TaxDistill: Improving Metagenomic Taxonomic Annotation via Distilled Genomic Foundation Models
Metagenomic taxonomic annotation is essential for interpreting complex microbial communities, yet reliable annotation remains challenging under reference database incompleteness and ambiguous taxonomic boundaries. Existing similarity-based tools are efficient, but they often produce noisy pseudo-labels in complex environments; learning-based post-hoc correction methods can further inherit this noise when trained on hard pseudo-labels generated by upstream classifiers. We propose TaxDistill, a plug-and-play knowledge distillation framework for reliable metagenomic taxonomic recalibration. TaxDistill uses the genomic foundation model GenomeOcean as a semantic teacher and performs one-way on...
| Authors | Rongye Ye, Lun Li, Zheng Luo et al. |
|---|---|
| Primary category | cs.LG |
| Published | 22 May 2026 |
| Last updated at source | 30 September 2026 |
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