Strengths and considerations
No source-reviewed explanatory claims are recorded here yet.
Task
regulatory sequence classification
Regulatory-sequence classification compares genomic models and tokenizers across established benchmark collections.
Sources
The impact of tokenizer selection in genomic language models · Methods §2.2 Benchmarks; Results §3.1; cached text lines 20–21, 43–44At 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 | Nucleotide Transformer tasks, Genomic Benchmarks and GUE.SourcesThe impact of tokenizer selection in genomic language models · Methods §2.2 Benchmarks; Results §3.1; cached text lines 20–21, 43–44 |
| Splits | The experiment reuses three published fine-tuning benchmark collections and repeats every task at least ten times. The broad catalogue label spans multiple task partitions; no single regulatory-element split is defined by that label.SourcesThe impact of tokenizer selection in genomic language models · §2.2 Benchmarks |
| Metrics | Mean accuracy and Matthews correlation coefficient are reported per fine-tuning task; multiclass tasks use macro averaging. Results remain task-specific across the three benchmark collections.SourcesThe impact of tokenizer selection in genomic language models · §2.2–2.3 Benchmarks and Metrics |
| Baselines | Attention-based and state-space genomic language models with different tokenizer choices.SourcesThe impact of tokenizer selection in genomic language models · Methods §2.2 Benchmarks; Results §3.1; cached text lines 20–21, 43–44 |
| Leakage controls | The benchmark reuses GB, NTTv2 and GUE task datasets and compares models pretrained on different corpora, including the human reference genome. Its main Methods do not report a unified overlap-removal audit between all task examples and every model’s pretraining corpus. · Not reported in inspected sourcesSourcesThe impact of tokenizer selection in genomic language models · §2 Materials and methods, model/pretraining and task-dataset descriptions; Table 1 |
| Uncertainty | Each fine-tuning task is replicated at least ten times; hyperparameter search differs by model family.SourcesThe impact of tokenizer selection in genomic language models · Methods §2.2 Benchmarks; Results §3.1; cached text lines 20–21, 43–44 |
| Entity type | Paper-specific computational evaluation protocol.SourcesThe impact of tokenizer selection in genomic language models · Methods §2.2 Benchmarks; Results §3.1; cached text lines 20–21, 43–44 |
| Organisms | Dataset-specific organisms in Nucleotide Transformer, Genomic Benchmarks and GUE.SourcesThe impact of tokenizer selection in genomic language models · Methods §2.2 Benchmarks; Results §3.1; cached text lines 20–21, 43–44 |
| Assays | Regulatory and other genomic classification labels.SourcesThe impact of tokenizer selection in genomic language models · Methods §2.2 Benchmarks; Results §3.1; cached text lines 20–21, 43–44 |
| Allowed inputs | Tokenized DNA sequence.SourcesThe impact of tokenizer selection in genomic language models · Methods §2.2 Benchmarks; Results §3.1; cached text lines 20–21, 43–44 |
| Adaptation | Repeated task fine-tuning compares tokenizer/model configurations.SourcesThe impact of tokenizer selection in genomic language models · Methods §2.2 Benchmarks; Results §3.1; cached text lines 20–21, 43–44 |
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
The impact of tokenizer selection in genomic language models · Methods §2.2 Benchmarks; Results §3.1; cached text lines 20–21, 43–44; Methods §2.2 Benchmarks; Results §3.1; cached text lines 20–21, 43–44; §2.2–2.3 Benchmarks and MetricsEvaluation methodology
Nucleotide Transformer tasks, Genomic Benchmarks and GUE. Mean accuracy and Matthews correlation coefficient are reported per fine-tuning task; multiclass tasks use macro averaging. Results remain task-specific across the three benchmark collections. Attention-based and state-space genomic language models with different tokenizer choices.
Sources
The impact of tokenizer selection in genomic language models · Methods §2.2 Benchmarks; Results §3.1; cached text lines 20–21, 43–44; §2.2–2.3 Benchmarks and Metrics; Methods §2.2 Benchmarks; Results §3.1; cached text lines 20–21, 43–44Recorded 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 |
|---|---|---|
| Caduceus (character tokens): regulatory sequence classification Configuration: Caduceus (character tokens)Task: regulatory sequence classificationDataset: genomic benchmark categories task-category MCC across benchmark datasets Independent external evaluation · Evaluation metadata: needs review | ||
| 0.778 MCC Unit: unitless · Direction: unknown | Uncertainty: not reported in legacy extract Scored: Not reported · Eligible: Not reported | source checkedThe impact of tokenizer selection in genomic language models · Table 2, Regulatory row, Caduceus (char) MCC 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 |
|---|---|---|
| The impact of tokenizer selection in genomic language models | journal full text in PMC | Read source DOI: 10.1093/bioinformatics/btaf456 |
What is still missing
- Models differ in architecture, size and pretraining as well as tokenizer, so Table 2 alone does not isolate tokenizer causality.
- Table 3 offers paired tokenizer comparisons; do not relabel whole-family comparisons as controlled tokenizer ablations.
- No per-cell uncertainty in Table 2.
Search and extraction details
primary comparison table screened
Searches
- "PMC12453675"
Evidence locations
- Tables 1–3
- Benchmark experimental setup
Strengths and limitations
Limitations and conditions
- Different hyperparameter-tuning procedures can affect the comparison. The broad catalogue label does not uniquely identify one dataset or executable protocol.
Sources
The impact of tokenizer selection in genomic language models · Methods §2.2 Benchmarks; Results §3.1; cached text lines 20–21, 43–44
Profile review details
Relevant full-paper computational evaluation sections, tables/captions and cited supplementary task passages were reviewed. Reporting omissions are scoped to the inspected sources. Original numerical results are unchanged.
Stable record: reported-task-cd127e56fb1f04Evidence 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 | The impact of tokenizer selection in genomic language models Methods §2.2 Benchmarks; Results §3.1; cached text lines 20–21, 43–44; Methods §2.2 Benchmarks; Results §3.1; cached text lines 20–21, 43–44; §2.2–2.3 Benchmarks and Metrics Version: journal full text in PMC | source checked automated source review · 2026-09-16 Audit detailsRelevant full-paper computational evaluation sections, tables/captions and cited supplementary task passages were reviewed. Reporting omissions are scoped to the inspected sources. Original numerical results are unchanged. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Diagram steps ["Allowed inputs: Tokenized DNA sequence.","Datasets: Nucleotide Transformer tasks, Genomic Benchmarks and GUE.","Metrics: Mean accuracy and Matthews correlation coefficient are reported per fine-tuning task; multiclass tasks use macro averaging. Results remain task-specific across the three benchmark collections."] Individual claims | The impact of tokenizer selection in genomic language models Methods §2.2 Benchmarks; Results §3.1; cached text lines 20–21, 43–44; Methods §2.2 Benchmarks; Results §3.1; cached text lines 20–21, 43–44; §2.2–2.3 Benchmarks and Metrics Version: journal full text in PMC | source checked automated source review · 2026-09-16 Audit detailsRelevant full-paper computational evaluation sections, tables/captions and cited supplementary task passages were reviewed. Reporting omissions are scoped to the inspected sources. Original numerical results are unchanged. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Diagram title Computational evaluation flow Individual claims | The impact of tokenizer selection in genomic language models Methods §2.2 Benchmarks; Results §3.1; cached text lines 20–21, 43–44; Methods §2.2 Benchmarks; Results §3.1; cached text lines 20–21, 43–44; §2.2–2.3 Benchmarks and Metrics Version: journal full text in PMC | source checked automated source review · 2026-09-16 Audit detailsRelevant full-paper computational evaluation sections, tables/captions and cited supplementary task passages were reviewed. Reporting omissions are scoped to the inspected sources. Original numerical results are unchanged. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Datasets Nucleotide Transformer tasks, Genomic Benchmarks and GUE. Individual claims | The impact of tokenizer selection in genomic language models Methods §2.2 Benchmarks; Results §3.1; cached text lines 20–21, 43–44 Version: journal full text in PMC | source checked automated source review · 2026-09-16 Audit detailsRelevant full-paper computational evaluation sections, tables/captions and cited supplementary task passages were reviewed. Reporting omissions are scoped to the inspected sources. Original numerical results are unchanged. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Splits The experiment reuses three published fine-tuning benchmark collections and repeats every task at least ten times. The broad catalogue label spans multiple task partitions; no single regulatory-element split is defined by that label. Individual claims | The impact of tokenizer selection in genomic language models §2.2 Benchmarks Version: journal full text in PMC | source checked automated source review · 2026-09-16 Audit detailsRelevant full-paper computational evaluation sections, tables/captions and cited supplementary task passages were reviewed. Reporting omissions are scoped to the inspected sources. Original numerical results are unchanged. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Adaptation Repeated task fine-tuning compares tokenizer/model configurations. Individual claims | The impact of tokenizer selection in genomic language models Methods §2.2 Benchmarks; Results §3.1; cached text lines 20–21, 43–44 Version: journal full text in PMC | source checked automated source review · 2026-09-16 Audit detailsRelevant full-paper computational evaluation sections, tables/captions and cited supplementary task passages were reviewed. Reporting omissions are scoped to the inspected sources. Original numerical results are unchanged. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Metrics Mean accuracy and Matthews correlation coefficient are reported per fine-tuning task; multiclass tasks use macro averaging. Results remain task-specific across the three benchmark collections. Individual claims | The impact of tokenizer selection in genomic language models §2.2–2.3 Benchmarks and Metrics Version: journal full text in PMC | source checked automated source review · 2026-09-16 Audit detailsRelevant full-paper computational evaluation sections, tables/captions and cited supplementary task passages were reviewed. Reporting omissions are scoped to the inspected sources. Original numerical results are unchanged. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Baselines Attention-based and state-space genomic language models with different tokenizer choices. Individual claims | The impact of tokenizer selection in genomic language models Methods §2.2 Benchmarks; Results §3.1; cached text lines 20–21, 43–44 Version: journal full text in PMC | source checked automated source review · 2026-09-16 Audit detailsRelevant full-paper computational evaluation sections, tables/captions and cited supplementary task passages were reviewed. Reporting omissions are scoped to the inspected sources. Original numerical results are unchanged. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Leakage controls The benchmark reuses GB, NTTv2 and GUE task datasets and compares models pretrained on different corpora, including the human reference genome. Its main Methods do not report a unified overlap-removal audit between all task examples and every model’s pretraining corpus. Individual claims | The impact of tokenizer selection in genomic language models §2 Materials and methods, model/pretraining and task-dataset descriptions; Table 1 Version: journal full text in PMC | unreported automated source review · 2026-09-16 Audit detailsRelevant full-paper computational evaluation sections, tables/captions and cited supplementary task passages were reviewed. Reporting omissions are scoped to the inspected sources. Original numerical results are unchanged. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Uncertainty Each fine-tuning task is replicated at least ten times; hyperparameter search differs by model family. Individual claims | The impact of tokenizer selection in genomic language models Methods §2.2 Benchmarks; Results §3.1; cached text lines 20–21, 43–44 Version: journal full text in PMC | source checked automated source review · 2026-09-16 Audit detailsRelevant full-paper computational evaluation sections, tables/captions and cited supplementary task passages were reviewed. Reporting omissions are scoped to the inspected sources. Original numerical results are unchanged. 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
- The impact of tokenizer selection in genomic language models · Original source · journal full text in PMC
- The impact of tokenizer selection in genomic language models · Original source · journal full text in PMC
Technical metadata and extraction receipts
Stable ID: reported-task-cd127e56fb1f04
- areas
- dna-genomes
- tasks
- regulatory sequence classification
- entity level
- task
- version
- Not reported
- task
- regulatory sequence classification
- 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-genomic-tokenizer-selection-2025; inspected locators: Tables 1–3; Benchmark experimental setup; searched queries: "PMC12453675"; gaps: Models differ in architecture, size and pretraining as well as tokenizer, so Table 2 alone does not isolate tokenizer causality.; Table 3 offers paired tokenizer comparisons; do not relabel whole-family comparisons as controlled tokenizer ablations.; No per-cell uncertainty in Table 2.; 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
- 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: genomic-tokenizer-selection-2025; source locator: Methods §2.2 Benchmarks; Results §3.1; cached text lines 20–21, 43–44; 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.