{
  "abstract": "Sample-specific molecular networks, which capture how an individual's gene regulatory structure may deviate from the population as a whole, are important tools for understanding heterogeneity in health and disease. However, most existing methods either do not accommodate uncertainty quantification or cannot handle missing data, non-Gaussian modalities, and longitudinal structure common in modern multi-omic studies. For example, copy number alterations in cancer genomic studies are non-Gaussian and often only partially observed, and their regulatory effects on gene expression may vary substantially across patients, variation that a single, population-averaged network cannot capture. To address these challenges, we propose PRISM (Personalized Relational Inference via Sample-specific Modeling), an empirical Bayes framework for estimating sample-specific covariance matrices from cross-sectional and longitudinal multi-omic data. PRISM models each individual sample's covariance matrix as drawn from an inverse-Wishart prior whose parameters are estimated from the data and allowed to vary across subpopulations or evolve over time, yielding a closed-form posterior and edge-level uncertainty quantification, ensuring scalability to high-dimensional omics data. In simulation experiments, PRISM achieves point-estimate accuracy comparable to or better than existing methods, substantially improved uncertainty calibration under non-Gaussian marginals, and lower computational cost, while maintaining robust coverage under moderate missingness. We apply PRISM to a yeast cell-cycle time course dataset, recovering time-varying biologically meaningful regulatory relationships independently documented in literature, and to paired copy number and gene expression data from ovarian cancer patients, identifying copy-number-expression interactions associated with survival, including TFs HNF4G and PAX8 which have independent evidence of clinical relevance in this cancer.",
  "abstract_shown": true,
  "affiliations": [
    "University of South Carolina"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "E. Saha"
    },
    {
      "name": "H. Chen"
    },
    {
      "name": "R. Ghosal"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8qs45t2ca0w0j.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.08.757613",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.08.757613v1.full",
  "id": "8qs45t2ca0w0j",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.08.757613v1",
  "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",
      "1312|Gene Regulatory Network Analysis"
    ]
  ],
  "openalex": "W7221103491",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.08.757613v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-08",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8qs45t2ca0w0j.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:26:49Z",
      "native_id": "10.64898/2026.10.08.757613",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.08.757613v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Empirical Bayes Estimation of Sample-Specific Correlation Networks for Cross-Sectional and Longitudinal Omics Data",
  "title_text": "Empirical Bayes Estimation of Sample-Specific Correlation Networks for Cross-Sectional and Longitudinal Omics Data",
  "type": "preprint",
  "updated_at": "2026-10-30T12:26:49Z",
  "url": "https://research.draconitic.com/paper/8qs45t2ca0w0j",
  "venue": "bioRxiv",
  "versions": []
}