{
  "abstract": "Background: The three-dimensional organization of the genome is a fundamental aspect of its function and regulation. High-throughput chromosome conformation capture techniques, such as Hi-C, have revolutionized our understanding of spatial genome organization. However, 3C-based methods are resource-intensive and technically demanding. This has driven the development of computational approaches for predicting Hi-C interaction matrices. Hi-cGAN, a novel approach based on conditional generative adversarial networks, offers a computational alternative to extensive wet-lab work by predicting Hi-C interaction matrices. This computational approach contributes to a broader exploration and understanding of genome architecture. Findings: The network pairs a convolutional generator with a convolutional discriminator, evaluated across bin sizes, inputs and cell types. It predicts a whole genome as a cool file at bin sizes from 2 to 25 kb, where Akita, C.Origami and Epiphany emit fixed windows of 1 Mb, 2 Mb and 990 kb. With the input chosen on a validation chromosome, agreement approaches Epiphany's and stays below the sequence-based C.Origami and Akita: over Akita's 411 held-out windows the mean correlation is 0.238 against 0.506. Boundary and loop calls agree less closely, placing the maps at the domain scale. Conclusions: Chromatin factor occupancy determines a substantial part of contact structure, and two tracks capture most of it. The most informative track depends on the resolution: CTCF and the cohesin subunits at 5 to 10 kb, active histone marks at 25 kb. Transfer to an unseen cell type costs about 0.12 SCC, and which method leads depends on the measure.",
  "abstract_shown": true,
  "affiliations": [
    "Hannover Medical School"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "R. Krauth"
    },
    {
      "name": "A. Kumar"
    },
    {
      "affs": [
        0
      ],
      "name": "J. Wolff"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ejp4f2jtscpc7.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.08.30.748103",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.08.30.748103v1.full",
  "id": "ejp4f2jtscpc7",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.08.30.748103v1",
  "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|Genomics and Chromatin Dynamics"
    ]
  ],
  "openalex": "W7207700934",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.08.30.748103v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-30",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/ejp4f2jtscpc7.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:12Z",
      "native_id": "10.64898/2026.08.30.748103",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.08.30.748103v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Hi-cGAN: Prediction of Hi-C interaction matrices with conditional generative adversarial networks",
  "title_text": "Hi-cGAN: Prediction of Hi-C interaction matrices with conditional generative adversarial networks",
  "type": "preprint",
  "updated_at": "2026-10-30T12:19:12Z",
  "url": "https://research.draconitic.com/paper/ejp4f2jtscpc7",
  "venue": "bioRxiv",
  "versions": []
}