Configuration

ENBED

ENBED is a byte-level encoder–decoder transformer for genomic sequence representation and sequence-to-sequence tasks.

Sources

Understanding the natural language of DNA using encoder–decoder foundation models with byte-level precision · 2 Methods/2.5 Application domains (paragraph 1); 5 Discussion (paragraph 1)

1 evaluation · 1 result

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

Understanding the natural language of DNA using encoder–decoder foundation models with byte-level precision · 1 Introduction/1.2 Our contributions (paragraph 1); 4 Ablation studies/4.1 Encoder–decoder architecture (paragraph 2)

Overview

Model type

Transformer representation pipeline; this record is the paper-specific evaluated configuration.

Sources

Biological inputs

DNA sequences at single-byte nucleotide resolution

Sources

Understanding the natural language of DNA using encoder–decoder foundation models with byte-level precision · 1 Introduction/1.2 Our contributions (paragraph 2); 5 Discussion (paragraph 1)

Outputs

Task-specific classifications or generated DNA sequences

Sources

Understanding the natural language of DNA using encoder–decoder foundation models with byte-level precision · 2 Methods/2.4 Applications of foundation models using transfer learning/2.4.2 Fine-tuning for downstream tasks (paragraph 1); 2 Methods/2.5 Application domains/2.5.1 Genomic benchmarks (paragraph 1)

Access

The authors provide implementation code at https://github.itap.purdue.edu/Clan-labs/ENBED and model weights through https://huggingface.co/malusare. A table-specific checkpoint hash is not supplied by these account-level links.

Sources

Understanding the natural language of DNA using encoder–decoder foundation models with byte-level precision · Data availability

limited source coverage · Automated source review, 2026-09-16. All specifications and missing details

Evaluations and results

1 evaluation · 1 result. Different protocols are not a single leaderboard.

Filter evaluations

Tested model or methodProtocolDatasetEvaluation setupMetricEvidence origin

Applied filters: All linked evaluations

Exact evaluated configurations and original reported results
Tested configuration	Protocol and dataset	Finding	Evidence and details
Configuration: ENBED	Task: Enhancer classification Dataset: Genomic Benchmarks Mouse Enhancers	90.3 Accuracy % · unknown Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source ENBED: Enhancer classification Reported Genomic Benchmarks classification accuracy. Aggregation: Not reported Understanding the natural language of DNA using encoder–decoder foundation models with byte-level precision · Table 2, Mouse Enhancers row, ENBED column

Source checking is not independent reproduction. Release 2026-09-29-06401fd5b220.

Use this model

How it works, versions and access

How it works

How the evaluated method works

Byte-level nucleotide inputs feed encoder and decoder transformer blocks with a subquadratic attention implementation. Masked-language pretraining precedes task-specific adaptation.

Sources

What was evaluated

The linked evaluation record identifies ENBED: Enhancer classification. Its dataset, split, adaptation and evidence origin remain attached to the reported results.

Sources

Understanding the natural language of DNA using encoder–decoder foundation models with byte-level precision · The named evaluation’s methods and comparison table; exact preserved evaluation IDs: evaluation-lit-003

Strengths, limitations and unresolved questions

Strengths and limitations

Strengths and considerations

Single-base input resolution retains substitutions and indels that can be obscured by multi-base token boundaries.
Sources
Understanding the natural language of DNA using encoder–decoder foundation models with byte-level precision · 1 Introduction/1.1 Limitations of previous work/1.1.2 Tokenization (paragraph 1); 3 Results/3.4 ENBED generates mutations using sequence-to-sequence transformation (paragraph 1)

Limitations and conditions

The evaluated downstream head and training corpus must be kept with each result; the GRCh38 configuration is not interchangeable with every ENBED model.
Sources
Understanding the natural language of DNA using encoder–decoder foundation models with byte-level precision · 2 Methods/2.4 Applications of foundation models using transfer learning/2.4.2 Fine-tuning for downstream tasks (paragraph 1); 3 Results/3.3 ENBED identifies biological function annotations (paragraph 1)

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-8db190bee6aae5

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	Transformer representation pipeline; this record is the paper-specific evaluated configuration. Sources Understanding the natural language of DNA using encoder–decoder foundation models with byte-level precision · 1 Introduction/1.2 Our contributions (paragraph 1); 4 Ablation studies/4.1 Encoder–decoder architecture (paragraph 2)
Architecture / procedure	Byte-level nucleotide inputs feed encoder and decoder transformer blocks with a subquadratic attention implementation. Masked-language pretraining precedes task-specific adaptation. Sources Understanding the natural language of DNA using encoder–decoder foundation models with byte-level precision · 1 Introduction/1.2 Our contributions (paragraph 1); 4 Ablation studies/4.1 Encoder–decoder architecture (paragraph 2)
Biological inputs	DNA sequences at single-byte nucleotide resolution Sources Understanding the natural language of DNA using encoder–decoder foundation models with byte-level precision · 1 Introduction/1.2 Our contributions (paragraph 2); 5 Discussion (paragraph 1)
Outputs	Task-specific classifications or generated DNA sequences Sources Understanding the natural language of DNA using encoder–decoder foundation models with byte-level precision · 2 Methods/2.4 Applications of foundation models using transfer learning/2.4.2 Fine-tuning for downstream tasks (paragraph 1); 2 Methods/2.5 Application domains/2.5.1 Genomic benchmarks (paragraph 1)
Parameters	1.2 billion trainable parameters in the full encoder–decoder model. Sources Understanding the natural language of DNA using encoder–decoder foundation models with byte-level precision · 4 Ablation studies (paragraph 1); 4 Ablation studies/4.1 Encoder–decoder architecture (paragraph 1)
Known versions / configuration	ENBED is the comparison-table label; that label does not specify an immutable weight revision. · Not reported in inspected sources Sources Understanding the natural language of DNA using encoder–decoder foundation models with byte-level precision · Model identification in the comparison table and corresponding Methods; immutable checkpoint revision is not supplied by the table label.
Training data / fitting	Reference-genome sequences; the GRCh38-labelled row is a distinct human-reference configuration. Sources Understanding the natural language of DNA using encoder–decoder foundation models with byte-level precision · 1 Introduction/1.2 Our contributions/1.2.1 Evaluation of performance on genomic benchmark datasets (paragraph 1); 3 Results/3.1 ENBED outperforms state-of-the-art models on GB datasets (paragraph 2)
Context limits	16,384 input/output tokens using local sliding-window plus global attention. The 512-token value in Methods describes the dense-attention hardware baseline, not ENBED’s final context. Sources Understanding the natural language of DNA using encoder–decoder foundation models with byte-level precision · 2 Methods/2.3 Attention (paragraph 2); 2 Methods/2.3 Attention/2.3.1 Sliding-window attention (paragraph 1)
Access	The authors provide implementation code at https://github.itap.purdue.edu/Clan-labs/ENBED and model weights through https://huggingface.co/malusare. A table-specific checkpoint hash is not supplied by these account-level links. Sources Understanding the natural language of DNA using encoder–decoder foundation models with byte-level precision · Data availability
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 Understanding the natural language of DNA using encoder–decoder foundation models with byte-level precision · Abstract (paragraph 1); 2 Methods/2.4 Applications of foundation models using transfer learning/2.4.1 Building the foundation model (paragraph 1)
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 Understanding the natural language of DNA using encoder–decoder foundation models with byte-level precision · Abstract (paragraph 1); 2 Methods/2.1 Encoder–decoder model architecture (paragraph 1)

Evidence

Source checking verifies the cited claim or transcription. It does not establish independent reproduction.

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.

19 evidence rows matching the loaded filters

Claims, original sources and review scope · Release 2026-09-29-06401fd5b220
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	Understanding the natural language of DNA using encoder–decoder foundation models with byte-level precision Original source ↗ 1 Introduction/1.2 Our contributions (paragraph 1); 4 Ablation studies/4.1 Encoder–decoder architecture (paragraph 2) Version: version of record Retrieved: 2026-09-16T10:33:35.378Z	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: `e95d4be70d32e61af5a92eda8ea66f25a2cc629e3f83e7b5241b13cde8bdb83b` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Diagram steps DNA sequences at single-byte nucleotide resolution ENBED Task-specific classifications or generated DNA sequences Individual claims	Understanding the natural language of DNA using encoder–decoder foundation models with byte-level precision Original source ↗ 1 Introduction/1.2 Our contributions (paragraph 1); 4 Ablation studies/4.1 Encoder–decoder architecture (paragraph 2) Version: version of record Retrieved: 2026-09-16T10:33:35.378Z	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: `e95d4be70d32e61af5a92eda8ea66f25a2cc629e3f83e7b5241b13cde8bdb83b` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Diagram title Evaluated procedure (conceptual) Individual claims	Understanding the natural language of DNA using encoder–decoder foundation models with byte-level precision Original source ↗ 1 Introduction/1.2 Our contributions (paragraph 1); 4 Ablation studies/4.1 Encoder–decoder architecture (paragraph 2) Version: version of record Retrieved: 2026-09-16T10:33:35.378Z	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: `e95d4be70d32e61af5a92eda8ea66f25a2cc629e3f83e7b5241b13cde8bdb83b` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Model type Transformer representation pipeline; this record is the paper-specific evaluated configuration. Individual claims	Understanding the natural language of DNA using encoder–decoder foundation models with byte-level precision Original source ↗ 1 Introduction/1.2 Our contributions (paragraph 1); 4 Ablation studies/4.1 Encoder–decoder architecture (paragraph 2) Version: version of record Retrieved: 2026-09-16T10:33:35.378Z	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: `e95d4be70d32e61af5a92eda8ea66f25a2cc629e3f83e7b5241b13cde8bdb83b` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Architecture / procedure Byte-level nucleotide inputs feed encoder and decoder transformer blocks with a subquadratic attention implementation. Masked-language pretraining precedes task-specific adaptation. Individual claims	Understanding the natural language of DNA using encoder–decoder foundation models with byte-level precision Original source ↗ 1 Introduction/1.2 Our contributions (paragraph 1); 4 Ablation studies/4.1 Encoder–decoder architecture (paragraph 2) Version: version of record Retrieved: 2026-09-16T10:33:35.378Z	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: `e95d4be70d32e61af5a92eda8ea66f25a2cc629e3f83e7b5241b13cde8bdb83b` 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	Understanding the natural language of DNA using encoder–decoder foundation models with byte-level precision Original source ↗ Abstract (paragraph 1); 2 Methods/2.1 Encoder–decoder model architecture (paragraph 1) Version: version of record Retrieved: 2026-09-16T10:33:35.378Z	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: `e95d4be70d32e61af5a92eda8ea66f25a2cc629e3f83e7b5241b13cde8bdb83b` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Biological inputs DNA sequences at single-byte nucleotide resolution Individual claims	Understanding the natural language of DNA using encoder–decoder foundation models with byte-level precision Original source ↗ 1 Introduction/1.2 Our contributions (paragraph 2); 5 Discussion (paragraph 1) Version: version of record Retrieved: 2026-09-16T10:33:35.378Z	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: `e95d4be70d32e61af5a92eda8ea66f25a2cc629e3f83e7b5241b13cde8bdb83b` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Outputs Task-specific classifications or generated DNA sequences Individual claims	Understanding the natural language of DNA using encoder–decoder foundation models with byte-level precision Original source ↗ 2 Methods/2.4 Applications of foundation models using transfer learning/2.4.2 Fine-tuning for downstream tasks (paragraph 1); 2 Methods/2.5 Application domains/2.5.1 Genomic benchmarks (paragraph 1) Version: version of record Retrieved: 2026-09-16T10:33:35.378Z	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: `e95d4be70d32e61af5a92eda8ea66f25a2cc629e3f83e7b5241b13cde8bdb83b` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Parameters 1.2 billion trainable parameters in the full encoder–decoder model. Individual claims	Understanding the natural language of DNA using encoder–decoder foundation models with byte-level precision Original source ↗ 4 Ablation studies (paragraph 1); 4 Ablation studies/4.1 Encoder–decoder architecture (paragraph 1) Version: version of record Retrieved: 2026-09-16T10:33:35.378Z	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.4.value` Source artifact SHA-256: `e95d4be70d32e61af5a92eda8ea66f25a2cc629e3f83e7b5241b13cde8bdb83b` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Known versions / configuration ENBED is the comparison-table label; that label does not specify an immutable weight revision. Individual claims	Understanding the natural language of DNA using encoder–decoder foundation models with byte-level precision Original source ↗ Model identification in the comparison table and corresponding Methods; immutable checkpoint revision is not supplied by the table label. Version: version of record Retrieved: 2026-09-16T10:33:35.378Z	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.5.value` Source artifact SHA-256: `e95d4be70d32e61af5a92eda8ea66f25a2cc629e3f83e7b5241b13cde8bdb83b` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact

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Release 2026-09-29-06401fd5b220 · Record review: needs review

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Understanding the natural language of DNA using encoder–decoder foundation models with byte-level precision · Original source · version of record

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Technical metadata and extraction receipts

Stable ID: reported-model-8db190bee6aae5

areas: dna-genomes
entity level: method
version: Not reported
reported name: ENBED
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: enbed-2024; source locator: 1 Introduction/1.2 Our contributions (paragraph 1); 4 Ablation studies/4.1 Encoder–decoder architecture (paragraph 2) | 2 Methods/2.5 Application domains (paragraph 1); 5 Discussion (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.

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model: ENBED: Enhancer classification

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