Model type
Thermodynamic RNA folding algorithm; this record is the paper-specific evaluated configuration.
Configuration
RNAfold
RNAfold is the thermodynamic RNA-structure baseline in the BPfold comparison.
Sources
Deep generalizable prediction of RNA secondary structure via base pair motif energy · Abstract (paragraph 2); Methods/Datasets and evaluation (paragraph 1)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
Deep generalizable prediction of RNA secondary structure via base pair motif energy · Abstract (paragraph 2); Methods/Base pair motif energy as thermodynamic prior (paragraph 1)At a glance
Biological inputs
RNA nucleotide sequence
Sources
Deep generalizable prediction of RNA secondary structure via base pair motif energy · Methods/Deep neural network with base pair attention (paragraph 5); Introduction (paragraph 1)Outputs
RNA secondary structure
Sources
Deep generalizable prediction of RNA secondary structure via base pair motif energy · Methods/Datasets and evaluation (paragraph 1); Discussion (paragraph 5)limited source coverage · Automated source review, 2026-09-16. All specifications and missing details
Evaluations and results
Release 2026-09-17-d277315f7d76 · 1 evaluation · 4 metric rows. Different protocols are not a single leaderboard.
| Metric and finding | Coverage and uncertainty | Evidence |
|---|---|---|
| RNAfold: RNA secondary structure Family-wise RNA secondary-structure evaluation; macro-average canonical base-pair metrics. Independent external evaluation · Evaluation metadata: needs review | ||
| 0.747 F1 Unit: unitless · Direction: higher | Uncertainty: not reported in legacy extract Scored: Not reported · Eligible: Not reported | source checkedDeep generalizable prediction of RNA secondary structure via base pair motif energy · Table 2, RNAfold row, PDB F1 column Source checking is not independent reproduction. |
| 0.776 Precision Unit: unitless · Direction: higher | Uncertainty: unreported Scored: Not reported · Eligible: Not reported | source checkedDeep generalizable prediction of RNA secondary structure via base pair motif energy · Table 2 (Tab2), row 10 RNAfold, column 8: PDB Precision Source checking is not independent reproduction. |
| 0.728 Recall Unit: unitless · Direction: higher | Uncertainty: unreported Scored: Not reported · Eligible: Not reported | source checkedDeep generalizable prediction of RNA secondary structure via base pair motif energy · Table 2 (Tab2), row 10 RNAfold, column 9: PDB Recall Source checking is not independent reproduction. |
| 0.749 INF Unit: unitless · Direction: higher | Uncertainty: unreported Scored: Not reported · Eligible: Not reported | source checkedDeep generalizable prediction of RNA secondary structure via base pair motif energy · Table 2 (Tab2), row 10 RNAfold, column 6: PDB INF Source checking is not independent reproduction. |
How it works
How the evaluated method works
ViennaRNA predicts a minimum-free-energy secondary structure using its thermodynamic energy model; the paper runs default settings.
Sources
Deep generalizable prediction of RNA secondary structure via base pair motif energy · Abstract (paragraph 2); Methods/Base pair motif energy as thermodynamic prior (paragraph 1)Underlying method and version boundaries
The ViennaRNA package computes minimum-free-energy structures, partition functions and associated structure probabilities. RNAfold is a procedure with energy parameters, not a neural language-model checkpoint.
Sources
ViennaRNA/ViennaRNA README.md · README.md; introduction, model description, pretrained-model and usage sections at pinned revisionWhat was evaluated
The linked evaluation record identifies RNAfold: RNA secondary structure. Its dataset, split, adaptation and evidence origin remain attached to the reported results.
Sources
Deep generalizable prediction of RNA secondary structure via base pair motif energy · The named evaluation’s methods and comparison table; exact preserved evaluation IDs: evaluation-lit-012Strengths and limitations
Strengths and considerations
- Provides a non-neural physical prior against which learned folding models can be assessed.
Sources
Deep generalizable prediction of RNA secondary structure via base pair motif energy · Discussion (paragraph 4); Introduction (paragraph 4)
Limitations and conditions
- The conventional nested-pair structure formulation does not model arbitrary pseudoknots or the complete in-vivo folding environment.
Sources
Deep generalizable prediction of RNA secondary structure via base pair motif energy · Introduction (paragraph 7); Results/Overview of the BPfold approach (paragraph 1)
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-52eee4cc67ca26Specifications
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 | Thermodynamic RNA folding algorithm; this record is the paper-specific evaluated configuration.SourcesViennaRNA/ViennaRNA README.md · README.md model description |
| Architecture / procedure | ViennaRNA predicts a minimum-free-energy secondary structure using its thermodynamic energy model; the paper runs default settings.SourcesDeep generalizable prediction of RNA secondary structure via base pair motif energy · Abstract (paragraph 2); Methods/Base pair motif energy as thermodynamic prior (paragraph 1) |
| Biological inputs | RNA nucleotide sequenceSourcesDeep generalizable prediction of RNA secondary structure via base pair motif energy · Methods/Deep neural network with base pair attention (paragraph 5); Introduction (paragraph 1) |
| Outputs | RNA secondary structureSourcesDeep generalizable prediction of RNA secondary structure via base pair motif energy · Methods/Datasets and evaluation (paragraph 1); Discussion (paragraph 5) |
| Parameters | Not applicable as a neural parameter count. · Not applicableSourcesDeep generalizable prediction of RNA secondary structure via base pair motif energy · Methods/Training strategy and structure refinement (paragraph 3); Results/Assessing the effectiveness of base pair motif energy (paragraph 3) |
| Known versions / configuration | ViennaRNA RNAfold version 2.6.4 · Not reported in inspected sourcesSourcesDeep generalizable prediction of RNA secondary structure via base pair motif energy · Model identification in the comparison table and corresponding Methods; immutable checkpoint revision is not supplied by the table label. |
| Training data / fitting | Experimentally informed thermodynamic parameters rather than neural pretraining.SourcesDeep generalizable prediction of RNA secondary structure via base pair motif energy · Methods/Training strategy and structure refinement (paragraph 3); Results/Evaluating BPfold on family-wise datasets (paragraph 3) |
| Context limits | RNA length is constrained by the RNAfold implementation, algorithm and available memory; there is no learned fixed-token context window. · Not applicableSourcesDeep generalizable prediction of RNA secondary structure via base pair motif energy · Supplementary information (paragraph 1); Code availability (paragraph 1) |
| Access | Official upstream implementation and usage documentation: https://github.com/ViennaRNA/ViennaRNA/blob/1ffec79f5e258896160f7362ced8263450f371dc/README.md. This pinned documentation revision is not automatically the evaluated weight revision.SourcesViennaRNA/ViennaRNA README.md · README.md; installation, model download and usage instructions |
| Code licence | ViennaRNA licence/disclaimer; see the pinned full text for scope and conditions. (upstream repository code at the cited revision; this does not establish every dependency or historical checkpoint licence).SourcesViennaRNA/ViennaRNA license.txt · license.txt; complete licence text |
| Weights licence | Not applicable: RNAfold uses thermodynamic energy parameters rather than pretrained neural weights. · Not applicableSourcesDeep generalizable prediction of RNA secondary structure via base pair motif energy · Methods/Training strategy and structure refinement (paragraph 3); Discussion (paragraph 2) |
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.
20 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 | Deep generalizable prediction of RNA secondary structure via base pair motif energy Abstract (paragraph 2); Methods/Base pair motif energy as thermodynamic prior (paragraph 1) 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 sequence","RNAfold","RNA secondary structure"] Individual claims | Deep generalizable prediction of RNA secondary structure via base pair motif energy Abstract (paragraph 2); Methods/Base pair motif energy as thermodynamic prior (paragraph 1) 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 | Deep generalizable prediction of RNA secondary structure via base pair motif energy Abstract (paragraph 2); Methods/Base pair motif energy as thermodynamic prior (paragraph 1) 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 Thermodynamic RNA folding algorithm; this record is the paper-specific evaluated configuration. Individual claims | ViennaRNA/ViennaRNA README.md README.md model description Version: 1ffec79f5e258896160f7362ced8263450f371dc | 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 ViennaRNA predicts a minimum-free-energy secondary structure using its thermodynamic energy model; the paper runs default settings. Individual claims | Deep generalizable prediction of RNA secondary structure via base pair motif energy Abstract (paragraph 2); Methods/Base pair motif energy as thermodynamic prior (paragraph 1) 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 Not applicable: RNAfold uses thermodynamic energy parameters rather than pretrained neural weights. Individual claims | Deep generalizable prediction of RNA secondary structure via base pair motif energy Methods/Training strategy and structure refinement (paragraph 3); Discussion (paragraph 2) Version: version of record | inapplicable 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 sequence Individual claims | Deep generalizable prediction of RNA secondary structure via base pair motif energy Methods/Deep neural network with base pair attention (paragraph 5); Introduction (paragraph 1) 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 Individual claims | Deep generalizable prediction of RNA secondary structure via base pair motif energy Methods/Datasets and evaluation (paragraph 1); Discussion (paragraph 5) 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 Not applicable as a neural parameter count. Individual claims | Deep generalizable prediction of RNA secondary structure via base pair motif energy Methods/Training strategy and structure refinement (paragraph 3); Results/Assessing the effectiveness of base pair motif energy (paragraph 3) Version: version of record | inapplicable 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 ViennaRNA RNAfold version 2.6.4 Individual claims | Deep generalizable prediction of RNA secondary structure via base pair motif energy Model identification in the comparison table and corresponding Methods; immutable checkpoint revision is not supplied by the table label. 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
- Deep generalizable prediction of RNA secondary structure via base pair motif energy · Original source · version of record
- ViennaRNA/ViennaRNA README.md · Original source · 1ffec79f5e258896160f7362ced8263450f371dc
- ViennaRNA/ViennaRNA license.txt · Original source · 1ffec79f5e258896160f7362ced8263450f371dc
Technical metadata and extraction receipts
Stable ID: reported-model-52eee4cc67ca26
- areas
- rna-transcriptomes
- entity level
- method
- version
- Not reported
- reported name
- RNAfold
- 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: bpfold-2025; evidence-reported-base-rnafold-readme-md; source locator: Abstract (paragraph 2); Methods/Base pair motif energy as thermodynamic prior (paragraph 1) | README.md model description | Abstract (paragraph 2); Methods/Datasets and evaluation (paragraph 1); 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.