{
  "id": 19174,
  "url": "https://arxiv.org/abs/2608.13256v1",
  "title": "Novel Knowledge-Guided Generative Methods for Synthetic Transcriptomic Data",
  "summary": "As biomedical research increasingly relies on data-intensive tools, the quality and utility of datasets are critical. Challenges such as imbalances, biases, and ethical or legal constraints often limit access to high-quality data. Synthetic data generation can help overcome these limitations. Here, we present a comparative analysis of generative models for transcriptomic data, investigating strategies to incorporate prior biological knowledge via gene graphs. This ensures that synthetic data cap",
  "authors": "Francesca Pia Panaccione, Sofia Mongardi, Marco Masseroli, Pietro Pinoli",
  "category": "research",
  "topics": "finance-investment,biotech",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-13T13:57:47.000Z",
  "fetched_at": "2026-08-14T05:10:49.168Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/19174",
  "original_url": "https://arxiv.org/abs/2608.13256v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}