{
  "id": 16701470,
  "title": "SPARK: Bayesian structure learning of enhancer-based gene regulatory networks from single-cell multiomics data and prior knowledge",
  "url": "https://urgent.news/2026/10/26/spark-bayesian-structure-learning-of-enhancer-based-gene-regulatory",
  "topic": "science",
  "section": "Science",
  "published": "2026-10-26T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.10.22.761021v1?rss=1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Enhancer-based gene regulatory networks (eGRNs) linking enhancers with recruited transcription factors (TFs) and downstream target genes can help decipher complex regulatory mechanisms, such as the function of GWAS variants at enhancers. Molecular assays that profile gene expression and chromatin state jointly from the same cells (such as 10x Multiome) are a rich source of input data for eGRN…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}