Configuration

Eco70PromBERT

Eco70PromBERT is the E. coli promoter-prediction transformer configuration evaluated in the CyaPromBERT study.

Sources

TSSNote-CyaPromBERT: Development of an integrated platform for highly accurate promoter prediction and visualization of Synechococcus sp. and Synechocystis sp. through a state-of-the-art natural language processing model BERT · Materials and methods/Datasets (paragraph 2); Materials and methods/Model training (paragraph 2)

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

TSSNote-CyaPromBERT: Development of an integrated platform for highly accurate promoter prediction and visualization of Synechococcus sp. and Synechocystis sp. through a state-of-the-art natural language processing model BERT · Results and discussion/Evaluating model performance compared to existing promoter prediction models using independent datasets from E. coli (paragraph 1); Results and discussion/Evaluating model performance compared to existing promoter prediction models using independent datasets from E. coli (paragraph 3)

Overview

Model type

Study-specific predictive method; this record is the paper-specific evaluated configuration.

Sources

TSSNote-CyaPromBERT: Development of an integrated platform for highly accurate promoter prediction and visualization of Synechococcus sp. and Synechocystis sp. through a state-of-the-art natural language processing model BERT · Results and discussion/Evaluating model performance compared to existing promoter prediction models using independent datasets from E. coli (paragraph 1); Results and discussion/Evaluating model performance compared to existing promoter prediction models using independent datasets from E. coli (paragraph 3)

Biological inputs

DNA promoter and non-promoter sequence windows

Sources

Outputs

Promoter classification

Sources

TSSNote-CyaPromBERT: Development of an integrated platform for highly accurate promoter prediction and visualization of Synechococcus sp. and Synechocystis sp. through a state-of-the-art natural language processing model BERT · Results and discussion/Evaluating model performance compared to existing promoter prediction models using independent datasets from E. coli (paragraph 3); Materials and methods/Datasets (paragraph 2)

Access

Official study implementation and usage documentation: https://github.com/hanepira/TSSnote-CyaPromBert/blob/e86f5449e2e2af3fead1b418ba721f38feb61318/README.md. This pinned documentation revision is not automatically the evaluated weight revision.

Sources

hanepira/TSSnote-CyaPromBert README.md · README.md; installation, model download and usage instructions

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: Eco70PromBERT	Task: E. coli sigma70 promoter prediction Dataset: Independent E. coli sigma70 test dataset	0.91 Promoter-class F1 unitless · unknown Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source Eco70PromBERT: E. coli sigma70 promoter prediction BERT-base with 1bp tokenizer; 110 promoters and 108 non-promoters. Aggregation: Not reported TSSNote-CyaPromBERT: Development of an integrated platform for highly accurate promoter prediction and visualization of Synechococcus sp. and Synechocystis sp. through a state-of-the-art natural language processing model BERT · TABLE 3, Eco70PromBERT (BERT-base + 1bp tokenizer) row, F1 score Promoter 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

The Eco70PromBERT-1 bp configuration uses BERT-base with a single-base tokenizer for E. coli σ70 promoter classification.

Sources

TSSNote-CyaPromBERT: Development of an integrated platform for highly accurate promoter prediction and visualization of Synechococcus sp. and Synechocystis sp. through a state-of-the-art natural language processing model BERT · Results and discussion/Evaluating model performance compared to existing promoter prediction models using independent datasets from E. coli (paragraph 1); Results and discussion/Evaluating model performance compared to existing promoter prediction models using independent datasets from E. coli (paragraph 3)

What was evaluated

The linked evaluation record identifies Eco70PromBERT: E. coli sigma70 promoter prediction. Its dataset, split, adaptation and evidence origin remain attached to the reported results.

Sources

TSSNote-CyaPromBERT: Development of an integrated platform for highly accurate promoter prediction and visualization of Synechococcus sp. and Synechocystis sp. through a state-of-the-art natural language processing model BERT · The named evaluation’s methods and comparison table; exact preserved evaluation IDs: evaluation-lit-035

Strengths, limitations and unresolved questions

Strengths and limitations

Strengths and considerations

Provides a learned-sequence comparator within an explicit promoter classification test.
Sources
TSSNote-CyaPromBERT: Development of an integrated platform for highly accurate promoter prediction and visualization of Synechococcus sp. and Synechocystis sp. through a state-of-the-art natural language processing model BERT · Results and discussion/Evaluating model performance compared to existing promoter prediction models using independent datasets from E. coli (paragraph 3); Results and discussion/Evaluating model performance compared to existing promoter prediction models using independent datasets from E. coli (paragraph 2)

Limitations and conditions

The E. coli result cannot be substituted for the cross-species cyanobacterial results in the same paper.
Sources
TSSNote-CyaPromBERT: Development of an integrated platform for highly accurate promoter prediction and visualization of Synechococcus sp. and Synechocystis sp. through a state-of-the-art natural language processing model BERT · Materials and methods/Model training (paragraph 7); Introduction (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-5b70fccb70bb70

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	Study-specific predictive method; this record is the paper-specific evaluated configuration. Sources TSSNote-CyaPromBERT: Development of an integrated platform for highly accurate promoter prediction and visualization of Synechococcus sp. and Synechocystis sp. through a state-of-the-art natural language processing model BERT · Results and discussion/Evaluating model performance compared to existing promoter prediction models using independent datasets from E. coli (paragraph 1); Results and discussion/Evaluating model performance compared to existing promoter prediction models using independent datasets from E. coli (paragraph 3)
Architecture / procedure	The Eco70PromBERT-1 bp configuration uses BERT-base with a single-base tokenizer for E. coli σ70 promoter classification. Sources TSSNote-CyaPromBERT: Development of an integrated platform for highly accurate promoter prediction and visualization of Synechococcus sp. and Synechocystis sp. through a state-of-the-art natural language processing model BERT · Results and discussion/Evaluating model performance compared to existing promoter prediction models using independent datasets from E. coli (paragraph 1); Results and discussion/Evaluating model performance compared to existing promoter prediction models using independent datasets from E. coli (paragraph 3)
Biological inputs	DNA promoter and non-promoter sequence windows Sources TSSNote-CyaPromBERT: Development of an integrated platform for highly accurate promoter prediction and visualization of Synechococcus sp. and Synechocystis sp. through a state-of-the-art natural language processing model BERT · Materials and methods/Datasets (paragraph 2); Results and discussion/Evaluating model performance compared to existing promoter prediction models using independent datasets from E. coli (paragraph 1)
Outputs	Promoter classification Sources TSSNote-CyaPromBERT: Development of an integrated platform for highly accurate promoter prediction and visualization of Synechococcus sp. and Synechocystis sp. through a state-of-the-art natural language processing model BERT · Results and discussion/Evaluating model performance compared to existing promoter prediction models using independent datasets from E. coli (paragraph 3); Materials and methods/Datasets (paragraph 2)
Parameters	86.8 million trainable parameters in the BERT-base configuration described by the study. Sources TSSNote-CyaPromBERT: Development of an integrated platform for highly accurate promoter prediction and visualization of Synechococcus sp. and Synechocystis sp. through a state-of-the-art natural language processing model BERT · Results and discussion/Interpreting the model’s behavior through Monte Carlo sampling and attention score visualization (paragraph 1); Results and discussion/Evaluating model performance compared to existing promoter prediction models using independent datasets from E. coli (paragraph 1)
Known versions / configuration	Eco70PromBERT is the comparison-table label; that label does not specify an immutable weight revision. · Not reported in inspected sources Sources TSSNote-CyaPromBERT: Development of an integrated platform for highly accurate promoter prediction and visualization of Synechococcus sp. and Synechocystis sp. through a state-of-the-art natural language processing model BERT · Model identification in the comparison table and corresponding Methods; immutable checkpoint revision is not supplied by the table label.
Training data / fitting	The paper’s E. coli σ70 promoter comparison; the cyanobacterial data and models are a separate experimental setting. Sources TSSNote-CyaPromBERT: Development of an integrated platform for highly accurate promoter prediction and visualization of Synechococcus sp. and Synechocystis sp. through a state-of-the-art natural language processing model BERT · Materials and methods/Datasets (paragraph 2); Results and discussion/Evaluating model performance compared to existing promoter prediction models using independent datasets from E. coli (paragraph 1)
Context limits	A maximum input/context length for this exact evaluated configuration is not established by the inspected sources. · Not reported in inspected sources Sources (2) TSSNote-CyaPromBERT: Development of an integrated platform for highly accurate promoter prediction and visualization of Synechococcus sp. and Synechocystis sp. through a state-of-the-art natural language processing model BERT; hanepira/TSSnote-CyaPromBert README.md · Materials and methods/Datasets; Materials and methods/Constructing promoter extracting module from dRNA-seq datasets; Materials and methods/Promoter and non-promoter sequences extraction; Materials and methods/Model training; inspected for explicit maximum input length (dataset lengths and family-wide limits are not substituted); README.md at pinned repository revision
Access	Official study implementation and usage documentation: https://github.com/hanepira/TSSnote-CyaPromBert/blob/e86f5449e2e2af3fead1b418ba721f38feb61318/README.md. This pinned documentation revision is not automatically the evaluated weight revision. Sources hanepira/TSSnote-CyaPromBert 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 hanepira/TSSnote-CyaPromBert 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 hanepira/TSSnote-CyaPromBert README.md · README.md; checkpoint/access documentation and licence scope

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.

20 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	TSSNote-CyaPromBERT: Development of an integrated platform for highly accurate promoter prediction and visualization of Synechococcus sp. and Synechocystis sp. through a state-of-the-art natural language processing model BERT Original source ↗ Results and discussion/Evaluating model performance compared to existing promoter prediction models using independent datasets from E. coli (paragraph 1); Results and discussion/Evaluating model performance compared to existing promoter prediction models using independent datasets from E. coli (paragraph 3) Version: version of record Retrieved: 2026-09-16T10:41:16.544033+00:00	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: `74278ccd77b2bc00a3f4434546545e8bdec8b0652a0e5d1862ec0f91decccd8d` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Diagram steps DNA promoter and non-promoter sequence windows Eco70PromBERT Promoter classification Individual claims	TSSNote-CyaPromBERT: Development of an integrated platform for highly accurate promoter prediction and visualization of Synechococcus sp. and Synechocystis sp. through a state-of-the-art natural language processing model BERT Original source ↗ Results and discussion/Evaluating model performance compared to existing promoter prediction models using independent datasets from E. coli (paragraph 1); Results and discussion/Evaluating model performance compared to existing promoter prediction models using independent datasets from E. coli (paragraph 3) Version: version of record Retrieved: 2026-09-16T10:41:16.544033+00:00	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: `74278ccd77b2bc00a3f4434546545e8bdec8b0652a0e5d1862ec0f91decccd8d` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Diagram title Evaluated procedure (conceptual) Individual claims	TSSNote-CyaPromBERT: Development of an integrated platform for highly accurate promoter prediction and visualization of Synechococcus sp. and Synechocystis sp. through a state-of-the-art natural language processing model BERT Original source ↗ Results and discussion/Evaluating model performance compared to existing promoter prediction models using independent datasets from E. coli (paragraph 1); Results and discussion/Evaluating model performance compared to existing promoter prediction models using independent datasets from E. coli (paragraph 3) Version: version of record Retrieved: 2026-09-16T10:41:16.544033+00:00	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: `74278ccd77b2bc00a3f4434546545e8bdec8b0652a0e5d1862ec0f91decccd8d` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Model type Study-specific predictive method; this record is the paper-specific evaluated configuration. Individual claims	TSSNote-CyaPromBERT: Development of an integrated platform for highly accurate promoter prediction and visualization of Synechococcus sp. and Synechocystis sp. through a state-of-the-art natural language processing model BERT Original source ↗ Results and discussion/Evaluating model performance compared to existing promoter prediction models using independent datasets from E. coli (paragraph 1); Results and discussion/Evaluating model performance compared to existing promoter prediction models using independent datasets from E. coli (paragraph 3) Version: version of record Retrieved: 2026-09-16T10:41:16.544033+00:00	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: `74278ccd77b2bc00a3f4434546545e8bdec8b0652a0e5d1862ec0f91decccd8d` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Architecture / procedure The Eco70PromBERT-1 bp configuration uses BERT-base with a single-base tokenizer for E. coli σ70 promoter classification. Individual claims	TSSNote-CyaPromBERT: Development of an integrated platform for highly accurate promoter prediction and visualization of Synechococcus sp. and Synechocystis sp. through a state-of-the-art natural language processing model BERT Original source ↗ Results and discussion/Evaluating model performance compared to existing promoter prediction models using independent datasets from E. coli (paragraph 1); Results and discussion/Evaluating model performance compared to existing promoter prediction models using independent datasets from E. coli (paragraph 3) Version: version of record Retrieved: 2026-09-16T10:41:16.544033+00:00	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: `74278ccd77b2bc00a3f4434546545e8bdec8b0652a0e5d1862ec0f91decccd8d` 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	hanepira/TSSnote-CyaPromBert README.md Original source ↗ README.md; checkpoint/access documentation and licence scope Version: e86f5449e2e2af3fead1b418ba721f38feb61318 Retrieved: 2026-09-16T19:54:13.729708+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: `c916c6c99b07fa3a440264eeefeac8a6b1508867da31c7233bc4d24367fd8bbd` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Biological inputs DNA promoter and non-promoter sequence windows Individual claims	TSSNote-CyaPromBERT: Development of an integrated platform for highly accurate promoter prediction and visualization of Synechococcus sp. and Synechocystis sp. through a state-of-the-art natural language processing model BERT Original source ↗ Materials and methods/Datasets (paragraph 2); Results and discussion/Evaluating model performance compared to existing promoter prediction models using independent datasets from E. coli (paragraph 1) Version: version of record Retrieved: 2026-09-16T10:41:16.544033+00:00	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: `74278ccd77b2bc00a3f4434546545e8bdec8b0652a0e5d1862ec0f91decccd8d` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Outputs Promoter classification Individual claims	TSSNote-CyaPromBERT: Development of an integrated platform for highly accurate promoter prediction and visualization of Synechococcus sp. and Synechocystis sp. through a state-of-the-art natural language processing model BERT Original source ↗ Results and discussion/Evaluating model performance compared to existing promoter prediction models using independent datasets from E. coli (paragraph 3); Materials and methods/Datasets (paragraph 2) Version: version of record Retrieved: 2026-09-16T10:41:16.544033+00:00	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: `74278ccd77b2bc00a3f4434546545e8bdec8b0652a0e5d1862ec0f91decccd8d` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Parameters 86.8 million trainable parameters in the BERT-base configuration described by the study. Individual claims	TSSNote-CyaPromBERT: Development of an integrated platform for highly accurate promoter prediction and visualization of Synechococcus sp. and Synechocystis sp. through a state-of-the-art natural language processing model BERT Original source ↗ Results and discussion/Interpreting the model’s behavior through Monte Carlo sampling and attention score visualization (paragraph 1); Results and discussion/Evaluating model performance compared to existing promoter prediction models using independent datasets from E. coli (paragraph 1) Version: version of record Retrieved: 2026-09-16T10:41:16.544033+00:00	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: `74278ccd77b2bc00a3f4434546545e8bdec8b0652a0e5d1862ec0f91decccd8d` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Known versions / configuration Eco70PromBERT is the comparison-table label; that label does not specify an immutable weight revision. Individual claims	TSSNote-CyaPromBERT: Development of an integrated platform for highly accurate promoter prediction and visualization of Synechococcus sp. and Synechocystis sp. through a state-of-the-art natural language processing model BERT 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: 2026-09-16T10:41:16.544033+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.5.value` Source artifact SHA-256: `74278ccd77b2bc00a3f4434546545e8bdec8b0652a0e5d1862ec0f91decccd8d` 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

2 source records and release history

TSSNote-CyaPromBERT: Development of an integrated platform for highly accurate promoter prediction and visualization of Synechococcus sp. and Synechocystis sp. through a state-of-the-art natural language processing model BERT · Original source · version of record
hanepira/TSSnote-CyaPromBert README.md · Original source · e86f5449e2e2af3fead1b418ba721f38feb61318

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Technical metadata and extraction receipts

Stable ID: reported-model-5b70fccb70bb70

areas: microbes-communities
entity level: method
version: Not reported
reported name: Eco70PromBERT
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: cyaprombert-2022; source locator: Results and discussion/Evaluating model performance compared to existing promoter prediction models using independent datasets from E. coli (paragraph 1); Results and discussion/Evaluating model performance compared to existing promoter prediction models using independent datasets from E. coli (paragraph 3) | Materials and methods/Datasets (paragraph 2); Materials and methods/Model training (paragraph 2); 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: Eco70PromBERT: E. coli sigma70 promoter prediction

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