{
  "id": 13473,
  "url": "https://arxiv.org/abs/2607.21343v1",
  "title": "M$^3$-Gen: Interpretable Multimodal Generation of Gene Expression Profiles Using Clinical and Imaging Data",
  "summary": "Integrating heterogeneous biomedical data, including clinical metadata, histopathology images, and molecular profiles, is crucial for comprehensive disease understanding. However, gene expression data acquisition remains constrained by high costs and privacy concerns, limiting its use in multimodal research and AI-driven applications. We present MultiModal Molecular Generation (M$^3$-Gen), a novel framework for the generation of gene expression profiles by conditioning a Generative Adversarial N",
  "authors": "Francesca Pia Panaccione, Carlo Sgaravatti, Marco Venere",
  "category": "research",
  "topics": "privacy-surveillance,healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-23T14:12:58.000Z",
  "fetched_at": "2026-07-25T05:10:48.796Z",
  "source_slug": "x-arxiv-cs-ai",
  "source_name": "arXiv cs.AI",
  "source_homepage": "https://arxiv.org/list/cs.AI/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/13473",
  "original_url": "https://arxiv.org/abs/2607.21343v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}