{
  "abstract": "Reliable microbial taxonomic inference is challenged by the rapid growth of newly sequenced organisms and incomplete reference coverage. While alignment- and database-driven tools remain essential for reference-backed identification, they are not designed for learned inference over species absent from genome-level training. We introduce HyperTaxa, a taxonomy-aligned representation learning framework for open-set microbial taxonomic inference. HyperTaxa encodes whole genomes as multiscale hyperspectral k-mer transition fields and aligns these representations to a taxonomy-derived semantic space using a two-stage Node2Vec-to-MDS procedure that combines local graph structure with global hierarchical distance preservation. The resulting space supports zero-shot inference over species excluded from genome-level training but represented in the taxonomy, generalized zero-shot inference over seen and unseen species, and cascaded genus-to-species prediction. Across benchmark and large-scale disjoint-species evaluations, HyperTaxa improves taxonomic prediction over pseudo-image and transformer-graph baselines, with the strongest gains at higher taxonomic ranks. Ablations further show that multiscale transition encoding and taxonomy-aware alignment are key contributors to generalization. These results position HyperTaxa as a complementary taxonomy-aware representation and candidate-generation framework for microbial identification under incomplete reference coverage and open-set uncertainty.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.25673",
  "author_total": 5,
  "authors": [
    {
      "name": "Akash Rao"
    },
    {
      "name": "Sushim Gupta"
    },
    {
      "name": "Vishalini Ramnath"
    },
    {
      "name": "Akhilesh Ramachandran"
    },
    {
      "name": "Sathyanarayanan Aakur"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/50b0t8hft8q9f.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.25673",
  "fulltext_url": null,
  "id": "50b0t8hft8q9f",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.25673",
  "language": null,
  "licence": "CC-BY-NC-SA-4.0",
  "licence_label": "CC BY-NC-SA 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.AI"
    ],
    [
      "arxiv",
      "q-bio.GN"
    ],
    [
      "openalex",
      "1312|Genomics and Phylogenetic Studies"
    ]
  ],
  "openalex": "W7223029980",
  "pdf_url": "https://arxiv.org/pdf/2610.25673",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-19",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/50b0t8hft8q9f.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:31:01Z",
      "native_id": "2610.25673",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.25673"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.GN",
      "subfield": "genetics-genomics"
    }
  ],
  "title": "HyperTaxa: Multiscale Genome Representations for Generalized Microbial Taxonomic Inference",
  "title_text": "HyperTaxa: Multiscale Genome Representations for Generalized Microbial Taxonomic Inference",
  "type": "preprint",
  "updated_at": "2026-10-30T12:33:50Z",
  "url": "https://research.draconitic.com/paper/50b0t8hft8q9f",
  "venue": "arXiv",
  "versions": []
}