Datasets
MirTarRAW positive/negative miRNA–mRNA pairs, plus DeepMirTarLeft.
Task
miRNA-mRNA interaction prediction
miRNA–mRNA interaction classification evaluates paired sequence representations on a benchmark split and an independent collection.
Sources
Retentive Network promotes efficient RNA language modeling of long sequences · Results: RNA–RNA interaction task; Methods: RNA–RNA interaction prediction; cached text lines 17, 79; task metric definitions and corresponding results table; matching task comparison table/ablation captionsOverview
Metrics
F1, precision, recall, accuracy and AUC for miRNA–mRNA interaction classification.
Allowed inputs
Paired miRNA and mRNA sequences.
Sources
Retentive Network promotes efficient RNA language modeling of long sequences · Results: RNA–RNA interaction task; Methods: RNA–RNA interaction prediction; cached text lines 17, 79; task metric definitions and corresponding results table; matching task comparison table/ablation captionsEvaluation 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
Retentive Network promotes efficient RNA language modeling of long sequences · Results: RNA–RNA interaction task; Methods: RNA–RNA interaction prediction; cached text lines 17, 79; task metric definitions and corresponding results table; matching task comparison table/ablation captionslimited 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 |
|---|---|---|---|
| Configuration: RNAret | Task: miRNA-mRNA interaction prediction Dataset: MirTarRAW | 0.962 F1 fraction · unknown Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceRNAret: miRNA-mRNA interaction prediction 5-mer RNAret classifier; 72/8/20 train/validation/test split Aggregation: Not reported Retentive Network promotes efficient RNA language modeling of long sequences · Table 1, MirTarRAW / 5-mer RNAret row, F1 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
MirTarRAW positive/negative miRNA–mRNA pairs, plus DeepMirTarLeft. MirTarRAW is partitioned 72:8:20 for training, validation and testing; DeepMirTarLeft is an additional independent dataset. F1, precision, recall, accuracy and AUC for miRNA–mRNA interaction classification. DeepMirTar, RNA-FM, RNABERT, RNA-MSM and RNAErnie in the interaction comparison; other RNA-task baselines are separate. MirTarRAW is divided 72%/8%/20% for training/validation/test, with DeepMirTarLeft as an additional test set. The RNAret methods do not specify a miRNA-identity, transcript-identity or homology-grouped holdout for this task. The paper’s explicit RNAStrAlign/ArchiveII overlap exclusion belongs to secondary-structure prediction and must not be transferred here.
Sources
Retentive Network promotes efficient RNA language modeling of long sequences · Results: RNA–RNA interaction task; Methods: RNA–RNA interaction prediction; cached text lines 17, 79; task metric definitions and corresponding results table; matching task comparison table/ablation captions; Results: miRNA-mRNA interaction prediction; Methods: Statistics and reproducibilityRecorded 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
No source-reviewed explanatory claims are recorded here yet.
Limitations and conditions
- A pair-level partition does not establish unseen-miRNA or unseen-transcript performance. RNAret’s secondary-structure overlap exclusions and another method’s uncertainty estimates do not apply to this interaction result.
Sources (3)
Retentive Network promotes efficient RNA language modeling of long sequences; rnaret-2026__42003_2026_9757_MOESM2_ESM.pdf; PMC7912887.xml · Results: miRNA-mRNA interaction prediction; Methods: Statistics and reproducibility; Table 1; Methods: Statistics and reproducibility; Supplementary Information; RNAret: miRNA-mRNA interaction prediction; miTAR: Abstract and Methods/Datasets
Profile review details
Targeted full-paper and supplement review of the outstanding task fields, with original dataset metadata checked where accessible. Source-scoped omissions are explicit; no independent benchmark reproduction or numerical-result change.
Stable record: reported-task-46e927bea10702Specifications
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 | MirTarRAW positive/negative miRNA–mRNA pairs, plus DeepMirTarLeft.SourcesRetentive Network promotes efficient RNA language modeling of long sequences · Results: RNA–RNA interaction task; Methods: RNA–RNA interaction prediction; cached text lines 17, 79; task metric definitions and corresponding results table; matching task comparison table/ablation captions |
| Splits | MirTarRAW is partitioned 72:8:20 for training, validation and testing; DeepMirTarLeft is an additional independent dataset.SourcesRetentive Network promotes efficient RNA language modeling of long sequences · Results: RNA–RNA interaction task; Methods: RNA–RNA interaction prediction; cached text lines 17, 79; task metric definitions and corresponding results table; matching task comparison table/ablation captions |
| Metrics | F1, precision, recall, accuracy and AUC for miRNA–mRNA interaction classification.SourcesRetentive Network promotes efficient RNA language modeling of long sequences · Results: RNA–RNA interaction task; Methods: RNA–RNA interaction prediction; cached text lines 17, 79; task metric definitions and corresponding results table; matching task comparison table/ablation captions |
| Baselines | DeepMirTar, RNA-FM, RNABERT, RNA-MSM and RNAErnie in the interaction comparison; other RNA-task baselines are separate.SourcesRetentive Network promotes efficient RNA language modeling of long sequences · Results: RNA–RNA interaction task; Methods: RNA–RNA interaction prediction; cached text lines 17, 79; task metric definitions and corresponding results table; matching task comparison table/ablation captions |
| Leakage controls | MirTarRAW is divided 72%/8%/20% for training/validation/test, with DeepMirTarLeft as an additional test set. The RNAret methods do not specify a miRNA-identity, transcript-identity or homology-grouped holdout for this task. The paper’s explicit RNAStrAlign/ArchiveII overlap exclusion belongs to secondary-structure prediction and must not be transferred here. · Not reported in inspected sourcesSourcesRetentive Network promotes efficient RNA language modeling of long sequences · Results: miRNA-mRNA interaction prediction; Methods: Statistics and reproducibility |
| Uncertainty | Table 1 reports point classification metrics. RNAret’s Statistics and reproducibility section and Supplementary Information do not specify repeated-run intervals or an uncertainty estimator for miRNA–mRNA prediction. Intervals reported in the original miTAR study would not quantify RNAret’s results. · Not reported in inspected sourcesSources (2)Retentive Network promotes efficient RNA language modeling of long sequences; rnaret-2026__42003_2026_9757_MOESM2_ESM.pdf · Table 1; Methods: Statistics and reproducibility; Supplementary Information |
| Entity type | Paper-specific computational evaluation protocol.SourcesRetentive Network promotes efficient RNA language modeling of long sequences · Results: RNA–RNA interaction task; Methods: RNA–RNA interaction prediction; cached text lines 17, 79; task metric definitions and corresponding results table; matching task comparison table/ablation captions |
| Organisms | Human miRNA–target pairs. The original miTAR study identifies its DeepMirTar and miRAW inputs as human datasets; MirTarRAW combines portions of these two datasets and DeepMirTarLeft is the withheld remainder of DeepMirTar.Sources (2)Retentive Network promotes efficient RNA language modeling of long sequences; PMC7912887.xml · RNAret: miRNA-mRNA interaction prediction; miTAR: Abstract and Methods/Datasets |
| Assays | miRNA–mRNA interaction labels.SourcesRetentive Network promotes efficient RNA language modeling of long sequences · Results: RNA–RNA interaction task; Methods: RNA–RNA interaction prediction; cached text lines 17, 79; task metric definitions and corresponding results table; matching task comparison table/ablation captions |
| Allowed inputs | Paired miRNA and mRNA sequences.SourcesRetentive Network promotes efficient RNA language modeling of long sequences · Results: RNA–RNA interaction task; Methods: RNA–RNA interaction prediction; cached text lines 17, 79; task metric definitions and corresponding results table; matching task comparison table/ablation captions |
| Adaptation | Supervised pair classification on the training portion with validation and independent testing.SourcesRetentive Network promotes efficient RNA language modeling of long sequences · Results: RNA–RNA interaction task; Methods: RNA–RNA interaction prediction; cached text lines 17, 79; task metric definitions and corresponding results table; matching task comparison table/ablation captions |
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. Dated primary-source discovery and protocol/table screening. Source checking does not mean experimental reproduction. Only separately extracted and independently reviewed numeric batches are publishable.
| Paper or primary resource | Version | Reference |
|---|---|---|
| Retentive Network promotes efficient RNA language modeling of long sequences | journal full text in PMC | Read source |
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.
- complete numerical transcription and independent cell review: Full primary artifact and table inventory preserved; no new numeric row is published from this audit alone.
- exact checkpoint hashes and per-method scored denominators: Table labels alone do not establish these fields; do not infer checkpoint or scored count from model name or dataset size.
Search and extraction details
primary comparison tables located
Searches
- Retentive Network promotes efficient RNA language modeling of long sequences 10.1038/s42003-026-09757-x
Evidence locations
- Table 1; XML table Tab1
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 summary of the cited evaluation; exact task configuration and source version remain part of the protocol. Individual claims | Retentive Network promotes efficient RNA language modeling of long sequences Results: RNA–RNA interaction task; Methods: RNA–RNA interaction prediction; cached text lines 17, 79; task metric definitions and corresponding results table; matching task comparison table/ablation captions Version: journal full text in PMC | source checked automated source review · 2026-09-16 Audit detailsTargeted full-paper and supplement review of the outstanding task fields, with original dataset metadata checked where accessible. Source-scoped omissions are explicit; no independent benchmark reproduction or numerical-result change. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
Diagram steps
| Retentive Network promotes efficient RNA language modeling of long sequences Results: RNA–RNA interaction task; Methods: RNA–RNA interaction prediction; cached text lines 17, 79; task metric definitions and corresponding results table; matching task comparison table/ablation captions Version: journal full text in PMC | source checked automated source review · 2026-09-16 Audit detailsTargeted full-paper and supplement review of the outstanding task fields, with original dataset metadata checked where accessible. Source-scoped omissions are explicit; no independent benchmark reproduction or numerical-result change. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Diagram title Computational evaluation flow Individual claims | Retentive Network promotes efficient RNA language modeling of long sequences Results: RNA–RNA interaction task; Methods: RNA–RNA interaction prediction; cached text lines 17, 79; task metric definitions and corresponding results table; matching task comparison table/ablation captions Version: journal full text in PMC | source checked automated source review · 2026-09-16 Audit detailsTargeted full-paper and supplement review of the outstanding task fields, with original dataset metadata checked where accessible. Source-scoped omissions are explicit; no independent benchmark reproduction or numerical-result change. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Datasets MirTarRAW positive/negative miRNA–mRNA pairs, plus DeepMirTarLeft. Individual claims | Retentive Network promotes efficient RNA language modeling of long sequences Results: RNA–RNA interaction task; Methods: RNA–RNA interaction prediction; cached text lines 17, 79; task metric definitions and corresponding results table; matching task comparison table/ablation captions Version: journal full text in PMC | source checked automated source review · 2026-09-16 Audit detailsTargeted full-paper and supplement review of the outstanding task fields, with original dataset metadata checked where accessible. Source-scoped omissions are explicit; no independent benchmark reproduction or numerical-result change. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Splits MirTarRAW is partitioned 72:8:20 for training, validation and testing; DeepMirTarLeft is an additional independent dataset. Individual claims | Retentive Network promotes efficient RNA language modeling of long sequences Results: RNA–RNA interaction task; Methods: RNA–RNA interaction prediction; cached text lines 17, 79; task metric definitions and corresponding results table; matching task comparison table/ablation captions Version: journal full text in PMC | source checked automated source review · 2026-09-16 Audit detailsTargeted full-paper and supplement review of the outstanding task fields, with original dataset metadata checked where accessible. Source-scoped omissions are explicit; no independent benchmark reproduction or numerical-result change. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Adaptation Supervised pair classification on the training portion with validation and independent testing. Individual claims | Retentive Network promotes efficient RNA language modeling of long sequences Results: RNA–RNA interaction task; Methods: RNA–RNA interaction prediction; cached text lines 17, 79; task metric definitions and corresponding results table; matching task comparison table/ablation captions Version: journal full text in PMC | source checked automated source review · 2026-09-16 Audit detailsTargeted full-paper and supplement review of the outstanding task fields, with original dataset metadata checked where accessible. Source-scoped omissions are explicit; no independent benchmark reproduction or numerical-result change. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Metrics F1, precision, recall, accuracy and AUC for miRNA–mRNA interaction classification. Individual claims | Retentive Network promotes efficient RNA language modeling of long sequences Results: RNA–RNA interaction task; Methods: RNA–RNA interaction prediction; cached text lines 17, 79; task metric definitions and corresponding results table; matching task comparison table/ablation captions Version: journal full text in PMC | source checked automated source review · 2026-09-16 Audit detailsTargeted full-paper and supplement review of the outstanding task fields, with original dataset metadata checked where accessible. Source-scoped omissions are explicit; no independent benchmark reproduction or numerical-result change. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Baselines DeepMirTar, RNA-FM, RNABERT, RNA-MSM and RNAErnie in the interaction comparison; other RNA-task baselines are separate. Individual claims | Retentive Network promotes efficient RNA language modeling of long sequences Results: RNA–RNA interaction task; Methods: RNA–RNA interaction prediction; cached text lines 17, 79; task metric definitions and corresponding results table; matching task comparison table/ablation captions Version: journal full text in PMC | source checked automated source review · 2026-09-16 Audit detailsTargeted full-paper and supplement review of the outstanding task fields, with original dataset metadata checked where accessible. Source-scoped omissions are explicit; no independent benchmark reproduction or numerical-result change. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Leakage controls MirTarRAW is divided 72%/8%/20% for training/validation/test, with DeepMirTarLeft as an additional test set. The RNAret methods do not specify a miRNA-identity, transcript-identity or homology-grouped holdout for this task. The paper’s explicit RNAStrAlign/ArchiveII overlap exclusion belongs to secondary-structure prediction and must not be transferred here. Individual claims | Retentive Network promotes efficient RNA language modeling of long sequences Results: miRNA-mRNA interaction prediction; Methods: Statistics and reproducibility Version: journal full text in PMC | unreported automated source review · 2026-09-16 Audit detailsTargeted full-paper and supplement review of the outstanding task fields, with original dataset metadata checked where accessible. Source-scoped omissions are explicit; no independent benchmark reproduction or numerical-result change. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Uncertainty Table 1 reports point classification metrics. RNAret’s Statistics and reproducibility section and Supplementary Information do not specify repeated-run intervals or an uncertainty estimator for miRNA–mRNA prediction. Intervals reported in the original miTAR study would not quantify RNAret’s results. Individual claims | rnaret-2026__42003_2026_9757_MOESM2_ESM.pdf Table 1; Methods: Statistics and reproducibility; Supplementary Information Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Retrieved 2026-09-16; sha256:8464ff052a60b946bd08fa18860f22b2f390fe93dd0549eb845c8e9a68cf5fcc | unreported automated source review · 2026-09-16 Audit detailsTargeted full-paper and supplement review of the outstanding task fields, with original dataset metadata checked where accessible. Source-scoped omissions are explicit; no independent benchmark reproduction or numerical-result change. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record Archive member: 42003_2026_9757_MOESM2_ESM.pdf |
Sources and history
View linked audit checks and correction history
Release 2026-09-29-06401fd5b220 · Record review: needs review
4 source records and release history
- Retentive Network promotes efficient RNA language modeling of long sequences · Original source · journal full text in PMC
- Retentive Network promotes efficient RNA language modeling of long sequences · Original source · journal full text in PMC
- rnaret-2026__42003_2026_9757_MOESM2_ESM.pdf · Original source · Retrieved 2026-09-16; sha256:8464ff052a60b946bd08fa18860f22b2f390fe93dd0549eb845c8e9a68cf5fcc
- PMC7912887.xml · Original source · Retrieved 2026-09-16; sha256:3504a0fa3b62698b7eff78418e682bf2484236ed05645597a8be4fa2e97b3efc
Technical metadata and extraction receipts
Stable ID: reported-task-46e927bea10702
- areas
- rna-transcriptomes
- tasks
- miRNA-mRNA interaction prediction
- entity level
- task
- version
- Not reported
- task
- miRNA-mRNA interaction prediction
- scope note
- Paper-specific evaluation task; protocol completeness requires further extraction.
- benchmark research
- review date: 2026-09-17; status: primary_comparison_tables_located; primary sources: evidence-expansion-rnaret-2026-e970e732; inspected locators: Table 1; XML table Tab1; searched queries: Retentive Network promotes efficient RNA language modeling of long sequences 10.1038/s42003-026-09757-x; gaps: complete numerical transcription and independent cell review: Full primary artifact and table inventory preserved; no new numeric row is published from this audit alone.; exact checkpoint hashes and per-method scored denominators: Table labels alone do not establish these fields; do not infer checkpoint or scored count from model name or dataset size.; claim scope: Dated primary-source discovery and protocol/table screening. Source checking does not mean experimental reproduction. Only separately extracted and independently reviewed numeric batches are publishable.
- historical missing metadata
- protocol version: 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: rnaret-2026; source locator: Results: RNA–RNA interaction task; Methods: RNA–RNA interaction prediction; cached text lines 17, 79; task metric definitions and corresponding results table; matching task comparison table/ablation captions; 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.
Related records
- dataset: MirTarRAW
- benchmark: RNAret: miRNA-mRNA interaction prediction