{
  "abstract": "Understanding how molecular interactions give rise to disease phenotypes across cellular contexts remains a central challenge in biomedical research. Here, we introduce a Disease-Cell-Protein (DCP) paradigm for modeling multi-scale disease biology, which jointly represents disease states, cellular composition, and protein interaction networks within a unified graph architecture. We instantiate this paradigm in the liver as LiverDCP by integrating LiverHomo, a harmonized single-cell atlas of liver diseases, with proteome-wide predicted protein-protein interactions to construct over 280 context-specific interactomes across diverse liver disease and cellular conditions. LiverDCP employs a multi-context representation learning strategy that enables joint training across hundreds of disease-cell environments, capturing shared interaction principles while preserving context-specific variation. LiverDCP incorporates pretrained protein sequence-derived features through a geometry-aware two-phase training scheme that preserves embedding structure while improving predictive performance. The resulting DCP protein embeddings reveal extensive rewiring of protein functional states across diseases, providing a transferable representation for downstream biomedical applications. Without GWAS supervision during representation learning, LiverDCP enables disease-risk gene classification and identifies cell types through which genetic risk may act. For therapeutic target discovery, LiverDCP recovers established Phase II+ MASH targets and prioritizes previously unrecognized candidates from the unannotated proteome, with 26 of the top 50 predictions showing independent PubMed evidence related to MASH biology. Context-specific interaction analysis further provides mechanistic hypotheses for less-characterized candidates. Together, these results establish DCP as a generalizable framework for connecting molecular interactions, cellular context, genetic risk, and therapeutic opportunities across complex diseases.",
  "abstract_shown": true,
  "affiliations": [
    "University of Texas Medical Branch"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "name": "Z. Shi"
    },
    {
      "name": "Z. Song"
    },
    {
      "name": "H. Stevenson-Lerner"
    },
    {
      "name": "B. Dong"
    },
    {
      "affs": [
        0
      ],
      "name": "H. Zhao"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/e0qh809tvv6z9.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.08.07.743628",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.08.07.743628v2.full",
  "id": "e0qh809tvv6z9",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.08.07.743628v2",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "systems biology"
    ],
    [
      "openalex",
      "1312|Bioinformatics and Genomic Networks"
    ]
  ],
  "openalex": "W7202086560",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.08.07.743628v2.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-07",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/e0qh809tvv6z9.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:25Z",
      "native_id": "10.64898/2026.08.07.743628",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.08.07.743628v2"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:systems biology",
      "subfield": "systems-synthetic"
    }
  ],
  "title": "LiverDCP: A Disease-Cell-Protein Framework for Multi-scale Modeling of Disease Biology",
  "title_text": "LiverDCP: A Disease-Cell-Protein Framework for Multi-scale Modeling of Disease Biology",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:25Z",
  "url": "https://research.draconitic.com/paper/e0qh809tvv6z9",
  "venue": "bioRxiv",
  "versions": []
}