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)

1 evaluation · 4 results

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)

Overview

Model type

Thermodynamic RNA folding algorithm; this record is the paper-specific evaluated configuration.

Sources

ViennaRNA/ViennaRNA README.md · README.md model description

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)

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.

Sources

ViennaRNA/ViennaRNA README.md · README.md; installation, model download and usage instructions

limited source coverage · Automated source review, 2026-09-16. All specifications and missing details

Evaluations and results

1 evaluation · 4 results. Different protocols are not a single leaderboard.

Filter evaluations

Tested model or methodProtocolDatasetEvaluation setupMetricEvidence origin

Applied filters: All linked evaluations

Exact evaluated configurations and original reported results
Tested configuration	Protocol and dataset	Finding	Evidence and details
Configuration: RNAfold	Protocol: PDB (RNA secondary structure) Dataset: PDB RNA set	0.747 F1 unitless · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source RNAfold: RNA secondary structure Family-wise RNA secondary-structure evaluation; macro-average canonical base-pair metrics. Aggregation: Not reported Deep generalizable prediction of RNA secondary structure via base pair motif energy · Table 2, RNAfold row, PDB F1 column
Configuration: RNAfold	Protocol: PDB (RNA secondary structure) Dataset: PDB RNA set	0.776 Precision unitless · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source RNAfold: RNA secondary structure Family-wise RNA secondary-structure evaluation; macro-average canonical base-pair metrics. Aggregation: Not reported Deep generalizable prediction of RNA secondary structure via base pair motif energy · Table 2 (Tab2), row 10 RNAfold, column 8: PDB Precision
Configuration: RNAfold	Protocol: PDB (RNA secondary structure) Dataset: PDB RNA set	0.728 Recall unitless · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source RNAfold: RNA secondary structure Family-wise RNA secondary-structure evaluation; macro-average canonical base-pair metrics. Aggregation: Not reported Deep generalizable prediction of RNA secondary structure via base pair motif energy · Table 2 (Tab2), row 10 RNAfold, column 9: PDB Recall
Configuration: RNAfold	Protocol: PDB (RNA secondary structure) Dataset: PDB RNA set	0.749 INF unitless · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source RNAfold: RNA secondary structure Family-wise RNA secondary-structure evaluation; macro-average canonical base-pair metrics. Aggregation: Not reported Deep 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. Release 2026-09-29-06401fd5b220.

Use this model

How it works, versions and access

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 revision

What 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-012

Strengths, limitations and unresolved questions

Strengths 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-52eee4cc67ca26

Specifications

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.

Inputs, outputs and configuration
Property	Description and evidence
Model type	Thermodynamic RNA folding algorithm; this record is the paper-specific evaluated configuration. Sources ViennaRNA/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. 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)
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)
Parameters	Not applicable as a neural parameter count. · Not applicable Sources 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)
Known versions / configuration	ViennaRNA RNAfold version 2.6.4 · Not reported in inspected sources Sources 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.
Training data / fitting	Experimentally informed thermodynamic parameters rather than neural pretraining. Sources Deep 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 applicable Sources Deep 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. Sources ViennaRNA/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). Sources ViennaRNA/ViennaRNA license.txt · license.txt; complete licence text
Weights licence	Not applicable: RNAfold uses thermodynamic energy parameters rather than pretrained neural weights. · Not applicable Sources Deep generalizable prediction of RNA secondary structure via base pair motif energy · Methods/Training strategy and structure refinement (paragraph 3); Discussion (paragraph 2)

Evidence

Source checking verifies the cited claim or transcription. It does not establish independent reproduction.

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

Claims, original sources and review scope · Release 2026-09-29-06401fd5b220
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 Original source ↗ Abstract (paragraph 2); Methods/Base pair motif energy as thermodynamic prior (paragraph 1) Version: version of record Retrieved: 2026-09-16T10:41:16.502000+00:00	source checked automated source review · 2026-09-16 Audit 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. Field: `attributes.profile.diagram.caption` Source artifact SHA-256: `976218bd172998a1a6e7ed1609ecb8cb2ee380fb48a8dc7b25bc05ea8b0a49af` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Diagram steps RNA nucleotide sequence RNAfold RNA secondary structure Individual claims	Deep generalizable prediction of RNA secondary structure via base pair motif energy Original source ↗ Abstract (paragraph 2); Methods/Base pair motif energy as thermodynamic prior (paragraph 1) Version: version of record Retrieved: 2026-09-16T10:41:16.502000+00:00	source checked automated source review · 2026-09-16 Audit 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. Field: `attributes.profile.diagram.steps` Source artifact SHA-256: `976218bd172998a1a6e7ed1609ecb8cb2ee380fb48a8dc7b25bc05ea8b0a49af` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Diagram title Evaluated procedure (conceptual) Individual claims	Deep generalizable prediction of RNA secondary structure via base pair motif energy Original source ↗ Abstract (paragraph 2); Methods/Base pair motif energy as thermodynamic prior (paragraph 1) Version: version of record Retrieved: 2026-09-16T10:41:16.502000+00:00	source checked automated source review · 2026-09-16 Audit 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. Field: `attributes.profile.diagram.title` Source artifact SHA-256: `976218bd172998a1a6e7ed1609ecb8cb2ee380fb48a8dc7b25bc05ea8b0a49af` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Model type Thermodynamic RNA folding algorithm; this record is the paper-specific evaluated configuration. Individual claims	ViennaRNA/ViennaRNA README.md Original source ↗ README.md model description Version: 1ffec79f5e258896160f7362ced8263450f371dc Retrieved: 2026-09-16T20:00:00.820987+00:00	source checked automated source review · 2026-09-16 Audit 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. Field: `attributes.profile.facts.0.value` Source artifact SHA-256: `d37146b01e4273062a5230c496a9af8414ee7ef14fcd905cf415959256d59c4e` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
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 Original source ↗ Abstract (paragraph 2); Methods/Base pair motif energy as thermodynamic prior (paragraph 1) Version: version of record Retrieved: 2026-09-16T10:41:16.502000+00:00	source checked automated source review · 2026-09-16 Audit 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. Field: `attributes.profile.facts.1.value` Source artifact SHA-256: `976218bd172998a1a6e7ed1609ecb8cb2ee380fb48a8dc7b25bc05ea8b0a49af` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
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 Original source ↗ Methods/Training strategy and structure refinement (paragraph 3); Discussion (paragraph 2) Version: version of record Retrieved: 2026-09-16T10:41:16.502000+00:00	inapplicable automated source review · 2026-09-16 Audit 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. Field: `attributes.profile.facts.10.value` Source artifact SHA-256: `976218bd172998a1a6e7ed1609ecb8cb2ee380fb48a8dc7b25bc05ea8b0a49af` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Biological inputs RNA nucleotide sequence Individual claims	Deep generalizable prediction of RNA secondary structure via base pair motif energy Original source ↗ Methods/Deep neural network with base pair attention (paragraph 5); Introduction (paragraph 1) Version: version of record Retrieved: 2026-09-16T10:41:16.502000+00:00	source checked automated source review · 2026-09-16 Audit 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. Field: `attributes.profile.facts.2.value` Source artifact SHA-256: `976218bd172998a1a6e7ed1609ecb8cb2ee380fb48a8dc7b25bc05ea8b0a49af` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Outputs RNA secondary structure Individual claims	Deep generalizable prediction of RNA secondary structure via base pair motif energy Original source ↗ Methods/Datasets and evaluation (paragraph 1); Discussion (paragraph 5) Version: version of record Retrieved: 2026-09-16T10:41:16.502000+00:00	source checked automated source review · 2026-09-16 Audit 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. Field: `attributes.profile.facts.3.value` Source artifact SHA-256: `976218bd172998a1a6e7ed1609ecb8cb2ee380fb48a8dc7b25bc05ea8b0a49af` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Parameters Not applicable as a neural parameter count. Individual claims	Deep generalizable prediction of RNA secondary structure via base pair motif energy Original source ↗ Methods/Training strategy and structure refinement (paragraph 3); Results/Assessing the effectiveness of base pair motif energy (paragraph 3) Version: version of record Retrieved: 2026-09-16T10:41:16.502000+00:00	inapplicable automated source review · 2026-09-16 Audit 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. Field: `attributes.profile.facts.4.value` Source artifact SHA-256: `976218bd172998a1a6e7ed1609ecb8cb2ee380fb48a8dc7b25bc05ea8b0a49af` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Known versions / configuration ViennaRNA RNAfold version 2.6.4 Individual claims	Deep generalizable prediction of RNA secondary structure via base pair motif energy Original source ↗ Model identification in the comparison table and corresponding Methods; immutable checkpoint revision is not supplied by the table label. Version: version of record Retrieved: 2026-09-16T10:41:16.502000+00:00	unreported automated source review · 2026-09-16 Audit 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. Field: `attributes.profile.facts.5.value` Source artifact SHA-256: `976218bd172998a1a6e7ed1609ecb8cb2ee380fb48a8dc7b25bc05ea8b0a49af` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact

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Sources and history

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Release 2026-09-29-06401fd5b220 · 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

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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.

Related records

model: RNAfold: RNA secondary structure

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