Configuration

RNAfold

RNAfold is the thermodynamic baseline and an upstream component considered in the DEBFold study.

Sources

DEBFold: Computational Identification of RNA Secondary Structures for Sequences across Structural Families Using Deep Learning · Methods and Data Sets/DEBFold Workflow (paragraph 1); Methods and Data Sets/DEBFold Workflow/Stage 1: Structure Location Folding Probability Estimation (paragraph 3)

1 evaluation · 1 metric row

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

At a glance

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

DEBFold: Computational Identification of RNA Secondary Structures for Sequences across Structural Families Using Deep Learning · Methods and Data Sets/DEBFold Workflow/Stage 1: Structure Location Folding Probability Estimation (paragraph 3); Methods and Data Sets/Family-Wise Processed RNA Structure Ground-Truth Data Set/Contamination-Free Family-Wise Independent Test Set for Evaluating Existing Deep-Learning-Based Structure Prediction Tools (paragraph 1)

Outputs

RNA secondary structure

Sources

DEBFold: Computational Identification of RNA Secondary Structures for Sequences across Structural Families Using Deep Learning · Conclusions (paragraph 1); Methods and Data Sets/Structure Prediction Evaluation Metrics (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.

Evaluation setupMetricEvidence origin

Results grouped by the exact reported evaluation
Metric and finding	Coverage and uncertainty	Evidence
RNAfold: RNA secondary structure Configuration: RNAfoldTask: RNA secondary structureDataset: DEBFold TestSetβ Median F1 on the prepared TestSetβ. Independent external evaluation · Evaluation metadata: needs review
52.3 Median F1 Unit: % · Direction: unknown	Uncertainty: not reported in legacy extract Scored: Not reported · Eligible: Not reported	source checkedDEBFold: Computational Identification of RNA Secondary Structures for Sequences across Structural Families Using Deep Learning · Table 1, RNAfold row, TestSetβ F1 (%) column 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

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

DEBFold: Computational Identification of RNA Secondary Structures for Sequences across Structural Families Using Deep Learning · The named evaluation’s methods and comparison table; exact preserved evaluation IDs: evaluation-lit-016

Strengths and limitations

Strengths and considerations

Provides a non-neural physical prior against which learned folding models can be assessed.
Sources
DEBFold: Computational Identification of RNA Secondary Structures for Sequences across Structural Families Using Deep Learning · Results and Discussion/DEBFold Is Robust against Different Thermodynamics-Constrained Optimization Algorithms (paragraph 1); Results and Discussion/DEBFold Outperforms Previous Thermodynamics-Based RNA Structure Prediction Tools (paragraph 1)

Limitations and conditions

The conventional nested-pair structure formulation does not model arbitrary pseudoknots or the complete in-vivo folding environment.
Sources
DEBFold: Computational Identification of RNA Secondary Structures for Sequences across Structural Families Using Deep Learning · Results and Discussion/Limitations of DEBFold (paragraph 1); Introduction (paragraph 5)

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-ed7f0db85facb1

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 DEBFold: Computational Identification of RNA Secondary Structures for Sequences across Structural Families Using Deep Learning · Methods and Data Sets/DEBFold Workflow/Stage 2: Score-Constrained Optimization Folding (paragraph 1); Methods and Data Sets/DEBFold Workflow (paragraph 1)
Biological inputs	RNA nucleotide sequence Sources DEBFold: Computational Identification of RNA Secondary Structures for Sequences across Structural Families Using Deep Learning · Methods and Data Sets/DEBFold Workflow/Stage 1: Structure Location Folding Probability Estimation (paragraph 3); Methods and Data Sets/Family-Wise Processed RNA Structure Ground-Truth Data Set/Contamination-Free Family-Wise Independent Test Set for Evaluating Existing Deep-Learning-Based Structure Prediction Tools (paragraph 1)
Outputs	RNA secondary structure Sources DEBFold: Computational Identification of RNA Secondary Structures for Sequences across Structural Families Using Deep Learning · Conclusions (paragraph 1); Methods and Data Sets/Structure Prediction Evaluation Metrics (paragraph 1)
Parameters	Not applicable as a neural parameter count. · Not applicable Sources DEBFold: Computational Identification of RNA Secondary Structures for Sequences across Structural Families Using Deep Learning · Methods and Data Sets/DEBFold Workflow/Stage 1: Structure Location Folding Probability Estimation (paragraph 3); Methods and Data Sets/DEBFold Workflow/Model Training Hyperparameters (paragraph 1)
Known versions / configuration	RNAfold as reported in the DEBFold study; do not assume the 2.6.4 version used in the later BPfold paper. · Not reported in inspected sources Sources DEBFold: Computational Identification of RNA Secondary Structures for Sequences across Structural Families Using Deep Learning · 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 DEBFold: Computational Identification of RNA Secondary Structures for Sequences across Structural Families Using Deep Learning · Methods and Data Sets/DEBFold Workflow/Model Training Hyperparameters (paragraph 1); Methods and Data Sets/DEBFold Workflow (paragraph 1)
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 DEBFold: Computational Identification of RNA Secondary Structures for Sequences across Structural Families Using Deep Learning · Table tbl3 (paragraph 1); Results and Discussion/DEBFold Is Robust against Different Thermodynamics-Constrained Optimization Algorithms (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 DEBFold: Computational Identification of RNA Secondary Structures for Sequences across Structural Families Using Deep Learning · Methods and Data Sets/DEBFold Workflow (paragraph 1); Methods and Data Sets/DEBFold Workflow/Stage 1: Structure Location Folding Probability Estimation (paragraph 3)

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 2026-09-17-d277315f7d76
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	DEBFold: Computational Identification of RNA Secondary Structures for Sequences across Structural Families Using Deep Learning Original source ↗ Methods and Data Sets/DEBFold Workflow/Stage 2: Score-Constrained Optimization Folding (paragraph 1); Methods and Data Sets/DEBFold Workflow (paragraph 1) Version: PMC11094721.1 Retrieved: 2026-09-16T10:41:16.509290+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: `e8f960eafb7f00edfdd81d4fb75c6de838e9b872b7e18875fc7a5bff2a2f72b3` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Diagram steps ["RNA nucleotide sequence","RNAfold","RNA secondary structure"] Individual claims	DEBFold: Computational Identification of RNA Secondary Structures for Sequences across Structural Families Using Deep Learning Original source ↗ Methods and Data Sets/DEBFold Workflow/Stage 2: Score-Constrained Optimization Folding (paragraph 1); Methods and Data Sets/DEBFold Workflow (paragraph 1) Version: PMC11094721.1 Retrieved: 2026-09-16T10:41:16.509290+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: `e8f960eafb7f00edfdd81d4fb75c6de838e9b872b7e18875fc7a5bff2a2f72b3` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Diagram title Evaluated procedure (conceptual) Individual claims	DEBFold: Computational Identification of RNA Secondary Structures for Sequences across Structural Families Using Deep Learning Original source ↗ Methods and Data Sets/DEBFold Workflow/Stage 2: Score-Constrained Optimization Folding (paragraph 1); Methods and Data Sets/DEBFold Workflow (paragraph 1) Version: PMC11094721.1 Retrieved: 2026-09-16T10:41:16.509290+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: `e8f960eafb7f00edfdd81d4fb75c6de838e9b872b7e18875fc7a5bff2a2f72b3` 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	DEBFold: Computational Identification of RNA Secondary Structures for Sequences across Structural Families Using Deep Learning Original source ↗ Methods and Data Sets/DEBFold Workflow/Stage 2: Score-Constrained Optimization Folding (paragraph 1); Methods and Data Sets/DEBFold Workflow (paragraph 1) Version: PMC11094721.1 Retrieved: 2026-09-16T10:41:16.509290+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: `e8f960eafb7f00edfdd81d4fb75c6de838e9b872b7e18875fc7a5bff2a2f72b3` 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	DEBFold: Computational Identification of RNA Secondary Structures for Sequences across Structural Families Using Deep Learning Original source ↗ Methods and Data Sets/DEBFold Workflow (paragraph 1); Methods and Data Sets/DEBFold Workflow/Stage 1: Structure Location Folding Probability Estimation (paragraph 3) Version: PMC11094721.1 Retrieved: 2026-09-16T10:41:16.509290+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: `e8f960eafb7f00edfdd81d4fb75c6de838e9b872b7e18875fc7a5bff2a2f72b3` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Biological inputs RNA nucleotide sequence Individual claims	DEBFold: Computational Identification of RNA Secondary Structures for Sequences across Structural Families Using Deep Learning Original source ↗ Methods and Data Sets/DEBFold Workflow/Stage 1: Structure Location Folding Probability Estimation (paragraph 3); Methods and Data Sets/Family-Wise Processed RNA Structure Ground-Truth Data Set/Contamination-Free Family-Wise Independent Test Set for Evaluating Existing Deep-Learning-Based Structure Prediction Tools (paragraph 1) Version: PMC11094721.1 Retrieved: 2026-09-16T10:41:16.509290+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: `e8f960eafb7f00edfdd81d4fb75c6de838e9b872b7e18875fc7a5bff2a2f72b3` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Outputs RNA secondary structure Individual claims	DEBFold: Computational Identification of RNA Secondary Structures for Sequences across Structural Families Using Deep Learning Original source ↗ Conclusions (paragraph 1); Methods and Data Sets/Structure Prediction Evaluation Metrics (paragraph 1) Version: PMC11094721.1 Retrieved: 2026-09-16T10:41:16.509290+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: `e8f960eafb7f00edfdd81d4fb75c6de838e9b872b7e18875fc7a5bff2a2f72b3` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Parameters Not applicable as a neural parameter count. Individual claims	DEBFold: Computational Identification of RNA Secondary Structures for Sequences across Structural Families Using Deep Learning Original source ↗ Methods and Data Sets/DEBFold Workflow/Stage 1: Structure Location Folding Probability Estimation (paragraph 3); Methods and Data Sets/DEBFold Workflow/Model Training Hyperparameters (paragraph 1) Version: PMC11094721.1 Retrieved: 2026-09-16T10:41:16.509290+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: `e8f960eafb7f00edfdd81d4fb75c6de838e9b872b7e18875fc7a5bff2a2f72b3` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Known versions / configuration RNAfold as reported in the DEBFold study; do not assume the 2.6.4 version used in the later BPfold paper. Individual claims	DEBFold: Computational Identification of RNA Secondary Structures for Sequences across Structural Families Using Deep Learning Original source ↗ Model identification in the comparison table and corresponding Methods; immutable checkpoint revision is not supplied by the table label. Version: PMC11094721.1 Retrieved: 2026-09-16T10:41:16.509290+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: `e8f960eafb7f00edfdd81d4fb75c6de838e9b872b7e18875fc7a5bff2a2f72b3` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact

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Sources and history

Release 2026-09-17-d277315f7d76 · Record review: needs review

3 source records and release history

DEBFold: Computational Identification of RNA Secondary Structures for Sequences across Structural Families Using Deep Learning · Original source · PMC11094721.1
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-ed7f0db85facb1

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: debfold-2024; evidence-reported-base-rnafold-readme-md; source locator: Methods and Data Sets/DEBFold Workflow/Stage 2: Score-Constrained Optimization Folding (paragraph 1); Methods and Data Sets/DEBFold Workflow (paragraph 1) | README.md model description | Methods and Data Sets/DEBFold Workflow (paragraph 1); Methods and Data Sets/DEBFold Workflow/Stage 1: Structure Location Folding Probability Estimation (paragraph 3); 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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