Configuration

iPro70-FMWin

iPro70-FMWin is an established σ70 promoter predictor used as a webserver comparator in the CyaPromBERT 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 · 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)

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

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 3); Results and discussion/Evaluating model performance compared to existing promoter prediction models using independent datasets from E. coli (paragraph 2)

At a glance

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 · Materials and methods/Model training (paragraph 3); Results and discussion/Evaluating model performance compared to existing promoter prediction models using independent datasets from E. coli (paragraph 2)

Biological inputs

E. coli σ70 promoter/non-promoter DNA 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 probabilities and class predictions

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)

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
iPro70-FMWin: E. coli sigma70 promoter prediction Configuration: iPro70-FMWinTask: E. coli sigma70 promoter predictionDataset: Independent E. coli sigma70 test dataset Compared on the same independent test dataset; 110 promoters and 108 non-promoters. Independent external evaluation · Evaluation metadata: needs review
0.90 Promoter-class F1 Unit: unitless · Direction: unknown	Uncertainty: not reported in legacy extract Scored: Not reported · Eligible: Not reported	source checkedTSSNote-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, iPro70-FMWin row, F1 score Promoter column Source checking is not independent reproduction.

How it works

How the evaluated method works

The original method extracts multi-window sequence features and uses AdaBoost-based feature selection, retaining 27 common features from 22,595 candidates before classifier evaluation. The CyaPromBERT study calls the existing server and uses its returned probabilities.

Sources

iPro70-FMWin original paper · Materials and methods / Benchmark dataset; Feature selection

What was evaluated

The linked evaluation record identifies iPro70-FMWin: 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-036

Strengths and limitations

Strengths and considerations

Provides a task-specialist comparator on the same independent E. coli dataset.
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); Materials and methods/Datasets (paragraph 2)

Limitations and conditions

The benchmark does not fully specify a reproducible server build or weight revision; webserver results are not a BERT-model 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 3); Results and discussion/Evaluating model performance compared to existing promoter prediction models using independent datasets from E. coli (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-23cb15b93c00ff

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 · Materials and methods/Model training (paragraph 3); Results and discussion/Evaluating model performance compared to existing promoter prediction models using independent datasets from E. coli (paragraph 2)
Architecture / procedure	The original method extracts multi-window sequence features and uses AdaBoost-based feature selection, retaining 27 common features from 22,595 candidates before classifier evaluation. The CyaPromBERT study calls the existing server and uses its returned probabilities. Sources iPro70-FMWin original paper · Materials and methods / Benchmark dataset; Feature selection
Biological inputs	E. coli σ70 promoter/non-promoter DNA 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 probabilities and class predictions 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)
Parameters	A neural parameter count is inapplicable. The original feature-selection procedure retains 27 features; the later server’s fitted classifier artifact is not pinned. · Not applicable Sources iPro70-FMWin original paper · Materials and methods / Feature selection
Known versions / configuration	iPro70-FMWin 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 original method paper uses 2,141 E. coli K-12 sequences from a RegulonDB 9.0-derived collection, including 741 σ70 promoters, with cross-validation. This is the underlying method’s training description; the later server build is not pinned. Sources iPro70-FMWin original paper · Materials and methods / Benchmark dataset; Feature selection
Context limits	81-bp windows spanning 60 bases upstream and 20 downstream of the transcription start site. Sources iPro70-FMWin original paper · Materials and methods / Benchmark dataset
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 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 2026-09-17-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	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/Model training (paragraph 3); Results and discussion/Evaluating model performance compared to existing promoter prediction models using independent datasets from E. coli (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.diagram.caption` Source artifact SHA-256: `74278ccd77b2bc00a3f4434546545e8bdec8b0652a0e5d1862ec0f91decccd8d` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Diagram steps ["E. coli σ70 promoter/non-promoter DNA windows","iPro70-FMWin","Promoter probabilities and class predictions"] 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/Model training (paragraph 3); Results and discussion/Evaluating model performance compared to existing promoter prediction models using independent datasets from E. coli (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.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 ↗ Materials and methods/Model training (paragraph 3); Results and discussion/Evaluating model performance compared to existing promoter prediction models using independent datasets from E. coli (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.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 ↗ Materials and methods/Model training (paragraph 3); Results and discussion/Evaluating model performance compared to existing promoter prediction models using independent datasets from E. coli (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.0.value` Source artifact SHA-256: `74278ccd77b2bc00a3f4434546545e8bdec8b0652a0e5d1862ec0f91decccd8d` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Architecture / procedure The original method extracts multi-window sequence features and uses AdaBoost-based feature selection, retaining 27 common features from 22,595 candidates before classifier evaluation. The CyaPromBERT study calls the existing server and uses its returned probabilities. Individual claims	iPro70-FMWin original paper Original source ↗ Materials and methods / Benchmark dataset; Feature selection Version: 10.1007/s00438-018-1487-5 Retrieved: 2026-09-16T20:40:28.842295+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: `e4fa53c5574961acf4a93bf251b71c248025c64b1b79e74da40ff73091d8b205` 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 E. coli σ70 promoter/non-promoter DNA 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 probabilities and class predictions 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.3.value` Source artifact SHA-256: `74278ccd77b2bc00a3f4434546545e8bdec8b0652a0e5d1862ec0f91decccd8d` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Parameters A neural parameter count is inapplicable. The original feature-selection procedure retains 27 features; the later server’s fitted classifier artifact is not pinned. Individual claims	iPro70-FMWin original paper Original source ↗ Materials and methods / Feature selection Version: 10.1007/s00438-018-1487-5 Retrieved: 2026-09-16T20:40:28.842295+00:00	inapplicable 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: `e4fa53c5574961acf4a93bf251b71c248025c64b1b79e74da40ff73091d8b205` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Known versions / configuration iPro70-FMWin 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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Sources and history

Release 2026-09-17-d277315f7d76 · Record review: needs review

3 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
iPro70-FMWin original paper · Original source · 10.1007/s00438-018-1487-5
hanepira/TSSnote-CyaPromBert README.md · Original source · e86f5449e2e2af3fead1b418ba721f38feb61318

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Technical metadata and extraction receipts

Stable ID: reported-model-23cb15b93c00ff

areas: microbes-communities
entity level: method
version: Not reported
reported name: iPro70-FMWin
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: evidence-reported-ipro70-original; cyaprombert-2022; source locator: Materials and methods / Benchmark dataset; Feature selection | Materials and methods/Model training (paragraph 3); Results and discussion/Evaluating model performance compared to existing promoter prediction models using independent datasets from E. coli (paragraph 2) | 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); 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: iPro70-FMWin: E. coli sigma70 promoter prediction

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