BERTax
BERT encoder pretrained on 1,500-nt genome fragments (3-mer tokens) and fine-tuned to predict superkingdom, phylum and, in the final model, genus.
Results are recorded on linked configurations and versions of this model. Their settings and evaluations are kept separate below.
Results on linked records
This model has no results of its own in this release. 3 linked records have results. They are not assigned to the underlying model: each record keeps its own settings and evaluations, and its scores are on its own page.
- BERTax (Closely related dataset, Mock et al. 2022) Configuration · configuration · 1 evaluation · 6 results
- BERTax (Distantly related dataset, Mock et al. 2022) Configuration · configuration · 1 evaluation · 12 results
- BERTax (Final model dataset, Mock et al. 2022) Configuration · configuration · 1 evaluation · 3 results
Overview
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Consult the linked sources for architecture or protocol details. Missing evidence is not evidence of a missing capability.
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How it works, versions and access
Strengths, limitations and unresolved questions
Evidence
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Evidence table
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Sources and history
Release 2026-10-22-1b7cf027e4f6 · Record review: source checked
1 source record and release history
- Taxonomic classification of DNA sequences beyond sequence similarity using deep neural networks · Original source · PNAS 119(35):e2122636119, published 2022-08-26; PMC9436379 full-text XML
Technical metadata and extraction receipts
Stable ID: dna-pathogen-20261012-bertax-model-bertax
- areas
- microbes-communities
- contexts
- research
- method types
- supervised_machine_learning
- reported name
- BERTax
- entity level
- family
- source locator
- Results 'Best Architecture Based on Simultaneous Classifications of Taxonomic Ranks'; Materials and Methods 'Pretraining', 'Architectures', 'Downloadable BERTax Version'
- access
- Code at https://github.com/f-kretschmer/bertax (GPL-3.0 per the repository; not pinned in this pass)
- missing metadata
- version: reason: inapplicable; note: Family record; each configuration is one trained model
- review
- method: source-hash-verification; scope-review; reviewer: claude; reviewer note: Separate Claude review agent (rev-mlvb), independent of the extracting agent; no human review claimed; date: 2026-10-12; artifact sha256: c0bb2f1bba8680b300076292389b39346ae58bea4bdf28647dd9462333b3bcd7; retrieval url: https://www.ebi.ac.uk/europepmc/webservices/rest/PMC9436379/fullTextXML; note: Checked against the re-downloaded article XML (SHA-256 matches) and SI.
Related records
- subject: training_data: dna-pathogen-20261012-bertax-model-bertax
- configuration of: BERTax (Closely related dataset, Mock et al. 2022)
- configuration of: BERTax (Distantly related dataset, Mock et al. 2022)
- configuration of: BERTax (Final model dataset, Mock et al. 2022)