{
  "abstract": "Summary Single-cell RNA sequencing (scRNA-seq) enables detailed characterization of cellular heterogeneity, yet understanding the full cellular and regulatory environment of complex tissues remains challenging. In the era of large single-cell atlases, this technology has become increasingly accessible, and datasets have grown in scale and statistical power. As a result, sample representation methods have emerged as a promising strategy to summarize patient-level biological variation. However, most existing approaches rely on unsupervised learning frameworks with ambiguous biological interpretability. Here we present a Framework for Learning Over REgulatory-Embedding Networks (FloREN), a supervised and interpretable sample representation method. FloREN models single-cell data as a heterogeneous network integrating cells and genes together with gene regulatory and cell-cell communication relationships. Through condition-aware embeddings and interpretable attention networks, FloREN enables improved sample stratification and biomarker discovery. In addition, the framework supports downstream analyses that found specific immune network mechanisms in immune-mediated inflammatory diseases (IMIDs).",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 26,
  "authors": [
    {
      "name": "Chloé Quéré"
    },
    {
      "name": "Valérie Devauchelle-Pensec"
    },
    {
      "name": "Philippe Thuillier"
    },
    {
      "name": "Florent Besson"
    },
    {
      "name": "Théo Guichaoua"
    },
    {
      "name": "Arthur Le Madec"
    },
    {
      "name": "Raphaële Seror"
    },
    {
      "name": "Marie Pean de Ponfilly"
    },
    {
      "name": "Daniel Wendling"
    },
    {
      "name": "Hatem Boulahdour"
    },
    {
      "name": "Tristan Pascart"
    },
    {
      "name": "Aurore Pacaud"
    },
    {
      "name": "Alban Bailliez"
    },
    {
      "name": "Thomas Eugène"
    },
    {
      "name": "Sebastien Ottaviani"
    },
    {
      "name": "Philippe Dieude"
    },
    {
      "name": "Khadija Ben Ali"
    },
    {
      "name": "Frederic Paycha"
    },
    {
      "name": "Augustin Latourte"
    },
    {
      "name": "Laurinda Carre"
    },
    {
      "name": "Bruno Fautrel"
    },
    {
      "name": "Patrice Cacoub"
    },
    {
      "name": "Serge Desarnaud"
    },
    {
      "name": "Pierre-Yves Salaün"
    },
    {
      "name": "Ronan Abgral"
    },
    {
      "name": "Solène Querellou"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bxrj548zrc8p8.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.07.12.738088",
  "fulltext_url": null,
  "id": "bxrj548zrc8p8",
  "issns": [
    "1619-7070",
    "1619-7089"
  ],
  "landing_url": "https://hal.science/hal-05837568v1",
  "language": "en",
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "hal",
      "0.sdv"
    ],
    [
      "hal",
      "1.sdv.imm"
    ],
    [
      "openalex",
      "1312|Single-cell and spatial transcriptomics"
    ]
  ],
  "openalex": "W7169811705",
  "pdf_url": null,
  "pmcid": null,
  "pmid": "42337036",
  "published": "2026-07-20",
  "publisher": "Springer Verlag (Germany) [1976-....]",
  "ris": "https://research.draconitic.com/paper/bxrj548zrc8p8.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:01Z",
      "native_id": "hal-05837568",
      "source": "hal",
      "url": "https://hal.science/hal-05837568v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "hal:1.sdv.imm",
      "subfield": "immunology"
    }
  ],
  "title": "FloREN: Decoding Immune Regulatory Networks through Interpretable Graph Transformer Patient Representations",
  "title_text": "FloREN: Decoding Immune Regulatory Networks through Interpretable Graph Transformer Patient Representations",
  "type": "preprint",
  "updated_at": "2026-10-30T12:18:01Z",
  "url": "https://research.draconitic.com/paper/bxrj548zrc8p8",
  "venue": "European Journal of Nuclear Medicine and Molecular Imaging",
  "versions": []
}