Model type
Study-specific predictive method; this record is the paper-specific evaluated configuration.
Pipeline
ProtT5 embeddings + ensemble classifier
EDLMPPI uses ProtT5 embeddings and an ensemble predictor to identify protein–protein interaction sites.
Sources
Learning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning · Conclusions (paragraph 1); Abstract (paragraph 2)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
Learning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning · Abstract (paragraph 2); Abstract (paragraph 1)At a glance
Biological inputs
Protein amino-acid sequences
Sources
Learning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning · Methods/Feature descriptors/Dynamic global contextual information (paragraph 2); Methods/Feature descriptors/Multi-source biological features (paragraph 4)Outputs
Per-residue protein–protein binding-site predictions
Sources
Learning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning · Methods/Datasets (paragraph 2); Abstract (paragraph 2)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 |
|---|---|---|
| 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. |
How it works
How the evaluated method works
ProtT5 converts the sequence into distributed residue representations, which feed the study’s ensemble deep-learning binding-site classifier.
Sources
Learning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning · Abstract (paragraph 2); Abstract (paragraph 1)What was evaluated
The linked evaluation record identifies ProtT5 embeddings + ensemble classifier: protein-protein binding-site prediction. Its dataset, split, adaptation and evidence origin remain attached to the reported results.
Sources
Learning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning · The named evaluation’s methods and comparison table; exact preserved evaluation IDs: evaluation-lit-b4-010Strengths and limitations
Strengths and considerations
- Uses pretrained sequence features for a label-scarce residue-level prediction task.
Sources
Learning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning · Conclusions (paragraph 1); Methods/Feature descriptors/Multi-source biological features (paragraph 2)
Limitations and conditions
- Residue-level binding-site prediction differs from deciding whether an arbitrary pair of proteins interacts.
Sources
Learning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning · Methods/Datasets (paragraph 2); Results and discussion/Protein binding domains analysis (paragraph 2)
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-fdac4c1ec8a433Specifications
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.SourcesLearning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning · Abstract (paragraph 2); Abstract (paragraph 1) |
| Architecture / procedure | ProtT5 converts the sequence into distributed residue representations, which feed the study’s ensemble deep-learning binding-site classifier.SourcesLearning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning · Abstract (paragraph 2); Abstract (paragraph 1) |
| Biological inputs | Protein amino-acid sequencesSourcesLearning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning · Methods/Feature descriptors/Dynamic global contextual information (paragraph 2); Methods/Feature descriptors/Multi-source biological features (paragraph 4) |
| Outputs | Per-residue protein–protein binding-site predictionsSourcesLearning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning · Methods/Datasets (paragraph 2); Abstract (paragraph 2) |
| Parameters | An aggregate parameter total for this exact evaluated configuration is not established by the inspected sources. · Not reported in inspected sourcesSources (2)Learning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning; houzl3416/EDLMPPI README.md · Results and discussion/Comparing EDLMPPI with different deep learning architectures; Results and discussion/Comparing EDLMPPI with other PPIs prediction methods; Methods/Datasets; Methods/Feature descriptors; Methods/Feature descriptors/Dynamic global contextual information; Methods/Feature descriptors/Multi-source biological features; Methods/Ensemble deep memory capsule network; Methods/Ensemble deep memory capsule network/Deep memory network; inspected for aggregate parameter count (component sizes are not added without an exact configuration); README.md at pinned repository revision |
| Known versions / configuration | ProtT5 embeddings + ensemble classifier is the comparison-table label; that label does not specify an immutable weight revision. · Not reported in inspected sourcesSourcesLearning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning · Model identification in the comparison table and corresponding Methods; immutable checkpoint revision is not supplied by the table label. |
| Training data / fitting | Dset_448, Dset_72 and Dset_164 are the study’s benchmark collections; their training/test roles are retained in the associated evaluation records.SourcesLearning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning · Methods/Datasets (paragraph 1); Methods/Datasets (paragraph 2) |
| Context limits | A maximum input/context length for this exact evaluated configuration is not established by the inspected sources. · Not reported in inspected sourcesSources (2)Learning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning; houzl3416/EDLMPPI README.md · Results and discussion/Comparing EDLMPPI with different deep learning architectures; Results and discussion/Comparing EDLMPPI with other PPIs prediction methods; Methods/Datasets; Methods/Feature descriptors; Methods/Feature descriptors/Dynamic global contextual information; Methods/Feature descriptors/Multi-source biological features; Methods/Ensemble deep memory capsule network; Methods/Ensemble deep memory capsule network/Deep memory network; inspected for explicit maximum input length (dataset lengths and family-wide limits are not substituted); README.md at pinned repository revision |
| Access | Official study implementation and usage documentation: https://github.com/houzl3416/EDLMPPI/blob/78e4a7b36bb83ccf4274786b859125178804f434/README.md. This pinned documentation revision is not automatically the evaluated weight revision.Sourceshouzl3416/EDLMPPI 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 sourcesSourceshouzl3416/EDLMPPI 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 sourcesSourceshouzl3416/EDLMPPI 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.
21 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 | Learning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning Abstract (paragraph 2); Abstract (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 ["Protein amino-acid sequences","ProtT5 embeddings + ensemble classifier","Per-residue protein–protein binding-site predictions"] Individual claims | Learning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning Abstract (paragraph 2); Abstract (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 | Learning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning Abstract (paragraph 2); Abstract (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 | Learning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning Abstract (paragraph 2); Abstract (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 ProtT5 converts the sequence into distributed residue representations, which feed the study’s ensemble deep-learning binding-site classifier. Individual claims | Learning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning Abstract (paragraph 2); Abstract (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 | houzl3416/EDLMPPI README.md README.md; checkpoint/access documentation and licence scope Version: 78e4a7b36bb83ccf4274786b859125178804f434 | 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 Protein amino-acid sequences Individual claims | Learning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning Methods/Feature descriptors/Dynamic global contextual information (paragraph 2); Methods/Feature descriptors/Multi-source biological features (paragraph 4) 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 Per-residue protein–protein binding-site predictions Individual claims | Learning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning Methods/Datasets (paragraph 2); Abstract (paragraph 2) 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 | houzl3416/EDLMPPI README.md Results and discussion/Comparing EDLMPPI with different deep learning architectures; Results and discussion/Comparing EDLMPPI with other PPIs prediction methods; Methods/Datasets; Methods/Feature descriptors; Methods/Feature descriptors/Dynamic global contextual information; Methods/Feature descriptors/Multi-source biological features; Methods/Ensemble deep memory capsule network; Methods/Ensemble deep memory capsule network/Deep memory network; 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: 78e4a7b36bb83ccf4274786b859125178804f434 | 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 | Learning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning Results and discussion/Comparing EDLMPPI with different deep learning architectures; Results and discussion/Comparing EDLMPPI with other PPIs prediction methods; Methods/Datasets; Methods/Feature descriptors; Methods/Feature descriptors/Dynamic global contextual information; Methods/Feature descriptors/Multi-source biological features; Methods/Ensemble deep memory capsule network; Methods/Ensemble deep memory capsule network/Deep memory network; 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
- Learning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning · Original source · version of record
- houzl3416/EDLMPPI README.md · Original source · 78e4a7b36bb83ccf4274786b859125178804f434
Technical metadata and extraction receipts
Stable ID: reported-model-fdac4c1ec8a433
- areas
- proteins-complexes
- entity level
- method
- version
- Not reported
- reported name
- ProtT5 embeddings + ensemble classifier
- 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: protein-binding-sites-2023; source locator: Abstract (paragraph 2); Abstract (paragraph 1) | Conclusions (paragraph 1); Abstract (paragraph 2); ambiguities: This is the paper-specific pipeline identity; unspecified component checkpoints or implementation versions are not inferred from its name.