This paper-specific evaluation tests enhancer recognition using Liu training dataset.
Explanatory profile: limited source coverage · Automated source review, 2026-09-16. This does not change the review status of its results.
| Property | Description and evidence |
|---|---|
| Record type | Paper-specific task; protocol incompletely extractedUtilizing a deep learning model based on BERT for identifying enhancers and their strength · Table 4, first-layer DNABERT2-Enhancer row, AUC column |
| Inputs | Liu training datasetUtilizing a deep learning model based on BERT for identifying enhancers and their strength · Table 4, first-layer DNABERT2-Enhancer row, AUC column |
| Assessment | AUCUtilizing a deep learning model based on BERT for identifying enhancers and their strength · Table 4, first-layer DNABERT2-Enhancer row, AUC column |
| Recorded split or evaluation setting | 5-fold cross-validationUtilizing a deep learning model based on BERT for identifying enhancers and their strength · Table 4, first-layer DNABERT2-Enhancer row, AUC column |
| Datasets | Not extracted or verified for this record. |
| Organisms | Not extracted or verified for this record. |
| Assays | Not extracted or verified for this record. |
| Adaptation | Not extracted or verified for this record. |
| Baselines | Not extracted or verified for this record. |
Outline of the existing paper extraction. Split membership, fitting details and scorer implementation remain incompletely reviewed.
The existing paper extraction describes: first-layer enhancer versus non-enhancer classifier. This description is retained with the exact evaluation records; it is not a new protocol reconstruction.
Utilizing a deep learning model based on BERT for identifying enhancers and their strength · Table 4, first-layer DNABERT2-Enhancer row, AUC columnRelease 2026-09-16-d74d282221a9 · 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. |
Catalogue extraction inspected; protocol claims remain limited to the cited evidence. Missing details are not presumed absent from the original paper.
Stable record: reported-task-86a628af87ff8fRelease 2026-09-16-d74d282221a9 · Record review: needs review
Stable ID: reported-task-86a628af87ff8f