Configuration

position-aware CNN

PDCNN is a position-aware convolutional enhancer classifier built from nucleotide distribution features.

Sources

A deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding · STAR★Methods/Method details/A model of convolutional neural network based on position awareness of positively and negatively modified nucleotide classes (paragraph 4); STAR★Methods/Method details/A model of convolutional neural network based on position awareness of positively and negatively modified nucleotide classes (paragraph 1)

1 evaluation · 1 metric row

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

A deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding · STAR★Methods/Method details/The position-aware encoding of positive and negative modification classes of nucleotides (paragraph 1); STAR★Methods/Method details/A model of convolutional neural network based on position awareness of positively and negatively modified nucleotide classes (paragraph 1)

At a glance

Model type

Convolutional neural network; this record is the paper-specific evaluated configuration.

Sources

Biological inputs

DNA windows encoded as positional nucleotide-distribution matrices

Sources

A deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding · STAR★Methods/Method details/The position-aware encoding of positive and negative modification classes of nucleotides (paragraph 5); STAR★Methods/Method details/The position-aware encoding of positive and negative modification classes of nucleotides (paragraph 6)

Outputs

Enhancer classification

Sources

A deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding · STAR★Methods/Quantification and statistical analysis/Model performance evaluation metrics (paragraph 1); Results and discussion/Comparison with existing DNA enhancer predictors (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.

Evaluation setupMetricEvidence origin

Results grouped by the exact reported evaluation
Metric and finding	Coverage and uncertainty	Evidence
position-aware CNN: enhancer prediction Configuration: position-aware CNNTask: enhancer predictionDataset: human enhancer dataset 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.

How it works

How the evaluated method works

The POCD-ND encoding measures positional nucleotide patterns; a CNN with two convolutional and fully connected stages is trained with cross-entropy.

Sources

What was evaluated

The linked evaluation record identifies position-aware CNN: enhancer prediction. Its dataset, split, adaptation and evidence origin remain attached to the reported results.

Sources

A deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding · The named evaluation’s methods and comparison table; exact preserved evaluation IDs: evaluation-lit-b4-004

Strengths and limitations

Strengths and considerations

Provides a conventional learned comparator with explicit position-dependent features rather than foundation-model pretraining.
Sources
A deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding · STAR★Methods/Method details/The position-aware encoding of positive and negative modification classes of nucleotides (paragraph 6); Introduction (paragraph 5)

Limitations and conditions

Feature encoding, sequence length and k-mer selection are part of the fitted pipeline and must match the evaluated setting.
Sources
A deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding · STAR★Methods/Method details/The position-aware encoding of positive and negative modification classes of nucleotides (paragraph 9); STAR★Methods/Method details/A model of convolutional neural network based on position awareness of positively and negatively modified nucleotide classes (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-d2c81acf1c42c4

Specifications

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.

Inputs, outputs and configuration
Property	Description and evidence
Model type	Convolutional neural network; this record is the paper-specific evaluated configuration. Sources A deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding · STAR★Methods/Method details/The position-aware encoding of positive and negative modification classes of nucleotides (paragraph 1); STAR★Methods/Method details/A model of convolutional neural network based on position awareness of positively and negatively modified nucleotide classes (paragraph 1)
Architecture / procedure	The POCD-ND encoding measures positional nucleotide patterns; a CNN with two convolutional and fully connected stages is trained with cross-entropy. Sources A deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding · STAR★Methods/Method details/The position-aware encoding of positive and negative modification classes of nucleotides (paragraph 1); STAR★Methods/Method details/A model of convolutional neural network based on position awareness of positively and negatively modified nucleotide classes (paragraph 1)
Biological inputs	DNA windows encoded as positional nucleotide-distribution matrices Sources A deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding · STAR★Methods/Method details/The position-aware encoding of positive and negative modification classes of nucleotides (paragraph 5); STAR★Methods/Method details/The position-aware encoding of positive and negative modification classes of nucleotides (paragraph 6)
Outputs	Enhancer classification Sources A deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding · STAR★Methods/Quantification and statistical analysis/Model performance evaluation metrics (paragraph 1); Results and discussion/Comparison with existing DNA enhancer predictors (paragraph 2)
Parameters	An aggregate parameter total for this exact evaluated configuration is not established by the inspected sources. · Not reported in inspected sources Sources (2) A deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding; xing1999/PDCNN README.md · Results and discussion/Impact of different feature encoding methods on model performance; Results and discussion/Comparison with classical machine learning methods; STAR★Methods/Resource availability/Lead contact; STAR★Methods/Resource availability/Materials availability; STAR★Methods/Method details/Datasets; STAR★Methods/Method details/The position-aware encoding of positive and negative modification classes of nucleotides; STAR★Methods/Method details/A model of convolutional neural network based on position awareness of positively and negatively modified nucleotide classes; STAR★Methods/Quantification and statistical analysis/Model performance evaluation metrics; inspected for aggregate parameter count (component sizes are not added without an exact configuration); README.md at pinned repository revision
Known versions / configuration	position-aware CNN is the comparison-table label; that label does not specify an immutable weight revision. · Not reported in inspected sources Sources A deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding · Model identification in the comparison table and corresponding Methods; immutable checkpoint revision is not supplied by the table label.
Training data / fitting	Human and mouse enhancer datasets described in the paper; sequence lengths and k-mer settings are tested separately. Sources A deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding · STAR★Methods/Method details/Datasets (paragraph 1); Results and discussion/Impact of different feature encoding methods on model performance (paragraph 1)
Context limits	The study selects 200-bp windows after comparing 50,100,150,200,250 and 300-bp inputs. Sources A deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding · Results and discussion/Comparison with classical machine learning methods (paragraph 2); STAR★Methods/Method details/The position-aware encoding of positive and negative modification classes of nucleotides (paragraph 5)
Access	Official study implementation and usage documentation: https://github.com/xing1999/PDCNN/blob/ff302344eb1a03bca4802408d03651cb4d2abc99/README.md. This pinned documentation revision is not automatically the evaluated weight revision. Sources xing1999/PDCNN 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 sources Sources xing1999/PDCNN 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 sources Sources xing1999/PDCNN 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.

20 evidence rows matching the loaded filters

Claims, original sources and review scope · Release 2026-09-17-d277315f7d76
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	A deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding Original source ↗ STAR★Methods/Method details/The position-aware encoding of positive and negative modification classes of nucleotides (paragraph 1); STAR★Methods/Method details/A model of convolutional neural network based on position awareness of positively and negatively modified nucleotide classes (paragraph 1) Version: version of record Retrieved: 2026-09-16T10:41:06Z	source checked automated source review · 2026-09-16 Audit 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. Field: `attributes.profile.diagram.caption` Source artifact SHA-256: `0183b6a111b1b02344cad35a571a1fd2c56257e406c5be1df69f7902c5d06749` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Diagram steps ["DNA windows encoded as positional nucleotide-distribution matrices","position-aware CNN","Enhancer classification"] Individual claims	A deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding Original source ↗ STAR★Methods/Method details/The position-aware encoding of positive and negative modification classes of nucleotides (paragraph 1); STAR★Methods/Method details/A model of convolutional neural network based on position awareness of positively and negatively modified nucleotide classes (paragraph 1) Version: version of record Retrieved: 2026-09-16T10:41:06Z	source checked automated source review · 2026-09-16 Audit 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. Field: `attributes.profile.diagram.steps` Source artifact SHA-256: `0183b6a111b1b02344cad35a571a1fd2c56257e406c5be1df69f7902c5d06749` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Diagram title Evaluated procedure (conceptual) Individual claims	A deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding Original source ↗ STAR★Methods/Method details/The position-aware encoding of positive and negative modification classes of nucleotides (paragraph 1); STAR★Methods/Method details/A model of convolutional neural network based on position awareness of positively and negatively modified nucleotide classes (paragraph 1) Version: version of record Retrieved: 2026-09-16T10:41:06Z	source checked automated source review · 2026-09-16 Audit 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. Field: `attributes.profile.diagram.title` Source artifact SHA-256: `0183b6a111b1b02344cad35a571a1fd2c56257e406c5be1df69f7902c5d06749` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Model type Convolutional neural network; this record is the paper-specific evaluated configuration. Individual claims	A deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding Original source ↗ STAR★Methods/Method details/The position-aware encoding of positive and negative modification classes of nucleotides (paragraph 1); STAR★Methods/Method details/A model of convolutional neural network based on position awareness of positively and negatively modified nucleotide classes (paragraph 1) Version: version of record Retrieved: 2026-09-16T10:41:06Z	source checked automated source review · 2026-09-16 Audit 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. Field: `attributes.profile.facts.0.value` Source artifact SHA-256: `0183b6a111b1b02344cad35a571a1fd2c56257e406c5be1df69f7902c5d06749` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Architecture / procedure The POCD-ND encoding measures positional nucleotide patterns; a CNN with two convolutional and fully connected stages is trained with cross-entropy. Individual claims	A deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding Original source ↗ STAR★Methods/Method details/The position-aware encoding of positive and negative modification classes of nucleotides (paragraph 1); STAR★Methods/Method details/A model of convolutional neural network based on position awareness of positively and negatively modified nucleotide classes (paragraph 1) Version: version of record Retrieved: 2026-09-16T10:41:06Z	source checked automated source review · 2026-09-16 Audit 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. Field: `attributes.profile.facts.1.value` Source artifact SHA-256: `0183b6a111b1b02344cad35a571a1fd2c56257e406c5be1df69f7902c5d06749` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
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	xing1999/PDCNN README.md Original source ↗ README.md; checkpoint/access documentation and licence scope Version: ff302344eb1a03bca4802408d03651cb4d2abc99 Retrieved: 2026-09-16T19:54:14.982961+00:00	unreported automated source review · 2026-09-16 Audit 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. Field: `attributes.profile.facts.10.value` Source artifact SHA-256: `77382e4a76f669866bb8e81551aba59b5993a1ff816c9b530a823940e6db1992` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Biological inputs DNA windows encoded as positional nucleotide-distribution matrices Individual claims	A deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding Original source ↗ STAR★Methods/Method details/The position-aware encoding of positive and negative modification classes of nucleotides (paragraph 5); STAR★Methods/Method details/The position-aware encoding of positive and negative modification classes of nucleotides (paragraph 6) Version: version of record Retrieved: 2026-09-16T10:41:06Z	source checked automated source review · 2026-09-16 Audit 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. Field: `attributes.profile.facts.2.value` Source artifact SHA-256: `0183b6a111b1b02344cad35a571a1fd2c56257e406c5be1df69f7902c5d06749` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Outputs Enhancer classification Individual claims	A deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding Original source ↗ STAR★Methods/Quantification and statistical analysis/Model performance evaluation metrics (paragraph 1); Results and discussion/Comparison with existing DNA enhancer predictors (paragraph 2) Version: version of record Retrieved: 2026-09-16T10:41:06Z	source checked automated source review · 2026-09-16 Audit 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. Field: `attributes.profile.facts.3.value` Source artifact SHA-256: `0183b6a111b1b02344cad35a571a1fd2c56257e406c5be1df69f7902c5d06749` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Parameters An aggregate parameter total for this exact evaluated configuration is not established by the inspected sources. Individual claims	A deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding Original source ↗ Results and discussion/Impact of different feature encoding methods on model performance; Results and discussion/Comparison with classical machine learning methods; STAR★Methods/Resource availability/Lead contact; STAR★Methods/Resource availability/Materials availability; STAR★Methods/Method details/Datasets; STAR★Methods/Method details/The position-aware encoding of positive and negative modification classes of nucleotides; STAR★Methods/Method details/A model of convolutional neural network based on position awareness of positively and negatively modified nucleotide classes; STAR★Methods/Quantification and statistical analysis/Model performance evaluation metrics; 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 Retrieved: 2026-09-16T10:41:06Z	unreported automated source review · 2026-09-16 Audit 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. Field: `attributes.profile.facts.4.value` Source artifact SHA-256: `0183b6a111b1b02344cad35a571a1fd2c56257e406c5be1df69f7902c5d06749` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Parameters An aggregate parameter total for this exact evaluated configuration is not established by the inspected sources. Individual claims	xing1999/PDCNN README.md Original source ↗ Results and discussion/Impact of different feature encoding methods on model performance; Results and discussion/Comparison with classical machine learning methods; STAR★Methods/Resource availability/Lead contact; STAR★Methods/Resource availability/Materials availability; STAR★Methods/Method details/Datasets; STAR★Methods/Method details/The position-aware encoding of positive and negative modification classes of nucleotides; STAR★Methods/Method details/A model of convolutional neural network based on position awareness of positively and negatively modified nucleotide classes; STAR★Methods/Quantification and statistical analysis/Model performance evaluation metrics; 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: ff302344eb1a03bca4802408d03651cb4d2abc99 Retrieved: 2026-09-16T19:54:14.982961+00:00	unreported automated source review · 2026-09-16 Audit 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. Field: `attributes.profile.facts.4.value` Source artifact SHA-256: `77382e4a76f669866bb8e81551aba59b5993a1ff816c9b530a823940e6db1992` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact

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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
xing1999/PDCNN README.md · Original source · ff302344eb1a03bca4802408d03651cb4d2abc99

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Technical metadata and extraction receipts

Stable ID: reported-model-d2c81acf1c42c4

areas: dna-genomes
entity level: method
version: Not reported
reported name: position-aware CNN
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 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: enhancer-position-encoding-2024; source locator: STAR★Methods/Method details/The position-aware encoding of positive and negative modification classes of nucleotides (paragraph 1); STAR★Methods/Method details/A model of convolutional neural network based on position awareness of positively and negatively modified nucleotide classes (paragraph 1) | STAR★Methods/Method details/A model of convolutional neural network based on position awareness of positively and negatively modified nucleotide classes (paragraph 4); STAR★Methods/Method details/A model of convolutional neural network based on position awareness of positively and negatively modified nucleotide classes (paragraph 1); 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.

Related records

model: position-aware CNN: enhancer prediction

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