{
  "abstract": "Machine-learning models of the human microbiome are trained mostly on stool samples from single cohorts, limiting cross-body-site representation and cross-study generalization. Progress is constrained less by algorithms than by the absence of a harmonized multi-body-site corpus carrying the technical metadata needed to model, rather than ignore, batch structure. Here we release Corpusome, a harmonized two-tier cross-body-site human microbiome corpus for representation learning: a harmonized corpus of 187,546 human microbiome samples integrating standardized profiles from curatedMetagenomicData, the American Gut Project, and the EBI MGnify platform. Corpusome follows a two-tier design preserving both functional depth and cross-body-site breadth: a shotgun tier (22,588 samples, 93 studies) with species- and pathway-level profiles, and a 16S tier (164,958 samples, from a full pull of 708 MGnify studies) with genus-level profiles extending coverage to oral, skin, respiratory, and urogenital sites. It spans six body sites and two modalities, with harmonized metadata for batch-aware modelling. Body-site signal exceeds technical/source variance in the 16S tier by approximately 2.4-fold.",
  "abstract_shown": true,
  "affiliations": [
    "Indiana University"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "H. Xuan"
    },
    {
      "name": "Y. Huang"
    },
    {
      "name": "J. Bian"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/57ehe2x2anabs.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.08.28.747922",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.08.28.747922v2.full",
  "id": "57ehe2x2anabs",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.08.28.747922v2",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "microbiology"
    ],
    [
      "openalex",
      "1312|Gut microbiota and health"
    ]
  ],
  "openalex": "W7204558254",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.08.28.747922v2.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-28",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/57ehe2x2anabs.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:25Z",
      "native_id": "10.64898/2026.08.28.747922",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.08.28.747922v2"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:microbiology",
      "subfield": "microbiology"
    }
  ],
  "title": "Corpusome, a cross-body-site human microbiome corpus for representation learning",
  "title_text": "Corpusome, a cross-body-site human microbiome corpus for representation learning",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:25Z",
  "url": "https://research.draconitic.com/paper/57ehe2x2anabs",
  "venue": "bioRxiv",
  "versions": []
}