{
  "abstract": "Gene regulation is a core biological mechanism crucial for organismal growth and development. Recent advances in deep learning, coupled with increasing computational resources, have enabled large-scale modelling of gene regulatory processes. While numerous models have been proposed, effectively capturing long-range genomic interactions remains a central challenge. Here, we introduce CellReg3D, a multiscale and multi-modal deep learning framework designed to model cell-type-specific gene regulation by integrating local sequence patterns with long-range genomic interactions. Existing approaches often overlook the contribution of 3D genome architecture and face limitations in combining sequence-based features with chromatin interaction data. Specifically, CellReg3D overcomes these gaps by leveraging a DNA language model to encode sequence-level information and incorporating Hi-C-derived 3D chromatin interactions to capture chromatin spatial organization. We demonstrate that CellReg3D effectively learns epigenetic patterns and chromatin activity states, achieving state-of-the-art performance in predicting epigenetic marks and gene expression profiles across multiple cell types. Using interpretable methods, we illustrate how the model identifies cell-type-specific regulatory mechanisms and prioritizes functional non-coding variants. Furthermore, we apply CellReg3D to enhance the interpretation of expression quantitative trait loci (eQTLs) and uncover novel potential gene regulatory interactions. Overall, CellReg3D offers a integrative framework for decoding regulatory genomics, unifying sequence and 3D genome structure data to advance variant prioritization, disease mechanism discovery, and personalized functional genomics.",
  "abstract_shown": true,
  "affiliations": [
    "Monash University"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 9,
  "authors": [
    {
      "name": "X. Wang"
    },
    {
      "name": "Y. Zhang"
    },
    {
      "name": "S. Chen"
    },
    {
      "name": "S. Imoto"
    },
    {
      "name": "G. I. Webb"
    },
    {
      "name": "A. V. Vasilakos"
    },
    {
      "name": "F. Li"
    },
    {
      "name": "K. Wang"
    },
    {
      "affs": [
        0
      ],
      "name": "J. Song"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/an72e8rcgpxnx.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.11.01.763421",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.11.01.763421v1.full",
  "id": "an72e8rcgpxnx",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.11.01.763421v1",
  "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": null,
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.11.01.763421v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/an72e8rcgpxnx.ris",
  "sources": [
    {
      "first_seen": "2026-11-05T00:21:36Z",
      "native_id": "10.64898/2026.11.01.763421",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.11.01.763421v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Decoding Cell-type Specific Gene Regulation with CellReg3D by Integrating DNA Large Language Models and 3D Genome Structure",
  "title_text": "Decoding Cell-type Specific Gene Regulation with CellReg3D by Integrating DNA Large Language Models and 3D Genome Structure",
  "type": "preprint",
  "updated_at": "2026-11-06T00:21:25Z",
  "url": "https://research.draconitic.com/paper/an72e8rcgpxnx",
  "venue": "bioRxiv",
  "versions": []
}