{
  "abstract": "Gene co-expression networks reflect transcription factor-associated regulation together with epigenetic influences such as DNA methylation, chromatin states, and histone modifications. To distinguish gene-gene dependencies that persist after accounting for methylation covariation, large-scale statistical inference conditioning on hundreds of thousands of molecular features is necessary, but the task remains computationally intractable for conventional Gaussian graphical modelling approaches. Here we present fastACCORD, a scalable computational framework for ultrahigh-dimensional partial correlation modeling. fastACCORD combines row-separable optimization, {ell}2 stabilization, and a semismooth Newton solver in a PyTorch implementation for CPU and CUDA-enabled GPU hardware. We applied it to matched transcriptomic and methylomic profiles from 16 TCGA cancer types and obtained joint networks containing >300,000 molecular features per cancer. The resulting multi-omic networks revealed cancer-specific methylation-expression dependencies, including recurrent methylation-associated expression repression of metabolic genes. Methylation-adjusted gene co-expression networks were sparser than networks estimated from mRNA data alone, but more enriched for ChIP-seq-supported TF-target relationships and curated co-regulon annotations. TF-target subnetworks further revealed cancer-specific architectures consistent with lineage identity, oncofetal reactivation, and tumor microenvironment-associated programs. Together, these analyses establish fastACCORD as a practical framework for ultrahigh-dimensional multi-omic partial correlation modeling and demonstrate how joint modeling of transcriptomic and epigenomic measurements can refine the interpretation of gene co-expression networks.",
  "abstract_shown": true,
  "affiliations": [
    "National University of Singapore"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "name": "S. Lee"
    },
    {
      "name": "Q. Zhao"
    },
    {
      "name": "D. Kim"
    },
    {
      "name": "S.-Y. Oh"
    },
    {
      "name": "J.-H. Won"
    },
    {
      "affs": [
        0
      ],
      "name": "H. Choi"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4eeafc3zjbs4k.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.20.753046",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.20.753046v1.full",
  "id": "4eeafc3zjbs4k",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.20.753046v1",
  "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|Bioinformatics and Genomic Networks"
    ]
  ],
  "openalex": "W7214283815",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.20.753046v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-20",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/4eeafc3zjbs4k.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:24:35Z",
      "native_id": "10.64898/2026.09.20.753046",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.20.753046v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "fastACCORD enables ultrahigh-dimensional partial correlation modeling for multi-omic data integration",
  "title_text": "fastACCORD enables ultrahigh-dimensional partial correlation modeling for multi-omic data integration",
  "type": "preprint",
  "updated_at": "2026-10-30T12:24:35Z",
  "url": "https://research.draconitic.com/paper/4eeafc3zjbs4k",
  "venue": "bioRxiv",
  "versions": []
}