Model type
Convolutional neural network; this record is the paper-specific evaluated configuration.
Configuration
TU-Fold (aug)
TU-Fold predicts RNA secondary structure as a set of multiclass decisions; this row includes augmentation.
Sources
RNA secondary structure prediction by conducting multi-class classifications · Materials and methods/Training method (paragraph 3); Results/Knowledge merge alleviates the performance drop in the cross-RNA-family evaluation (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
RNA secondary structure prediction by conducting multi-class classifications · Materials and methods/Training method (paragraph 1); Abstract (paragraph 3)At a glance
Biological inputs
RNA nucleotide sequences
Sources
RNA secondary structure prediction by conducting multi-class classifications · CRediT authorship contribution statement (paragraph 1); Discussion (paragraph 3)Outputs
RNA secondary-structure pairings
Sources
RNA secondary structure prediction by conducting multi-class classifications · Materials and methods/Training method (paragraph 3); Materials and methods/Experimental settings (paragraph 6)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 |
|---|---|---|
| TU-Fold (aug): RNA secondary structure Three-fold training and evaluation; source reports mean and standard deviation. Author-reported evaluation · Evaluation metadata: needs review | ||
| 0.947 F1 Unit: unitless · Direction: unknown | Uncertainty: ± 0.002 standard deviation Scored: Not reported · Eligible: Not reported | source checkedRNA secondary structure prediction by conducting multi-class classifications · Table 2, TU-Fold (aug) row, Overall F1 column Source checking is not independent reproduction. |
How it works
How the evaluated method works
Attention and convolutional components generate a matrix-based secondary-structure prediction, with multiclass training designed to reduce the need for complex validity post-processing.
Sources
RNA secondary structure prediction by conducting multi-class classifications · Materials and methods/Training method (paragraph 1); Abstract (paragraph 3)What was evaluated
The linked evaluation record identifies TU-Fold (aug): RNA secondary structure. Its dataset, split, adaptation and evidence origin remain attached to the reported results.
Sources
RNA secondary structure prediction by conducting multi-class classifications · The named evaluation’s methods and comparison table; exact preserved evaluation IDs: evaluation-lit-013Strengths and limitations
Strengths and considerations
- Separately examines structural validity and generalisation across RNA families.
Sources
RNA secondary structure prediction by conducting multi-class classifications · Results/Data augmentation further improves the performance (paragraph 2); Materials and methods/Experimental settings (paragraph 1)
Limitations and conditions
- The augmented setting is distinct from the unaugmented model and from the separate strategy proposed for cross-family transfer.
Sources
RNA secondary structure prediction by conducting multi-class classifications · Materials and methods/Knowledge merge (paragraph 2); Discussion (paragraph 2)
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-d1cd9a425f9bbdSpecifications
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 | Convolutional neural network; this record is the paper-specific evaluated configuration.SourcesRNA secondary structure prediction by conducting multi-class classifications · Materials and methods/Training method (paragraph 1); Abstract (paragraph 3) |
| Architecture / procedure | Attention and convolutional components generate a matrix-based secondary-structure prediction, with multiclass training designed to reduce the need for complex validity post-processing.SourcesRNA secondary structure prediction by conducting multi-class classifications · Materials and methods/Training method (paragraph 1); Abstract (paragraph 3) |
| Biological inputs | RNA nucleotide sequencesSourcesRNA secondary structure prediction by conducting multi-class classifications · CRediT authorship contribution statement (paragraph 1); Discussion (paragraph 3) |
| Outputs | RNA secondary-structure pairingsSourcesRNA secondary structure prediction by conducting multi-class classifications · Materials and methods/Training method (paragraph 3); Materials and methods/Experimental settings (paragraph 6) |
| Parameters | An aggregate parameter total for this exact evaluated configuration is not established by the inspected sources. · Not reported in inspected sourcesSources (2)RNA secondary structure prediction by conducting multi-class classifications; ygjiyn/tu_fold README.md · Materials and methods/Training method; Materials and methods/Evaluation method; Materials and methods/Data augmentation; Materials and methods/Knowledge merge; Materials and methods/Experimental settings; Results/TU-fold outperforms existing methods without using post-processing steps; inspected for aggregate parameter count (component sizes are not added without an exact configuration); README.md at pinned repository revision |
| Known versions / configuration | TU-Fold (aug) is the comparison-table label; that label does not specify an immutable weight revision. · Not reported in inspected sourcesSourcesRNA secondary structure prediction by conducting multi-class classifications · Model identification in the comparison table and corresponding Methods; immutable checkpoint revision is not supplied by the table label. |
| Training data / fitting | The paper’s within-family and cross-family RNA-structure partitions; augmentation is part of this named configuration.SourcesRNA secondary structure prediction by conducting multi-class classifications · Materials and methods/Data augmentation (paragraph 1); Materials and methods/Training method (paragraph 3) |
| 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)RNA secondary structure prediction by conducting multi-class classifications; ygjiyn/tu_fold README.md · Materials and methods/Training method; Materials and methods/Evaluation method; Materials and methods/Data augmentation; Materials and methods/Knowledge merge; Materials and methods/Experimental settings; Results/TU-fold outperforms existing methods without using post-processing steps; 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/ygjiyn/tu_fold/blob/f0532b6bf38b2f57baf0ba6afce7766bfc64899b/README.md. This pinned documentation revision is not automatically the evaluated weight revision.Sourcesygjiyn/tu_fold 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).Sourcesygjiyn/tu_fold 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 sourcesSourcesygjiyn/tu_fold 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 | RNA secondary structure prediction by conducting multi-class classifications Materials and methods/Training method (paragraph 1); Abstract (paragraph 3) Version: version of record | 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 ["RNA nucleotide sequences","TU-Fold (aug)","RNA secondary-structure pairings"] Individual claims | RNA secondary structure prediction by conducting multi-class classifications Materials and methods/Training method (paragraph 1); Abstract (paragraph 3) Version: version of record | 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 | RNA secondary structure prediction by conducting multi-class classifications Materials and methods/Training method (paragraph 1); Abstract (paragraph 3) Version: version of record | 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 Convolutional neural network; this record is the paper-specific evaluated configuration. Individual claims | RNA secondary structure prediction by conducting multi-class classifications Materials and methods/Training method (paragraph 1); Abstract (paragraph 3) Version: version of record | 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 Attention and convolutional components generate a matrix-based secondary-structure prediction, with multiclass training designed to reduce the need for complex validity post-processing. Individual claims | RNA secondary structure prediction by conducting multi-class classifications Materials and methods/Training method (paragraph 1); Abstract (paragraph 3) Version: version of record | 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 | ygjiyn/tu_fold README.md README.md; checkpoint/access documentation and licence scope Version: f0532b6bf38b2f57baf0ba6afce7766bfc64899b | 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 RNA nucleotide sequences Individual claims | RNA secondary structure prediction by conducting multi-class classifications CRediT authorship contribution statement (paragraph 1); Discussion (paragraph 3) Version: version of record | 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 RNA secondary-structure pairings Individual claims | RNA secondary structure prediction by conducting multi-class classifications Materials and methods/Training method (paragraph 3); Materials and methods/Experimental settings (paragraph 6) Version: version of record | 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 | ygjiyn/tu_fold README.md Materials and methods/Training method; Materials and methods/Evaluation method; Materials and methods/Data augmentation; Materials and methods/Knowledge merge; Materials and methods/Experimental settings; Results/TU-fold outperforms existing methods without using post-processing steps; 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: f0532b6bf38b2f57baf0ba6afce7766bfc64899b | 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 | RNA secondary structure prediction by conducting multi-class classifications Materials and methods/Training method; Materials and methods/Evaluation method; Materials and methods/Data augmentation; Materials and methods/Knowledge merge; Materials and methods/Experimental settings; Results/TU-fold outperforms existing methods without using post-processing steps; 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: version of record | 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
3 source records and release history
- RNA secondary structure prediction by conducting multi-class classifications · Original source · version of record
- ygjiyn/tu_fold README.md · Original source · f0532b6bf38b2f57baf0ba6afce7766bfc64899b
- ygjiyn/tu_fold LICENSE · Original source · f0532b6bf38b2f57baf0ba6afce7766bfc64899b
Technical metadata and extraction receipts
Stable ID: reported-model-d1cd9a425f9bbd
- areas
- rna-transcriptomes
- entity level
- method
- version
- Not reported
- reported name
- TU-Fold (aug)
- 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 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: tu-fold-2025; source locator: Materials and methods/Training method (paragraph 1); Abstract (paragraph 3) | Materials and methods/Training method (paragraph 3); Results/Knowledge merge alleviates the performance drop in the cross-RNA-family evaluation (paragraph 2); 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.