{
  "abstract": "Health digital twins (HDTs) promise patient-specific modeling and decision support but current approaches remain structurally fragmented: monolithic models that address a single organ or task lack cross-scale fidelity, while system-level twins lack generalizable architectural frameworks. We propose OmniBioTwin, a System-of-Twinned-Systems (SoTS) framework that organizes HDTs as modular computational entities coupled through explicit interaction operators within a multi-layer network architecture. The framework comprises seven coordinated layers - spanning data integration, autonomous twin modeling, cross-scale coupling, temporal synchronization, and human-in-the-loop decision support. We demonstrate OmniBioTwin by instantiating a multiscale twin for glucagon-like peptide-1 (GLP-1) signaling pathways in Alzheimer's disease, illustrating how molecular, cellular, and organ-level twins can be composed and coupled within a unified system.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "Journal reference: 2026 IEEE 14th International Conference on Healthcare Informatics (ICHI)",
  "arxiv": "2606.11264",
  "author_total": 3,
  "authors": [
    {
      "name": "Zhaohui Wang"
    },
    {
      "name": "Yu Huang"
    },
    {
      "name": "Jiang Bian"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4h1vnnx02arr6.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2606.11264",
  "fulltext_url": null,
  "id": "4h1vnnx02arr6",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2606.11264",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.AI"
    ],
    [
      "arxiv",
      "q-bio.QM"
    ],
    [
      "openalex",
      "2739|Technology and Human Factors in Education and Health"
    ]
  ],
  "openalex": "W7164364093",
  "pdf_url": "https://arxiv.org/pdf/2606.11264",
  "pmcid": null,
  "pmid": null,
  "published": "2026-06-09",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/4h1vnnx02arr6.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:19Z",
      "native_id": "2606.11264",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2606.11264"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.QM",
      "subfield": "bioinformatics"
    }
  ],
  "title": "OmniBioTwin: A System-of-Twinned-Systems Framework for Health Digital Twins",
  "title_text": "OmniBioTwin: A System-of-Twinned-Systems Framework for Health Digital Twins",
  "type": "preprint",
  "updated_at": "2026-10-30T12:19:19Z",
  "url": "https://research.draconitic.com/paper/4h1vnnx02arr6",
  "venue": "arXiv",
  "versions": [
    {
      "doi": "10.1109/ichi69079.2026.00266",
      "pid": null,
      "relation": "published-as",
      "title": null,
      "venue": null
    }
  ]
}