{
  "abstract": "Inter-patient molecular heterogeneity is a fundamental challenge in precision oncology: population-level multi-omics networks reveal average biology aggregated across the population but obscure individual variations that drive differential clinical outcomes. We introduce SIREN (Sample-specific Inference via Regularized Empirical-Bayes Networks), a method that estimates one partial correlation network per sample across omics layers by combining a population-level empirical Bayes prior with a rank-1 individual-specific update. Since a sample-specific precision matrix cannot be estimated from a single observation, SIREN uses a conjugate Inverse Wishart prior whose mean is the Oracle Approximating Shrinkage estimator, yielding closed-form individual-specific posteriors without MCMC. On simulated heterogeneous populations, SIREN achieves superior edge recovery over population-average methods including OAS, Ledoit-Wolf, and graphical Lasso, while remaining competitive in homogeneous settings. Applied to paired transcriptomic and methylomic profiles from lung adenocarcinoma, SIREN identifies individual-specific gene-methylation regulatory edges that stratify patients by survival in ways population-level analysis cannot, implicating chromatin remodeling and WNT signaling pathways in epigenetic heterogeneity. SIREN is computationally scalable and available as a Python package.",
  "abstract_shown": true,
  "affiliations": [
    "Harvard T. H. Chan School of Public Health"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "E. Saha"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/03w0fgwgnq2cm.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.05.25.727641",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.05.25.727641v2.full",
  "id": "03w0fgwgnq2cm",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.05.25.727641v2",
  "language": null,
  "licence": "CC-BY-NC",
  "licence_label": "CC BY-NC",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ],
    [
      "openalex",
      "2613|Statistical Methods and Inference"
    ]
  ],
  "openalex": "W7162642851",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.05.25.727641v2.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-05-25",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/03w0fgwgnq2cm.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:43Z",
      "native_id": "10.64898/2026.05.25.727641",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.05.25.727641v2"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Individual-Specific Gaussian Graphical Models for Heterogeneous Populations with Application to Epigenetic Gene Regulation in Lung Adenocarcinoma",
  "title_text": "Individual-Specific Gaussian Graphical Models for Heterogeneous Populations with Application to Epigenetic Gene Regulation in Lung Adenocarcinoma",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:43Z",
  "url": "https://research.draconitic.com/paper/03w0fgwgnq2cm",
  "venue": "bioRxiv",
  "versions": []
}