this record is the paper-specific evaluated configuration.
Configuration
structure-informed pLM
This structure-informed protein language model uses structural prediction objectives during training to improve variant-effect scoring.
Sources
Structure-Informed Protein Language Models are Robust Predictors for Variant Effects (reviewed HTML snapshot) · Structure-Informed Protein Language Models; Cross-Modal Masked Learning (Denoising) Framework; Table 4How it worksEvaluated procedure (conceptual)
Conceptual input–method–output guide. Check the procedure text and linked evaluation for fitted components, additional inputs and exact settings.
Sources
Structure-Informed Protein Language Models are Robust Predictors for Variant Effects (reviewed HTML snapshot) · Structure-Informed Protein Language Models; Cross-Modal Masked Learning (Denoising) Framework; Table 4At a glance
Biological inputs
Protein sequence at inference; sequence/structure pairs supply additional training supervision
Sources
Structure-Informed Protein Language Models are Robust Predictors for Variant Effects (reviewed HTML snapshot) · Structure-Informed Protein Language Models; Cross-Modal Masked Learning (Denoising) Framework; Table 4Outputs
Sequence-based variant-effect scores
Sources
Structure-Informed Protein Language Models are Robust Predictors for Variant Effects (reviewed HTML snapshot) · Structure-Informed Protein Language Models; Cross-Modal Masked Learning (Denoising) Framework; Table 4limited source coverage · Automated source review, 2026-09-16. All specifications and missing details
Evaluations and results
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. |
How it works
How the evaluated method works
Cross-modal masked learning combines amino-acid recovery with secondary-structure, relative-solvent-accessibility and contact-map prediction heads. The linked AA+SS+RSA+CM configuration uses all four objectives.
Sources
Structure-Informed Protein Language Models are Robust Predictors for Variant Effects (reviewed HTML snapshot) · Structure-Informed Protein Language Models; Cross-Modal Masked Learning (Denoising) Framework; Table 4What was evaluated
The linked evaluation record identifies structure-informed pLM: protein variant-effect classification. Its dataset, split, adaptation and evidence origin remain attached to the reported results.
Sources
Structure-Informed Protein Language Models are Robust Predictors for Variant Effects (reviewed HTML snapshot) · The named evaluation’s methods and comparison table; exact preserved evaluation IDs: evaluation-b2-structure-informed-plm-2025Strengths and limitations
Strengths and considerations
- Structural context regularises training without requiring a variant structure at inference.
Sources
Structure-Informed Protein Language Models are Robust Predictors for Variant Effects (reviewed HTML snapshot) · Structure-Informed Protein Language Models; Cross-Modal Masked Learning (Denoising) Framework; Table 4
Limitations and conditions
- The training structural information and family-specific adaptation are part of the evaluated method; it is not equivalent to an unadapted sequence-only encoder.
Sources
Structure-Informed Protein Language Models are Robust Predictors for Variant Effects (reviewed HTML snapshot) · Structure-Informed Protein Language Models; Cross-Modal Masked Learning (Denoising) Framework; Table 4
Profile review details
Primary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction.
Stable record: reported-model-035a3ab36a3a6aSpecifications
Inputs, training, access and other details
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 |
|---|---|
| Model type | Study-specific predictive method; this record is the paper-specific evaluated configuration.SourcesStructure-Informed Protein Language Models are Robust Predictors for Variant Effects (reviewed HTML snapshot) · Structure-Informed Protein Language Models; Cross-Modal Masked Learning (Denoising) Framework; Table 4 |
| Architecture / procedure | Cross-modal masked learning combines amino-acid recovery with secondary-structure, relative-solvent-accessibility and contact-map prediction heads. The linked AA+SS+RSA+CM configuration uses all four objectives.SourcesStructure-Informed Protein Language Models are Robust Predictors for Variant Effects (reviewed HTML snapshot) · Structure-Informed Protein Language Models; Cross-Modal Masked Learning (Denoising) Framework; Table 4 |
| Biological inputs | Protein sequence at inference; sequence/structure pairs supply additional training supervisionSourcesStructure-Informed Protein Language Models are Robust Predictors for Variant Effects (reviewed HTML snapshot) · Structure-Informed Protein Language Models; Cross-Modal Masked Learning (Denoising) Framework; Table 4 |
| Outputs | Sequence-based variant-effect scoresSourcesStructure-Informed Protein Language Models are Robust Predictors for Variant Effects (reviewed HTML snapshot) · Structure-Informed Protein Language Models; Cross-Modal Masked Learning (Denoising) Framework; Table 4 |
| Parameters | An aggregate parameter total for this exact evaluated configuration is not established by the inspected sources. · Not reported in inspected sourcesSources (2)Structure-Informed Protein Language Models are Robust Predictors for Variant Effects (reviewed HTML snapshot); Shen-Lab/Structure-informed_PLM readMe.md · Complete primary text and named comparison table; inspected for aggregate parameter count (component sizes are not added without an exact configuration); readMe.md at pinned repository revision |
| Known versions / configuration | structure-informed pLM is the comparison-table label; that label does not specify an immutable weight revision. · Not reported in inspected sourcesSourcesStructure-Informed Protein Language Models are Robust Predictors for Variant Effects (reviewed HTML snapshot) · Model identification in the comparison table and corresponding Methods; immutable checkpoint revision is not supplied by the table label. |
| Training data / fitting | Family datasets combine sequence information with experimental PDB and predicted AlphaFold structures.SourcesStructure-Informed Protein Language Models are Robust Predictors for Variant Effects (reviewed HTML snapshot) · Structure-Informed Protein Language Models; Cross-Modal Masked Learning (Denoising) Framework; Table 4 |
| Context limits | A maximum input/context length for this exact evaluated configuration is not established by the inspected sources. · Not reported in inspected sourcesSources (2)Structure-Informed Protein Language Models are Robust Predictors for Variant Effects (reviewed HTML snapshot); Shen-Lab/Structure-informed_PLM readMe.md · Complete primary text and named comparison table; inspected for explicit maximum input length (dataset lengths and family-wide limits are not substituted); readMe.md at pinned repository revision |
| Access | Official study implementation and usage documentation: https://github.com/Shen-Lab/Structure-informed_PLM/blob/2307b101f9bf08223729a68f52b8a6fb21f18991/readMe.md. This pinned documentation revision is not automatically the evaluated weight revision.SourcesShen-Lab/Structure-informed_PLM readMe.md · readMe.md; installation, model download and usage instructions |
| Code licence | MIT (study repository code at the cited revision; this does not establish every dependency or historical checkpoint licence).SourcesShen-Lab/Structure-informed_PLM LICENSE · LICENSE; complete licence text |
| Weights licence | The inspected model-access documentation does not explicitly identify terms for this exact evaluated checkpoint or fitted head; repository code terms are shown separately. · Not reported in inspected sourcesSourcesShen-Lab/Structure-informed_PLM readMe.md · readMe.md; checkpoint/access documentation and licence scope |
Evidence table
Inspect claims, sources and review details
Trace 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.
21 evidence rows matching the loaded filters
| Property and statement | Original source and location | Review and provenance |
|---|---|---|
| Diagram caption Conceptual input–method–output guide. Check the procedure text and linked evaluation for fitted components, additional inputs and exact settings. Individual claims | Structure-Informed Protein Language Models are Robust Predictors for Variant Effects (reviewed HTML snapshot) Structure-Informed Protein Language Models; Cross-Modal Masked Learning (Denoising) Framework; Table 4 Version: Human Genetics 2025 journal article (online 2024) | source checked automated source review · 2026-09-16 Audit detailsPrimary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Diagram steps ["Protein sequence at inference","structure-informed pLM","Sequence-based variant-effect scores"] Individual claims | Structure-Informed Protein Language Models are Robust Predictors for Variant Effects (reviewed HTML snapshot) Structure-Informed Protein Language Models; Cross-Modal Masked Learning (Denoising) Framework; Table 4 Version: Human Genetics 2025 journal article (online 2024) | source checked automated source review · 2026-09-16 Audit detailsPrimary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Diagram title Evaluated procedure (conceptual) Individual claims | Structure-Informed Protein Language Models are Robust Predictors for Variant Effects (reviewed HTML snapshot) Structure-Informed Protein Language Models; Cross-Modal Masked Learning (Denoising) Framework; Table 4 Version: Human Genetics 2025 journal article (online 2024) | source checked automated source review · 2026-09-16 Audit detailsPrimary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Model type Study-specific predictive method; this record is the paper-specific evaluated configuration. Individual claims | Structure-Informed Protein Language Models are Robust Predictors for Variant Effects (reviewed HTML snapshot) Structure-Informed Protein Language Models; Cross-Modal Masked Learning (Denoising) Framework; Table 4 Version: Human Genetics 2025 journal article (online 2024) | source checked automated source review · 2026-09-16 Audit detailsPrimary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Architecture / procedure Cross-modal masked learning combines amino-acid recovery with secondary-structure, relative-solvent-accessibility and contact-map prediction heads. The linked AA+SS+RSA+CM configuration uses all four objectives. Individual claims | Structure-Informed Protein Language Models are Robust Predictors for Variant Effects (reviewed HTML snapshot) Structure-Informed Protein Language Models; Cross-Modal Masked Learning (Denoising) Framework; Table 4 Version: Human Genetics 2025 journal article (online 2024) | source checked automated source review · 2026-09-16 Audit detailsPrimary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Weights licence The inspected model-access documentation does not explicitly identify terms for this exact evaluated checkpoint or fitted head; repository code terms are shown separately. Individual claims | Shen-Lab/Structure-informed_PLM readMe.md readMe.md; checkpoint/access documentation and licence scope Version: 2307b101f9bf08223729a68f52b8a6fb21f18991 | unreported automated source review · 2026-09-16 Audit detailsPrimary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Biological inputs Protein sequence at inference; sequence/structure pairs supply additional training supervision Individual claims | Structure-Informed Protein Language Models are Robust Predictors for Variant Effects (reviewed HTML snapshot) Structure-Informed Protein Language Models; Cross-Modal Masked Learning (Denoising) Framework; Table 4 Version: Human Genetics 2025 journal article (online 2024) | source checked automated source review · 2026-09-16 Audit detailsPrimary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Outputs Sequence-based variant-effect scores Individual claims | Structure-Informed Protein Language Models are Robust Predictors for Variant Effects (reviewed HTML snapshot) Structure-Informed Protein Language Models; Cross-Modal Masked Learning (Denoising) Framework; Table 4 Version: Human Genetics 2025 journal article (online 2024) | source checked automated source review · 2026-09-16 Audit detailsPrimary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Parameters An aggregate parameter total for this exact evaluated configuration is not established by the inspected sources. Individual claims | Structure-Informed Protein Language Models are Robust Predictors for Variant Effects (reviewed HTML snapshot) Complete primary text and named comparison table; inspected for aggregate parameter count (component sizes are not added without an exact configuration); readMe.md at pinned repository revision Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Human Genetics 2025 journal article (online 2024) | unreported automated source review · 2026-09-16 Audit detailsPrimary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Parameters An aggregate parameter total for this exact evaluated configuration is not established by the inspected sources. Individual claims | Shen-Lab/Structure-informed_PLM readMe.md Complete primary text and named comparison table; inspected for aggregate parameter count (component sizes are not added without an exact configuration); readMe.md at pinned repository revision Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: 2307b101f9bf08223729a68f52b8a6fb21f18991 | unreported automated source review · 2026-09-16 Audit detailsPrimary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
Sources and history
Release 2026-09-17-d277315f7d76 · Record review: needs review
4 source records and release history
- Structure-Informed Protein Language Models are Robust Predictors for Variant Effects · Original source · Human Genetics 2025 journal article (online 2024)
- Structure-Informed Protein Language Models are Robust Predictors for Variant Effects (reviewed HTML snapshot) · Original source · Human Genetics 2025 journal article (online 2024)
- Shen-Lab/Structure-informed_PLM readMe.md · Original source · 2307b101f9bf08223729a68f52b8a6fb21f18991
- Shen-Lab/Structure-informed_PLM LICENSE · Original source · 2307b101f9bf08223729a68f52b8a6fb21f18991
Technical metadata and extraction receipts
Stable ID: reported-model-035a3ab36a3a6a
- areas
- proteins-complexes
- entity level
- method
- version
- not stated in table
- reported name
- structure-informed pLM
- historical missing metadata
- checkpoint revision: not_reported_in_legacy_extract; training data: not_reported_in_legacy_extract; licence: not_reported_in_legacy_extract
- metadata review scope
- historical_missing_metadata preserves the original discovery state. Current descriptive evidence and missingness are recorded in profile.facts; numerical-result review is separate.
- legacy kinds
- model
- entity classification
- review date: 2026-09-17; rationale: This source-scoped entry preserves the method/configuration actually named in an evaluation. It is neither a global family identity nor proof of an immutable checkpoint; the linked evaluation retains adaptation, fitting and scoring details.; source ids: evidence-reported-structure-informed-current-html; source locator: Structure-Informed Protein Language Models; Cross-Modal Masked Learning (Denoising) Framework; Table 4; ambiguities: Configuration means the source-labelled evaluated identity. It does not establish missing checkpoint hashes, default settings or equivalence to same-named records in other papers.