Datasets
CLIPdb-derived human RNA-binding annotations with sampled, deduplicated negative pairs.
Task
mRNA-protein interaction prediction
mRNA–protein interaction prediction explicitly contrasts familiar-protein pair prediction with transfer to unseen RNA-binding proteins.
Sources
Generalizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity · Datasets and methods; ground-truth dataset construction; cached text lines 8–13; task metric definitions and corresponding results table; matching task comparison table/ablation captionsOverview
Metrics
AUROC, AUPRC, F1, precision, recall and specificity, reported separately for each partitioning regime.
Allowed inputs
RNA–protein pairs.
Sources
Generalizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity · Datasets and methods; ground-truth dataset construction; cached text lines 8–13; 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
Generalizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity · Datasets and methods; ground-truth dataset construction; cached text lines 8–13; 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 |
|---|---|---|---|
| Pipeline: ProteinBERT LLM-encoding model | Task: mRNA-protein interaction prediction Dataset: mRNA-RBP pairs | 71.5% AUROC percent · unknown Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceProteinBERT LLM-encoding model: mRNA-protein interaction prediction LLM encoding of protein partner; RBP-aware partition tests generalization to unseen protein diversity Aggregation: Not reported Generalizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity · Table 2, RBP-aware test set 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
CLIPdb-derived human RNA-binding annotations with sampled, deduplicated negative pairs. A random-pair test permits protein overlap; an RBP-aware test holds out all test-protein identities. AUROC, AUPRC, F1, precision, recall and specificity, reported separately for each partitioning regime. One-hot attention model, ProteinBERT embedding model and structure-aware encoding; split definitions are compared separately. The protein-held-out test is constructed specifically to expose identity leakage hidden by random pair sampling. 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
Generalizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity · Datasets and methods; ground-truth dataset construction; cached text lines 8–13; task metric definitions and corresponding results table; matching task comparison table/ablation captionsRecorded 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
- RBP-aware evaluation separates unseen-protein transfer from easier random-pair testing.
Sources
Generalizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity · Datasets and methods; ground-truth dataset construction; cached text lines 8–13; task metric definitions and corresponding results table; matching task comparison table/ablation captions
Limitations and conditions
- Unobserved interactions are used to construct negatives and may include undiscovered positives. Classifier configuration must remain attached to the partitioning regime.
Sources
Generalizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity · Datasets and methods; ground-truth dataset construction; cached text lines 8–13; task metric definitions and corresponding results table; matching task comparison table/ablation captions
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-d7e6274011946eSpecifications
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 | CLIPdb-derived human RNA-binding annotations with sampled, deduplicated negative pairs.SourcesGeneralizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity · Datasets and methods; ground-truth dataset construction; cached text lines 8–13; task metric definitions and corresponding results table; matching task comparison table/ablation captions |
| Splits | A random-pair test permits protein overlap; an RBP-aware test holds out all test-protein identities.SourcesGeneralizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity · Datasets and methods; ground-truth dataset construction; cached text lines 8–13; task metric definitions and corresponding results table; matching task comparison table/ablation captions |
| Metrics | AUROC, AUPRC, F1, precision, recall and specificity, reported separately for each partitioning regime.SourcesGeneralizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity · Datasets and methods; ground-truth dataset construction; cached text lines 8–13; task metric definitions and corresponding results table; matching task comparison table/ablation captions |
| Baselines | One-hot attention model, ProteinBERT embedding model and structure-aware encoding; split definitions are compared separately.SourcesGeneralizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity · Datasets and methods; ground-truth dataset construction; cached text lines 8–13; task metric definitions and corresponding results table; matching task comparison table/ablation captions |
| Leakage controls | The protein-held-out test is constructed specifically to expose identity leakage hidden by random pair sampling.SourcesGeneralizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity · Datasets and methods; ground-truth dataset construction; cached text lines 8–13; task metric definitions and corresponding results table; matching task comparison table/ablation captions |
| 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 sourcesSourcesGeneralizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity · Datasets and methods; ground-truth dataset construction; cached text lines 8–13; task metric definitions and corresponding results table; matching task comparison table/ablation captions |
| Entity type | Paper-specific computational evaluation protocol.SourcesGeneralizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity · Datasets and methods; ground-truth dataset construction; cached text lines 8–13; task metric definitions and corresponding results table; matching task comparison table/ablation captions |
| Organisms | Human.SourcesGeneralizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity · Datasets and methods; ground-truth dataset construction; cached text lines 8–13; task metric definitions and corresponding results table; matching task comparison table/ablation captions |
| Assays | CLIPdb RNA-binding annotations.SourcesGeneralizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity · Datasets and methods; ground-truth dataset construction; cached text lines 8–13; task metric definitions and corresponding results table; matching task comparison table/ablation captions |
| Allowed inputs | RNA–protein pairs.SourcesGeneralizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity · Datasets and methods; ground-truth dataset construction; cached text lines 8–13; task metric definitions and corresponding results table; matching task comparison table/ablation captions |
| Adaptation | Supervised interaction prediction; random-pair and held-out-protein tests measure different transfer settings.SourcesGeneralizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity · Datasets and methods; ground-truth dataset construction; cached text lines 8–13; 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 |
|---|---|---|
| Generalizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity | version of record | 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
- Generalizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity 10.1186/s13321-026-01197-3
Evidence locations
- Table 4; XML table Tab4
- Table 5; XML table Tab5
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 | Generalizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity Datasets and methods; ground-truth dataset construction; cached text lines 8–13; task metric definitions and corresponding results table; matching task comparison table/ablation captions Version: version of record | 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
| Generalizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity Datasets and methods; ground-truth dataset construction; cached text lines 8–13; task metric definitions and corresponding results table; matching task comparison table/ablation captions Version: version of record | 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 | Generalizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity Datasets and methods; ground-truth dataset construction; cached text lines 8–13; task metric definitions and corresponding results table; matching task comparison table/ablation captions Version: version of record | 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 CLIPdb-derived human RNA-binding annotations with sampled, deduplicated negative pairs. Individual claims | Generalizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity Datasets and methods; ground-truth dataset construction; cached text lines 8–13; task metric definitions and corresponding results table; matching task comparison table/ablation captions Version: version of record | 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 A random-pair test permits protein overlap; an RBP-aware test holds out all test-protein identities. Individual claims | Generalizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity Datasets and methods; ground-truth dataset construction; cached text lines 8–13; task metric definitions and corresponding results table; matching task comparison table/ablation captions Version: version of record | 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 interaction prediction; random-pair and held-out-protein tests measure different transfer settings. Individual claims | Generalizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity Datasets and methods; ground-truth dataset construction; cached text lines 8–13; task metric definitions and corresponding results table; matching task comparison table/ablation captions Version: version of record | 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 AUROC, AUPRC, F1, precision, recall and specificity, reported separately for each partitioning regime. Individual claims | Generalizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity Datasets and methods; ground-truth dataset construction; cached text lines 8–13; task metric definitions and corresponding results table; matching task comparison table/ablation captions Version: version of record | 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 One-hot attention model, ProteinBERT embedding model and structure-aware encoding; split definitions are compared separately. Individual claims | Generalizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity Datasets and methods; ground-truth dataset construction; cached text lines 8–13; task metric definitions and corresponding results table; matching task comparison table/ablation captions Version: version of record | 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 protein-held-out test is constructed specifically to expose identity leakage hidden by random pair sampling. Individual claims | Generalizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity Datasets and methods; ground-truth dataset construction; cached text lines 8–13; task metric definitions and corresponding results table; matching task comparison table/ablation captions Version: version of record | 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 | Generalizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity Datasets and methods; ground-truth dataset construction; cached text lines 8–13; task metric definitions and corresponding results table; matching task comparison table/ablation captions Version: version of record | 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
- Generalizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity · Original source · version of record
- Generalizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity · Original source · version of record
Technical metadata and extraction receipts
Stable ID: reported-task-d7e6274011946e
- areas
- rna-transcriptomes
- tasks
- mRNA-protein interaction prediction
- entity level
- task
- version
- Not reported
- task
- mRNA-protein 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-mrna-protein-diversity-2026-94f9fe22; inspected locators: Table 4; XML table Tab4; Table 5; XML table Tab5; searched queries: Generalizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity 10.1186/s13321-026-01197-3; 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: mrna-protein-diversity-2026; source locator: Datasets and methods; ground-truth dataset construction; cached text lines 8–13; 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.