Strengths and considerations
No source-reviewed explanatory claims are recorded here yet.
Task
protein-protein binding-site prediction
Protein-interaction binding-site prediction evaluates residue labels on nonredundant protein collections.
Sources
Learning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning · Methods: Datasets; Evaluation performance; cached text lines 57–59, 110–112; matching task comparison table/ablation captionsAt a glance
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 | PDB-derived Dset collections and a BioLip-derived Dset_1291 collection.SourcesLearning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning · Methods: Datasets; Evaluation performance; cached text lines 57–59, 110–112; matching task comparison table/ablation captions |
| Splits | Dset_843 supplies training sequences and Dset_448 an independent test subset for the BioLip setting.SourcesLearning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning · Methods: Datasets; Evaluation performance; cached text lines 57–59, 110–112; matching task comparison table/ablation captions |
| Metrics | Sensitivity, specificity, precision, accuracy, F1, MCC, AUROC and average precision.SourcesLearning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning · Methods: Datasets; Evaluation performance; cached text lines 57–59, 110–112; matching task comparison table/ablation captions |
| Baselines | Feature-descriptor ablations, ESM-1b/ProGen2/ProtT5 embeddings and task-specific SCRIBER/DELPHI comparisons.SourcesLearning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning · Methods: Datasets; Evaluation performance; cached text lines 57–59, 110–112; matching task comparison table/ablation captions |
| Leakage controls | The dataset construction describes sequence-similarity reduction before the train/test subdivision.SourcesLearning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning · Methods: Datasets; Evaluation performance; cached text lines 57–59, 110–112; 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 sourcesSourcesLearning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning · Methods: Datasets; Evaluation performance; cached text lines 57–59, 110–112; matching task comparison table/ablation captions |
| Entity type | Paper-specific computational evaluation protocol.SourcesLearning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning · Methods: Datasets; Evaluation performance; cached text lines 57–59, 110–112; matching task comparison table/ablation captions |
| Organisms | The benchmark uses PDB- and BioLip-derived protein collections selected for structure quality, sequence redundancy and interaction annotations. The Datasets section does not report their species distribution or a species-specific sampling rule. · Not reported in inspected sourcesSourcesLearning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning · Methods: Datasets; Supplementary Table 1 description |
| Assays | Protein–protein binding-residue annotations.SourcesLearning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning · Methods: Datasets; Evaluation performance; cached text lines 57–59, 110–112; matching task comparison table/ablation captions |
| Allowed inputs | Protein sequence/representation for binding-site prediction.SourcesLearning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning · Methods: Datasets; Evaluation performance; cached text lines 57–59, 110–112; matching task comparison table/ablation captions |
| Adaptation | Supervised residue classification using the defined Dset training and independent test sets.SourcesLearning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning · Methods: Datasets; Evaluation performance; cached text lines 57–59, 110–112; matching task comparison table/ablation captions |
How it works
How it worksComputational evaluation flow
Conceptual summary of the cited evaluation; exact task configuration and source version remain part of the protocol.
Sources
Learning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning · Methods: Datasets; Evaluation performance; cached text lines 57–59, 110–112; matching task comparison table/ablation captionsEvaluation methodology
PDB-derived Dset collections and a BioLip-derived Dset_1291 collection. Dset_843 supplies training sequences and Dset_448 an independent test subset for the BioLip setting. Sensitivity, specificity, precision, accuracy, F1, MCC, AUROC and average precision. Feature-descriptor ablations, ESM-1b/ProGen2/ProtT5 embeddings and task-specific SCRIBER/DELPHI comparisons. The dataset construction describes sequence-similarity reduction before the train/test subdivision. 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
Learning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning · Methods: Datasets; Evaluation performance; cached text lines 57–59, 110–112; matching task comparison table/ablation captionsRecorded evaluations
Each evaluation records what was tested and under which conditions.
Tested entities 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 |
|---|---|---|
| ProtT5 embeddings + ensemble classifier: protein-protein binding-site prediction Pipeline: ProtT5 embeddings + ensemble classifierTask: protein-protein binding-site predictionDataset: Dset_448 Explainable ensemble binding-site predictor using ProtT5 features Author-reported evaluation · Evaluation metadata: needs review | ||
| 0.810 AUROC Unit: fraction · Direction: unknown | Uncertainty: not reported in legacy extract Scored: Not reported · Eligible: Not reported | source checkedLearning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning · Table 2, Dset_448 section, ProtT5 row, AUROC column Source checking is not 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 |
|---|---|---|
| Learning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning | version of record | Read source |
What is still missing
- 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
- Learning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning 10.1038/s42003-023-04462-5
Evidence locations
- Table 5; XML table Tab5
Strengths and limitations
Limitations and conditions
- Multiple Dset collections differ in sequence coverage and training provenance. Results must retain the named training/test combination.
Sources
Learning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning · Methods: Datasets; Evaluation performance; cached text lines 57–59, 110–112; matching task comparison table/ablation captions
Profile review details
Relevant full-paper computational evaluation sections, tables/captions and cited supplementary task passages were reviewed. Reporting omissions are scoped to the inspected sources. Original numerical results are unchanged.
Stable record: reported-task-f0ed5188dbb6d4Evidence 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.
17 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 | Learning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning Methods: Datasets; Evaluation performance; cached text lines 57–59, 110–112; matching task comparison table/ablation captions Version: version of record | source checked automated source review · 2026-09-16 Audit detailsRelevant full-paper computational evaluation sections, tables/captions and cited supplementary task passages were reviewed. Reporting omissions are scoped to the inspected sources. Original numerical results are unchanged. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Diagram steps ["Input: Protein sequence/representation for binding-site prediction.","Evaluation: Supervised residue classification using the defined Dset training and independent test sets.","Readout: Sensitivity, specificity, precision, accuracy, F1, MCC, AUROC and average precision."] Individual claims | Learning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning Methods: Datasets; Evaluation performance; cached text lines 57–59, 110–112; matching task comparison table/ablation captions Version: version of record | source checked automated source review · 2026-09-16 Audit detailsRelevant full-paper computational evaluation sections, tables/captions and cited supplementary task passages were reviewed. Reporting omissions are scoped to the inspected sources. Original numerical results are unchanged. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Diagram title Computational evaluation flow Individual claims | Learning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning Methods: Datasets; Evaluation performance; cached text lines 57–59, 110–112; matching task comparison table/ablation captions Version: version of record | source checked automated source review · 2026-09-16 Audit detailsRelevant full-paper computational evaluation sections, tables/captions and cited supplementary task passages were reviewed. Reporting omissions are scoped to the inspected sources. Original numerical results are unchanged. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Datasets PDB-derived Dset collections and a BioLip-derived Dset_1291 collection. Individual claims | Learning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning Methods: Datasets; Evaluation performance; cached text lines 57–59, 110–112; matching task comparison table/ablation captions Version: version of record | source checked automated source review · 2026-09-16 Audit detailsRelevant full-paper computational evaluation sections, tables/captions and cited supplementary task passages were reviewed. Reporting omissions are scoped to the inspected sources. Original numerical results are unchanged. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Splits Dset_843 supplies training sequences and Dset_448 an independent test subset for the BioLip setting. Individual claims | Learning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning Methods: Datasets; Evaluation performance; cached text lines 57–59, 110–112; matching task comparison table/ablation captions Version: version of record | source checked automated source review · 2026-09-16 Audit detailsRelevant full-paper computational evaluation sections, tables/captions and cited supplementary task passages were reviewed. Reporting omissions are scoped to the inspected sources. Original numerical results are unchanged. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Adaptation Supervised residue classification using the defined Dset training and independent test sets. Individual claims | Learning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning Methods: Datasets; Evaluation performance; cached text lines 57–59, 110–112; matching task comparison table/ablation captions Version: version of record | source checked automated source review · 2026-09-16 Audit detailsRelevant full-paper computational evaluation sections, tables/captions and cited supplementary task passages were reviewed. Reporting omissions are scoped to the inspected sources. Original numerical results are unchanged. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Metrics Sensitivity, specificity, precision, accuracy, F1, MCC, AUROC and average precision. Individual claims | Learning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning Methods: Datasets; Evaluation performance; cached text lines 57–59, 110–112; matching task comparison table/ablation captions Version: version of record | source checked automated source review · 2026-09-16 Audit detailsRelevant full-paper computational evaluation sections, tables/captions and cited supplementary task passages were reviewed. Reporting omissions are scoped to the inspected sources. Original numerical results are unchanged. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Baselines Feature-descriptor ablations, ESM-1b/ProGen2/ProtT5 embeddings and task-specific SCRIBER/DELPHI comparisons. Individual claims | Learning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning Methods: Datasets; Evaluation performance; cached text lines 57–59, 110–112; matching task comparison table/ablation captions Version: version of record | source checked automated source review · 2026-09-16 Audit detailsRelevant full-paper computational evaluation sections, tables/captions and cited supplementary task passages were reviewed. Reporting omissions are scoped to the inspected sources. Original numerical results are unchanged. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Leakage controls The dataset construction describes sequence-similarity reduction before the train/test subdivision. Individual claims | Learning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning Methods: Datasets; Evaluation performance; cached text lines 57–59, 110–112; matching task comparison table/ablation captions Version: version of record | source checked automated source review · 2026-09-16 Audit detailsRelevant full-paper computational evaluation sections, tables/captions and cited supplementary task passages were reviewed. Reporting omissions are scoped to the inspected sources. Original numerical results are unchanged. 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 | Learning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning Methods: Datasets; Evaluation performance; cached text lines 57–59, 110–112; matching task comparison table/ablation captions Version: version of record | unreported automated source review · 2026-09-16 Audit detailsRelevant full-paper computational evaluation sections, tables/captions and cited supplementary task passages were reviewed. Reporting omissions are scoped to the inspected sources. Original numerical results are unchanged. 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
- Learning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning · Original source · version of record
- Learning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning · Original source · version of record
Technical metadata and extraction receipts
Stable ID: reported-task-f0ed5188dbb6d4
- areas
- proteins-complexes
- tasks
- protein-protein binding-site prediction
- entity level
- task
- version
- Not reported
- task
- protein-protein binding-site 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-protein-binding-sites-2023-491711aa; inspected locators: Table 5; XML table Tab5; searched queries: Learning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning 10.1038/s42003-023-04462-5; 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; 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: protein-binding-sites-2023; source locator: Methods: Datasets; Evaluation performance; cached text lines 57–59, 110–112; 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.