Model type
DNA sequence transformer; this record is the paper-specific evaluated configuration.
Configuration
DNABERT2-Enhancer
DNABERT2-Enhancer combines a fine-tuned DNA transformer with a CNN to classify enhancers and their activity.
Sources
Utilizing a deep learning model based on BERT for identifying enhancers and their strength · Abstract (paragraph 1); Results/Comparison of the proposed model with existing methods (paragraph 4)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
Utilizing a deep learning model based on BERT for identifying enhancers and their strength · Materials and methods/DNABERT2-enhancer model (paragraph 1); Materials and methods/DNABERT-2 model (paragraph 4)At a glance
Biological inputs
DNA sequence windows
Sources
Utilizing a deep learning model based on BERT for identifying enhancers and their strength · Conclusion (paragraph 1); Discussion (paragraph 3)Outputs
Enhancer/non-enhancer labels and strong/weak enhancer classification
Sources
Utilizing a deep learning model based on BERT for identifying enhancers and their strength · Introduction (paragraph 7); Materials and methods/Benchmark dataset (paragraph 4)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 |
|---|---|---|
| DNABERT2-Enhancer: enhancer recognition first-layer enhancer versus non-enhancer classifier Author-reported evaluation · Evaluation metadata: needs review | ||
| 0.965 AUC Unit: fraction · Direction: unknown | Uncertainty: not reported in legacy extract Scored: Not reported · Eligible: Not reported | source checkedUtilizing a deep learning model based on BERT for identifying enhancers and their strength · Table 4, first-layer DNABERT2-Enhancer row, AUC column Source checking is not independent reproduction. |
How it works
How the evaluated method works
DNABERT-2 initialises a BERT feature extractor. Transfer learning adapts it to enhancer data, and a convolutional network classifies the resulting sequence features.
Sources
Utilizing a deep learning model based on BERT for identifying enhancers and their strength · Materials and methods/DNABERT2-enhancer model (paragraph 1); Materials and methods/DNABERT-2 model (paragraph 4)Underlying method and version boundaries
DNABERT-2 replaces overlapping k-mer tokens with byte-pair encoding and uses ALiBi positional biases. The official 117M model produces 768-dimensional token representations; downstream classifiers and pooling choices are separate configuration details.
Sources
MAGICS-LAB/DNABERT_2 README.md · README.md; introduction, model description, pretrained-model and usage sections at pinned revisionWhat was evaluated
The linked evaluation record identifies DNABERT2-Enhancer: enhancer recognition. Its dataset, split, adaptation and evidence origin remain attached to the reported results.
Sources
Utilizing a deep learning model based on BERT for identifying enhancers and their strength · The named evaluation’s methods and comparison table; exact preserved evaluation IDs: evaluation-b2-dnabert2-enhancer-2025Strengths and limitations
Strengths and considerations
- Explicitly separates enhancer identification from enhancer-strength classification.
Sources
Utilizing a deep learning model based on BERT for identifying enhancers and their strength · Results (paragraph 4); Introduction (paragraph 7)
Limitations and conditions
- The balanced training sets and cell-line-specific labels limit direct extrapolation to genome-wide enhancer prevalence.
Sources
Utilizing a deep learning model based on BERT for identifying enhancers and their strength · Results/Comparison of the proposed model with existing methods (paragraph 4); Materials and methods/Benchmark dataset (paragraph 3)
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-d0e594ec3c0430Specifications
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 | DNA sequence transformer; this record is the paper-specific evaluated configuration.SourcesMAGICS-LAB/DNABERT_2 README.md · README.md model description |
| Architecture / procedure | DNABERT-2 initialises a BERT feature extractor. Transfer learning adapts it to enhancer data, and a convolutional network classifies the resulting sequence features.SourcesUtilizing a deep learning model based on BERT for identifying enhancers and their strength · Materials and methods/DNABERT2-enhancer model (paragraph 1); Materials and methods/DNABERT-2 model (paragraph 4) |
| Biological inputs | DNA sequence windowsSourcesUtilizing a deep learning model based on BERT for identifying enhancers and their strength · Conclusion (paragraph 1); Discussion (paragraph 3) |
| Outputs | Enhancer/non-enhancer labels and strong/weak enhancer classificationSourcesUtilizing a deep learning model based on BERT for identifying enhancers and their strength · Introduction (paragraph 7); Materials and methods/Benchmark dataset (paragraph 4) |
| Parameters | An aggregate parameter total for this exact evaluated configuration is not established by the inspected sources. · Not reported in inspected sourcesSources (2)Utilizing a deep learning model based on BERT for identifying enhancers and their strength; MAGICS-LAB/DNABERT_2 README.md · Materials and methods/Benchmark dataset; Materials and methods/DNABERT2-enhancer model; Materials and methods/DNABERT-2 model; Materials and methods/CNN model; Results/Comparison of the proposed model with existing methods; inspected for aggregate parameter count (component sizes are not added without an exact configuration); README.md at pinned repository revision |
| Known versions / configuration | DNABERT2-Enhancer is the comparison-table label; that label does not specify an immutable weight revision. · Not reported in inspected sourcesSourcesUtilizing a deep learning model based on BERT for identifying enhancers and their strength · Model identification in the comparison table and corresponding Methods; immutable checkpoint revision is not supplied by the table label. |
| Training data / fitting | Liu and Basith enhancer datasets; Basith includes eight cell-line subsets, 204–2,000-bp sequences and a 60% CD-HIT redundancy threshold.SourcesUtilizing a deep learning model based on BERT for identifying enhancers and their strength · Materials and methods/Benchmark dataset (paragraph 3); Materials and methods/Benchmark dataset (paragraph 1) |
| Context limits | The Basith benchmark includes 204–2,000-bp sequences; this is benchmark input coverage, not a validated universal model limit.SourcesUtilizing a deep learning model based on BERT for identifying enhancers and their strength · Results (paragraph 3); Materials and methods/DNABERT2-enhancer model (paragraph 1) |
| Access | Official upstream implementation and usage documentation: https://github.com/MAGICS-LAB/DNABERT_2/blob/f25bed9ee20db966dff39e5c1571249d04e36404/README.md. This pinned documentation revision is not automatically the evaluated weight revision.SourcesMAGICS-LAB/DNABERT_2 README.md · README.md; installation, model download and usage instructions |
| Code licence | Apache 2.0 (upstream repository code at the cited revision; this does not establish every dependency or historical checkpoint licence).SourcesMAGICS-LAB/DNABERT_2 LICENSE · LICENSE; complete licence text |
| 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 sourcesSourcesMAGICS-LAB/DNABERT_2 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 | Utilizing a deep learning model based on BERT for identifying enhancers and their strength Materials and methods/DNABERT2-enhancer model (paragraph 1); Materials and methods/DNABERT-2 model (paragraph 4) Version: journal full text in PMC | 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 ["DNA sequence windows","DNABERT2-Enhancer","Enhancer/non-enhancer labels and strong/weak enhancer classification"] Individual claims | Utilizing a deep learning model based on BERT for identifying enhancers and their strength Materials and methods/DNABERT2-enhancer model (paragraph 1); Materials and methods/DNABERT-2 model (paragraph 4) Version: journal full text in PMC | 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 | Utilizing a deep learning model based on BERT for identifying enhancers and their strength Materials and methods/DNABERT2-enhancer model (paragraph 1); Materials and methods/DNABERT-2 model (paragraph 4) Version: journal full text in PMC | 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 DNA sequence transformer; this record is the paper-specific evaluated configuration. Individual claims | MAGICS-LAB/DNABERT_2 README.md README.md model description Version: f25bed9ee20db966dff39e5c1571249d04e36404 | 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 DNABERT-2 initialises a BERT feature extractor. Transfer learning adapts it to enhancer data, and a convolutional network classifies the resulting sequence features. Individual claims | Utilizing a deep learning model based on BERT for identifying enhancers and their strength Materials and methods/DNABERT2-enhancer model (paragraph 1); Materials and methods/DNABERT-2 model (paragraph 4) Version: journal full text in PMC | 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 | MAGICS-LAB/DNABERT_2 README.md README.md; checkpoint/access documentation and licence scope Version: f25bed9ee20db966dff39e5c1571249d04e36404 | 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 DNA sequence windows Individual claims | Utilizing a deep learning model based on BERT for identifying enhancers and their strength Conclusion (paragraph 1); Discussion (paragraph 3) Version: journal full text in PMC | 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 Enhancer/non-enhancer labels and strong/weak enhancer classification Individual claims | Utilizing a deep learning model based on BERT for identifying enhancers and their strength Introduction (paragraph 7); Materials and methods/Benchmark dataset (paragraph 4) Version: journal full text in PMC | 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 | Utilizing a deep learning model based on BERT for identifying enhancers and their strength Materials and methods/Benchmark dataset; Materials and methods/DNABERT2-enhancer model; Materials and methods/DNABERT-2 model; Materials and methods/CNN model; Results/Comparison of the proposed model with existing methods; 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: journal full text in PMC | 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 | MAGICS-LAB/DNABERT_2 README.md Materials and methods/Benchmark dataset; Materials and methods/DNABERT2-enhancer model; Materials and methods/DNABERT-2 model; Materials and methods/CNN model; Results/Comparison of the proposed model with existing methods; 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: f25bed9ee20db966dff39e5c1571249d04e36404 | 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
3 source records and release history
- Utilizing a deep learning model based on BERT for identifying enhancers and their strength · Original source · journal full text in PMC
- MAGICS-LAB/DNABERT_2 README.md · Original source · f25bed9ee20db966dff39e5c1571249d04e36404
- MAGICS-LAB/DNABERT_2 LICENSE · Original source · f25bed9ee20db966dff39e5c1571249d04e36404
Technical metadata and extraction receipts
Stable ID: reported-model-d0e594ec3c0430
- areas
- dna-genomes
- entity level
- method
- version
- not stated in table
- reported name
- DNABERT2-Enhancer
- historical missing metadata
- 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 source-scoped entry preserves the method/configuration actually named in an evaluation. It is neither a global family identity nor proof of an immutable checkpoint; the linked evaluation retains adaptation, fitting and scoring details.; source ids: dnabert2-enhancer-2025; evidence-reported-base-dnabert2-readme-md; source locator: Materials and methods/DNABERT2-enhancer model (paragraph 1); Materials and methods/DNABERT-2 model (paragraph 4) | README.md model description | Abstract (paragraph 1); Results/Comparison of the proposed model with existing methods (paragraph 4); ambiguities: Configuration means the source-labelled evaluated identity. It does not establish missing checkpoint hashes, default settings or equivalence to same-named records in other papers.