{
  "abstract": "Breast cancer outcome prediction remains challenging for underrepresented populations because genomic datasets are demographically imbalanced and conventional multi-omics integration largely relies on undirected molecular similarity. We developed DAG-HEART, a directed acyclic graph-guided multi-omics transfer-learning framework that extends our previous transfer learning strategy with data augmentation. Using TCGA-BRCA mRNA, miRNA, and DNA-methylation data, DAG-HEART was evaluated for progression-free interval prediction in a data-minority group. DAG-guided nonlinear integration consistently improved predictive performance relative to direction-agnostic and correlation-based representations, while biologically motivated directional constraints generally outperformed reversed or unconstrained structures. Recurrently selected features converged on extracellular-matrix and regulatory pathways and supported clinically meaningful risk stratification. DAG-HEART provides an interpretable strategy for combining directed multi-omics structure with transfer learning under data imbalance across racial groups.",
  "abstract_shown": true,
  "affiliations": [
    "University of Nebraska Medical Center"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "M. Baek"
    },
    {
      "name": "J. Wang"
    },
    {
      "affs": [
        0
      ],
      "name": "S. Wan"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/73y68jk46bq37.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.08.31.748384",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.08.31.748384v1.full",
  "id": "73y68jk46bq37",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.08.31.748384v1",
  "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",
      "bioinformatics"
    ],
    [
      "openalex",
      "1306|Breast Cancer Treatment Studies"
    ]
  ],
  "openalex": "W7207800282",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.08.31.748384v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-31",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/73y68jk46bq37.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:56Z",
      "native_id": "10.64898/2026.08.31.748384",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.08.31.748384v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "DAG-HEART: Directed Acyclic Graph-Guided Health Equity-Aware Representation Transfer Learning Framework for Breast Cancer",
  "title_text": "DAG-HEART: Directed Acyclic Graph-Guided Health Equity-Aware Representation Transfer Learning Framework for Breast Cancer",
  "type": "preprint",
  "updated_at": "2026-10-30T12:18:56Z",
  "url": "https://research.draconitic.com/paper/73y68jk46bq37",
  "venue": "bioRxiv",
  "versions": []
}