Model type
Study-specific predictive method; this record is the paper-specific evaluated configuration.
Pipeline
Vaxign-DL + ESM
Vaxign-DL plus ESM combines protein-language-model features with engineered features to predict bacterial protective antigens.
Sources
Enhancing Vaxign-DL for Vaccine Candidate Prediction with added ESM-Generated Features · Results/Performance analysis ESM enhance Vaxign-DL model (paragraph 3); Methods/ESM generation of new features based on protein sequences (paragraph 2)How 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
Enhancing Vaxign-DL for Vaccine Candidate Prediction with added ESM-Generated Features · Methods/ESM generation of new features based on protein sequences (paragraph 1); Results/Performance analysis ESM enhance Vaxign-DL model (paragraph 3)At a glance
Biological inputs
Protein sequence-derived learned and engineered features
Sources
Enhancing Vaxign-DL for Vaccine Candidate Prediction with added ESM-Generated Features · Results/Performance analysis ESM enhance Vaxign-DL model (paragraph 3); Results/Hyperparameter Optimization Study/Leave-one-pathogen-out Validation (paragraph 1)Outputs
Predicted protective-antigen labels
Sources
Enhancing Vaxign-DL for Vaccine Candidate Prediction with added ESM-Generated Features · Results/Performance analysis ESM enhance Vaxign-DL model (paragraph 3); Methods/Collection of Positive and Negative protein sequences (paragraph 1)limited 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 |
|---|---|---|
| Vaxign-DL + ESM: vaccine-antigen candidate prediction Pipeline: Vaxign-DL + ESMTask: vaccine-antigen candidate predictionDataset: vaccine candidate validation Combined skip architecture, four layers, ESM-generated sequence features Author-reported evaluation · Evaluation metadata: needs review | ||
| 0.92 AUPRC Unit: fraction · Direction: unknown | Uncertainty: ±0.013 Scored: Not reported · Eligible: Not reported | source checkedEnhancing Vaxign-DL for Vaccine Candidate Prediction with added ESM-Generated Features · Table 2, 4 Layers row, AUPRC column Source checking is not independent reproduction. |
How it works
How the evaluated method works
ESM-1b (33 layers, 650M parameters) supplies 1,280 sequence features, combined with 509 existing features in the deep protective-antigen classifier. This paper does not use ESM-2 for the reported configuration.
Sources
Enhancing Vaxign-DL for Vaccine Candidate Prediction with added ESM-Generated Features · Methods/ESM generation of new features based on protein sequences (paragraph 1); Results/Performance analysis ESM enhance Vaxign-DL model (paragraph 3)What was evaluated
The linked evaluation record identifies Vaxign-DL + ESM: vaccine-antigen candidate prediction. Its dataset, split, adaptation and evidence origin remain attached to the reported results.
Sources
Enhancing Vaxign-DL for Vaccine Candidate Prediction with added ESM-Generated Features · The named evaluation’s methods and comparison table; exact preserved evaluation IDs: evaluation-lit-b4-012Strengths and limitations
Strengths and considerations
- Tests whether pretrained features add information beyond an established engineered-feature pipeline.
Sources
Enhancing Vaxign-DL for Vaccine Candidate Prediction with added ESM-Generated Features · Methods/Deep learning pipeline (paragraph 1); Methods/ESM generation of new features based on protein sequences (paragraph 3)
Limitations and conditions
- The source is a preprint; computational antigen prioritisation is not vaccine-efficacy or safety validation.
Sources
Enhancing Vaxign-DL for Vaccine Candidate Prediction with added ESM-Generated Features · Results/Performance analysis ESM enhance Vaxign-DL model (paragraph 3); Introduction (paragraph 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-8ad3e0cefde796Specifications
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.SourcesEnhancing Vaxign-DL for Vaccine Candidate Prediction with added ESM-Generated Features · Methods/ESM generation of new features based on protein sequences (paragraph 1); Results/Performance analysis ESM enhance Vaxign-DL model (paragraph 3) |
| Architecture / procedure | ESM-1b (33 layers, 650M parameters) supplies 1,280 sequence features, combined with 509 existing features in the deep protective-antigen classifier. This paper does not use ESM-2 for the reported configuration.SourcesEnhancing Vaxign-DL for Vaccine Candidate Prediction with added ESM-Generated Features · Methods/ESM generation of new features based on protein sequences (paragraph 1); Results/Performance analysis ESM enhance Vaxign-DL model (paragraph 3) |
| Biological inputs | Protein sequence-derived learned and engineered featuresSourcesEnhancing Vaxign-DL for Vaccine Candidate Prediction with added ESM-Generated Features · Results/Performance analysis ESM enhance Vaxign-DL model (paragraph 3); Results/Hyperparameter Optimization Study/Leave-one-pathogen-out Validation (paragraph 1) |
| Outputs | Predicted protective-antigen labelsSourcesEnhancing Vaxign-DL for Vaccine Candidate Prediction with added ESM-Generated Features · Results/Performance analysis ESM enhance Vaxign-DL model (paragraph 3); Methods/Collection of Positive and Negative protein sequences (paragraph 1) |
| Parameters | 650-million-parameter ESM-1b feature extractor; the downstream classifier is additional.SourcesEnhancing Vaxign-DL for Vaccine Candidate Prediction with added ESM-Generated Features · Results/Performance analysis ESM enhance Vaxign-DL model (paragraph 3); Methods/Deep learning pipeline (paragraph 1) |
| Known versions / configuration | Vaxign-DL + ESM is the comparison-table label; that label does not specify an immutable weight revision. · Not reported in inspected sourcesSourcesEnhancing Vaxign-DL for Vaccine Candidate Prediction with added ESM-Generated Features · Model identification in the comparison table and corresponding Methods; immutable checkpoint revision is not supplied by the table label. |
| Training data / fitting | Protegen-derived positive antigens, with 397 positives after the described homology filtering and separately constructed negatives; leave-one-pathogen-out validation is included.SourcesEnhancing Vaxign-DL for Vaccine Candidate Prediction with added ESM-Generated Features · Introduction (paragraph 4); Abstract (paragraph 1) |
| Context limits | The study either skips proteins longer than 1,024 amino acids or truncates them to the first 1,024; Skip and Cut are separate evaluated settings.SourcesEnhancing Vaxign-DL for Vaccine Candidate Prediction with added ESM-Generated Features · Results/Hyperparameter Optimization Study/Comparison of Two ESM methods for Processing Long Sequence Proteins (paragraph 1); Methods/ESM generation of new features based on protein sequences (paragraph 3) |
| Access | The paper describes the ESM-1b extension but does not establish a separate released checkpoint for this fitted Vaxign-DL plus ESM pipeline. · Not reported in inspected sourcesSourcesEnhancing Vaxign-DL for Vaccine Candidate Prediction with added ESM-Generated Features · Complete paper; ESM-1b feature extraction and neural-network fitting descriptions; exact-name implementation search |
| Code licence | No explicit code licence was established from the paper’s availability statement and inspected repository-root documentation. · Not reported in inspected sourcesSourcesEnhancing Vaxign-DL for Vaccine Candidate Prediction with added ESM-Generated Features · Discussion (paragraph 6); Discussion (paragraph 5) |
| 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 sourcesSourcesEnhancing Vaxign-DL for Vaccine Candidate Prediction with added ESM-Generated Features · Methods/Deep learning pipeline (paragraph 2); Discussion (paragraph 6) |
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.
19 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 | Enhancing Vaxign-DL for Vaccine Candidate Prediction with added ESM-Generated Features Methods/ESM generation of new features based on protein sequences (paragraph 1); Results/Performance analysis ESM enhance Vaxign-DL model (paragraph 3) Version: preprint version in PMC | 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-derived learned and engineered features","Vaxign-DL + ESM","Predicted protective-antigen labels"] Individual claims | Enhancing Vaxign-DL for Vaccine Candidate Prediction with added ESM-Generated Features Methods/ESM generation of new features based on protein sequences (paragraph 1); Results/Performance analysis ESM enhance Vaxign-DL model (paragraph 3) Version: preprint version in PMC | 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 | Enhancing Vaxign-DL for Vaccine Candidate Prediction with added ESM-Generated Features Methods/ESM generation of new features based on protein sequences (paragraph 1); Results/Performance analysis ESM enhance Vaxign-DL model (paragraph 3) Version: preprint version in PMC | 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 | Enhancing Vaxign-DL for Vaccine Candidate Prediction with added ESM-Generated Features Methods/ESM generation of new features based on protein sequences (paragraph 1); Results/Performance analysis ESM enhance Vaxign-DL model (paragraph 3) Version: preprint version in PMC | 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 ESM-1b (33 layers, 650M parameters) supplies 1,280 sequence features, combined with 509 existing features in the deep protective-antigen classifier. This paper does not use ESM-2 for the reported configuration. Individual claims | Enhancing Vaxign-DL for Vaccine Candidate Prediction with added ESM-Generated Features Methods/ESM generation of new features based on protein sequences (paragraph 1); Results/Performance analysis ESM enhance Vaxign-DL model (paragraph 3) Version: preprint version in PMC | 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 | Enhancing Vaxign-DL for Vaccine Candidate Prediction with added ESM-Generated Features Methods/Deep learning pipeline (paragraph 2); Discussion (paragraph 6) Version: preprint version in PMC | 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-derived learned and engineered features Individual claims | Enhancing Vaxign-DL for Vaccine Candidate Prediction with added ESM-Generated Features Results/Performance analysis ESM enhance Vaxign-DL model (paragraph 3); Results/Hyperparameter Optimization Study/Leave-one-pathogen-out Validation (paragraph 1) Version: preprint version in PMC | 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 Predicted protective-antigen labels Individual claims | Enhancing Vaxign-DL for Vaccine Candidate Prediction with added ESM-Generated Features Results/Performance analysis ESM enhance Vaxign-DL model (paragraph 3); Methods/Collection of Positive and Negative protein sequences (paragraph 1) Version: preprint version in PMC | 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 650-million-parameter ESM-1b feature extractor; the downstream classifier is additional. Individual claims | Enhancing Vaxign-DL for Vaccine Candidate Prediction with added ESM-Generated Features Results/Performance analysis ESM enhance Vaxign-DL model (paragraph 3); Methods/Deep learning pipeline (paragraph 1) Version: preprint version in PMC | 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 |
| Known versions / configuration Vaxign-DL + ESM is the comparison-table label; that label does not specify an immutable weight revision. Individual claims | Enhancing Vaxign-DL for Vaccine Candidate Prediction with added ESM-Generated Features Model identification in the comparison table and corresponding Methods; immutable checkpoint revision is not supplied by the table label. Version: preprint version in PMC | 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
1 source records and release history
- Enhancing Vaxign-DL for Vaccine Candidate Prediction with added ESM-Generated Features · Original source · preprint version in PMC
Technical metadata and extraction receipts
Stable ID: reported-model-8ad3e0cefde796
- areas
- proteins-complexes
- entity level
- method
- version
- Not reported
- reported name
- Vaxign-DL + ESM
- historical missing metadata
- version: not_reported_in_legacy_extract; 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 record identifies a composed analysis workflow with separately identifiable upstream models, representations or tools and a downstream prediction/scoring procedure. Results belong to that complete composition rather than to an upstream model alone.; source ids: vaxign-esm-2024; source locator: Methods/ESM generation of new features based on protein sequences (paragraph 1); Results/Performance analysis ESM enhance Vaxign-DL model (paragraph 3) | Results/Performance analysis ESM enhance Vaxign-DL model (paragraph 3); Methods/ESM generation of new features based on protein sequences (paragraph 2); ambiguities: This is the paper-specific pipeline identity; unspecified component checkpoints or implementation versions are not inferred from its name.