Datasets
RNA-KG-derived multispecies interaction pairs; sampled negatives preserve pair-type frequencies with a 20:1 negative-to-positive ratio.
Task
non-coding RNA pairwise interaction prediction
Noncoding RNA-pair classification evaluates a strongly imbalanced interaction dataset with validation-tuned decision thresholds.
Sources
Computational understanding of non-coding RNA pairwise interactions · Methods: Dataset; Experimental evaluation: Data preparation and splitting; Evaluation metrics; cached text lines 15–17, 66–75; comparative evaluation and ablation passagesOverview
Metrics
Accuracy, balanced accuracy, precision, recall, F1, AUROC and AUPRC, both overall and by interacting pair type.
Allowed inputs
Pairs of noncoding RNA sequences.
Sources
Computational understanding of non-coding RNA pairwise interactions · Methods: Dataset; Experimental evaluation: Data preparation and splitting; Evaluation metrics; cached text lines 15–17, 66–75; comparative evaluation and ablation passagesEvaluation procedure diagram
How it worksComputational evaluation flow
Conceptual summary of the cited evaluation; exact task configuration and source version remain part of the protocol.
Sources
Computational understanding of non-coding RNA pairwise interactions · Methods: Dataset; Experimental evaluation: Data preparation and splitting; Evaluation metrics; cached text lines 15–17, 66–75; comparative evaluation and ablation passageslimited source coverage · Automated source review, 2026-09-16. All specifications and missing details
Results
Results are available, but no reviewed comparison panel is linked in this release.
All evaluations
1 evaluation · 1 result. Different protocols are not a single leaderboard.
Filter evaluations
Applied filters: All linked evaluations
| Tested configuration | Protocol and dataset | Finding | Evidence and details |
|---|---|---|---|
| Pipeline: CUPID Data-aug-Avg | Task: non-coding RNA pairwise interaction prediction Dataset: ncRNA interaction pairs | 0.919 AUROC fraction · unknown Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceCUPID Data-aug-Avg: non-coding RNA pairwise interaction prediction Data augmentation with average pooling for molecule-level ncRNA embeddings Aggregation: Not reported Computational understanding of non-coding RNA pairwise interactions · Table 1, CUPID > Data-aug-Avg row, AUROC column |
Source checking is not independent reproduction. Release 2026-09-29-06401fd5b220.
Methods and evaluation design
Procedure, tasks and evaluated configurations
How it works
Evaluation methodology
RNA-KG-derived multispecies interaction pairs; sampled negatives preserve pair-type frequencies with a 20:1 negative-to-positive ratio. Stratified 90:10 train/test partition; a further validation subset is drawn from training data. Accuracy, balanced accuracy, precision, recall, F1, AUROC and AUPRC, both overall and by interacting pair type. Random classifier, IntaRNA and CUPID pooling/data-augmentation ablations. The threshold is selected by validation MCC; sequence-disjoint separation of interacting entities remains unextracted. The cited text-accessible evaluation sections give no confidence-interval, resampling or repeat-run error-bar specification. Image-only tables and uninspected supplements are outside this absence claim.
Sources
Computational understanding of non-coding RNA pairwise interactions · Methods: Dataset; Experimental evaluation: Data preparation and splitting; Evaluation metrics; cached text lines 15–17, 66–75; comparative evaluation and ablation passagesRecorded evaluations
Each evaluation records what was tested and under which conditions.
Run instructions
No runnable recipe has been reviewed for this task. Dataset access, model requirements, licences and compute requirements must be checked against its sources before execution.
A task describes a biological question. Choose a linked protocol to obtain concrete split and scoring instructions.
Strengths, limitations and unresolved questions
Strengths and limitations
Strengths and considerations
- A random classifier and explicit pooling/augmentation ablations isolate different sources of performance.
Sources
Computational understanding of non-coding RNA pairwise interactions · Methods: Dataset; Experimental evaluation: Data preparation and splitting; Evaluation metrics; cached text lines 15–17, 66–75; comparative evaluation and ablation passages
Limitations and conditions
- A pair-level split does not by itself establish transfer to unseen RNAs. AUPRC depends on the sampled class prevalence.
Sources
Computational understanding of non-coding RNA pairwise interactions · Methods: Dataset; Experimental evaluation: Data preparation and splitting; Evaluation metrics; cached text lines 15–17, 66–75; comparative evaluation and ablation passages
Profile review details
Task-specific computational methodology and field context checked in the cited primary-source artifact. Source-backed fields, inapplicable evaluator dimensions and unresolved details are distinguished. Numerical results were not reproduced.
Stable record: reported-task-c7ce06b753b8b6Specifications
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 |
|---|---|
| Datasets | RNA-KG-derived multispecies interaction pairs; sampled negatives preserve pair-type frequencies with a 20:1 negative-to-positive ratio.SourcesComputational understanding of non-coding RNA pairwise interactions · Methods: Dataset; Experimental evaluation: Data preparation and splitting; Evaluation metrics; cached text lines 15–17, 66–75; comparative evaluation and ablation passages |
| Splits | Stratified 90:10 train/test partition; a further validation subset is drawn from training data.SourcesComputational understanding of non-coding RNA pairwise interactions · Methods: Dataset; Experimental evaluation: Data preparation and splitting; Evaluation metrics; cached text lines 15–17, 66–75; comparative evaluation and ablation passages |
| Metrics | Accuracy, balanced accuracy, precision, recall, F1, AUROC and AUPRC, both overall and by interacting pair type.SourcesComputational understanding of non-coding RNA pairwise interactions · Methods: Dataset; Experimental evaluation: Data preparation and splitting; Evaluation metrics; cached text lines 15–17, 66–75; comparative evaluation and ablation passages |
| Baselines | Random classifier, IntaRNA and CUPID pooling/data-augmentation ablations.SourcesComputational understanding of non-coding RNA pairwise interactions · Methods: Dataset; Experimental evaluation: Data preparation and splitting; Evaluation metrics; cached text lines 15–17, 66–75; comparative evaluation and ablation passages |
| Leakage controls | The threshold is selected by validation MCC; sequence-disjoint separation of interacting entities remains unextracted.SourcesComputational understanding of non-coding RNA pairwise interactions · Methods: Dataset; Experimental evaluation: Data preparation and splitting; Evaluation metrics; cached text lines 15–17, 66–75; comparative evaluation and ablation passages |
| Uncertainty | The cited text-accessible evaluation sections give no confidence-interval, resampling or repeat-run error-bar specification. Image-only tables and uninspected supplements are outside this absence claim. · Not reported in inspected sourcesSourcesComputational understanding of non-coding RNA pairwise interactions · Methods: Dataset; Experimental evaluation: Data preparation and splitting; Evaluation metrics; cached text lines 15–17, 66–75; comparative evaluation and ablation passages |
| Entity type | Paper-specific computational evaluation protocol.SourcesComputational understanding of non-coding RNA pairwise interactions · Methods: Dataset; Experimental evaluation: Data preparation and splitting; Evaluation metrics; cached text lines 15–17, 66–75; comparative evaluation and ablation passages |
| Organisms | Multiple species represented in RNA-KG.SourcesComputational understanding of non-coding RNA pairwise interactions · Methods: Dataset; Experimental evaluation: Data preparation and splitting; Evaluation metrics; cached text lines 15–17, 66–75; comparative evaluation and ablation passages |
| Assays | RNA–RNA interaction annotations with sampled negative pairs.SourcesComputational understanding of non-coding RNA pairwise interactions · Methods: Dataset; Experimental evaluation: Data preparation and splitting; Evaluation metrics; cached text lines 15–17, 66–75; comparative evaluation and ablation passages |
| Allowed inputs | Pairs of noncoding RNA sequences.SourcesComputational understanding of non-coding RNA pairwise interactions · Methods: Dataset; Experimental evaluation: Data preparation and splitting; Evaluation metrics; cached text lines 15–17, 66–75; comparative evaluation and ablation passages |
| Adaptation | Supervised pair classification using a stratified train/test partition and training-derived validation subset.SourcesComputational understanding of non-coding RNA pairwise interactions · Methods: Dataset; Experimental evaluation: Data preparation and splitting; Evaluation metrics; cached text lines 15–17, 66–75; comparative evaluation and ablation passages |
Evidence
Source checking verifies the cited claim or transcription. It does not establish independent reproduction.
Papers and result coverage
Last literature check: 2026-09-17. Primary-paper discovery and source inspection. Source-checked results are not independently reproduced experiments.
| Paper or primary resource | Version | Reference |
|---|---|---|
| Computational understanding of non-coding RNA pairwise interactions | PMC archival version PMC12957212.1 | Read source DOI: 10.3389/frai.2026.1749205 |
Historical gaps recorded on 2026-09-17
The catalogue now holds 1 result rows for this benchmark. A note below about pending extraction describes the state on 2026-09-17 and may since have been answered by a later batch. The result rows and their sources are the current record.
- Interaction-pair splitting does not guarantee unseen RNA identities across partitions.
- Random baseline AUPRC reflects class prevalence; do not treat it as a fitted model run.
- CUPID variants are distinct adaptations; no uncertainty printed.
Search and extraction details
primary comparison table screened
Searches
- "PMC12957212"
Evidence locations
- Table 1
- Methods: Data preparation and splitting; Data augmentation; Training hyper-parameters
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.
18 evidence rows matching the loaded filters
| Property and statement | Original source and location | Review and provenance |
|---|---|---|
| Diagram caption Conceptual summary of the cited evaluation; exact task configuration and source version remain part of the protocol. Individual claims | Computational understanding of non-coding RNA pairwise interactions Methods: Dataset; Experimental evaluation: Data preparation and splitting; Evaluation metrics; cached text lines 15–17, 66–75; comparative evaluation and ablation passages Version: PMC archival version PMC12957212.1 | source checked automated source review · 2026-09-16 Audit detailsTask-specific computational methodology and field context checked in the cited primary-source artifact. Source-backed fields, inapplicable evaluator dimensions and unresolved details are distinguished. Numerical results were not reproduced. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
Diagram steps
| Computational understanding of non-coding RNA pairwise interactions Methods: Dataset; Experimental evaluation: Data preparation and splitting; Evaluation metrics; cached text lines 15–17, 66–75; comparative evaluation and ablation passages Version: PMC archival version PMC12957212.1 | source checked automated source review · 2026-09-16 Audit detailsTask-specific computational methodology and field context checked in the cited primary-source artifact. Source-backed fields, inapplicable evaluator dimensions and unresolved details are distinguished. Numerical results were not reproduced. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Diagram title Computational evaluation flow Individual claims | Computational understanding of non-coding RNA pairwise interactions Methods: Dataset; Experimental evaluation: Data preparation and splitting; Evaluation metrics; cached text lines 15–17, 66–75; comparative evaluation and ablation passages Version: PMC archival version PMC12957212.1 | source checked automated source review · 2026-09-16 Audit detailsTask-specific computational methodology and field context checked in the cited primary-source artifact. Source-backed fields, inapplicable evaluator dimensions and unresolved details are distinguished. Numerical results were not reproduced. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Datasets RNA-KG-derived multispecies interaction pairs; sampled negatives preserve pair-type frequencies with a 20:1 negative-to-positive ratio. Individual claims | Computational understanding of non-coding RNA pairwise interactions Methods: Dataset; Experimental evaluation: Data preparation and splitting; Evaluation metrics; cached text lines 15–17, 66–75; comparative evaluation and ablation passages Version: PMC archival version PMC12957212.1 | source checked automated source review · 2026-09-16 Audit detailsTask-specific computational methodology and field context checked in the cited primary-source artifact. Source-backed fields, inapplicable evaluator dimensions and unresolved details are distinguished. Numerical results were not reproduced. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Splits Stratified 90:10 train/test partition; a further validation subset is drawn from training data. Individual claims | Computational understanding of non-coding RNA pairwise interactions Methods: Dataset; Experimental evaluation: Data preparation and splitting; Evaluation metrics; cached text lines 15–17, 66–75; comparative evaluation and ablation passages Version: PMC archival version PMC12957212.1 | source checked automated source review · 2026-09-16 Audit detailsTask-specific computational methodology and field context checked in the cited primary-source artifact. Source-backed fields, inapplicable evaluator dimensions and unresolved details are distinguished. Numerical results were not reproduced. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Adaptation Supervised pair classification using a stratified train/test partition and training-derived validation subset. Individual claims | Computational understanding of non-coding RNA pairwise interactions Methods: Dataset; Experimental evaluation: Data preparation and splitting; Evaluation metrics; cached text lines 15–17, 66–75; comparative evaluation and ablation passages Version: PMC archival version PMC12957212.1 | source checked automated source review · 2026-09-16 Audit detailsTask-specific computational methodology and field context checked in the cited primary-source artifact. Source-backed fields, inapplicable evaluator dimensions and unresolved details are distinguished. Numerical results were not reproduced. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Metrics Accuracy, balanced accuracy, precision, recall, F1, AUROC and AUPRC, both overall and by interacting pair type. Individual claims | Computational understanding of non-coding RNA pairwise interactions Methods: Dataset; Experimental evaluation: Data preparation and splitting; Evaluation metrics; cached text lines 15–17, 66–75; comparative evaluation and ablation passages Version: PMC archival version PMC12957212.1 | source checked automated source review · 2026-09-16 Audit detailsTask-specific computational methodology and field context checked in the cited primary-source artifact. Source-backed fields, inapplicable evaluator dimensions and unresolved details are distinguished. Numerical results were not reproduced. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Baselines Random classifier, IntaRNA and CUPID pooling/data-augmentation ablations. Individual claims | Computational understanding of non-coding RNA pairwise interactions Methods: Dataset; Experimental evaluation: Data preparation and splitting; Evaluation metrics; cached text lines 15–17, 66–75; comparative evaluation and ablation passages Version: PMC archival version PMC12957212.1 | source checked automated source review · 2026-09-16 Audit detailsTask-specific computational methodology and field context checked in the cited primary-source artifact. Source-backed fields, inapplicable evaluator dimensions and unresolved details are distinguished. Numerical results were not reproduced. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Leakage controls The threshold is selected by validation MCC; sequence-disjoint separation of interacting entities remains unextracted. Individual claims | Computational understanding of non-coding RNA pairwise interactions Methods: Dataset; Experimental evaluation: Data preparation and splitting; Evaluation metrics; cached text lines 15–17, 66–75; comparative evaluation and ablation passages Version: PMC archival version PMC12957212.1 | source checked automated source review · 2026-09-16 Audit detailsTask-specific computational methodology and field context checked in the cited primary-source artifact. Source-backed fields, inapplicable evaluator dimensions and unresolved details are distinguished. Numerical results were not reproduced. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Uncertainty The cited text-accessible evaluation sections give no confidence-interval, resampling or repeat-run error-bar specification. Image-only tables and uninspected supplements are outside this absence claim. Individual claims | Computational understanding of non-coding RNA pairwise interactions Methods: Dataset; Experimental evaluation: Data preparation and splitting; Evaluation metrics; cached text lines 15–17, 66–75; comparative evaluation and ablation passages Version: PMC archival version PMC12957212.1 | unreported automated source review · 2026-09-16 Audit detailsTask-specific computational methodology and field context checked in the cited primary-source artifact. Source-backed fields, inapplicable evaluator dimensions and unresolved details are distinguished. Numerical results were not reproduced. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
Sources and history
View linked audit checks and correction history
Release 2026-09-29-06401fd5b220 · Record review: needs review
2 source records and release history
- Computational understanding of non-coding RNA pairwise interactions · Original source · PMC archival version PMC12957212.1
- Computational understanding of non-coding RNA pairwise interactions · Original source · PMC archival version PMC12957212.1
Technical metadata and extraction receipts
Stable ID: reported-task-c7ce06b753b8b6
- areas
- rna-transcriptomes
- tasks
- non-coding RNA pairwise interaction prediction
- entity level
- task
- version
- Not reported
- task
- non-coding RNA pairwise interaction prediction
- scope note
- Paper-specific evaluation task; protocol completeness requires further extraction.
- benchmark research
- review date: 2026-09-17; status: primary_comparison_table_screened; primary sources: expansion-p3-cupid-rna-interactions-2026; inspected locators: Table 1; Methods: Data preparation and splitting; Data augmentation; Training hyper-parameters; searched queries: "PMC12957212"; gaps: Interaction-pair splitting does not guarantee unseen RNA identities across partitions.; Random baseline AUPRC reflects class prevalence; do not treat it as a fitted model run.; CUPID variants are distinct adaptations; no uncertainty printed.; claim scope: Primary-paper discovery and source inspection. Source-checked results are not independently reproduced experiments.
- historical missing metadata
- protocol version: not_reported_in_legacy_extract; split: 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
- benchmark
- entity classification
- review date: 2026-09-17; rationale: This source-scoped record identifies the biological prediction task and holds its paper context. Preserve the existing task identity; exact split, model adaptation and scoring remain in linked evaluations or separate protocol records.; source ids: cupid-rna-interactions-2026; source locator: Methods: Dataset; Experimental evaluation: Data preparation and splitting; Evaluation metrics; cached text lines 15–17, 66–75; comparative evaluation and ablation passages; ambiguities: A paper- or suite-specific task may constrain some inputs or metrics; that alone does not make it interchangeable with a complete versioned protocol. No protocol equivalence is inferred.; Some legacy profile Entity type facts use the generic phrase computational evaluation protocol. That boilerplate is not sufficient to establish a single fixed protocol identity or to merge this task with another protocol record.