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CANN: Commutative Algebra Neural Networks for Virus Classification — arXiv:2610.00283

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.00283
License (as archived): CC0-1.0
Archived: 2026-10-07T12:24:58.619Z
SHA-256 of the archived text: 33770d7914021e549fcee8b6a620c75aa1895abfce03dac327ddd4e639373273
Stable URL of this page: https://metraipolis.com/documento/dc_eb897dd1aba2

How to cite

CANN: Commutative Algebra Neural Networks for Virus Classification — arXiv:2610.00283 — arXiv (en), https://arxiv.org/abs/2610.00283 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_eb897dd1aba2 (Metraipolis archive dc_eb897dd1aba2, sha256 33770d7914021e549fcee8b6a620c75aa1895abfce03dac327ddd4e639373273)

BibTeX

@misc{dceb897dd1aba2,
  title = {CANN: Commutative Algebra Neural Networks for Virus Classification — arXiv:2610.00283},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.00283},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_eb897dd1aba2; sha256 33770d7914021e549fcee8b6a620c75aa1895abfce03dac327ddd4e639373273},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.00283. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

The extensive diversity of viral genomes challenges alignment-free representations to capture both nucleotide composition and the higher-order organization of recurring sequence patterns. Conventional $k$-mer representations quantify pattern abundance but do not explicitly encode how these patterns are organized across scales. Here, we introduce CANN, an alignment-free framework that combines persistent commutative-algebraic descriptors with neural representation learning for viral family classification. CANN uses persistent facet descriptors to encode the multiscale organization of $k$-mer patterns and complementary $k$-mer counts to capture sequence composition. Convolutional and Transformer encoders independently learn latent representations of these features, and cosine distances within their respective embedding spaces are averaged before $k$-nearest-neighbor classification. Across multiple NCBI viral genome collections, CANN achieves robust family-level performance, with the fusion of encoder-specific distances consistently improving classification relative to either encoder alone. Evaluation on genomes present in the 2026 NCBI collection but absent from the 2024 reference set further supports generalization to unseen members of families already represented in the reference data. Together, these findings show that persistent commutative-algebraic descriptors can complement neural representations of viral genomes, linking algebraic descriptions of genomic organization with data-driven classification.

Tema degli esploratori: scienze della vita e salute (retro-catalogo arXiv: annunci del 2026-10-02 in q-bio.GN).
Categorie arXiv: Genomics (q-bio.GN).
Pagina del lavoro: https://arxiv.org/abs/2610.00283 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-07T11:57:39.018Z dalla pagina https://arxiv.org/catchup/q-bio.GN/2026-10-02?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).