mRNA–protein interaction prediction explicitly contrasts familiar-protein pair prediction with transfer to unseen RNA-binding proteins.
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 |
Conceptual summary of the cited evaluation; exact task configuration and source version remain part of the protocol.
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.
Each evaluation records what was tested and under which conditions.
Release 2026-09-17-d277315f7d76 · 1 evaluation · 1 metric row. Different protocols are not a single leaderboard.
| Metric and finding | Coverage and uncertainty | Evidence |
|---|---|---|
| ProteinBERT LLM-encoding model: mRNA-protein interaction prediction Pipeline: ProteinBERT LLM-encoding modelTask: mRNA-protein interaction predictionDataset: mRNA-RBP pairs LLM encoding of protein partner; RBP-aware partition tests generalization to unseen protein diversity Author-reported evaluation · Evaluation metadata: needs review | ||
| 71.5% AUROC Unit: percent · Direction: unknown | Uncertainty: not reported in legacy extract Scored: Not reported · Eligible: Not reported | source checkedGeneralizable 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. |
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 |
primary comparison tables located
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-d7e6274011946eTrace 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 ["Input: RNA–protein pairs.","Evaluation: Supervised interaction prediction; random-pair and held-out-protein tests measure different transfer settings.","Readout: 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 |
| 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 |
Release 2026-09-17-d277315f7d76 · Record review: needs review
Stable ID: reported-task-d7e6274011946e