Strengths and considerations
No source-reviewed explanatory claims are recorded here yet.
Task
enhancer prediction
Enhancer classification uses species-specific VISTA sequence collections with separate validation and test partitions.
Sources
A deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding · Methods: Datasets; Model performance evaluation metrics; cached text lines 60–62, 87–89; 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 | Human hg19 and mouse mm9 positive/negative VISTA enhancer sequences.SourcesA deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding · Methods: Datasets; Model performance evaluation metrics; cached text lines 60–62, 87–89; matching task comparison table/ablation captions |
| Splits | Each species dataset is divided into training, validation and testing in an 80:10:10 ratio.SourcesA deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding · Methods: Datasets; Model performance evaluation metrics; cached text lines 60–62, 87–89; matching task comparison table/ablation captions |
| Metrics | Sensitivity, specificity, accuracy, MCC and ROC-AUC.SourcesA deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding · Methods: Datasets; Model performance evaluation metrics; cached text lines 60–62, 87–89; matching task comparison table/ablation captions |
| Baselines | CNN, random forest, logistic regression, KNN, SVM and XGBoost under the compared feature encodings.SourcesA deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding · Methods: Datasets; Model performance evaluation metrics; cached text lines 60–62, 87–89; matching task comparison table/ablation captions |
| Leakage controls | The authors report CD-HIT-based removal of highly similar sequences before splitting.SourcesA deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding · Methods: Datasets; Model performance evaluation metrics; cached text lines 60–62, 87–89; 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 sourcesSourcesA deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding · Methods: Datasets; Model performance evaluation metrics; cached text lines 60–62, 87–89; matching task comparison table/ablation captions |
| Entity type | Paper-specific computational evaluation protocol.SourcesA deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding · Methods: Datasets; Model performance evaluation metrics; cached text lines 60–62, 87–89; matching task comparison table/ablation captions |
| Organisms | Human hg19 and mouse mm9.SourcesA deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding · Methods: Datasets; Model performance evaluation metrics; cached text lines 60–62, 87–89; matching task comparison table/ablation captions |
| Assays | VISTA enhancer annotations.SourcesA deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding · Methods: Datasets; Model performance evaluation metrics; cached text lines 60–62, 87–89; matching task comparison table/ablation captions |
| Allowed inputs | DNA enhancer and negative sequences.SourcesA deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding · Methods: Datasets; Model performance evaluation metrics; cached text lines 60–62, 87–89; matching task comparison table/ablation captions |
| Adaptation | Supervised classification with separate train/validation/test partitions for each species.SourcesA deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding · Methods: Datasets; Model performance evaluation metrics; cached text lines 60–62, 87–89; 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
A deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding · Methods: Datasets; Model performance evaluation metrics; cached text lines 60–62, 87–89; matching task comparison table/ablation captionsEvaluation methodology
Human hg19 and mouse mm9 positive/negative VISTA enhancer sequences. Each species dataset is divided into training, validation and testing in an 80:10:10 ratio. Sensitivity, specificity, accuracy, MCC and ROC-AUC. CNN, random forest, logistic regression, KNN, SVM and XGBoost under the compared feature encodings. The authors report CD-HIT-based removal of highly similar sequences before splitting. 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
A deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding · Methods: Datasets; Model performance evaluation metrics; cached text lines 60–62, 87–89; 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 |
|---|---|---|
| position-aware CNN: enhancer prediction Nucleotide position-aware feature encoding; average assessment of CNN classifier Author-reported evaluation · Evaluation metadata: needs review | ||
| 0.94 AUROC Unit: fraction · Direction: unknown | Uncertainty: not reported in legacy extract Scored: Not reported · Eligible: Not reported | source checkedA deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding · Table 2, Human section, CNN row, AUC column Source checking is not independent reproduction. |
Papers and result coverage
Last literature check: 2026-09-17. Primary-paper discovery and source inspection. Source-checked results are not independently reproduced experiments.
| Paper or primary resource | Version | Reference |
|---|---|---|
| A deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding | version of record | Read source DOI: 10.1016/j.isci.2024.110030 |
What is still missing
- Published Fig. 5/6 advanced-model comparisons require figure/source-data review; do not pretend Table 3 gives scores.
- Avoid mixing human and mouse populations or treating source-level similarity filtering as an exact split manifest.
Search and extraction details
primary comparison table screened
Searches
- "PMC11167433"
Evidence locations
- Table 2
- STAR Methods: Datasets
- Results and discussion
Strengths and limitations
Limitations and conditions
- The exact identity-filter implementation, comparator settings remain unextracted; sequence filtering alone does not establish chromosome-held-out evaluation.
Sources
A deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding · Methods: Datasets; Model performance evaluation metrics; cached text lines 60–62, 87–89; matching task comparison table/ablation captions
Profile review details
Task-specific computational methodology and field context checked in the cited primary-source artifact. Source-backed fields, inapplicable evaluator dimensions and unresolved details are distinguished. Numerical results were not reproduced.
Stable record: reported-task-64607443a9ba15Evidence 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 | A deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding Methods: Datasets; Model performance evaluation metrics; cached text lines 60–62, 87–89; matching task comparison table/ablation captions Version: version of record | source checked automated source review · 2026-09-16 Audit detailsTask-specific computational methodology and field context checked in the cited primary-source artifact. Source-backed fields, inapplicable evaluator dimensions and unresolved details are distinguished. Numerical results were not reproduced. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Diagram steps ["Input: DNA enhancer and negative sequences.","Evaluation: Supervised classification with separate train/validation/test partitions for each species.","Readout: Sensitivity, specificity, accuracy, MCC and ROC-AUC."] Individual claims | A deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding Methods: Datasets; Model performance evaluation metrics; cached text lines 60–62, 87–89; matching task comparison table/ablation captions Version: version of record | source checked automated source review · 2026-09-16 Audit detailsTask-specific computational methodology and field context checked in the cited primary-source artifact. Source-backed fields, inapplicable evaluator dimensions and unresolved details are distinguished. Numerical results were not reproduced. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Diagram title Computational evaluation flow Individual claims | A deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding Methods: Datasets; Model performance evaluation metrics; cached text lines 60–62, 87–89; matching task comparison table/ablation captions Version: version of record | source checked automated source review · 2026-09-16 Audit detailsTask-specific computational methodology and field context checked in the cited primary-source artifact. Source-backed fields, inapplicable evaluator dimensions and unresolved details are distinguished. Numerical results were not reproduced. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Datasets Human hg19 and mouse mm9 positive/negative VISTA enhancer sequences. Individual claims | A deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding Methods: Datasets; Model performance evaluation metrics; cached text lines 60–62, 87–89; matching task comparison table/ablation captions Version: version of record | source checked automated source review · 2026-09-16 Audit detailsTask-specific computational methodology and field context checked in the cited primary-source artifact. Source-backed fields, inapplicable evaluator dimensions and unresolved details are distinguished. Numerical results were not reproduced. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Splits Each species dataset is divided into training, validation and testing in an 80:10:10 ratio. Individual claims | A deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding Methods: Datasets; Model performance evaluation metrics; cached text lines 60–62, 87–89; matching task comparison table/ablation captions Version: version of record | source checked automated source review · 2026-09-16 Audit detailsTask-specific computational methodology and field context checked in the cited primary-source artifact. Source-backed fields, inapplicable evaluator dimensions and unresolved details are distinguished. Numerical results were not reproduced. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Adaptation Supervised classification with separate train/validation/test partitions for each species. Individual claims | A deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding Methods: Datasets; Model performance evaluation metrics; cached text lines 60–62, 87–89; matching task comparison table/ablation captions Version: version of record | source checked automated source review · 2026-09-16 Audit detailsTask-specific computational methodology and field context checked in the cited primary-source artifact. Source-backed fields, inapplicable evaluator dimensions and unresolved details are distinguished. Numerical results were not reproduced. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Metrics Sensitivity, specificity, accuracy, MCC and ROC-AUC. Individual claims | A deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding Methods: Datasets; Model performance evaluation metrics; cached text lines 60–62, 87–89; matching task comparison table/ablation captions Version: version of record | source checked automated source review · 2026-09-16 Audit detailsTask-specific computational methodology and field context checked in the cited primary-source artifact. Source-backed fields, inapplicable evaluator dimensions and unresolved details are distinguished. Numerical results were not reproduced. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Baselines CNN, random forest, logistic regression, KNN, SVM and XGBoost under the compared feature encodings. Individual claims | A deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding Methods: Datasets; Model performance evaluation metrics; cached text lines 60–62, 87–89; matching task comparison table/ablation captions Version: version of record | source checked automated source review · 2026-09-16 Audit detailsTask-specific computational methodology and field context checked in the cited primary-source artifact. Source-backed fields, inapplicable evaluator dimensions and unresolved details are distinguished. Numerical results were not reproduced. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Leakage controls The authors report CD-HIT-based removal of highly similar sequences before splitting. Individual claims | A deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding Methods: Datasets; Model performance evaluation metrics; cached text lines 60–62, 87–89; matching task comparison table/ablation captions Version: version of record | source checked automated source review · 2026-09-16 Audit detailsTask-specific computational methodology and field context checked in the cited primary-source artifact. Source-backed fields, inapplicable evaluator dimensions and unresolved details are distinguished. Numerical results were not reproduced. 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 | A deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding Methods: Datasets; Model performance evaluation metrics; cached text lines 60–62, 87–89; matching task comparison table/ablation captions Version: version of record | unreported automated source review · 2026-09-16 Audit detailsTask-specific computational methodology and field context checked in the cited primary-source artifact. Source-backed fields, inapplicable evaluator dimensions and unresolved details are distinguished. Numerical results were not reproduced. 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
- A deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding · Original source · version of record
- A deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding · Original source · version of record
Technical metadata and extraction receipts
Stable ID: reported-task-64607443a9ba15
- areas
- dna-genomes
- tasks
- enhancer prediction
- entity level
- task
- version
- Not reported
- task
- enhancer prediction
- scope note
- Paper-specific evaluation task; protocol completeness requires further extraction.
- benchmark research
- review date: 2026-09-17; status: primary_comparison_table_screened; primary sources: expansion-p3-enhancer-position-encoding-2024; inspected locators: Table 2; STAR Methods: Datasets; Results and discussion; searched queries: "PMC11167433"; gaps: Published Fig. 5/6 advanced-model comparisons require figure/source-data review; do not pretend Table 3 gives scores.; Avoid mixing human and mouse populations or treating source-level similarity filtering as an exact split manifest.; claim scope: Primary-paper discovery and source inspection. Source-checked results are not independently reproduced experiments.
- 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: enhancer-position-encoding-2024; source locator: Methods: Datasets; Model performance evaluation metrics; cached text lines 60–62, 87–89; 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.
Related records
- dataset: human enhancer dataset
- benchmark: position-aware CNN: enhancer prediction