Model type
Study-specific predictive method; this record is the paper-specific evaluated configuration.
Pipeline
ProteinBERT LLM-encoding model
This mRNA–protein interaction predictor uses ProteinBERT to encode RNA-binding-protein sequences.
Sources
Generalizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity · Results and discussions/Limitations of sequence-based deep-learning models (paragraph 6); Datasets and methods (paragraph 1)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
Generalizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity · Datasets and methods/The modern attention-based deep network for mRPI identification/Part 2: binding pattern integration (paragraph 1); Datasets and methods/The implemented RBP encoding schemes (paragraph 1)At a glance
Biological inputs
mRNA and RNA-binding-protein sequences
Sources
Generalizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity · Results and discussions/Limitations of sequence-based deep-learning models (paragraph 6); Datasets and methods/Evaluation metrics (paragraph 1)Outputs
mRNA–protein interaction predictions
Sources
Generalizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity · Results and discussions/Limitations of sequence-based deep-learning models (paragraph 6); Results and discussions/Limitations of sequence-based deep-learning models (paragraph 5)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.
| 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. |
How it works
How the evaluated method works
ProteinBERT-derived protein features enter the paper’s attention-based mRNA–protein prediction framework alongside the RNA representation.
Sources
Generalizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity · Datasets and methods/The modern attention-based deep network for mRPI identification/Part 2: binding pattern integration (paragraph 1); Datasets and methods/The implemented RBP encoding schemes (paragraph 1)What was evaluated
The linked evaluation record identifies ProteinBERT LLM-encoding model: mRNA-protein interaction prediction. Its dataset, split, adaptation and evidence origin remain attached to the reported results.
Sources
Generalizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity · The named evaluation’s methods and comparison table; exact preserved evaluation IDs: evaluation-lit-b4-008Strengths and limitations
Strengths and considerations
- The benchmark explicitly tests whether an interaction model generalises to proteins excluded from training.
Sources
Generalizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity · Abstract (paragraph 1); Introduction (paragraph 4)
Limitations and conditions
- Random interaction-level splits permit protein overlap; their performance must not be treated as unseen-RBP generalisation.
Sources
Generalizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity · Results and discussions/Limitations of sequence-based deep-learning models (paragraph 2); Results and discussions/RBP-aware cross-validation further confirmed the requirement for the RBP-aware test set and RBP diversity (paragraph 1)
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-41ae49bb40ed8eSpecifications
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 |
|---|---|
| Model type | Study-specific predictive method; this record is the paper-specific evaluated configuration.SourcesGeneralizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity · Datasets and methods/The modern attention-based deep network for mRPI identification/Part 2: binding pattern integration (paragraph 1); Datasets and methods/The implemented RBP encoding schemes (paragraph 1) |
| Architecture / procedure | ProteinBERT-derived protein features enter the paper’s attention-based mRNA–protein prediction framework alongside the RNA representation.SourcesGeneralizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity · Datasets and methods/The modern attention-based deep network for mRPI identification/Part 2: binding pattern integration (paragraph 1); Datasets and methods/The implemented RBP encoding schemes (paragraph 1) |
| Biological inputs | mRNA and RNA-binding-protein sequencesSourcesGeneralizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity · Results and discussions/Limitations of sequence-based deep-learning models (paragraph 6); Datasets and methods/Evaluation metrics (paragraph 1) |
| Outputs | mRNA–protein interaction predictionsSourcesGeneralizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity · Results and discussions/Limitations of sequence-based deep-learning models (paragraph 6); Results and discussions/Limitations of sequence-based deep-learning models (paragraph 5) |
| Parameters | An aggregate parameter total for this exact evaluated configuration is not established by the inspected sources. · Not reported in inspected sourcesSources (2)Generalizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity; cobisLab/mRPI-issue README.md · Datasets and methods; Datasets and methods/Preparation of the mRNA-protein interaction ground-truth dataset; Datasets and methods/The evaluated data partitioning schemes; Datasets and methods/The implemented RBP encoding schemes; Datasets and methods/The implemented RBP encoding schemes/One-hot encoding of RBPs and mRNA fragments; Datasets and methods/The implemented RBP encoding schemes/LLM-encoding of RBPs; Datasets and methods/The implemented RBP encoding schemes/Structure-aware encoding of RBPs; Datasets and methods/The modern attention-based deep network for mRPI identification; inspected for aggregate parameter count (component sizes are not added without an exact configuration); README.md at pinned repository revision |
| Known versions / configuration | ProteinBERT LLM-encoding model is the comparison-table label; that label does not specify an immutable weight revision. · Not reported in inspected sourcesSourcesGeneralizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity · Model identification in the comparison table and corresponding Methods; immutable checkpoint revision is not supplied by the table label. |
| Training data / fitting | CLIP-derived interaction pairs with separate random-pair and RBP-aware partitions.SourcesGeneralizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity · Datasets and methods (paragraph 1); Datasets and methods/The evaluated data partitioning schemes (paragraph 1) |
| Context limits | Protein inputs are padded to 2,804 residues and mRNA fragments to 1,024 nucleotides in the evaluated dataset.SourcesGeneralizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity · Datasets and methods/The implemented RBP encoding schemes (paragraph 1); Datasets and methods/The implemented RBP encoding schemes/One-hot encoding of RBPs and mRNA fragments (paragraph 1) |
| Access | Official study implementation and usage documentation: https://github.com/cobisLab/mRPI-issue/blob/0f2d27666c4876f00d4c4e6cb3bec0e1629d214c/README.md. This pinned documentation revision is not automatically the evaluated weight revision.SourcescobisLab/mRPI-issue README.md · README.md; installation, model download and usage instructions |
| Code licence | No explicit code licence was established from the paper’s availability statement and inspected repository-root documentation. · Not reported in inspected sourcesSourcescobisLab/mRPI-issue README.md · README.md and repository-root licence-file search |
| Weights licence | The inspected model-access documentation does not explicitly identify terms for this exact evaluated checkpoint or fitted head; repository code terms are shown separately. · Not reported in inspected sourcesSourcescobisLab/mRPI-issue README.md · README.md; checkpoint/access documentation and licence scope |
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
| 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 | Generalizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity Datasets and methods/The modern attention-based deep network for mRPI identification/Part 2: binding pattern integration (paragraph 1); Datasets and methods/The implemented RBP encoding schemes (paragraph 1) Version: version of record | source checked automated source review · 2026-09-16 Audit detailsPrimary 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: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Diagram steps ["mRNA and RNA-binding-protein sequences","ProteinBERT LLM-encoding model","mRNA–protein interaction predictions"] Individual claims | Generalizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity Datasets and methods/The modern attention-based deep network for mRPI identification/Part 2: binding pattern integration (paragraph 1); Datasets and methods/The implemented RBP encoding schemes (paragraph 1) Version: version of record | source checked automated source review · 2026-09-16 Audit detailsPrimary 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: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Diagram title Evaluated procedure (conceptual) Individual claims | Generalizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity Datasets and methods/The modern attention-based deep network for mRPI identification/Part 2: binding pattern integration (paragraph 1); Datasets and methods/The implemented RBP encoding schemes (paragraph 1) Version: version of record | source checked automated source review · 2026-09-16 Audit detailsPrimary 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: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Model type Study-specific predictive method; this record is the paper-specific evaluated configuration. Individual claims | Generalizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity Datasets and methods/The modern attention-based deep network for mRPI identification/Part 2: binding pattern integration (paragraph 1); Datasets and methods/The implemented RBP encoding schemes (paragraph 1) Version: version of record | source checked automated source review · 2026-09-16 Audit detailsPrimary 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: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Architecture / procedure ProteinBERT-derived protein features enter the paper’s attention-based mRNA–protein prediction framework alongside the RNA representation. Individual claims | Generalizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity Datasets and methods/The modern attention-based deep network for mRPI identification/Part 2: binding pattern integration (paragraph 1); Datasets and methods/The implemented RBP encoding schemes (paragraph 1) Version: version of record | source checked automated source review · 2026-09-16 Audit detailsPrimary 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: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Weights licence The inspected model-access documentation does not explicitly identify terms for this exact evaluated checkpoint or fitted head; repository code terms are shown separately. Individual claims | cobisLab/mRPI-issue README.md README.md; checkpoint/access documentation and licence scope Version: 0f2d27666c4876f00d4c4e6cb3bec0e1629d214c | unreported automated source review · 2026-09-16 Audit detailsPrimary 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: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Biological inputs mRNA and RNA-binding-protein sequences Individual claims | Generalizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity Results and discussions/Limitations of sequence-based deep-learning models (paragraph 6); Datasets and methods/Evaluation metrics (paragraph 1) Version: version of record | source checked automated source review · 2026-09-16 Audit detailsPrimary 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: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Outputs mRNA–protein interaction predictions Individual claims | Generalizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity Results and discussions/Limitations of sequence-based deep-learning models (paragraph 6); Results and discussions/Limitations of sequence-based deep-learning models (paragraph 5) Version: version of record | source checked automated source review · 2026-09-16 Audit detailsPrimary 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: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Parameters An aggregate parameter total for this exact evaluated configuration is not established by the inspected sources. Individual claims | cobisLab/mRPI-issue README.md Datasets and methods; Datasets and methods/Preparation of the mRNA-protein interaction ground-truth dataset; Datasets and methods/The evaluated data partitioning schemes; Datasets and methods/The implemented RBP encoding schemes; Datasets and methods/The implemented RBP encoding schemes/One-hot encoding of RBPs and mRNA fragments; Datasets and methods/The implemented RBP encoding schemes/LLM-encoding of RBPs; Datasets and methods/The implemented RBP encoding schemes/Structure-aware encoding of RBPs; Datasets and methods/The modern attention-based deep network for mRPI identification; inspected for aggregate parameter count (component sizes are not added without an exact configuration); README.md at pinned repository revision Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: 0f2d27666c4876f00d4c4e6cb3bec0e1629d214c | unreported automated source review · 2026-09-16 Audit detailsPrimary 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: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Parameters An aggregate parameter total for this exact evaluated configuration is not established by the inspected sources. Individual claims | Generalizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity Datasets and methods; Datasets and methods/Preparation of the mRNA-protein interaction ground-truth dataset; Datasets and methods/The evaluated data partitioning schemes; Datasets and methods/The implemented RBP encoding schemes; Datasets and methods/The implemented RBP encoding schemes/One-hot encoding of RBPs and mRNA fragments; Datasets and methods/The implemented RBP encoding schemes/LLM-encoding of RBPs; Datasets and methods/The implemented RBP encoding schemes/Structure-aware encoding of RBPs; Datasets and methods/The modern attention-based deep network for mRPI identification; inspected for aggregate parameter count (component sizes are not added without an exact configuration); README.md at pinned repository revision Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: version of record | unreported automated source review · 2026-09-16 Audit detailsPrimary 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: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
Sources and history
Release 2026-09-17-d277315f7d76 · 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
- cobisLab/mRPI-issue README.md · Original source · 0f2d27666c4876f00d4c4e6cb3bec0e1629d214c
Technical metadata and extraction receipts
Stable ID: reported-model-41ae49bb40ed8e
- areas
- rna-transcriptomes
- entity level
- method
- version
- Not reported
- reported name
- ProteinBERT LLM-encoding model
- 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 record identifies a composed analysis workflow with separately identifiable upstream models, representations or tools and a downstream prediction/scoring procedure. Results belong to that complete composition rather than to an upstream model alone.; source ids: mrna-protein-diversity-2026; source locator: Datasets and methods/The modern attention-based deep network for mRPI identification/Part 2: binding pattern integration (paragraph 1); Datasets and methods/The implemented RBP encoding schemes (paragraph 1) | Results and discussions/Limitations of sequence-based deep-learning models (paragraph 6); Datasets and methods (paragraph 1); ambiguities: This is the paper-specific pipeline identity; unspecified component checkpoints or implementation versions are not inferred from its name.